scripts/MERS.2_recombination.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T07:01:37.220976",
     "start_time": "2017-08-07T07:01:36.204338"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe style=\"border: 0; width: 400px; height: 472px;\" src=\"https://bandcamp.com/EmbeddedPlayer/album=29809561/size=large/bgcol=ffffff/linkcol=e99708/artwork=small/transparent=true/\" seamless><a href=\"http://romowerikoito.bandcamp.com/album/nawam-r\">NAWAMAR by Romowe Rikoito</a></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.patheffects as path_effects\n",
    "import datetime as dt\n",
    "import re\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import imp\n",
    "import scipy.special\n",
    "import scipy.misc\n",
    "from IPython.display import HTML\n",
    "from StringIO import StringIO\n",
    "from zipfile import ZipFile\n",
    "\n",
    "bt = imp.load_source('baltic', '/Users/evogytis/Documents/BLAB_baltic/baltic.py')\n",
    "\n",
    "typeface='Helvetica Neue'\n",
    "mpl.rcParams['font.weight']=300\n",
    "mpl.rcParams['axes.labelweight']=300\n",
    "mpl.rcParams['font.family']=typeface\n",
    "mpl.rcParams['font.size']=22\n",
    "\n",
    "def convertDate(x,start,end):\n",
    "    \"\"\" Converts calendar dates between given formats \"\"\"\n",
    "    return dt.datetime.strftime(dt.datetime.strptime(x,start),end)\n",
    "\n",
    "zipfile = ZipFile('./../data/phylo/structured-coalescent.zip')\n",
    "tree12=bt.loadNexus(zipfile.open('MERS_274_sCoal.combinedTyped.mcc.tree').readlines())\n",
    "\n",
    "zipfile = ZipFile('./../data/phylo/structured-coalescent-2fragments.zip')\n",
    "frag1=bt.loadNexus(zipfile.open('structured-coalescent-2fragments/MERS_274_2frag_sCoal.CDS1_1,2.mcc.tree').readlines())\n",
    "frag2=bt.loadNexus(zipfile.open('structured-coalescent-2fragments/MERS_274_2frag_sCoal.CDS2_1,2.mcc.tree').readlines())\n",
    "\n",
    "tree12.root.traits['type']='origin'\n",
    "\n",
    "zipfile=ZipFile('./../data/phylo/sequences.zip')\n",
    "seqs={}\n",
    "path_to_alignment='sequences/MERS_CoV_274.fasta'\n",
    "for line in zipfile.open(path_to_alignment).readlines():\n",
    "    if '>' in line:\n",
    "        name=line.strip('\\n')[1:]\n",
    "    else:\n",
    "        seq=line.strip('\\n')\n",
    "        seqs[name]=seq\n",
    "\n",
    "path='<iframe style=\"border: 0; width: 400px; height: 472px;\" src=\"https://bandcamp.com/EmbeddedPlayer/album=29809561/size=large/bgcol=ffffff/linkcol=e99708/artwork=small/transparent=true/\" seamless><a href=\"http://romowerikoito.bandcamp.com/album/nawam-r\">NAWAMAR by Romowe Rikoito</a></iframe>'\n",
    "\n",
    "print 'Done!'\n",
    "HTML(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T09:42:42.404730",
     "start_time": "2017-08-01T09:42:42.377119"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t 1 & KSA-378 & KJ713296 & camel & 2013-11 \\\\\n",
      "\t 2 & KSA-363 & KJ713298 & camel & 2013-11 \\\\\n",
      "\t 3 & KSA-503 & KJ713297 & camel & 2013-11 \\\\\n",
      "\t 4 & KSA-376 & KJ713299 & camel & 2013-11 \\\\\n",
      "\t 5 & KSA-505 & KJ713295 & camel & 2013-11 \\\\\n",
      "\t 6 & Jeddah-1 & KF917527 & camel & 2013-11-08 \\\\\n",
      "\t 7 & NRCE-HKU205 & KJ477102 & camel & 2013-11-15 \\\\\n",
      "\t 8 & KFU-HKU1 & KJ650297 & camel & 2013-11-30 \\\\\n",
      "\t 9 & KFU-HKU13 & KJ650295 & camel & 2013-12-30 \\\\\n",
      "\t 10 & Camel\\_Egypt\\_NRCE-HKU271 &  & camel & 2013-12-30 \\\\\n",
      "\t 11 & Camel\\_Egypt\\_NRCE-HKU270 &  & camel & 2013-12-30 \\\\\n",
      "\t 12 & KFU-HKU19Dam & KJ650296 & camel & 2013-12-30 \\\\\n",
      "\t 13 & Qatar\\_2\\_2014 & KJ650098 & camel & 2014-02-16 \\\\\n",
      "\t 14 & UAE/D469-14 & KU242424 & camel & 2014-03-04 \\\\\n",
      "\t 15 & UAE/D511-14 & KU242423 & camel & 2014-03-12 \\\\\n",
      "\t 16 & Jeddah/F13A/2014 & KT368824 & camel & 2014-05 \\\\\n",
      "\t 17 & UAE/D1164.10/2014 & KP719928 & camel & 2014-06 \\\\\n",
      "\t 18 & UAE/D1339.2/2014 & KP719931 & camel & 2014-06 \\\\\n",
      "\t 19 & UAE/D1164.11/2014 & KP719929 & camel & 2014-06 \\\\\n",
      "\t 20 & UAE/D1164.9/2014 & KP719927 & camel & 2014-06 \\\\\n",
      "\t 21 & UAE/D1209/2014 & KP719933 & camel & 2014-06 \\\\\n",
      "\t 22 & UAE/D1164.14/2014 & KP719930 & camel & 2014-06 \\\\\n",
      "\t 23 & UAE/D1243.12/2014 & KP719932 & camel & 2014-06 \\\\\n",
      "\t 24 & D1164.1/14 & KX108937 & camel & 2014-06-02 \\\\\n",
      "\t 25 & Riyadh/Ry23N/2014 & KT368825 & camel & 2014-07 \\\\\n",
      "\t 26 & Riyadh/Ry84N/2014 & KT368826 & camel & 2014-07 \\\\\n",
      "\t 27 & Jeddah/S93/2014 & KT368855 & camel & 2014-09 \\\\\n",
      "\t 28 & Jeddah/401/2014 & KT368827 & camel & 2014-09 \\\\\n",
      "\t 29 & Jeddah/S100/2014 & KT368853 & camel & 2014-09 \\\\\n",
      "\t 30 & Jeddah/S99/2014 & KT368857 & camel & 2014-09 \\\\\n",
      "\t 31 & Jeddah/S94/2014 & KT368856 & camel & 2014-09 \\\\\n",
      "\t 32 & Jeddah/S73/2014 & KT368854 & camel & 2014-09 \\\\\n",
      "\t 33 & Jeddah/O47b/2014 & KT368852 & camel & 2014-10 \\\\\n",
      "\t 34 & Jeddah/O23b/2014 & KT368849 & camel & 2014-10 \\\\\n",
      "\t 35 & Jeddah/O24/2014 & KT368850 & camel & 2014-10 \\\\\n",
      "\t 36 & Jeddah/O30/2014 & KT368851 & camel & 2014-10 \\\\\n",
      "\t 37 & Jeddah/N51/2014 & KT368846 & camel & 2014-11 \\\\\n",
      "\t 38 & Jeddah/N68b/2014 & KT368848 & camel & 2014-11 \\\\\n",
      "\t 39 & Jeddah/N62b/2014 & KT368847 & camel & 2014-11 \\\\\n",
      "\t 40 & Jeddah/D40/2014 & KT368834 & camel & 2014-12 \\\\\n",
      "\t 41 & Jeddah/D90/2014 & KT368844 & camel & 2014-12 \\\\\n",
      "\t 42 & Jeddah/D88/2014 & KT368843 & camel & 2014-12 \\\\\n",
      "\t 43 & Jeddah/D36/2014 & KT368832 & camel & 2014-12 \\\\\n",
      "\t 44 & Jeddah/D35/2014 & KT368831 & camel & 2014-12 \\\\\n",
      "\t 45 & Jeddah/D92/2014 & KT368845 & camel & 2014-12 \\\\\n",
      "\t 46 & Jeddah/D49/2014 & KT368841 & camel & 2014-12 \\\\\n",
      "\t 47 & Jeddah/D34/2014 & KT368830 & camel & 2014-12 \\\\\n",
      "\t 48 & Jeddah/D33b/2014 & KT368829 & camel & 2014-12 \\\\\n",
      "\t 49 & Jeddah/D42/2014 & KT368835 & camel & 2014-12 \\\\\n",
      "\t 50 & Jeddah/D50b/2014 & KT368842 & camel & 2014-12 \\\\\n",
      "\t 51 & Jeddah/D45/2014 & KT368837 & camel & 2014-12 \\\\\n",
      "\t 52 & Jeddah/D46b/2014 & KT368838 & camel & 2014-12 \\\\\n",
      "\t 53 & Jeddah/D43b/2014 & KT368836 & camel & 2014-12 \\\\\n",
      "\t 54 & Jeddah/D100/2014 & KT368828 & camel & 2014-12 \\\\\n",
      "\t 55 & Jeddah/D47/2014 & KT368839 & camel & 2014-12 \\\\\n",
      "\t 56 & Jeddah/D38b/2014 & KT368833 & camel & 2014-12 \\\\\n",
      "\t 57 & Jeddah/D48/2014 & KT368840 & camel & 2014-12 \\\\\n",
      "\t 58 & D2597.2/14 & KX108938 & camel & 2014-12-13 \\\\\n",
      "\t 59 & Egypt\\_NRCE-NC163/2014 & KU740200 & camel & 2014-12-17 \\\\\n",
      "\t 60 & Jeddah/Jd7/2015 & KT368861 & camel & 2015-01 \\\\\n",
      "\t 61 & Jeddah/Jd86/2015 & KT368863 & camel & 2015-01 \\\\\n",
      "\t 62 & Jeddah/Jd90/2015 & KT368865 & camel & 2015-01 \\\\\n",
      "\t 63 & Jeddah/Jd1b/2015 & KT368858 & camel & 2015-01 \\\\\n",
      "\t 64 & Jeddah/Jd4/2015 & KT368859 & camel & 2015-01 \\\\\n",
      "\t 65 & Jeddah/Jd85/2015 & KT368862 & camel & 2015-01 \\\\\n",
      "\t 66 & Jeddah/Jd6b/2015 & KT368860 & camel & 2015-01 \\\\\n",
      "\t 67 & Jeddah/Jd87/2015 & KT368864 & camel & 2015-01 \\\\\n",
      "\t 68 & D252/15 & KX108939 & camel & 2015-01-30 \\\\\n",
      "\t 69 & Jeddah/Jd199/2015 & KT368867 & camel & 2015-02 \\\\\n",
      "\t 70 & Jeddah/Jd175/2015 & KT368866 & camel & 2015-02 \\\\\n",
      "\t 71 & D374/15 & KX108940 & camel & 2015-02-12 \\\\\n",
      "\t 72 & D383/15 & KX108941 & camel & 2015-02-14 \\\\\n",
      "\t 73 & D389/15 & KX108942 & camel & 2015-02-15 \\\\\n",
      "\t 74 & Riyadh/Ry63/2015 & KT368876 & camel & 2015-03 \\\\\n",
      "\t 75 & Riyadh/Ry136/2015 & KT368868 & camel & 2015-03 \\\\\n",
      "\t 76 & Riyadh/Ry178/2015 & KT368874 & camel & 2015-03 \\\\\n",
      "\t 77 & Riyadh/Ry162/2015 & KT368871 & camel & 2015-03 \\\\\n",
      "\t 78 & Riyadh/Ry86/2015 & KT368879 & camel & 2015-03 \\\\\n",
      "\t 79 & Taif/T150/2015 & KT368889 & camel & 2015-03 \\\\\n",
      "\t 80 & Riyadh/Ry137/2015 & KT368869 & camel & 2015-03 \\\\\n",
      "\t 81 & Riyadh/Ry179/2015 & KT368875 & camel & 2015-03 \\\\\n",
      "\t 82 & Riyadh/Ry177/2015 & KT368873 & camel & 2015-03 \\\\\n",
      "\t 83 & Riyadh/Ry79/2015 & KT368878 & camel & 2015-03 \\\\\n",
      "\t 84 & Riyadh/Ry173/2015 & KT368872 & camel & 2015-03 \\\\\n",
      "\t 85 & Taif/T157b/2015 & KT368890 & camel & 2015-03 \\\\\n",
      "\t 86 & Riyadh/Ry159b/2015 & KT368870 & camel & 2015-03 \\\\\n",
      "\t 87 & Riyadh/Ry64/2015 & KT368877 & camel & 2015-03 \\\\\n",
      "\t 88 & Taif/T3/2015 & KT368880 & camel & 2015-04 \\\\\n",
      "\t 89 & Taif/T16/2015 & KT368882 & camel & 2015-04 \\\\\n",
      "\t 90 & Taif/T22/2015 & KT368883 & camel & 2015-04 \\\\\n",
      "\t 91 & Taif/T92/2015 & KT368887 & camel & 2015-04 \\\\\n",
      "\t 92 & Taif/T7/2015 & KT368881 & camel & 2015-04 \\\\\n",
      "\t 93 & Taif/T91b/2015 & KT368886 & camel & 2015-04 \\\\\n",
      "\t 94 & Taif/T68/2015 & KT368884 & camel & 2015-04 \\\\\n",
      "\t 95 & Taif/T89/2015 & KT368885 & camel & 2015-04 \\\\\n",
      "\t 96 & Taif/T98/2015 & KT368888 & camel & 2015-04 \\\\\n",
      "\t 97 & D998/15 & KX108943 & camel & 2015-04-23 \\\\\n",
      "\t 98 & D1157/15 & KX108944 & camel & 2015-05-12 \\\\\n",
      "\t 99 & D1189.1/15 & KX108946 & camel & 2015-05-18 \\\\\n",
      "\t 100 & D1271/15 & KX108945 & camel & 2015-05-29 \\\\\n",
      "\t 101 & Jordan-N3/2012 & KC776174 & human & 2012-04-15 \\\\\n",
      "\t 102 & EMC/2012 & JX869059 & human & 2012-06-13 \\\\\n",
      "\t 103 & England/1/2012 & KC164505 & human & 2012-09-11 \\\\\n",
      "\t 104 & Riyadh\\_1\\_2012 & KF600612 & human & 2012-10-23 \\\\\n",
      "\t 105 & Riyadh\\_2\\_2012 & KF600652 & human & 2012-10-30 \\\\\n",
      "\t 106 & Riyadh\\_3\\_2013 & KF600613 & human & 2013-02-05 \\\\\n",
      "\t 107 & England/3/2013 & KM210278 & human & 2013-02-10 \\\\\n",
      "\t 108 & England/2/2013 & KM015348 & human & 2013-02-10 \\\\\n",
      "\t 109 & England/4/2013 & KM210277 & human & 2013-02-13 \\\\\n",
      "\t 110 & Riyadh\\_4\\_2013 & KJ156952 & human & 2013-03-01 \\\\\n",
      "\t 111 & Munich/AbuDhabi/2013 & KF192507 & human & 2013-03-22 \\\\\n",
      "\t 112 & Al-Hasa\\_2\\_2013 & KF186566 & human & 2013-04-21 \\\\\n",
      "\t 113 & Al-Hasa\\_3\\_2013 & KF186565 & human & 2013-04-22 \\\\\n",
      "\t 114 & UAE-FRA1\\_1627-2013\\_BAL & KJ361500 & human & 2013-04-26 \\\\\n",
      "\t 115 & Al-Hasa\\_4\\_2013 & KF186564 & human & 2013-05-01 \\\\\n",
      "\t 116 & Al-Hasa\\_7\\_2013 & KF600623 & human & 2013-05-01 \\\\\n",
      "\t 117 & Al-Hasa\\_8\\_2013 & KF600618 & human & 2013-05-01 \\\\\n",
      "\t 118 & Al-Hasa\\_25\\_2013 & KJ156866 & human & 2013-05-02 \\\\\n",
      "\t 119 & Al-Hasa\\_11\\_2013 & KF600629 & human & 2013-05-03 \\\\\n",
      "\t 120 & Al-Hasa\\_12\\_2013 & KF600627 & human & 2013-05-07 \\\\\n",
      "\t 121 & Al-Hasa\\_14\\_2013 & KF600615 & human & 2013-05-08 \\\\\n",
      "\t 122 & Al-Hasa\\_1\\_2013 & KF186567 & human & 2013-05-09 \\\\\n",
      "\t 123 & Al-Hasa\\_15\\_2013 & KF600645 & human & 2013-05-11 \\\\\n",
      "\t 124 & Al-Hasa\\_16\\_2013 & KF600644 & human & 2013-05-12 \\\\\n",
      "\t 125 & Buraidah\\_1\\_2013 & KF600630 & human & 2013-05-13 \\\\\n",
      "\t 126 & Al-Hasa\\_23\\_2013 & KJ156860 & human & 2013-05-13 \\\\\n",
      "\t 127 & Al-Hasa\\_17\\_2013 & KF600647 & human & 2013-05-15 \\\\\n",
      "\t 128 & Al-Hasa\\_19\\_2013 & KF600632 & human & 2013-05-23 \\\\\n",
      "\t 129 & Al-Hasa\\_18\\_2013 & KF600651 & human & 2013-05-23 \\\\\n",
      "\t 130 & Al-Hasa\\_21\\_2013 & KF600634 & human & 2013-05-30 \\\\\n",
      "\t 131 & Hafr-Al-Batin\\_1\\_2013 & KF600628 & human & 2013-06-04 \\\\\n",
      "\t 132 & Wadi-Ad-Dawasir\\_1\\_2013 & KJ156881 & human & 2013-06-12 \\\\\n",
      "\t 133 & Taif\\_1\\_2013 & KJ156949 & human & 2013-06-12 \\\\\n",
      "\t 134 & Taif\\_2\\_2013 & KJ156896 & human & 2013-06-12 \\\\\n",
      "\t 135 & Taif\\_3\\_2013 & KJ156938 & human & 2013-06-13 \\\\\n",
      "\t 136 & Al-Hasa\\_26\\_2013 & KJ156882 & human & 2013-06-18 \\\\\n",
      "\t 137 & Al-Hasa\\_27\\_2013 & KJ156943 & human & 2013-06-19 \\\\\n",
      "\t 138 & Al-Hasa\\_28\\_2013 & KJ156887 & human & 2013-06-22 \\\\\n",
      "\t 139 & Riyadh\\_6\\_2013 & KJ156879 & human & 2013-07-02 \\\\\n",
