07 What is the inter-subtypic relationships? Includes circos plots..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import scipy.spatial.distance as distance \n",
    "import scipy.cluster.hierarchy as sch \n",
    "import matplotlib.gridspec as gridspec \n",
    "import matplotlib.patches as patches\n",
    "import math\n",
    "import bokeh.mpl\n",
    "\n",
    "from collections import Counter #, defaultdict\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "G = nx.read_gpickle('20141103 All IRD Final Graph.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# [edge for edge in G.edges(data=True) if edge[2]['edge_type'] == 'reassortant']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2106\n"
     ]
    }
   ],
   "source": [
    "# Check to make sure that reassortant viruses do not have transmission edges going into them.\n",
    "reassortant_viruses = set(edge[1] for edge in G.edges(data=True) if edge[2]['edge_type'] == 'reassortant')\n",
    "print(len(reassortant_viruses))\n",
    "for v in reassortant_viruses:\n",
    "    for edge in G.in_edges(v, data=True):\n",
    "        if edge[2]['edge_type'] == 'full_complement':\n",
    "            print(v) #should not print anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def normalize_counter(counter):\n",
    "    total = sum(counter.values())\n",
    "    \n",
    "    normalized = dict()\n",
    "    for k, v in counter.items():\n",
    "        normalized[k] = float(v) / float(total)\n",
    "    return normalized"
   ]
  },
  {
   "cell_type": "code",
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      "Counter({('H3N8', 'H3N8'): 1, ('H8N4', 'H3N8'): 1})\n",
      "Counter({('H3N8', 'H4N8'): 1, ('H4N6', 'H4N8'): 1})\n",
      "Counter({('H4N6', 'H9N2'): 1, ('H9N2', 'H9N2'): 1})\n",
      "Counter({('H10N3', 'H12N5'): 1, ('H12N5', 'H12N5'): 1})\n",
      "Counter({('H3N8', 'H3N2'): 1, ('H6N2', 'H3N2'): 1})\n",
      "Counter({('H5N5', 'H5N2'): 1, ('H4N2', 'H5N2'): 1})\n",
      "Counter({('H4N6', 'H4N8'): 4, ('H11N8', 'H4N8'): 1, ('H10N2', 'H4N8'): 1, ('H6N8', 'H4N8'): 1, ('H10N8', 'H4N8'): 1, ('H3N8', 'H4N8'): 1, ('H4N8', 'H4N8'): 1, ('H5N1', 'H4N8'): 1})\n",
      "Counter({('H5N2', 'H5N3'): 5, ('H2N3', 'H5N3'): 1, ('H5N3', 'H5N3'): 1, ('H2N1', 'H5N3'): 1})\n",
      "Counter({('H3N2', 'H4N2'): 1, ('H4N8', 'H4N2'): 1})\n",
      "Counter({('H4N6', 'H4N3'): 1, ('H2N3', 'H4N3'): 1})\n",
      "Counter({('H3N8', 'H3N2'): 1, ('H5N2', 'H3N2'): 1})\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Code that normalizes the in-edge counts to 1.0\n",
    "For example, if we have a node that has 6 reassortant in_edges belonging to 3 source pair combinations that could explain its\n",
    "existence, and we wanted to count the number of different subtypes that gave it its genes, we first count, and then normalize\n",
