03 What is the transition profile for each subgraph?.ipynb

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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import brewer2mpl\n",
    "import colorsys\n",
    "import math\n",
    "import dendropy as dp\n",
    "import json\n",
    "import os\n",
    "\n",
    "from datetime import datetime\n",
    "from Bio import AlignIO, SeqIO\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio.Seq import Seq\n",
    "from Levenshtein import distance\n",
    "from itertools import combinations, product, permutations\n",
    "from time import time\n",
    "from __future__ import division\n",
    "from collections import Counter, defaultdict\n",
    "from copy import deepcopy\n",
    "from random import shuffle, choice, sample\n",
    "from scipy.stats.mstats import mquantiles\n",
    "from scipy.stats import norm, expon, poisson, binom\n",
    "from scipy.misc import comb\n",
    "from IPython.display import Math, clear_output\n",
    "from networkx.readwrite import json_graph \n",
    "\n",
    "%matplotlib inline"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "A transition profile is the host-to-host transition profile for each subgraph."
   ]
  },
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   "cell_type": "code",
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   "source": [
    "if os.getcwd().split('/')[-1] == 'All_IRD_Analysis_Run2':\n",
    "    os.chdir('subgraph_pkl')\n",
    "subgraphs = dict()\n",
    "counter = 0\n",
    "for f in os.listdir(os.getcwd()):\n",
    "    if f.split('.')[-1] == 'pkl':\n",
    "        i = f.split('_')[0]\n",
    "        g = nx.read_gpickle('{0}'.format(f))\n",
    "        subgraphs[counter] = g\n",
    "        counter += 1\n",
    "    \n",
    "if os.getcwd().split('/')[-1] == 'subgraph_pkl':\n",
    "    os.chdir('..')\n"
   ]
  },
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   "cell_type": "code",
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       " 717: 1,\n",
       " 718: 1,\n",
       " 719: 1,\n",
       " 720: 2,\n",
       " 721: 1,\n",
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       " 732: 1,\n",
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       " 778: 2,\n",
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       " 782: 1,\n",
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       " 835: 21,\n",
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       " 924: 4,\n",
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       " 939: 1,\n",
       " 940: 1,\n",
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       " 946: 54,\n",
       " 947: 1,\n",
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       " 949: 1,\n",
       " 950: 1,\n",
       " 951: 1,\n",
       " 952: 1,\n",
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       " 954: 1,\n",
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       " 962: 1,\n",
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       " 964: 1,\n",
       " 965: 1,\n",
       " 966: 1,\n",
       " 967: 1,\n",
       " 968: 3,\n",
       " 969: 1,\n",
       " 970: 1,\n",
       " 971: 1,\n",
       " 972: 1,\n",
       " 973: 1,\n",
       " 974: 1,\n",
       " 975: 1,\n",
       " 976: 1,\n",
       " 977: 1,\n",
       " 978: 1,\n",
       " 979: 4,\n",
       " 980: 1,\n",
       " 981: 1,\n",
       " 982: 1,\n",
       " 983: 1,\n",
       " 984: 1,\n",
       " 985: 1,\n",
       " 986: 1,\n",
       " 987: 1,\n",
       " 988: 1,\n",
       " 989: 1,\n",
       " 990: 1,\n",
       " 991: 1,\n",
       " 992: 1,\n",
       " 993: 1,\n",
       " 994: 1,\n",
       " 995: 1,\n",
       " 996: 1,\n",
       " 997: 1,\n",
       " 998: 1,\n",
       " 999: 1,\n",
       " ...}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgraph_sizes = dict()\n",
    "for i, subgraph in subgraphs.items():\n",
    "    subgraph_sizes[i] = len(subgraph.nodes())\n",
    "subgraph_sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's make the transition matrix for each subgraph.\n",
    "def get_graph_of_interest(subgraph_list, subgraph_idx):\n",
    "    goi = nx.DiGraph(subgraph_list[subgraph_idx])\n",
    "    for source, sink in goi.edges():\n",
    "        if goi.node[source]['collection_date'] >= goi.node[sink]['collection_date']:\n",
    "            goi.remove_edge(source, sink)\n",
    "            \n",
    "    return goi\n",
    "\n",
    "# Initialize a dictionary for the transition matrix.\n",
    "def initialize_transition_matrix(labels):\n",
    "    \"\"\"\n",
    "    Pass in a list of labels that define what the transitions are.\n",
    "    \"\"\"\n",
    "    transition_matrix = dict()\n",
    "    for t1 in labels:\n",
    "        if t1 not in transition_matrix.keys():\n",
    "            transition_matrix[t1] = dict()\n",
    "        for t2 in labels:\n",
    "            transition_matrix[t1][t2] = 0\n",
    "            \n",
    "    return transition_matrix\n",
    "\n",
    "# Count the source-sink relationships\n",
    "def compute_transition_matrix(goi, transition_matrix):\n",
    "    for source, sink, data in goi.edges(data=True):\n",
    "        source_host = goi.node[source]['host_species']\n",
    "        sink_host = goi.node[sink]['host_species']\n",
    "\n",
    "        if '/' in source_host:\n",
    "            source_host = source_host.split('/')[1]\n",