      "\t 140 & Riyadh\\_5\\_2013 & KJ156944 & human & 2013-07-02 \\\\\n",
      "\t 141 & Riyadh\\_7\\_2013 & KJ156937 & human & 2013-07-15 \\\\\n",
      "\t 142 & Riyadh\\_8\\_2013 & KJ156880 & human & 2013-07-17 \\\\\n",
      "\t 143 & Riyadh\\_9\\_2013 & KJ156869 & human & 2013-07-17 \\\\\n",
      "\t 144 & Hafr-Al-Batin\\_2\\_2013 & KJ156910 & human & 2013-08-05 \\\\\n",
      "\t 145 & Asir\\_2\\_2013 & KJ156863 & human & 2013-08-05 \\\\\n",
      "\t 146 & Riyadh\\_11\\_2013 & KJ156946 & human & 2013-08-06 \\\\\n",
      "\t 147 & Riyadh\\_12\\_2013 & KJ156926 & human & 2013-08-08 \\\\\n",
      "\t 148 & Riyadh\\_13\\_2013 & KJ156888 & human & 2013-08-13 \\\\\n",
      "\t 149 & Riyadh\\_14\\_2013 & KJ156934 & human & 2013-08-15 \\\\\n",
      "\t 150 & Hafr-Al-Batin\\_4\\_2013 & KJ156931 & human & 2013-08-25 \\\\\n",
      "\t 151 & Hafr-Al-Batin\\_5\\_2013 & KJ156951 & human & 2013-08-25 \\\\\n",
      "\t 152 & Riyadh\\_17\\_2013 & KJ156918 & human & 2013-08-26 \\\\\n",
      "\t 153 & Hafr-Al-Batin\\_6\\_2013 & KJ156874 & human & 2013-08-28 \\\\\n",
      "\t 154 & Riyadh\\_10\\_2013 & KJ156891 & human & 2013-09-05 \\\\\n",
      "\t 155 & Madinah\\_3b\\_2013 & KJ156916 & human & 2013-09-11 \\\\\n",
      "\t 156 & Qatar3 & KF961221 & human & 2013-10-13 \\\\\n",
      "\t 157 & Qatar4 & KF961222 & human & 2013-10-17 \\\\\n",
      "\t 158 & Oman\\_2285\\_2013 & KT156560 & human & 2013-10-28 \\\\\n",
      "\t 159 & Jeddah-1 & KF958702 & human & 2013-11-05 \\\\\n",
      "\t 160 & AbuDhabi\\_UAE\\_9\\_2013 & KP209312 & human & 2013-11-15 \\\\\n",
      "\t 161 & Oman\\_2874\\_2013 & KT156561 & human & 2013-12-28 \\\\\n",
      "\t 162 & AbuDhabi/Gayathi\\_UAE\\_2\\_2014 & KP209310 & human & 2014-03-07 \\\\\n",
      "\t 163 & Jeddah\\_C7569/KSA & KM027256 & human & 2014-04-03 \\\\\n",
      "\t 164 & Jeddah\\_C7149/KSA & KM027255 & human & 2014-04-05 \\\\\n",
      "\t 165 & Jeddah\\_C7770/KSA & KM027257 & human & 2014-04-07 \\\\\n",
      "\t 166 & AbuDhabi\\_UAE\\_8\\_2014 & KP209306 & human & 2014-04-07 \\\\\n",
      "\t 167 & AbuDhabi\\_UAE\\_16\\_2014 & KP209308 & human & 2014-04-10 \\\\\n",
      "\t 168 & AbuDhabi\\_UAE\\_18\\_2014 & KP209307 & human & 2014-04-10 \\\\\n",
      "\t 169 & Jeddah\\_C8826/KSA & KM027258 & human & 2014-04-12 \\\\\n",
      "\t 170 & AbuDhabi\\_UAE\\_26\\_2014 & KP209313 & human & 2014-04-13 \\\\\n",
      "\t 171 & Jeddah\\_C9055/KSA & KM027259 & human & 2014-04-14 \\\\\n",
      "\t 172 & Makkah\\_C9355/KSA/Makkah & KM027261 & human & 2014-04-15 \\\\\n",
      "\t 173 & AbuDhabi\\_UAE\\_33\\_2014 & KP209311 & human & 2014-04-17 \\\\\n",
      "\t 174 & AbuDhabi\\_UAE\\_30\\_2014 & KP209309 & human & 2014-04-19 \\\\\n",
      "\t 175 & Jeddah\\_C10306/KSA & KM027260 & human & 2014-04-21 \\\\\n",
      "\t 176 & Riyadh\\_2014KSA\\_683/KSA/2014 & KM027262 & human & 2014-04-22 \\\\\n",
      "\t 177 & Riyadh-KKUH-90b &  & human & 2014-04-24 \\\\\n",
      "\t 178 & Riyadh-KKUH-105 &  & human & 2014-04-25 \\\\\n",
      "\t 179 & Riyadh-KKUH-104 &  & human & 2014-04-25 \\\\\n",
      "\t 180 & KFMC-1 & KT121580 & human & 2014-04-28 \\\\\n",
      "\t 181 & KFMC-8 & KT121579 & human & 2014-04-30 \\\\\n",
      "\t 182 & Indiana/USA-1\\_SaudiArabia\\_2014 & KJ813439 & human & 2014-04-30 \\\\\n",
      "\t 183 & KFMC-10 & KT121578 & human & 2014-05-01 \\\\\n",
      "\t 184 & KFMC-7 & KT121581 & human & 2014-05-03 \\\\\n",
      "\t 185 & Riyadh-KKUH-291 &  & human & 2014-05-06 \\\\\n",
      "\t 186 & KFMC-9 & KT121574 & human & 2014-05-07 \\\\\n",
      "\t 187 & KFMC-3 & KT121573 & human & 2014-05-09 \\\\\n",
      "\t 188 & Florida/USA-2\\_SaudiArabia\\_2014 & KJ829365 & human & 2014-05-10 \\\\\n",
      "\t 189 & KFMC-2 & KT121577 & human & 2014-05-11 \\\\\n",
      "\t 190 & KFMC-4 & KT121575 & human & 2014-05-12 \\\\\n",
      "\t 191 & KFMC-5 & KT121572 & human & 2014-05-12 \\\\\n",
      "\t 192 & Riyadh-KKUH-368 &  & human & 2014-05-13 \\\\\n",
      "\t 193 & KFMC-6 & KT121576 & human & 2014-05-18 \\\\\n",
      "\t 194 & Riyadh\\_2014KSA\\_158/KSA/2014 & KM027281 & human & 2014-05-20 \\\\\n",
      "\t 195 & Jeddah-KFH-285TA &  & human & 2014-06-03 \\\\\n",
      "\t 196 & Jeddah-KFH-605TD &  & human & 2014-06-09 \\\\\n",
      "\t 197 & Jeddah-KFH-668TD &  & human & 2014-06-09 \\\\\n",
      "\t 198 & Jeddah-KFH-899NF &  & human & 2014-06-16 \\\\\n",
      "\t 199 & Jeddah-KFH-949NSG1 &  & human & 2014-06-18 \\\\\n",
      "\t 200 & Riyadh-KKUH-643 &  & human & 2014-11-02 \\\\\n",
      "\t 201 & Taif/KSA-7032/2014 & KU710264 & human & 2014-11-04 \\\\\n",
      "\t 202 & Riyadh-KKUH-665 &  & human & 2014-11-19 \\\\\n",
      "\t 203 & Riyadh-KSA-2049/2015 & KR011266 & human & 2015-01-06 \\\\\n",
      "\t 204 & Riyadh-KSA-2343/2015 & KR011264 & human & 2015-01-21 \\\\\n",
      "\t 205 & Riyadh-KSA-2345/2015 & KR011263 & human & 2015-01-21 \\\\\n",
      "\t 206 & Riyadh-KSA-2466/2015 & KR011265 & human & 2015-01-26 \\\\\n",
      "\t 207 & Kharj-KSA-2599/2015 & KT806052 & human & 2015-02-02 \\\\\n",
      "\t 208 & Kharj-KSA-2598/2015 & KT806053 & human & 2015-02-02 \\\\\n",
      "\t 209 & Riyadh-KSA-2716/2015 & KT806051 & human & 2015-02-05 \\\\\n",
      "\t 210 & Khobar-KSA-6736/2015 & KT806048 & human & 2015-02-07 \\\\\n",
      "\t 211 & Jeddah-KSA-C20843/2015 & KT806044 & human & 2015-02-09 \\\\\n",
      "\t 212 & Jeddah-KSA-C20860/2015 & KT806055 & human & 2015-02-10 \\\\\n",
      "\t 213 & Riyadh\\_KSA\\_2959\\_2015 & KT026453 & human & 2015-02-10 \\\\\n",
      "\t 214 & Riyadh-KSA-3065/2015 & KT806050 & human & 2015-02-12 \\\\\n",
      "\t 215 & Najran-KSA-C20915/2015 & KT806054 & human & 2015-02-13 \\\\\n",
      "\t 216 & Riyadh-KSA-3181/2015 & KT806049 & human & 2015-02-15 \\\\\n",
      "\t 217 & Riyadh\\_KKUH\\_0734 &  & human & 2015-02-18 \\\\\n",
      "\t 218 & Jeddah-KSA-C21271/2015 & KT806045 & human & 2015-02-22 \\\\\n",
      "\t 219 & Riyadh\\_KKUH\\_0755 &  & human & 2015-02-23 \\\\\n",
      "\t 220 & Riyadh\\_KKUH\\_0756 &  & human & 2015-02-23 \\\\\n",
      "\t 221 & Riyadh\\_KKUH\\_0780 &  & human & 2015-02-25 \\\\\n",
      "\t 222 & Riyadh\\_KKUH\\_0801 &  & human & 2015-02-27 \\\\\n",
      "\t 223 & Riyadh\\_KKUH\\_0826 &  & human & 2015-02-28 \\\\\n",
      "\t 224 & Riyadh\\_KKUH\\_0818 &  & human & 2015-02-28 \\\\\n",
      "\t 225 & Riyadh\\_KSA\\_4050\\_2015 & KT026454 & human & 2015-03-01 \\\\\n",
      "\t 226 & Riyadh\\_KKUH\\_0944 &  & human & 2015-03-02 \\\\\n",
      "\t 227 & Riyadh\\_KKUH\\_0939 &  & human & 2015-03-02 \\\\\n",
      "\t 228 & Riyadh\\_KKUH\\_1080 &  & human & 2015-03-03 \\\\\n",
      "\t 229 & Riyadh\\_KKUH\\_1066 &  & human & 2015-03-03 \\\\\n",
      "\t 230 & Riyadh\\_KKUH\\_1145 &  & human & 2015-03-04 \\\\\n",
      "\t 231 & Riyadh\\_KKUH\\_1217 &  & human & 2015-03-04 \\\\\n",
      "\t 232 & Riyadh\\_KKUH\\_1461 &  & human & 2015-03-08 \\\\\n",
      "\t 233 & Riyadh\\_KKUH\\_1470 &  & human & 2015-03-08 \\\\\n",
      "\t 234 & Riyadh\\_KKUH\\_1522 &  & human & 2015-03-09 \\\\\n",
      "\t 235 & Germany3/UAE-Dubai/Abu-Dhabi &  & human & 2015-03-11 \\\\\n",
      "\t 236 & Hufuf-KSA-9158/2015 & KT806047 & human & 2015-03-27 \\\\\n",
      "\t 237 & Hufuf-KSA-11002/2015 & KT806046 & human & 2015-05-10 \\\\\n",
      "\t 238 & KOR/KNIH/002\\_05\\_2015 & KT029139 & human & 2015-05-20 \\\\\n",
      "\t 239 & ChinaGD01 & KT036372 & human & 2015-05-28 \\\\\n",
      "\t 240 & KOR/Seoul/014-2015 & KX034093 & human & 2015-05-30 \\\\\n",
      "\t 241 & KOREA/Seoul/014-1-2015 & KT374052 & human & 2015-05-31 \\\\\n",
      "\t 242 & KOREA/Seoul/035-1-2015 & KT374054 & human & 2015-06-03 \\\\\n",
      "\t 243 & KOR/Seoul/066-2015 & KX034095 & human & 2015-06-04 \\\\\n",
      "\t 244 & Korea/Seoul/SNU1-035/2015 & KU308549 & human & 2015-06-08 \\\\\n",
      "\t 245 & KOR/CNUH\\_SNU/030\\_06\\_2015 & KT868868 & human & 2015-06-08 \\\\\n",
      "\t 246 & KOR/CNUH\\_SNU/024\\_06\\_2015 & KT868867 & human & 2015-06-08 \\\\\n",
      "\t 247 & KOR/CNUH\\_SNU/054\\_06\\_2015 & KT868871 & human & 2015-06-09 \\\\\n",
      "\t 248 & KOR/CNUH\\_SNU/038\\_06\\_2015 & KT868870 & human & 2015-06-10 \\\\\n",
      "\t 249 & KOR/CNUH\\_SNU/148\\_06\\_2015 & KT868876 & human & 2015-06-10 \\\\\n",
      "\t 250 & KOR/CNUH\\_SNU/122\\_06\\_2015 & KT868875 & human & 2015-06-10 \\\\\n",
      "\t 251 & KOR/CNUH\\_SNU/082\\_06\\_2015 & KT868872 & human & 2015-06-10 \\\\\n",
      "\t 252 & KOR/CNUH\\_SNU/085\\_06\\_2015 & KT868873 & human & 2015-06-10 \\\\\n",
      "\t 253 & KOR/CNUH\\_SNU/016\\_06\\_2015 & KT868865 & human & 2015-06-11 \\\\\n",
      "\t 254 & KOR/CNUH\\_SNU/023\\_06\\_2015 & KT868866 & human & 2015-06-11 \\\\\n",
      "\t 255 & KOR/CNUH\\_SNU/031\\_06\\_2015 & KT868869 & human & 2015-06-11 \\\\\n",
      "\t 256 & KOR/CNUH\\_SNU/110\\_06\\_2015 & KT868874 & human & 2015-06-11 \\\\\n",
      "\t 257 & KOR/Seoul/050-1-2015 & KX034094 & human & 2015-06-11 \\\\\n",
      "\t 258 & THA/CU/17\\_06\\_2015 & KT225476 & human & 2015-06-17 \\\\\n",
      "\t 259 & KOR/Seoul/077-2-2015 & KX034096 & human & 2015-06-17 \\\\\n",
      "\t 260 & KOR/Seoul/080-3-2015 & KX034097 & human & 2015-06-17 \\\\\n",
      "\t 261 & KOREA/Seoul/163-1-2015 & KT374051 & human & 2015-06-19 \\\\\n",
      "\t 262 & KOREA/Seoul/168-1-2015 & KT374056 & human & 2015-06-21 \\\\\n",
      "\t 263 & KOR/Seoul/162-1-2015 & KX034098 & human & 2015-06-22 \\\\\n",
      "\t 264 & KOR/CNUH\\_SNU/172\\_06\\_2015 & KT868877 & human & 2015-06-22 \\\\\n",
      "\t 265 & KOR/Seoul/169-2015 & KX034099 & human & 2015-06-26 \\\\\n",
      "\t 266 & KOR/Seoul/177-3-2015 & KX034100 & human & 2015-07-03 \\\\\n",
      "\t 267 & Jeddah-KSA-3RS2702/2015 & KU851859 & human & 2015-07-12 \\\\\n",
      "\t 268 & Riyadh-KSA-16120/2015 & KU851861 & human & 2015-08-24 \\\\\n",
      "\t 269 & Riyadh-KSA-16117/2015 & KU851862 & human & 2015-08-24 \\\\\n",
      "\t 270 & Riyadh-KSA-16121/2015 & KU851860 & human & 2015-08-24 \\\\\n",
      "\t 271 & Riyadh-KSA-16098/2015 & KU851864 & human & 2015-08-24 \\\\\n",
      "\t 272 & Riyadh-KSA-16077/2015 & KU851863 & human & 2015-08-27 \\\\\n",
      "\t 273 & Jordan\\_1\\_2015 & KU233363 & human & 2015-09-17 \\\\\n",
      "\t 274 & Jordan\\_10\\_2015 & KU233362 & human & 2015-09-17 \\\\\n"
     ]
    }
   ],
   "source": [
    "for n,name in enumerate(sorted(seqs.keys(),key=lambda x:(x.split('|')[2],bt.decimalDate(x.split('|')[-1],variable=True)))):\n",
    "    #print name\n",
    "    strain,accession,host,date=name.split('|')\n",
    "    print '\\t %d & %s & %s & %s & %s \\\\\\\\' % (n+1,strain.replace('_','\\_'),accession,host,date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T09:42:50.233058",
     "start_time": "2017-08-01T09:42:49.553080"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "for k in [x for x in tree12.Objects if x.branchType=='node']:\n",
    "    out=open('./../data/phylo/MERS_clade%s_size%d.fna'%(k.index,len(k.leaves)),'w')\n",
    "\n",
    "    for s in k.leaves:\n",
    "        n=tree12.tipMap[s]\n",
    "        print>>out,'>%s\\n%s'%(n,seqs[n])\n",
    "\n",
    "    out.close()\n",
    "    \n",
    "print 'Done!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T09:46:20.745790",