    "the counter.\n",
    "\"\"\"\n",
    "for v in reassortant_viruses:\n",
    "    in_edges = G.in_edges(v, data=True)\n",
    "    sources = [s for s, _, _ in in_edges]\n",
    "#     print(sources)\n",
    "    \n",
    "    sink_subtype = G.node[v]['subtype']\n",
    "    subtype_relationship_counts = Counter()\n",
    "    for source in sources:\n",
    "        source_subtype = G.node[source]['subtype']\n",
    "        subtype_relationship_counts[(source_subtype, sink_subtype)] += 1\n",
    "        \n",
    "    print(subtype_relationship_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, I think the correct normalization is simply a single count of subtypes represented for each \n",
    "source, i.e. no need to normalize. This is because reassortment is a rare thing, so a single count matters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Warning: this cell takes some time to run.\n",
    "subtypeG = nx.DiGraph()\n",
    "\n",
    "for v in reassortant_viruses:\n",
    "    in_edges = G.in_edges(v, data=True)\n",
    "    sources = [s for s, _, _ in in_edges]\n",
    "    source_subtypes = set([G.node[s]['subtype'] for s in sources])\n",
    "#     print(source_subtypes)\n",
    "    \n",
    "    sink_subtype = G.node[v]['subtype']\n",
    "    \n",
    "    for source_subtype in source_subtypes:\n",
    "        if (source_subtype, sink_subtype) not in G.edges():\n",
    "            subtypeG.add_edge(source_subtype, sink_subtype, count=1)\n",
    "        else:\n",
    "            subtypeG.edge[source_subtype][sink_subtype]['count'] += 1\n",
    "        \n",
    "#     print(subtype_relationship_counts)\n",
    "\n",
    "# Save the subtype graph to disk.\n",
    "nx.write_gpickle(subtypeG, '07 inter-subtype interactions.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('H10N8', 'H6N8', {'count': 1}),\n",
       " ('H10N8', 'H4N8', {'count': 1}),\n",
       " ('H10N8', 'H1N3', {'count': 1}),\n",
       " ('H10N8', 'H10N8', {'count': 1}),\n",
       " ('H10N8', 'H10N2', {'count': 1}),\n",
       " ('H10N8', 'H10N4', {'count': 1}),\n",
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       " ('H10N7', 'H10N6', {'count': 1}),\n",
       " ('H10N7', 'H10N7', {'count': 1}),\n",
       " ('H10N7', 'Mixed', {'count': 1}),\n",
       " ('H10N7', 'H12N5', {'count': 1}),\n",
       " ('H10N7', 'H10N2', {'count': 1}),\n",
       " ('H10N7', 'H2N7', {'count': 1}),\n",
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       " ('H2N3', 'H11N2', {'count': 1}),\n",
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       " ('H2N3', 'H11N9', {'count': 1}),\n",
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       " ('H2N3', 'H3N8', {'count': 1}),\n",