    "        if '/' in sink_host:\n",
    "            sink_host = sink_host.split('/')[1]\n",
    "        if sink_host not in host_types: \n",
    "            sink_host = 'Other'\n",
    "        if source_host not in host_types:\n",
    "            source_host = 'Other'\n",
    "\n",
    "        transition_matrix[source_host][sink_host] += 1\n",
    "        \n",
    "    return transition_matrix\n",
    "\n",
    "# # Sample code below\n",
    "# goi = get_graph_of_interest(subgraphs, 1661)\n",
    "# transition_matrix = initialize_transition_matrix(host_types)\n",
    "# transition_matrix = compute_transition_matrix(goi, transition_matrix)\n",
    "# transition_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.,   1.,   3.,  11.,   4.,   6.,  13.,  45.,  43.,  17.]),\n",
       " array([ 7.691758 ,  7.7222042,  7.7526504,  7.7830966,  7.8135428,\n",
       "         7.843989 ,  7.8744352,  7.9048814,  7.9353276,  7.9657738,  7.99622  ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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vaS1SAjo9Ii6RtAA4S9KHgSXAQRXGYGZm49S1pqTxclOS2eBwU1IXI+jlpiQzM+tPTgxm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116314b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a histogram of the PWIs represented\n",
    "def get_pwi_histogram(subgraphs, idx):\n",
    "    pwis = []\n",
    "    goi = subgraphs[idx]\n",
    "    \n",
    "    for source, sink, data in goi.edges(data=True):\n",
    "        pwis.append(data['pwi'])\n",
    "        \n",
    "    fig = plt.figure()\n",
    "    plt.xlabel('PWI')\n",
    "    plt.ylabel('Counts')\n",
    "    plt.title('Subgraph {0} PWIs Represented'.format(idx))\n",
    "    if len(set(pwis)) == 1:\n",
    "        fig = plt.hist(pwis, range=(pwis[0]-0.5, pwis[0]+0.5))\n",
    "    else:\n",
    "        fig = plt.hist(pwis)\n",
    "    return fig\n",
    "\n",
    "get_pwi_histogram(subgraphs, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AwBnUarWWz+I5QP+zeQ4AAABwBu+8806Mjo7G8ePHT35n9xygv3l5DgAAAHAGs+2ee30O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\nAAAAAAAjzwEAAAAAYOQ5AAAAAACMPAcAAAAAgJHnAAAAAAAw8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104433750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_network(goi):\n",
    "    fig = plt.figure(figsize=(20, 20))\n",
    "    colormap = {'Human':'red', 'Avian':'blue', 'Swine':'green', 'Environment':'orange', 'Other':'pink'}\n",
    "    labels = list()\n",
    "    for n, d, in goi.nodes(data=True):\n",
    "        host = d['host_species']\n",
    "        if '/' in host:\n",
    "            host = host.split('/')[1]\n",
    "        if host not in colormap.keys():\n",
    "            host = 'Other'\n",
    "        labels.append(host)\n",
    "    colors = [colormap[l] for l in labels]\n",
    "    fig = nx.draw(goi, node_color=colors)\n",
    "    return fig\n",
    "plot_network(subgraphs[546])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def table_transition_matrix(transition_matrix_dictionary):\n",
    "    tmatrix_df = pd.DataFrame(transition_matrix_dictionary).T\n",
    "    tmatrix_df.loc['Environment', 'Avian']\n",
    "    return tmatrix_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_transition_matrix(tmatrix_df, subgraph_idx):\n",
    "    fig = plt.figure(figsize=(6,6))\n",
    "    fig = plt.pcolor(tmatrix_df, cmap='Greens')\n",
    "    plt.xticks(np.arange(len(tmatrix_df.index))+0.5, tmatrix_df.index)\n",
    "    plt.xlabel('Destination')\n",
    "    plt.yticks(np.arange(len(tmatrix_df.index))+0.5, tmatrix_df.index)\n",
    "    plt.ylabel('Source')\n",
    "    plt.title('Subgraph {0} Transition Matrix'.format(subgraph_idx))\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[d['pwi'] for _,_,d in subgraphs[56].edges(data=True)]"
   ]
  },
  {
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   "source": [
    "import os\n",
    "\n",
    "if 'subgraph_plots' not in os.listdir(os.getcwd()):\n",
    "    os.mkdir('subgraph_plots')\n",
    "\n",
    "host_types = ['Avian', 'Human' , 'Swine', 'Environment', 'Other']\n",
    "\n",
    "for i, subgraph in subgraphs.items():\n",
    "    if len(subgraph.edges()) > 1:\n",
    "        goi = subgraphs[i]\n",
    "\n",
    "        transition_matrix = initialize_transition_matrix(host_types)\n",
    "        transition_matrix = compute_transition_matrix(goi, transition_matrix)\n",
    "        network_plot = plot_network(goi)\n",
    "        plt.savefig('subgraph_plots/{0} Network.pdf'.format(i))\n",
    "        clear_output() \n",
    "        plt.close()\n",
    "        # Plot transition matrix and save PDF & CSV versions of them. \n",
    "        tmatrix_df = table_transition_matrix(transition_matrix)\n",
    "        tmatrix_df.to_csv('subgraph_plots/{0} Transition Matrix.csv'.format(i))\n",
    "        plt.figure()\n",
    "        transition_matrix_plot = plot_transition_matrix(tmatrix_df, i)\n",
    "        plt.savefig('subgraph_plots/{0} Transition Matrix.pdf'.format(i))\n",
    "        plt.close()\n",
    "        clear_output()\n",
    "        \n",
    "        pwi_histogram = get_pwi_histogram(subgraphs, i)\n",
    "        plt.savefig('subgraph_plots/{0} PWI Histogram.pdf'.format(i))\n",
    "        plt.close()\n",
    "        clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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