     "start_time": "2017-08-01T09:42:52.210426"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade5_size33.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade60275_size2.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade6213_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade62798_size6.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade62800_size4.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade68546_size4.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade6_size32.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade70045_size2.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade78413_size10.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade78414_size8.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade78415_size4.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade78416_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade78417_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade7934_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade79539_size4.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade79540_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade79964_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade7_size30.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade7_size31.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade8036_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade81543_size4.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade81544_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade82867_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade83596_size11.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade83599_size8.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade83600_size4.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade83601_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade83602_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade849_size5.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade85202_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade852_size5.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade86778_size4.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade86779_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade86780_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade87553_size6.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade87554_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade88849_size4.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade88850_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade89276_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade8_size29.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade8_size30.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade92857_size27.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade92858_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade92859_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade93707_size27.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade93708_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade93709_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95111_size24.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95112_size6.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95113_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95114_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95959_size24.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95960_size6.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95961_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95962_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade97070_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade97500_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade97914_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade98343_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade99823_size18.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade9_size28.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade9_size29.fna\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "path_to_phi='/Users/evogytis/Downloads/PhiPack/Phi'\n",
    "for filename in /Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade*.fna; do\n",
    "    path_to_alignment=\"$filename\"\n",
    "    echo \"$path_to_alignment\"\n",
    "    name=${filename##*/}\n",
    "    base=${name%.fna}\n",
    "    #echo \"${base}_phi.txt\"\n",
    "    rm \"./../data/phylo/${base}_phi.txt\"\n",
    "    \"$path_to_phi\" -f \"$path_to_alignment\" >>\"./../data/phylo/${base}_phi.txt\"\n",
    "done\n",
    "echo Done!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T09:58:26.905335",
     "start_time": "2017-08-01T09:46:20.748431"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade0_size274.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade100240_size17.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade100241_size16.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade100242_size15.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade100243_size13.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade100667_size12.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade100668_size9.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade86780_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade87553_size6.fna\n",
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      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade88849_size4.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade88850_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade89276_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade8_size29.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade8_size30.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade92857_size27.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade92858_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade92859_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade93707_size27.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade93708_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade93709_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95111_size24.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95112_size6.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95113_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95114_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95959_size24.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95960_size6.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95961_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade95962_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade97070_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade97500_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade97914_size3.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade98343_size2.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade99823_size18.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade9_size28.fna\n",
      "/Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade9_size29.fna\n",
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\tNeed a p-value of 2.457468e-09 to survive multiple comparisons correction\n",
      "\n",
      "\tSINGLE-BP-RUN Progress --  0.001% \t\t(1.000000e+00)    \r",
      "\tSINGLE-BP-RUN Progress --  0.002% \t\t(4.557147e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.004% \t\t(4.242424e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.005% \t\t(4.242424e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.006% \t\t(4.242424e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.008% \t\t(4.242424e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.009% \t\t(1.868743e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.010% \t\t(1.868743e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.012% \t\t(1.693548e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.013% \t\t(1.539589e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.014% \t\t(1.539589e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.016% \t\t(1.539589e-02)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.020% \t\t(1.539589e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.021% \t\t(1.539589e-02)    \r",
      "\tSINGLE-BP-RUN Progress --  0.022% \t\t(8.678456e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.024% \t\t(8.678456e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.025% \t\t(8.678456e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.026% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.028% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.029% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.030% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.032% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.033% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.034% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.036% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.037% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.038% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.040% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.041% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.042% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.044% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.045% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.046% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.048% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.049% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.050% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.052% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.053% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.054% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.056% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.057% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.058% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.060% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.061% \t\t(2.745324e-04)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.064% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.065% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.066% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.068% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.069% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.070% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.072% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.073% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.074% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.076% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.077% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.078% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.080% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.081% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.082% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.084% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.085% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.086% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.088% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.089% \t\t(2.745324e-04)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.092% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.093% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.094% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.096% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.097% \t\t(2.745324e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.098% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.100% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.101% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.102% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.104% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.105% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.106% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.108% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.109% \t\t(1.042825e-04)    \r",
      "\tSINGLE-BP-RUN Progress --  0.110% \t\t(1.042825e-04)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.413% \t\t(3.390118e-05)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.713% \t\t(3.390118e-05)    \r",
      "\tSINGLE-BP-RUN Progress --  0.715% \t\t(3.390118e-05)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.719% \t\t(3.390118e-05)    \r",
      "\tSINGLE-BP-RUN Progress --  0.720% \t\t(3.390118e-05)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.724% \t\t(3.390118e-05)    \r",
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      "\tSINGLE-BP-RUN Progress --  0.729% \t\t(3.390118e-05)    \r",
      "\tSINGLE-BP-RUN Progress --  0.731% \t\t(6.319947e-08)    \r",