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       " ('H2N3', 'H10N6', {'count': 1}),\n",
       " ('H2N3', 'H7N2', {'count': 1}),\n",
       " ('H2N3', 'Mixed', {'count': 1}),\n",
       " ('H2N3', 'H6N2', {'count': 1}),\n",
       " ('H2N3', 'H2N5', {'count': 1}),\n",
       " ('H2N3', 'H2N6', {'count': 1}),\n",
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       " ('H2N3', 'H2N1', {'count': 1}),\n",
       " ('H2N3', 'H2N2', {'count': 1}),\n",
       " ('H2N3', 'H2N3', {'count': 1}),\n",
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       " ('Mixed', 'H3N1', {'count': 1}),\n",
       " ('Mixed', 'H10N1', {'count': 1}),\n",
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       " ('Mixed', 'H3N2', {'count': 1}),\n",
       " ('Mixed', 'H10N2', {'count': 1}),\n",
       " ('Mixed', 'H3N6', {'count': 1}),\n",
       " ('H9N8', 'H12N4', {'count': 1}),\n",
       " ('H5N7', 'H5N7', {'count': 1}),\n",
       " ('H5N3', 'H11N8', {'count': 1}),\n",
       " ('H5N3', 'H2N7', {'count': 1}),\n",
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       " ('H5N3', 'H4N3', {'count': 1}),\n",
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       " ('H5N3', 'H5N1', {'count': 1}),\n",
       " ('H5N3', 'H12N5', {'count': 1}),\n",
       " ('H5N3', 'H10N8', {'count': 1}),\n",
       " ('H5N3', 'H9N2', {'count': 1}),\n",
       " ('H5N3', 'H7N3', {'count': 1}),\n",
       " ('H5N3', 'Mixed', {'count': 1}),\n",
       " ('H5N3', 'H6N2', {'count': 1}),\n",
       " ('H5N3', 'H6N3', {'count': 1}),\n",
       " ('H5N3', 'H7N1', {'count': 1}),\n",
       " ('H5N3', 'H10N3', {'count': 1}),\n",
       " ('H5N3', 'H10N6', {'count': 1}),\n",
       " ('H12N8', 'H4N8', {'count': 1}),\n",
       " ('H12N8', 'H1N8', {'count': 1}),\n",
       " ('H12N8', 'H12N3', {'count': 1}),\n",
       " ('H12N8', 'H12N8', {'count': 1}),\n",
       " ('H12N8', 'H12N4', {'count': 1}),\n",
       " ('H12N8', 'H12N7', {'count': 1}),\n",
       " ('H12N8', 'H3N8', {'count': 1}),\n",
       " ('H4N7', 'H3N8', {'count': 1}),\n",
       " ('H3N9', 'H4N9', {'count': 1}),\n",
       " ('H3N9', 'H2N3', {'count': 1}),\n",
       " ('H3N9', 'H11N9', {'count': 1}),\n",
       " ('H3N9', 'H2N9', {'count': 1}),\n",
       " ('H3N9', 'H5N2', {'count': 1}),\n",
       " ('H3N9', 'H3N9', {'count': 1}),\n",
       " ('H3N9', 'H3N8', {'count': 1}),\n",
       " ('H3N9', 'H3N2', {'count': 1}),\n",
       " ('H3N9', 'H3N6', {'count': 1}),\n",
       " ('H3N8', 'H7N2', {'count': 1}),\n",
       " ('H3N8', 'H11N7', {'count': 1}),\n",
       " ('H3N8', 'H11N2', {'count': 1}),\n",
       " ('H3N8', 'H11N9', {'count': 1}),\n",
       " ('H3N8', 'H11N8', {'count': 1}),\n",
       " ('H3N8', 'H12N5', {'count': 1}),\n",
       " ('H3N8', 'H3N9', {'count': 1}),\n",
       " ('H3N8', 'H3N8', {'count': 1}),\n",
       " ('H3N8', 'H3N1', {'count': 1}),\n",
       " ('H3N8', 'H3N3', {'count': 1}),\n",
       " ('H3N8', 'H3N2', {'count': 1}),\n",
       " ('H3N8', 'H3N5', {'count': 1}),\n",
       " ('H3N8', 'H3N7', {'count': 1}),\n",
       " ('H3N8', 'H3N6', {'count': 1}),\n",
       " ('H3N8', 'H10N8', {'count': 1}),\n",
       " ('H3N8', 'H4N8', {'count': 1}),\n",
       " ('H3N8', 'H4N9', {'count': 1}),\n",
       " ('H3N8', 'H5N8', {'count': 1}),\n",
       " ('H3N8', 'H4N2', {'count': 1}),\n",
       " ('H3N8', 'H4N1', {'count': 1}),\n",
       " ('H3N8', 'H4N6', {'count': 1}),\n",
       " ('H3N8', 'H4N7', {'count': 1}),\n",
       " ('H3N8', 'H6N8', {'count': 1}),\n",
       " ('H3N8', 'H13N6', {'count': 1}),\n",
       " ('H3N8', 'H7N9', {'count': 1}),\n",
       " ('H3N8', 'H7N8', {'count': 1}),\n",
       " ('H3N8', 'H7N5', {'count': 1}),\n",
       " ('H3N8', 'H7N4', {'count': 1}),\n",
       " ('H3N8', 'H6N2', {'count': 1}),\n",
       " ('H3N8', 'H7N3', {'count': 1}),\n",
       " ('H3N8', 'H10N7', {'count': 1}),\n",
       " ('H3N8', 'H12N7', {'count': 1}),\n",
       " ('H3N8', 'H6N7', {'count': 1}),\n",
       " ('H3N8', 'H6N1', {'count': 1}),\n",
       " ('H3N8', 'Mixed', {'count': 1}),\n",
       " ('H3N8', 'H12N8', {'count': 1}),\n",
       " ('H3N8', 'H2N1', {'count': 1}),\n",
       " ('H3N8', 'H1N9', {'count': 1}),\n",
       " ('H3N8', 'H2N7', {'count': 1}),\n",
       " ('H3N8', 'H5N2', {'count': 1}),\n",
       " ('H3N8', 'H2N2', {'count': 1}),\n",
       " ('H3N8', 'H2N3', {'count': 1}),\n",
       " ('H3N8', 'H14N2', {'count': 1}),\n",
       " ('H3N8', 'H1N1', {'count': 1}),\n",
       " ('H3N8', 'H2N8', {'count': 1}),\n",
       " ('H3N8', 'H9N2', {'count': 1}),\n",
       " ('H3N8', 'H9N7', {'count': 1}),\n",
       " ('H3N8', 'H8N4', {'count': 1}),\n",
       " ('H3N8', 'H9N8', {'count': 1}),\n",
       " ('H11N6', 'H4N6', {'count': 1}),\n",
       " ('H4N4', 'H5N4', {'count': 1}),\n",
       " ('H3N1', 'H3N6', {'count': 1}),\n",
       " ('H3N1', 'H11N3', {'count': 1}),\n",
       " ('H3N1', 'H7N1', {'count': 1}),\n",
       " ('H3N1', 'H1N1', {'count': 1}),\n",
       " ('H3N1', 'H4N1', {'count': 1}),\n",
       " ('H3N1', 'H3N8', {'count': 1}),\n",
       " ('H3N1', 'H3N1', {'count': 1}),\n",
       " ('H3N1', 'H10N1', {'count': 1}),\n",
       " ('H3N1', 'H3N2', {'count': 1}),\n",
       " ('H3N1', 'H3N5', {'count': 1}),\n",
       " ('H3N1', 'H8N1', {'count': 1}),\n",
       " ('H3N3', 'H4N3', {'count': 1}),\n",
       " ('H3N3', 'H5N3', {'count': 1}),\n",
       " ('H3N3', 'H12N5', {'count': 1}),\n",
       " ('H3N3', 'H3N8', {'count': 1}),\n",
       " ('H3N3', 'H3N3', {'count': 1}),\n",
       " ('H3N3', 'H3N2', {'count': 1}),\n",
       " ('H3N3', 'H3N6', {'count': 1}),\n",