      "\tSINGLE-BP-RUN Progress --  0.733% \t\t(1.421297e-08)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.017% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.018% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.020% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.022% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.024% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.025% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.026% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.028% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.029% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.030% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.032% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.033% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.034% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.036% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.038% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.040% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.041% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.042% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.044% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.328% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.330% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.638% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.640% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.641% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.643% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.644% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.645% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.647% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.648% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.649% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.651% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.652% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.653% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.655% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.656% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.657% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.659% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.660% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.661% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.663% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.664% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.665% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.667% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.668% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.669% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.671% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.672% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.673% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.675% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.676% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.679% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.691% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.695% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.701% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.703% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.704% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.705% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.707% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.708% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.709% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.716% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.717% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.719% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.725% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.727% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.731% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.732% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.733% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.735% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.736% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.737% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.739% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.740% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.741% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.744% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.748% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.751% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.925% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.926% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.930% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.931% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.933% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.934% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.938% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.939% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.941% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  1.947% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.949% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.950% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.951% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.953% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.954% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.955% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.957% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.958% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.959% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.961% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.962% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.963% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.965% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.966% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.967% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.969% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.970% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.971% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.973% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.974% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.975% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.977% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.978% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.979% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.981% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.982% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.983% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.985% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.986% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.987% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.989% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.990% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.991% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.993% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.994% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.995% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.997% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.998% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  1.999% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.001% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.002% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.003% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.005% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.006% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.007% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.009% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.010% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.011% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.013% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.014% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.015% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.017% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.018% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.019% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.021% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.022% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.024% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.025% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.026% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.028% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.029% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.030% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.032% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.033% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.034% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.036% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.037% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.038% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.040% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.041% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.042% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.044% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.045% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.046% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.048% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.049% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.050% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.052% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.053% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.054% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.056% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.057% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.058% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.060% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.061% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.062% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.064% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.065% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.066% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.068% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.069% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  2.072% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  2.074% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.076% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  2.101% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  2.257% \t\t(2.230757e-09)    \r",
      "\tSINGLE-BP-RUN Progress --  2.259% \t\t(2.230757e-09)    \r",
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      "\tSINGLE-BP-RUN Progress --  2.560% \t\t(8.906590e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  2.860% \t\t(8.906590e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.161% \t\t(8.906590e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.463% \t\t(8.906590e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.465% \t\t(8.906590e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.771% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.772% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.773% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.775% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.776% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.777% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.779% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.780% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.781% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.783% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.784% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.785% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.787% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.788% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.790% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.791% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.792% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.794% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.795% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.796% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.798% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.799% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.800% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.802% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.803% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.806% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.807% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.811% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.812% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.816% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.818% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.826% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.827% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.828% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.830% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.831% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.832% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.834% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.836% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.838% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.839% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.840% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.842% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.843% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.846% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.850% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.851% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.852% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.860% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.866% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.867% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.868% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.870% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.871% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.872% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.874% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.878% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.879% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.884% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.888% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.925% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.926% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.927% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.929% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.930% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.931% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.933% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.935% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.937% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  3.938% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  3.941% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  4.001% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.002% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  4.072% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  4.076% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.077% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  4.080% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.081% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.082% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  4.391% \t\t(3.459643e-11)    \r",
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      "\tSINGLE-BP-RUN Progress --  4.701% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.703% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.704% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.705% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.707% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.708% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.709% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.711% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.712% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.713% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.715% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.716% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.717% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.719% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.720% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.721% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.723% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.724% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.725% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.727% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.728% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.729% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.731% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.732% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.733% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.735% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.736% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.737% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.739% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.740% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.741% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.743% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.744% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.745% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.747% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.748% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.749% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.751% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.752% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.753% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.755% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.756% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.757% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.759% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.760% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.763% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.764% \t\t(3.459643e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.765% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.767% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.768% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.769% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.771% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.772% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.773% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.775% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.776% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.777% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.779% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.780% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.781% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.783% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.784% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.785% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.787% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.788% \t\t(1.750240e-11)    \r",
      "\tSINGLE-BP-RUN Progress --  4.789% \t\t(1.750240e-11)    \r",
      "\tSI**OUTPUT MUTED**"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "path_to_3seq='/Users/evogytis/Downloads/3seq_110812/3seq'\n",
    "for filename in /Users/evogytis/Documents/MERS_2.0/clean/data/alignments/clades/MERS_clade*.fna; do\n",
    "    path_to_alignment=\"$filename\"\n",
    "    echo \"$path_to_alignment\"\n",
    "    name=${filename##*/}\n",
    "    base=${name%.fna}\n",
    "    #echo \"${base}_phi.txt\"\n",
    "    \n",
    "    rm \"/./../data/phylo/${base}_3seq.txt\"\n",
    "    \"$path_to_3seq\" -single \"$path_to_alignment\" >> \"./../data/phylo/${base}_3seq.txt\"\n",
    "    #Yes | \"$path_to_3seq\" -fullrun \"$path_to_alignment\" 8192 >> \"/Users/evogytis/Downloads/${base}_3seq.txt\"\n",
    "done\n",
    "echo Done!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T09:58:27.606673",
     "start_time": "2017-08-01T09:58:26.908672"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.118, '51706': 0.0456, '51707': 0.374, '51708': 0.56, '51709': 0.568, '11250': nan, '11251': nan, '126997': nan, '126996': nan, '126995': nan, '62799': nan, '62798': nan, '215318': nan, '215319': nan, '60560': nan, '39508': nan, '39509': nan, '189469': 1.0, '52142': nan, '137297': nan, '131352': nan, '4': 0.149, '100670': nan, '100671': 0.913, '78415': nan, '5': 0.471, '78417': nan, '78416': nan, '78413': 0.24, '101095': nan, '101096': nan, '101091': 0.909, '101090': 0.193, '108111': nan, '97914': nan, '8036': nan, '170835': nan, '135984': 1.0, '135985': nan, '135986': nan, '154904': nan, '79964': nan, '154903': nan, '154902': nan, '95960': 1.0, '95961': nan, '95962': nan, '228619': nan, '228618': 1.0, '228617': 1.0, '228616': 0.11, '228615': 0.0654, '228614': 0.0667, '126438': nan, '35591': nan, '59846': nan, '59845': nan, '59844': nan, '158482': nan, '158483': nan, '160511': nan, '160510': nan, '849': nan, '160512': nan, '203192': nan, '141352': nan, '92857': 0.211, '122579': nan, '190129': nan, '190128': nan, '190127': nan, '29095': nan, '29094': nan, '29093': nan, '29092': 1.0, '29091': 1.0, '29090': 1.0, '69619': nan, '116843': nan, '116846': nan, '51221': nan, '51220': nan, '142701': nan, '230885': 0.0667, '17351': nan, '82867': nan, '140928': nan, '97070': nan, '215286': 1.0, '16224': 1.0, '16225': nan, '101514': 1.0, '101515': nan, '101516': nan, '101517': nan, '204875': nan, '204874': 1.0, '219715': nan, '204871': 0.18, '204870': 0.271, '204873': 0.148, '204872': 0.148, '51645': nan, '95959': 0.193, '51646': nan, '215291': nan, '215290': nan, '219293': nan, '7934': nan, '35169': nan, '124316': nan, '124315': nan, '108534': nan, '108533': nan, '196319': nan, '173575': nan, '173574': 1.0, '198356': nan, '244452': nan, '135676': nan, '143562': nan, '134813': nan, '134812': 1.0, '215288': nan, '83601': nan, '162682': nan, '213093': 1.0, '26604': nan, '213097': nan, '213096': nan, '213095': nan, '213094': 1.0, '198776': nan, '198775': nan, '51197': 0.00141}\n"
     ]
    }
   ],
   "source": [
    "root='./../data/phylo/'\n",
    "\n",
    "results_3seq={}\n",
    "pval_3seq=re.compile('Rejection of the null hypothesis of clonal evolution at p = ([0-9\\.e\\-]+)')\n",
    "\n",
    "for filename in os.listdir(root):\n",
    "    if filename.endswith(\"_3seq.txt\"):\n",
    "        node_idx=filename.split('_')[1].strip('clade')\n",
    "\n",
    "        for line in open(root+filename,'r'):\n",
    "            cerberus=pval_3seq.search(line)\n",
    "            if cerberus:\n",
    "                pass\n",
    "                pval=float(cerberus.group(1))\n",
    "                \n",
    "                results_3seq[node_idx]=pval\n",
    "\n",
    "results_phi={}\n",
    "pval_phi=re.compile('PHI \\(Normal\\): +([0-9\\-\\+\\.Ee]+)')\n",
    "for filename in os.listdir(root):\n",
    "    if filename.endswith(\"_phi.txt\"):\n",
    "        node_idx=filename.split('_')[1].strip('clade')\n",
    "\n",
    "        for line in open(root+filename,'r'):\n",
    "            cerberus=pval_phi.search(line)\n",
    "            if cerberus:\n",
    "                pass\n",
    "                try:\n",
    "                    pval=float(cerberus.group(1))\n",
    "                except ValueError:\n",
    "                    pval=np.nan\n",
    "                    \n",
    "                results_phi[node_idx]=pval\n",
    "                \n",
    "                \n",
    "print results_3seq\n",
    "print results_phi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T09:58:52.067056",
     "start_time": "2017-08-01T09:58:27.609525"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6ysyKFSswdOhQSJKElStX4s0330RiTNmPAYmJiXj33XexZs0aAMCgQYPw0Ucf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b040d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,15),facecolor='w')\n",
    "\n",
    "every=12\n",
    "xDates=['%04d-%02d-01'%(y,m) for y in range(2009,2016) for m in range(1,13)]\n",
    "\n",
    "[ax.axvspan(bt.decimalDate(xDates[x]),bt.decimalDate(xDates[x])+1/float(12),facecolor='k',edgecolor='none',alpha=0.04) for x in range(0,len(xDates),2)]\n",
    "ax.set_xticks([bt.decimalDate(x)+1/24.0 for x in xDates if (int(x.split('-')[1])-1)%every==0])\n",
    "\n",
    "ax.set_xticklabels([convertDate(x,'%Y-%m-%d','%Y') if x.split('-')[1]=='01' else convertDate(x,'%Y-%m-%d','%b') for x in xDates if (int(x.split('-')[1])-1)%every==0])\n",
    "ax.tick_params(axis='x',labelsize=28,size=0)\n",
    "\n",
    "[ax.axvline(i,ls='--',lw=1,color='grey',zorder=0) for i in range(2000,2020)]\n",
    "\n",
    "#genomeTree=bt.loadNexus(subtree_root+'MERS_307.HinfectC.combined.mcc.tree')\n",
    "order=[k.name for k in tree12.traverse_tree()]\n",
    "\n",
    "skip=0\n",
    "branchWidth=2\n",
    "types=['h','c']\n",
    "\n",
    "cmap=mpl.cm.Vega10\n",
    "for t,tp in enumerate(types):\n",
    "    effects=[path_effects.Stroke(linewidth=4, foreground='white'),\n",
    "                 path_effects.Stroke(linewidth=1, foreground=cmap(t))] ## black text, white outline\n",
    "    \n",
    "    if tp=='h':\n",
    "        tp='human'\n",
    "    else:\n",
    "        tp='camel'\n",
    "    \n",
    "    ax.text(0.05,0.1+t/25.0,'%s'%(tp),color=cmap(t),size=30,transform=ax.transAxes,path_effects=effects)\n",
    "    \n",
    "ax.scatter(0.06,0.12+(1+t)/25.0,s=1000,color='k',marker='1',transform=ax.transAxes)\n",
    "ax.scatter(0.06,0.12+(2+t)/25.0,s=1000,color='k',marker='2',transform=ax.transAxes)\n",
    "\n",
    "effects=[path_effects.Stroke(linewidth=4, foreground='white'),\n",
    "                 path_effects.Stroke(linewidth=1, foreground='k')] ## black text, white outline\n",
    "ax.text(0.08,0.12+(1+t)/25.0,'PHI',size=30,color='k',ha='left',va='center',transform=ax.transAxes,path_effects=effects)\n",
    "ax.text(0.08,0.12+(2+t)/25.0,'3Seq',size=30,color='k',ha='left',va='center',transform=ax.transAxes,path_effects=effects)\n",