       " ('H3N2', 'H11N2', {'count': 1}),\n",
       " ('H3N2', 'H12N3', {'count': 1}),\n",
       " ('H3N2', 'H12N5', {'count': 1}),\n",
       " ('H3N2', 'H3N9', {'count': 1}),\n",
       " ('H3N2', 'H3N8', {'count': 1}),\n",
       " ('H3N2', 'H3N1', {'count': 1}),\n",
       " ('H3N2', 'H3N3', {'count': 1}),\n",
       " ('H3N2', 'H3N2', {'count': 1}),\n",
       " ('H3N2', 'H3N5', {'count': 1}),\n",
       " ('H3N2', 'H3N6', {'count': 1}),\n",
       " ('H3N2', 'H4N2', {'count': 1}),\n",
       " ('H3N2', 'H4N6', {'count': 1}),\n",
       " ('H3N2', 'H5N2', {'count': 1}),\n",
       " ('H3N2', 'H6N8', {'count': 1}),\n",
       " ('H3N2', 'H6N1', {'count': 1}),\n",
       " ('H3N2', 'H6N2', {'count': 1}),\n",
       " ('H3N2', 'H10N6', {'count': 1}),\n",
       " ('H3N2', 'H10N7', {'count': 1}),\n",
       " ('H3N2', 'Mixed', {'count': 1}),\n",
       " ('H3N2', 'H2N7', {'count': 1}),\n",
       " ('H3N2', 'H2N2', {'count': 1}),\n",
       " ('H3N2', 'H2N3', {'count': 1}),\n",
       " ('H3N2', 'H1N2', {'count': 1}),\n",
       " ('H3N2', 'H1N1', {'count': 1}),\n",
       " ('H3N2', 'H9N2', {'count': 1}),\n",
       " ('H3N2', 'H7N3', {'count': 1}),\n",
       " ('H3N2', 'H7N2', {'count': 1}),\n",
       " ('H3N5', 'H2N5', {'count': 1}),\n",
       " ('H3N5', 'H11N3', {'count': 1}),\n",
       " ('H3N5', 'H3N8', {'count': 1}),\n",
       " ('H3N5', 'H1N1', {'count': 1}),\n",
       " ('H3N5', 'H9N2', {'count': 1}),\n",
       " ('H3N5', 'H3N2', {'count': 1}),\n",
       " ('H3N5', 'H3N5', {'count': 1}),\n",
       " ('H3N5', 'H6N5', {'count': 1}),\n",
       " ('H3N7', 'H4N8', {'count': 1}),\n",
       " ('H3N6', 'H11N6', {'count': 1}),\n",
       " ('H3N6', 'H11N8', {'count': 1}),\n",
       " ('H3N6', 'H12N5', {'count': 1}),\n",
       " ('H3N6', 'H3N8', {'count': 1}),\n",
       " ('H3N6', 'H3N1', {'count': 1}),\n",
       " ('H3N6', 'H3N3', {'count': 1}),\n",
       " ('H3N6', 'H3N2', {'count': 1}),\n",
       " ('H3N6', 'H3N5', {'count': 1}),\n",
       " ('H3N6', 'H3N6', {'count': 1}),\n",
       " ('H3N6', 'H4N1', {'count': 1}),\n",
       " ('H3N6', 'H4N6', {'count': 1}),\n",
       " ('H3N6', 'H6N2', {'count': 1}),\n",
       " ('H3N6', 'H7N6', {'count': 1}),\n",
       " ('H3N6', 'H10N5', {'count': 1}),\n",
       " ('H3N6', 'H7N3', {'count': 1}),\n",
       " ('H3N6', 'H7N2', {'count': 1}),\n",
       " ('H3N6', 'Mixed', {'count': 1}),\n",
       " ('H3N6', 'H7N7', {'count': 1}),\n",
       " ('H3N6', 'H2N7', {'count': 1}),\n",
       " ('H3N6', 'H1N1', {'count': 1}),\n",
       " ('H3N6', 'H10N6', {'count': 1}),\n",
       " ('H3N6', 'H8N4', {'count': 1}),\n",
       " ('H4N8', 'H11N3', {'count': 1}),\n",
       " ('H4N8', 'H11N2', {'count': 1}),\n",
       " ('H4N8', 'H4N7', {'count': 1}),\n",
       " ('H4N8', 'H3N8', {'count': 1}),\n",