    "    \n",
    "for k in tree12.Objects:\n",
    "    c='k'\n",
    "    if isinstance(k.traits['type'],str):\n",
    "        c=cmap(types.index(k.traits['type']))\n",
    "    \n",
    "    z=1\n",
    "    if k.traits['type']=='h':\n",
    "        z=2\n",
    "\n",
    "    x=k.absoluteTime\n",
    "    y=k.y\n",
    "\n",
    "    xp=k.parent.absoluteTime\n",
    "    if xp==0:\n",
    "        xp=x\n",
    "    \n",
    "    if k.branchType=='leaf':\n",
    "        ax.scatter(x,y,s=20,facecolor=c,edgecolor='none',zorder=4)\n",
    "        ax.scatter(x,y,s=40,facecolor='w',edgecolor='none',zorder=3)\n",
    "    else:\n",
    "        node_idx='%s'%(k.index)\n",
    "        \n",
    "        yl=k.children[0].y+skip\n",
    "        yr=k.children[-1].y+skip\n",
    "        ax.plot([x,x],[yl,yr],color=c,lw=branchWidth,zorder=z)\n",
    "        \n",
    "        \n",
    "        if results_phi.has_key(node_idx):\n",
    "            if results_phi[node_idx]<=0.05:\n",
    "                ax.scatter(x,y,s=200,color='k',marker='1',zorder=10)\n",
    "                \n",
    "        if results_3seq.has_key(node_idx):\n",
    "            if results_3seq[node_idx]<=0.05:\n",
    "                ax.scatter(x,y,s=200,color='k',marker='2',zorder=10)\n",
    "\n",
    "    if k.parent.traits.has_key('type') and k.traits['type']!=k.parent.traits['type']:\n",
    "        ax.plot([x,xp],[y,y],color=cmap(1),lw=branchWidth,zorder=z-1)\n",
    "    else:\n",
    "        ax.plot([x,xp],[y,y],color=c,lw=branchWidth,zorder=z)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "ax.tick_params(size=0)\n",
    "#ax.set_xticklabels([])\n",
    "ax.set_yticklabels([])\n",
    "\n",
    "ax.set_xlim(bt.decimalDate('2009-11-01'),bt.decimalDate('2015-10-01'))\n",
    "ax.set_ylim(-5,tree12.ySpan+5)\n",
    "plt.savefig('./../figures/mers_recombination_tree.png',dpi=300,bbox_inches='tight') ## save to file\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posterior clade sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T07:05:44.308680",
     "start_time": "2017-08-07T07:05:19.139778"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MERS_274_sCoal.combined.subtrees\n"
     ]
    }
   ],
   "source": [
    "## analyse posteriors\n",
    "zipfile=ZipFile('./../data/phylo/structured-coalescent.zip')\n",
    "subtrees_path=['MERS_274_sCoal.combined.subtrees']\n",
    "tipRe = re.compile(\"'[A-Za-z0-9\\_\\|\\-\\/]+'\")\n",
    "\n",
    "subtree_posteriors={}\n",
    "N_intros=[]\n",
    "\n",
    "mcmcCounter=0\n",
    "human_tips=[]\n",
    "burnin=0\n",
    "\n",
    "for run in range(len(subtrees_path)):\n",
    "    print subtrees_path[run]\n",
    "    for line in zipfile.open(subtrees_path[run]).readlines():\n",
    "        l=line.strip().split('\\t')\n",
    "        if len(l)>0 and l[0]!='state' and int(l[0])>burnin:\n",
    "            subtree_leaves=[tipRe.findall(string) for string in l[1:]]\n",
    "            \n",
    "            N_intros.append(len(subtree_leaves))\n",
    "                \n",
    "            for leaves in subtree_leaves:\n",
    "                \n",
    "                lf=','.join(sorted(leaves))\n",
    "                for tip in leaves:\n",
    "                    if tip not in human_tips:\n",
    "                        human_tips.append(tip)\n",
    "                if subtree_posteriors.has_key(lf):\n",
    "                    subtree_posteriors[lf]+=1\n",
    "                else:\n",
    "                    subtree_posteriors[lf]=1\n",
    "                    \n",
    "            mcmcCounter+=1\n",
    "            \n",
    "for clade in subtree_posteriors.keys():\n",
    "    subtree_posteriors[clade]=subtree_posteriors[clade]/float(mcmcCounter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T10:05:20.671515",
     "start_time": "2017-08-01T10:03:25.266228"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "unique_clades=subtree_posteriors.keys()\n",
    "\n",
    "bestMatch={}\n",
    "for tip in human_tips: ## iterate over tips\n",
    "    tipPresence=[subtree for subtree in subtree_posteriors.keys() if tip in subtree.split(',')] ## identify clades which contain the tip\n",
    "    best=sorted(tipPresence,key=lambda x:-subtree_posteriors[x])[0] ## get the clade with the highest posterior probability\n",
    "    \n",
    "#     print tip,best,subtree_posteriors[best]/float(mcmcCounter),[subtree_posteriors[x]/float(mcmcCounter) for x in tipPresence]\n",
    "    bestMatch[tip]=best ## remember highest probability membership of tip\n",
    "\n",
    "# human_tips=sorted(human_tips,key=lambda x:-subtree_posteriors[bestMatch[x]]) ## sort tips according to their best membership score\n",
    "human_tips=sorted(human_tips,key=lambda x:-order.index(x.strip(\"'\"))) ## sort tips according to their order in tree\n",
    "\n",
    "# human_tips=sorted(human_tips,key=lambda x:-bt.decimalDate(x.strip(\"'\").split('|')[-1],variable=True)) ## sort tips according to their collection date\n",
    "\n",
    "\n",
    "empty=np.zeros((len(human_tips),len(human_tips)))\n",
    "empty.fill(np.nan)\n",
    "analysis=pd.DataFrame(empty,index=human_tips,columns=human_tips)\n",
    "\n",
    "for tipA in human_tips:\n",
    "    for tipB in human_tips:\n",
    "        tipOverlap=[subtree for subtree in unique_clades if tipA in subtree.split(',') and tipB in subtree.split(',')] ## identify clades with both tips\n",
    "        \n",
    "        #print [(subtree1_posteriors.has_key(x),subtree2_posteriors.has_key(x)) for x in tipOverlap]\n",
    "        \n",
    "        if len(tipOverlap)>0 and human_tips.index(tipA)>=human_tips.index(tipB): ## non-zero clades, lower triangle\n",
    "            #print tipA,tipB,len(tipOverlap),sum([subtree_posteriors[x]/float(mcmcCounter) for x in tipOverlap])\n",
    "\n",
    "            if subtree_posteriors.has_key(tipA): ## if tip has been observed on its own\n",
    "                if tipA==tipB: ## if plotting against itself\n",
    "                    analysis[tipA][tipB]=subtree_posteriors[tipA] ## report singleton probability, not integrated probability (which will be 1.0)\n",
    "                else:\n",
    "                    analysis[tipA][tipB]=sum([subtree_posteriors[x] for x in tipOverlap if subtree_posteriors.has_key(x)]) ## if different report integrated probability\n",
    "            else:\n",
    "                analysis[tipA][tipB]=sum([subtree_posteriors[x] for x in tipOverlap if subtree_posteriors.has_key(x)]) ## everything else integrated probability too\n",
    "        \n",
    "print 'Done!'\n",
    "# for subtree in sorted(subtree_posteriors.keys(),key=lambda x:-len(x.split(','))):\n",
    "#     print subtree_posteriors[subtree]/float(mcmcCounter),subtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T10:06:31.792869",
     "start_time": "2017-08-01T10:06:30.509116"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    },
    {
     "data": {
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UaQAAgDxXUlISq1evznUM/kmRBgAAyHOTJ0+Oiy++OGpra3MdhbBHGgBohoPF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\n/p5cNFaSH3/8Uffff7/uuecejy8i+yOCNAAAAAD4ufj4eO3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Pjx8/rnHjxik+Pr7YpWCH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\nQJAGAAAAAMAEgjQAAAAAACb8f4zDu0NNCBeIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122acaf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ## start figure\n",
    "fig,ax = plt.subplots(figsize=(15,15),facecolor='w')\n",
    "\n",
    "print len(human_tips)\n",
    "\n",
    "# ## mask NaNs\n",
    "masked_array = np.ma.array(np.array(analysis),mask=np.isnan(analysis))\n",
    "\n",
    "# cmap.set_bad('k',1.)\n",
    "cmap=mpl.cm.get_cmap('viridis_r',20)\n",
    "# ## plot heatmap\n",
    "heatmap = ax.pcolor(masked_array,edgecolors='none', linewidths=0,cmap=cmap,alpha=1)\n",
    "\n",
    "# ## make plot pretty\n",
    "# ax.set_xticks(np.arange(0.5,len(human_tips)+0.5))\n",
    "# ax.set_xticklabels([x for x in human_tips],rotation=90)\n",
    "# ax.set_yticks(np.arange(0.5,len(human_tips)+0.5))\n",
    "# ax.set_yticklabels(human_tips)\n",
    "# ax.set_xlabel('destination',size=30)\n",
    "# ax.set_ylabel('origin',size=30)\n",
    "\n",
    "ax.tick_params(length=0,labelsize=1)\n",
    "\n",
    "ax.set_xlabel('viruses',size=28)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "#ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "#ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "axcb = fig.add_axes([0.95, 0.15, 0.02, 0.6], frame_on=False)\n",
    "cb=mpl.colorbar.ColorbarBase(axcb,cmap=cmap,norm=mpl.colors.Normalize(vmin=0.0,vmax=1.0),orientation='vertical',alpha=1.0)\n",
    "\n",
    "axcb.yaxis.set_label_position(\"left\")\n",
    "axcb.tick_params(axis='y',which='both',direction='out',size=5,labelsize=26,width=1,pad=5)\n",
    "\n",
    "# #ax.text(0.01,0.95,'%s'%break_description,size=26,va='bottom',ha='left',transform=ax.transAxes)\n",
    "axcb.set_ylabel('marginal posterior probability of membership',{'fontsize':28})\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T07:10:23.840666",
     "start_time": "2017-08-07T07:09:08.952638"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MERS_274_2frag_sCoal.CDS1_1,2.combined.subT.txt\n"
     ]
    }
   ],
   "source": [
    "## analyse posteriors of two frag trees\n",
    "subtree_root='/Users/evogytis/Documents/MERS_2.0/clean/analyses/beast/frag/'\n",
    "\n",
    "subtrees_path1=['MERS_274_2frag_sCoal.CDS1_1,2.combined.subT.txt'] ## files generated with samogitia.py from 2 fragment trees\n",
    "subtrees_path2=['MERS_274_2frag_sCoal.CDS2_1,2.combined.subT.txt']\n",
    "\n",
    "tipRe = re.compile(\"'([A-Za-z0-9\\_\\|\\-\\/]+)'\")\n",
    "\n",
    "subtree_posteriors1={}\n",
    "subtree_posteriors2={}\n",
    "N_intros1=[]\n",
    "N_intros2=[]\n",
    "\n",
    "mcmcCounter=0\n",
    "human_tips1=[]\n",
    "human_tips2=[]\n",
    "\n",
    "diffs=[]\n",
    "\n",
    "burnin=0\n",
    "mcmcCounter=0\n",
    "\n",
    "for run in range(len(subtrees_path1)):\n",
    "    print subtrees_path1[run]\n",
    "    f1=open(subtree_root+subtrees_path1[run],'r')\n",
    "    f2=open(subtree_root+subtrees_path2[run],'r')\n",
    "    for line1,line2 in zip(f1,f2):\n",
    "        l1=line1.strip().split('\\t')\n",
    "        l2=line2.strip().split('\\t')\n",
    "        \n",
    "        if len(l1)>0 and l1[0]!='state' and int(l1[0])>burnin:\n",
    "            subtree_leaves1=[tipRe.findall(string) for string in l1[1:]]\n",
    "            subtree_leaves2=[tipRe.findall(string) for string in l2[1:]]\n",
    "            \n",
    "            N_intros1.append(len(subtree_leaves1))\n",
    "            N_intros2.append(len(subtree_leaves2))\n",
    "            \n",
    "            diffs.append(len(subtree_leaves1)-len(subtree_leaves2))\n",
    "            \n",
    "            for leaves in subtree_leaves1:\n",
    "                \n",
    "                lf=','.join(sorted(leaves))\n",
    "                for tip in leaves:\n",
    "                    if tip not in human_tips1:\n",
    "                        human_tips1.append(tip.strip(\"'\"))\n",
    "                if subtree_posteriors1.has_key(lf):\n",
    "                    subtree_posteriors1[lf]+=1\n",
    "                else:\n",
    "                    subtree_posteriors1[lf]=1\n",
    "                    \n",
    "            for leaves in subtree_leaves2:\n",
    "                lf=','.join(sorted(leaves))\n",
    "                for tip in leaves:\n",
    "                    if tip not in human_tips2:\n",
    "                        human_tips2.append(tip.strip(\"'\"))\n",
    "                if subtree_posteriors2.has_key(lf):\n",
    "                    subtree_posteriors2[lf]+=1\n",
    "                else:\n",
    "                    subtree_posteriors2[lf]=1\n",
    "                    \n",
    "            mcmcCounter+=1\n",
    "            \n",
    "for clade in subtree_posteriors1.keys():\n",
    "    subtree_posteriors1[clade]=subtree_posteriors1[clade]/float(mcmcCounter)\n",
    "    \n",
    "for clade in subtree_posteriors2.keys():\n",
    "    subtree_posteriors2[clade]=subtree_posteriors2[clade]/float(mcmcCounter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T07:13:48.147327",
     "start_time": "2017-08-07T07:13:47.557916"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    }
   ],
   "source": [
    "human_embedding=frag2.reduceTree([k for k in frag2.Objects if k.branchType=='leaf' and 'human' in k.name])\n",
    "\n",
    "human_embedding.sortBranches(descending=False)\n",
    "order=[x.name.strip(\"'\") for x in human_embedding.traverse_tree()][::-1]\n",
    "print len(order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T07:19:52.526734",
     "start_time": "2017-08-07T07:13:56.359178"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174 174\n",
      "174 174\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "unique_clades=list(set(subtree_posteriors1.keys()+subtree_posteriors2.keys()))\n",
    "\n",
    "bestMatch1={}\n",
    "bestMatch2={}\n",
    "for tip in human_tips1: ## iterate over tips\n",
    "    tipPresence1=[subtree for subtree in subtree_posteriors1.keys() if tip in subtree.split(',')] ## identify clades which contain the tip\n",
    "    best1=sorted(tipPresence1,key=lambda x:-subtree_posteriors1[x])[0] ## get the clade with the highest posterior probability\n",
    "    bestMatch1[tip]=best1 ## remember highest probability membership of tip\n",
    "\n",
    "for tip in human_tips2:\n",
    "    tipPresence2=[subtree for subtree in subtree_posteriors2.keys() if tip in subtree.split(',')]\n",
    "    best2=sorted(tipPresence2,key=lambda x:-subtree_posteriors2[x])[0]\n",
    "    bestMatch2[tip]=best2\n",
    "\n",
    "shared_clades=[clade for clade in unique_clades if subtree_posteriors1.has_key(clade) and subtree_posteriors2.has_key(clade)]\n",
    "shared_tips=sorted([clade for clade in unique_clades if subtree_posteriors1.has_key(clade) and subtree_posteriors2.has_key(clade) and clade.count(',')==0],key=lambda x:order.index(x.strip(\"'\")))\n",
    "\n",
    "print len(human_tips1),len(human_tips2)\n",
    "\n",
    "human_tips1=sorted(shared_tips,key=lambda x:-subtree_posteriors2[bestMatch2[x]])+sorted(set(human_tips1)-set(shared_tips),key=lambda x:-subtree_posteriors1[bestMatch1[x]]) ## sort tips according to their best membership score\n",
    "human_tips2=sorted(shared_tips,key=lambda x:-subtree_posteriors2[bestMatch2[x]])+sorted(set(human_tips2)-set(shared_tips),key=lambda x:-subtree_posteriors2[bestMatch2[x]]) ## sort tips according to their best \n",
    "\n",
    "print len(human_tips1),len(human_tips2)\n",
    "\n",
    "# human_tips=sorted(human_tips,key=lambda x:-bt.decimalDate(x.strip(\"'\").split('|')[-1],variable=True)) ## sort tips according to their collection date\n",
    "\n",
    "#empty=np.zeros((len(shared_tips),len(shared_tips)))\n",
    "empty=np.zeros((len(order),len(order)))\n",
    "empty.fill(np.nan)\n",
    "#analysis=pd.DataFrame(empty,index=shared_tips,columns=shared_tips)\n",
    "analysis=pd.DataFrame(empty,index=order,columns=order)\n",
    "\n",
    "for tipA in shared_tips:\n",
    "    #tipOverlap1=[subtree for subtree in subtree_posteriors1.keys() if tipA in subtree.split(',')]\n",
    "    for tipB in shared_tips:\n",
    "        #tipOverlap=[subtree for subtree in shared_clades if tipA in subtree.split(',') and tipB in subtree.split(',')]\n",
    "        tipOverlap=[subtree for subtree in unique_clades if tipA in subtree.split(',') and tipB in subtree.split(',')]\n",
    "        \n",
    "        \n",
    "        \n",
    "#         tipOverlap1=[subtree for subtree in subtree_posteriors1.keys() if tipA in subtree.split(',')]\n",
    "#         tipOverlap2=[subtree for subtree in subtree_posteriors2.keys() if tipB in subtree.split(',')]\n",
    "        \n",
    "#         if len(tipOverlap)>0:\n",
    "#             if tipA==tipB:\n",
    "#                 if subtree_posteriors1.has_key(tipA) and subtree_posteriors2.has_key(tipB):\n",
    "#                     analysis[tipB][tipA]=np.mean([subtree_posteriors1[tipA],subtree_posteriors1[tipB]])\n",
    "#                 elif subtree_posteriors1.has_key(tipA):\n",
    "#                     analysis[tipB][tipA]=subtree_posteriors1[tipA]\n",
    "#                 elif subtree_posteriors2.has_key(tipB):\n",
    "#                     analysis[tipB][tipA]=subtree_posteriors2[tipB]\n",
    "\n",
    "#             elif human_tips1.index(tipA)>human_tips2.index(tipB):\n",
    "#                 analysis[tipB][tipA]=sum([subtree_posteriors1[x] for x in tipOverlap])\n",
    "\n",
    "#             elif human_tips1.index(tipA)<human_tips2.index(tipB):\n",
    "#                 analysis[tipB][tipA]=sum([subtree_posteriors2[x] for x in tipOverlap])\n",
    "\n",
    "        \n",
    "        if len(tipOverlap)>0:\n",
    "            if human_tips1.index(tipA)>=human_tips2.index(tipB): ## non-zero clades, lower triangle\n",
    "            \n",
    "                if subtree_posteriors1.has_key(tipA): ## if tip has been observed on its own\n",
    "                    if tipA==tipB: ## if plotting against itself\n",
    "                        analysis[tipA][tipB]=subtree_posteriors1[tipA]\n",
    "                    else:\n",
    "                        analysis[tipA][tipB]=sum([subtree_posteriors1[x] for x in tipOverlap if subtree_posteriors1.has_key(x)])\n",
    "                else:\n",
    "                    analysis[tipA][tipB]=sum([subtree_posteriors1[x] for x in tipOverlap if subtree_posteriors1.has_key(x)])\n",
    "                    \n",
    "            else:\n",
    "                if subtree_posteriors2.has_key(tipB): ## if tip has been observed on its own\n",
    "                    if tipA==tipB: ## if plotting against itself\n",
    "                        analysis[tipA][tipB]=subtree_posteriors2[tipB]\n",
    "                    else:\n",
    "                        analysis[tipA][tipB]=sum([subtree_posteriors2[x] for x in tipOverlap if subtree_posteriors2.has_key(x)])\n",
    "                else:\n",
    "                    analysis[tipA][tipB]=sum([subtree_posteriors2[x] for x in tipOverlap if subtree_posteriors2.has_key(x)])\n",
    "\n",
    "print 'Done!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T07:25:53.176676",
     "start_time": "2017-08-07T07:25:40.580970"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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o8eLFGQoi0uUZrLfeequmT5+uF198UWlpaQVyzytSUlL09NNP6/3331efPn0yPedVq1YpISFB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139cf150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25,20),facecolor='w')\n",
    "gs = gridspec.GridSpec(1, 2,width_ratios=[1,4],hspace=0.15,wspace=0.0)\n",
    "\n",
    "ax1 = plt.subplot(gs[0]) ## lower plot\n",
    "ax2 = plt.subplot(gs[1],sharey=ax1)\n",
    "\n",
    "branchWidth=2\n",
    "\n",
    "clist=[(0.0,mpl.cm.Vega10(0)),\n",
    "       (0.5,'grey'),\n",
    "       (1.0,mpl.cm.Vega10(1))]\n",
    "cmap = mpl.colors.LinearSegmentedColormap.from_list('mycmap',clist)\n",
    "\n",
    "\n",
    "for k in human_embedding.Objects:\n",
    "    c='grey'\n",
    "    xp=0\n",
    "    z=1\n",
    "    if k.traits.has_key('type'):\n",
    "        if k.traits['type']=='c':\n",
    "            c=cmap(1.0)\n",
    "        elif k.traits['type']=='h':\n",
    "            c=cmap(0.0)\n",
    "        xp=k.parent.absoluteTime\n",
    "\n",
    "    \n",
    "    x=k.absoluteTime\n",
    "    y=k.y-0.5\n",
    "\n",
    "    \n",
    "    if xp==0:\n",
    "        xp=x\n",
    "\n",
    "    if k.branchType=='leaf':\n",
    "        if 'camel' in k.name:\n",
    "            c=cmap(1.0)\n",
    "        elif 'human' in k.name:\n",
    "            c=cmap(0.0)\n",
    "        ax1.scatter(x,y,s=20,facecolor=c,edgecolor='none',zorder=4)\n",
    "        ax1.scatter(x,y,s=40,facecolor='w',edgecolor='none',zorder=3)\n",
    "        #ax1.plot([x,2020.0],[y,y],ls='-',color='k',lw=1)\n",
    "    else:\n",
    "        yl=k.children[0].y-0.5\n",
    "        yr=k.children[-1].y-0.5\n",
    "        ax1.plot([x,x],[yl,yr],color=c,lw=branchWidth,zorder=z)\n",
    "    #c='grey'\n",
    "    ax1.plot([x,xp],[y,y],color=c,lw=branchWidth,zorder=z)\n",
    "\n",
    "for k in human_embedding.Objects:\n",
    "    if k.parent.traits.has_key('type') and k.traits['type']!=k.parent.traits['type']:\n",
    "        if k.branchType=='node':\n",
    "            lvs=[order.index(frag2.tipMap[x]) for x in k.leaves]\n",
    "\n",
    "            ax2.axhline(max(lvs)+1,color='k',lw=1)\n",
    "            ax1.plot([k.absoluteTime,2020.0],[max(lvs)+1,max(lvs)+1],color='k',lw=1)\n",
    "\n",
    "        elif k.branchType=='leaf':\n",
    "            ax1.plot([k.absoluteTime,2020.0],[k.y,k.y],color='k',lw=1)\n",
    "            ax2.axhline(k.y,color='k',lw=1)\n",
    "    \n",
    "ax1.set_yticklabels([])\n",
    "ax1.tick_params(axis='y',size=0)\n",
    "ax1.set_xlim(2010.0,2016.0)\n",
    "ax1.set_ylim(0,human_embedding.ySpan)\n",
    "\n",
    "ax1.text(0.05,0.95,'A',size=50,transform=ax1.transAxes)\n",
    "\n",
    "\n",
    "ax2.set_ylim(0,human_embedding.ySpan)\n",
    "\n",
    "# ## mask NaNs\n",
    "masked_array = np.ma.array(np.array(analysis).T,mask=np.isnan(analysis))\n",
    "\n",
    "\n",
    "cmap=mpl.cm.get_cmap('inferno_r',20)\n",
    "\n",
    "# ## plot heatmap\n",
    "heatmap = ax2.pcolormesh(masked_array,edgecolors='none', linewidths=0,cmap=cmap,alpha=1)\n",
    "\n",
    "ax2.tick_params(length=0,labelsize=1)\n",
    "ax2.set_xlabel('human virus in fragment 1',size=32)\n",
    "ax2.yaxis.set_label_position('right')\n",
    "ax2.set_ylabel('human virus in fragment 2',size=32)\n",
    "\n",
    "ax2.spines['top'].set_visible(False)\n",
    "\n",
    "axcb = fig.add_axes([0.95, 0.15, 0.02, 0.6], frame_on=False)\n",
    "cb=mpl.colorbar.ColorbarBase(axcb,cmap=cmap,norm=mpl.colors.Normalize(vmin=0.0,vmax=1.0),orientation='vertical',alpha=1.0)\n",
    "\n",
    "axcb.yaxis.set_label_position(\"left\")\n",
    "axcb.tick_params(axis='y',which='both',direction='out',size=5,labelsize=26,width=1,pad=5)\n",
    "\n",
    "axcb.set_ylabel('marginal posterior probability of membership',{'fontsize':32})\n",
    "plt.savefig('./../figures/mers_fragments.png',dpi=150,bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T10:53:13.605527",
     "start_time": "2017-08-01T10:53:02.722869"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52.9267164759 46.7589990001 1.63295744917 2.59535051661\n",
      "Shared clades account for 0.9701 and 0.9474 of all clades in posteriors in frag1 and frag 2, respectively when ignoring clades at less than 1.0 frequency\n"
     ]
    },
    {
     "data": {
      "image/png": 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TVu1Mw+cpJ3Eio8jhNrzUCug1Cvjr1QjQqxEdpEPnVgHw16nLH/NWI8hHjZgQb7QK9Ya3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\nx/0f0wuEe6pQqMAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122b6c4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1=0\n",
    "g2=0\n",
    "b1=0\n",
    "b2=0\n",
    "\n",
    "olap=[clade for clade in unique_clades if subtree_posteriors1.has_key(clade) and subtree_posteriors2.has_key(clade)]\n",
    "\n",
    "for o in olap:\n",
    "    g1+=subtree_posteriors1[o]\n",
    "    g2+=subtree_posteriors2[o]\n",
    "\n",
    "cutoff=1.0\n",
    "for o in subtree_posteriors1.keys():\n",
    "    if o not in olap and subtree_posteriors1[o]<cutoff:\n",
    "        #print o\n",
    "        b1+=subtree_posteriors1[o]\n",
    "\n",
    "for o in subtree_posteriors2.keys():\n",
    "    if o not in olap and subtree_posteriors2[o]<cutoff:\n",
    "        b2+=subtree_posteriors2[o]\n",
    "\n",
    "print g1,g2,b1,b2\n",
    "print 'Shared clades account for %.4f and %.4f of all clades in posteriors in frag1 and frag 2, respectively when ignoring clades at less than %s frequency'%(g1/(g1+b1),g2/(g2+b2),cutoff)\n",
    "\n",
    "fig = plt.figure(figsize=(10,10),facecolor='w')\n",
    "\n",
    "gs = gridspec.GridSpec(3, 3,width_ratios=[1,8,2],height_ratios=[2,8,1],hspace=0.0,wspace=0.0)\n",
    "\n",
    "ax = plt.subplot(gs[4]) ## center\n",
    "\n",
    "\n",
    "left = plt.subplot(gs[3],sharey=ax) ## left\n",
    "bottom = plt.subplot(gs[7],sharex=ax) ## bottom\n",
    "\n",
    "\n",
    "cumulative_right=plt.subplot(gs[5],sharey=ax)\n",
    "cumulative_top=plt.subplot(gs[1],sharex=ax)\n",
    "\n",
    "s=60\n",
    "\n",
    "xo=[subtree_posteriors1[o] for o in olap]\n",
    "yo=[subtree_posteriors2[o] for o in olap]\n",
    "norm=mpl.colors.Normalize(0,2)\n",
    "ax.scatter(xo,yo,s=s,c=np.sum((xo,yo),axis=0),cmap=mpl.cm.pink_r,norm=norm,zorder=10)\n",
    "ax.scatter(xo,yo,s=s*2,facecolor='k',edgecolor='none',zorder=9)\n",
    "\n",
    "\n",
    "\n",
    "xxu=[subtree_posteriors1[o] for o in subtree_posteriors1.keys() if o not in olap]\n",
    "xyu=np.random.uniform(size=len(subtree_posteriors1)-len(olap))\n",
    "bottom.scatter(xxu,xyu,s=s,c=xxu,cmap=mpl.cm.Blues,zorder=10)\n",