       " ('H4N8', 'H3N3', {'count': 1}),\n",
       " ('H4N8', 'H4N8', {'count': 1}),\n",
       " ('H4N8', 'H4N9', {'count': 1}),\n",
       " ('H4N8', 'H4N2', {'count': 1}),\n",
       " ('H4N8', 'H4N3', {'count': 1}),\n",
       " ('H4N8', 'H4N1', {'count': 1}),\n",
       " ('H4N8', 'H4N6', {'count': 1}),\n",
       " ('H4N8', 'H5N2', {'count': 1}),\n",
       " ('H4N8', 'H4N5', {'count': 1}),\n",
       " ('H4N8', 'H6N8', {'count': 1}),\n",
       " ('H4N8', 'H6N6', {'count': 1}),\n",
       " ('H4N8', 'H10N2', {'count': 1}),\n",
       " ('H4N8', 'H7N3', {'count': 1}),\n",
       " ('H4N8', 'Mixed', {'count': 1}),\n",
       " ('H4N8', 'H6N2', {'count': 1}),\n",
       " ('H4N8', 'H1N8', {'count': 1}),\n",
       " ('H4N8', 'H2N3', {'count': 1}),\n",
       " ('H4N8', 'H9N2', {'count': 1}),\n",
       " ('H4N8', 'H10N6', {'count': 1}),\n",
       " ('H4N8', 'H8N4', {'count': 1}),\n",
       " ('H4N8', 'H9N8', {'count': 1}),\n",
       " ('H4N9', 'H4N9', {'count': 1}),\n",
       " ('H4N9', 'H6N9', {'count': 1}),\n",
       " ('H4N9', 'H4N6', {'count': 1}),\n",
       " ('H4N9', 'H4N1', {'count': 1}),\n",
       " ('H4N9', 'H11N9', {'count': 1}),\n",
       " ('H4N9', 'H10N9', {'count': 1}),\n",
       " ('H4N9', 'H10N5', {'count': 1}),\n",
       " ('H5N9', 'H1N9', {'count': 1}),\n",
       " ('H5N9', 'H5N9', {'count': 1}),\n",
       " ('H5N9', 'H5N3', {'count': 1}),\n",
       " ('H5N9', 'H5N4', {'count': 1}),\n",
       " ('H5N9', 'H11N9', {'count': 1}),\n",
       " ('H5N9', 'H5N2', {'count': 1}),\n",
       " ('H5N9', 'H8N4', {'count': 1}),\n",
       " ('H5N8', 'H5N8', {'count': 1}),\n",
       " ('H4N2', 'H11N2', {'count': 1}),\n",
       " ('H4N2', 'H12N3', {'count': 1}),\n",
       " ('H4N2', 'H11N9', {'count': 1}),\n",
       " ('H4N2', 'H11N8', {'count': 1}),\n",
       " ('H4N2', 'H12N5', {'count': 1}),\n",
       " ('H4N2', 'H3N8', {'count': 1}),\n",
       " ('H4N2', 'H3N2', {'count': 1}),\n",
       " ('H4N2', 'H4N8', {'count': 1}),\n",
       " ('H4N2', 'H4N2', {'count': 1}),\n",
       " ('H4N2', 'H4N3', {'count': 1}),\n",
       " ('H4N2', 'H4N1', {'count': 1}),\n",
       " ('H4N2', 'H4N6', {'count': 1}),\n",
       " ('H4N2', 'H5N2', {'count': 1}),\n",
       " ('H4N2', 'H6N8', {'count': 1}),\n",
       " ('H4N2', 'H6N1', {'count': 1}),\n",
       " ('H4N2', 'H6N2', {'count': 1}),\n",
       " ('H4N2', 'H10N3', {'count': 1}),\n",
       " ('H4N2', 'H7N3', {'count': 1}),\n",
       " ('H4N2', 'H7N2', {'count': 1}),\n",
       " ('H4N2', 'Mixed', {'count': 1}),\n",
       " ('H4N2', 'H2N7', {'count': 1}),\n",
       " ('H4N2', 'H2N2', {'count': 1}),\n",
       " ('H4N2', 'H1N2', {'count': 1}),\n",
       " ('H4N2', 'H1N1', {'count': 1}),\n",
       " ('H4N2', 'H9N2', {'count': 1}),\n",