    "bottom.scatter(xxu,xyu,s=s*2,facecolor='k',edgecolor='none',zorder=9)\n",
    "\n",
    "\n",
    "yxu=np.random.uniform(size=len(subtree_posteriors2)-len(olap))\n",
    "yyu=[subtree_posteriors2[o] for o in subtree_posteriors2.keys() if o not in olap]\n",
    "left.scatter(yxu,yyu,s=s,c=yyu,cmap=mpl.cm.Reds,zorder=10)\n",
    "left.scatter(yxu,yyu,s=s*2,facecolor='k',edgecolor='none',zorder=9)\n",
    "\n",
    "\n",
    "sxo=sorted(xo)\n",
    "syo=sorted(yo)\n",
    "cumulative_right.plot(np.cumsum(syo)/sum(syo+yyu),syo,c=mpl.cm.Reds(0.85))\n",
    "cumulative_top.plot(sxo,np.cumsum(sxo)/sum(sxo+xxu),c=mpl.cm.Blues(0.85))\n",
    "\n",
    "\n",
    "ax.grid()\n",
    "left.grid(axis='y')\n",
    "bottom.grid(axis='x')\n",
    "cumulative_top.grid()\n",
    "cumulative_right.grid()\n",
    "\n",
    "left.set_ylabel('posterior probability for clade in fragment 2')\n",
    "bottom.set_xlabel('posterior probability for clade in fragment 1')\n",
    "\n",
    "plt.setp(ax.get_xticklabels(), visible=False)\n",
    "plt.setp(ax.get_yticklabels(), visible=False)\n",
    "plt.setp(cumulative_top.get_xticklabels(), visible=False)\n",
    "plt.setp(cumulative_right.get_yticklabels(), visible=False)\n",
    "\n",
    "cumulative_top.set_yticks(np.linspace(0,1,6))\n",
    "cumulative_right.set_xticks(np.linspace(0,1,6))\n",
    "\n",
    "cumulative_top.set_yticklabels([])\n",
    "cumulative_right.set_xticklabels([])\n",
    "\n",
    "#cumulative_right.set_xlabel('cumulative clade probability')\n",
    "\n",
    "ax.tick_params(size=0)\n",
    "left.tick_params(axis='x',size=0)\n",
    "bottom.tick_params(axis='y',size=0)\n",
    "cumulative_right.tick_params(size=0)\n",
    "cumulative_top.tick_params(size=0)\n",
    "\n",
    "left.set_xticklabels([])\n",
    "bottom.set_yticklabels([])\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "bottom.spines['top'].set_visible(False)\n",
    "left.spines['right'].set_visible(False)\n",
    "cumulative_top.spines['bottom'].set_visible(False)\n",
    "cumulative_right.spines['left'].set_visible(False)\n",
    "\n",
    "cumulative_top.set_ylim(-0.01,1.05)\n",
    "cumulative_right.set_xlim(-0.01,1.05)\n",
    "plt.savefig('./../figures/mers_flower.png',dpi=300,bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T07:31:00.543181",
     "start_time": "2017-08-07T07:30:34.445886"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30130\n"
     ]
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "from Bio import AlignIO\n",
    "tree_path='MERS_CoV_274_rename.labelled_tree.newick'\n",
    "asr_path='MERS_CoV_274_rename.ML_sequence.fasta'\n",
    "cross_path='MERS_CoV_274_rename.position_cross_reference.txt'\n",
    "zipfile=ZipFile('./../data/phylo/CFML_results.zip')\n",
    "\n",
    "for line in zipfile.open(cross_path).readlines():\n",
    "    l=map(int,line.strip('\\n').split(','))\n",
    "#     site_map={x-1:i for i,x in enumerate(l) if x!=0}\n",
    "    site_map={i:x-1 for i,x in enumerate(l) if x!=0}\n",
    "    L=len(l)\n",
    "print L\n",
    "alignment = AlignIO.read(zipfile.open(asr_path), \"fasta\")\n",
    "\n",
    "seq_map={}\n",
    "for seq in alignment:\n",
    "#     seq_map[seq.id]=str(seq.seq)\n",
    "    seq_map[seq.id]=''.join([seq.seq[site_map[i]] for i in sorted(site_map.keys())])\n",
    "\n",
    "ll=bt.tree()\n",
    "\n",
    "for line in zipfile.open(tree_path).readlines():\n",
    "    l=line.strip('\\n')\n",
    "    bt.make_tree(l,ll)\n",
    "\n",
    "ll.traverse_tree()\n",
    "ll.sortBranches()\n",
    "\n",
    "\n",
    "for k in ll.Objects:\n",
    "    if isinstance(k,bt.leaf): ## if leaf - add trait\n",
    "        k.traits['id']=k.numName\n",
    "    else:\n",
    "        k.traits['id']=k.traits['label']\n",
    "    \n",
    "    \n",
    "    if k.branchType=='node':\n",
    "        node_id=k.traits['label']\n",
    "        k.traits['seq']=seq_map[node_id]\n",
    "#         k.traits['seq']=''.join([seq_map[node_id][site_map[i]] for i in sorted(site_map.keys())])\n",
    "    else:\n",
    "        k.traits['seq']=seq_map[k.numName]\n",
    "#         k.traits['seq']=''.join([seq_map[k.numName][site_map[i]] for i in sorted(site_map.keys())])\n",
    "        \n",
    "#print [k.traits for k in ll.Objects]\n",
    "for k in ll.Objects:\n",
    "    if k.traits.has_key('seq') and k.parent.traits.has_key('seq'):\n",
    "        cur_seq=k.traits['seq']\n",
    "        anc_seq=k.parent.traits['seq']\n",
    "        \n",
    "        k.traits['mu']=['%s_%s%s'%(i,m[0],m[1]) for i,m in enumerate(zip(anc_seq,cur_seq)) if m[0]!=m[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T10:55:04.683077",
     "start_time": "2017-08-01T10:54:55.732306"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "mutations={}\n",
    "\n",
    "for k in ll.Objects: ## iterate over objects\n",
    "\n",
    "    if isinstance(k,bt.leaf): ## if leaf - add trait\n",
    "        k.traits['id']=k.numName\n",
    "    else:\n",
    "        k.traits['id']=k.traits['label']\n",
    "\n",
    "\n",
    "    key=k.traits['id']\n",
    "    k.traits['mu']=[]\n",
    "\n",
    "    if k.traits.has_key('id') and k.parent.traits.has_key('id'):\n",
    "        keyp=k.parent.traits['id']\n",
    "\n",
    "        for i in range(len(seq_map[key])): \n",
    "            #print i\n",
    "            if seq_map[key][i]!=seq_map[keyp][i]: ## for every difference (i.e. mutation) along a branch\n",
    "                nucleotide_then=seq_map[keyp][i]\n",
    "                nucleotide_now=seq_map[key][i]\n",
    "\n",
    "                mutation=[nucleotide_then,nucleotide_now]\n",
    "#                 mutation=sorted(mutation)\n",
    "\n",
    "                mu='%s'%(''.join(mutation))\n",
    "\n",
    "#                 site=poly_to_actual[i]\n",
    "                site=i\n",
    "\n",
    "                full_mu='%s_%s'%(site,mu)\n",
    "\n",
    "                if mutations.has_key(full_mu):\n",
    "                    mutations[full_mu]+=1\n",
    "                else:\n",
    "                    mutations[full_mu]=1\n",
    "\n",
    "#                 if mutations[site].has_key(mu):\n",
    "#                     mutations[site][mu]+=1\n",
    "#                 else:\n",
    "#                     mutations[site][mu]=1\n",
    "\n",
    "#                 k.traits['mu'][site]=mu\n",
    "\n",
    "                k.traits['mu'].append(full_mu)\n",
    "ll.drawTree()  \n",
    "print 'Done!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-01T10:56:00.377944",
     "start_time": "2017-08-01T10:55:29.325860"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "547\n"
     ]
    },
    {
     "data": {
      "image/png": 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ApEnusJfVGjyFO394ONoUj5o7AAyJBB4AjJ/kDq3Y1OkzBpI7AAAAxORcExPJsiwkSRKy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\nt6H6QwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122a07190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_tips=len([k for k in ll.Objects if k.branchType=='leaf'])\n",
    "L_aln=len(seq_map.values()[0])\n",
    "\n",
    "empty=np.zeros((N_tips,L_aln))\n",
    "empty.fill(np.nan)\n",
    "analysis=pd.DataFrame(empty,index=range(N_tips),columns=range(L_aln))\n",
    "\n",
    "\n",
    "norm=mpl.colors.Normalize(1,len(ll.nodes)+1,clip=False)\n",
    "perturb=list(np.random.choice(ll.nodes,len(ll.nodes),replace=False))\n",
    "\n",
    "from matplotlib import colors\n",
    "\n",
    "cmap=mpl.cm.Set1\n",
    "\n",
    "valid_nucleotides=['A','C','T','G','U']\n",
    "\n",
    "# plotTree=ll.collapseNodes('length',lambda x:x<=1e-07)\n",
    "plotTree=ll\n",
    "plotTree.sortBranches()\n",
    "print len(plotTree.Objects)\n",
    "\n",
    "for k in sorted(plotTree.Objects,key=lambda w:-w.height): ## start from earliest objects\n",
    "\n",
    "    key=k.traits['id']\n",
    "    derived_alleles={int(mut.split('_')[0]):mut.split('_')[1] for mut in k.traits['mu']} ## get mutations on branch\n",
    "\n",
    "    for site in sorted(derived_alleles.keys()): ## iterate over sites changed\n",
    "        mu='%s_%s'%(site,derived_alleles[site]) ## format mutation\n",
    "\n",
    "        #all_alleles=[seq_map[seq][site] for seq in seq_map.keys() if 'NODE' not in seq and seq_map[seq][site] in valid_nucleotides] ## get the site in all sequences\n",
    "\n",
    "        if k.branchType=='node': ## only care for nodes\n",
    "\n",
    "            leaves=k.leaves\n",
    "            children=[w for w in plotTree.Objects if w.branchType=='leaf' and w.numName in leaves] ## get every child\n",
    "            \n",
    "            children_allele=[seq_map[child][site] for child in leaves if seq_map[child][site] in valid_nucleotides] ## get the site's current state in children\n",
    "            \n",
    "            for n in children:\n",
    "                if seq_map[n.numName][site]==derived_alleles[site][-1] and seq_map[n.numName][site] in valid_nucleotides: ## site same as ancestor\n",
    "                    if mutations[mu]>1: ## ... but homoplasious\n",
    "                        analysis[site][n.y]=1\n",
    "                    else: ## all good, single mutation in ancestor inherited by one descendant\n",
    "                        analysis[site][n.y]=2\n",
    "                    \n",
    "                if seq_map[n.numName][site]!=derived_alleles[site][-1] and seq_map[n.numName][site] in valid_nucleotides: ## site reverted?\n",
    "                    analysis[site][n.y]=0\n",
    "                    \n",
    "fig = plt.figure(figsize=(20,10),facecolor='w')\n",
    "\n",
    "## define subplots\n",
    "gs = gridspec.GridSpec(1, 2,width_ratios=[1,5],hspace=0.0,wspace=0.0)\n",
    "\n",
    "ax1 = plt.subplot(gs[1])\n",
    "ax2 = plt.subplot(gs[0],sharey=ax1)\n",
    "\n",
    "\n",
    "## mask NaNs\n",
    "analysis.dropna(axis=1,how='any',thresh=1,inplace=True) ## drop NaNs, singleton mutations mostly\n",
    "masked_array = np.ma.array(np.array(analysis),mask=np.isnan(analysis))\n",
    "\n",
    "heatmap = ax1.pcolor(masked_array,edgecolors='none', linewidths=0,cmap=cmap,alpha=1)\n",
    "# heatmap = ax1.pcolormesh(np.array(analysis),edgecolors='none', linewidths=0,cmap=cmap,norm=norm,alpha=1)\n",
    "\n",
    "ax1.set_ylim(0,N_tips+1)\n",
    "\n",
    "ax1.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(1))\n",
    "\n",
    "branchWidth=2\n",
    "tipSize=30\n",
    "for k in plotTree.Objects:\n",
    "    key=k.traits['id']\n",
    "    y=k.y+0.5\n",
    "    #yp=k.parent.y\n",
    "    b=k.length\n",
    "    x=k.height\n",
    "    xp=k.parent.height \n",
    "    \n",
    "    if xp==None or xp==0.0:\n",
    "        xp=x\n",
    "    \n",
    "    c='k'\n",
    "    if isinstance(k,bt.leaf):\n",
    "        if 'camel' in k.numName:\n",
    "            tc=mpl.cm.Vega10(1)\n",
    "        else:\n",
    "            tc=mpl.cm.Vega10(0)\n",
    "\n",
    "        ax2.add_patch(plt.Rectangle((x,y-0.5),1.0,1.0,facecolor=tc,edgecolor='none'))\n",
    "\n",
    "    elif isinstance(k,bt.node):\n",
    "        yl=k.children[0].y+0.5\n",
    "        yr=k.children[-1].y+0.5\n",
    "\n",
    "        c='k'\n",
    "        #print k.index,plotTree.nodes.index(k),norm(plotTree.nodes.index(k)),cmap(norm(plotTree.nodes.index(k)))\n",
    "        ax2.plot([x,x],[yl,yr],color=c,lw=branchWidth,zorder=98) \n",
    "        \n",
    "    ax2.plot([x,xp],[y,y],color=c,lw=branchWidth,zorder=98)\n",
    "\n",
    "ax2.set_xlim(-0.0001,ll.treeHeight+0.0001)\n",
    "\n",
    "\n",
    "# ax1.spines['top'].set_visible(False)\n",
    "# ax1.spines['right'].set_visible(False)\n",
    "# ax1.spines['bottom'].set_visible(False)\n",
    "# ax1.spines['left'].set_visible(False)\n",
    "\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "# ax2.spines['bottom'].set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "\n",
    "ax1.set_yticklabels([])\n",
    "ax1.set_xticklabels([])\n",
    "ax2.set_yticklabels([])\n",
    "\n",
    "ax1.tick_params(size=0)\n",
    "ax2.tick_params(axis='x',labelsize=14)\n",
    "ax2.tick_params(axis='y',size=0)\n",
    "\n",
    "ax1.set_xlabel('relative genomic position',size=30)\n",
    "ax2.set_xlabel('divergence',size=30)\n",
    "\n",
    "plt.savefig('./../figures/mers_incompatibilities.png',dpi=300,bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-02T12:40:26.728463",
     "start_time": "2017-08-02T12:40:26.642655"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(274, 636)\n"
     ]
    }
   ],
   "source": [
    "print analysis.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}