       " ('H4N2', 'H9N1', {'count': 1}),\n",
       " ('H4N2', 'H13N2', {'count': 1}),\n",
       " ('H4N2', 'H8N4', {'count': 1}),\n",
       " ('H4N3', 'H11N7', {'count': 1}),\n",
       " ('H4N3', 'H6N3', {'count': 1}),\n",
       " ('H4N3', 'H11N3', {'count': 1}),\n",
       " ('H4N3', 'H2N3', {'count': 1}),\n",
       " ('H4N3', 'H1N2', {'count': 1}),\n",
       " ('H4N3', 'H4N6', {'count': 1}),\n",
       " ('H4N3', 'H9N2', {'count': 1}),\n",
       " ('H4N3', 'H7N3', {'count': 1}),\n",
       " ('H4N3', 'H6N2', {'count': 1}),\n",
       " ('H4N3', 'H3N2', {'count': 1}),\n",
       " ('H4N3', 'H8N4', {'count': 1}),\n",
       " ('H4N3', 'H6N6', {'count': 1}),\n",
       " ('H5N5', 'H5N8', {'count': 1}),\n",
       " ('H5N5', 'H4N2', {'count': 1}),\n",
       " ('H5N5', 'H5N6', {'count': 1}),\n",
       " ('H5N5', 'H5N2', {'count': 1}),\n",
       " ('H5N5', 'H5N1', {'count': 1}),\n",
       " ('H5N5', 'H4N5', {'count': 1}),\n",
       " ('H5N5', 'H5N7', {'count': 1}),\n",
       " ('H4N1', 'H9N2', {'count': 1}),\n",
       " ('H4N1', 'H4N8', {'count': 1}),\n",
       " ('H4N1', 'H10N1', {'count': 1}),\n",
       " ('H4N6', 'H11N7', {'count': 1}),\n",
       " ('H4N6', 'H11N6', {'count': 1}),\n",
       " ('H4N6', 'H11N3', {'count': 1}),\n",
       " ('H4N6', 'H4N6', {'count': 1}),\n",
       " ('H4N6', 'H11N9', {'count': 1}),\n",
       " ('H4N6', 'H12N7', {'count': 1}),\n",
       " ('H4N6', 'H4N7', {'count': 1}),\n",
       " ('H4N6', 'H3N8', {'count': 1}),\n",
       " ('H4N6', 'H13N6', {'count': 1}),\n",
       " ('H4N6', 'H3N3', {'count': 1}),\n",
       " ('H4N6', 'H3N2', {'count': 1}),\n",
       " ('H4N6', 'H3N6', {'count': 1}),\n",
       " ('H4N6', 'H10N8', {'count': 1}),\n",
       " ('H4N6', 'H4N8', {'count': 1}),\n",
       " ('H4N6', 'H4N9', {'count': 1}),\n",
       " ('H4N6', 'H5N9', {'count': 1}),\n",
       " ('H4N6', 'H5N8', {'count': 1}),\n",
       " ('H4N6', 'H4N2', {'count': 1}),\n",
       " ('H4N6', 'H4N3', {'count': 1}),\n",
       " ('H4N6', 'H4N1', {'count': 1}),\n",
       " ('H4N6', 'H5N3', {'count': 1}),\n",
       " ('H4N6', 'H5N2', {'count': 1}),\n",
       " ('H4N6', 'H4N4', {'count': 1}),\n",
       " ('H4N6', 'H4N5', {'count': 1}),\n",
       " ('H4N6', 'H6N8', {'count': 1}),\n",
       " ('H4N6', 'H6N9', {'count': 1}),\n",
       " ('H4N6', 'H6N6', {'count': 1}),\n",
       " ('H4N6', 'H6N2', {'count': 1}),\n",
       " ('H4N6', 'H7N6', {'count': 1}),\n",
       " ('H4N6', 'H6N5', {'count': 1}),\n",
       " ('H4N6', 'H10N6', {'count': 1}),\n",
       " ('H4N6', 'H12N6', {'count': 1}),\n",
       " ('H4N6', 'H11N8', {'count': 1}),\n",
       " ('H4N6', 'H2N7', {'count': 1}),\n",
       " ('H4N6', 'Mixed', {'count': 1}),\n",
       " ('H4N6', 'H14N6', {'count': 1}),\n",
       " ('H4N6', 'H12N5', {'count': 1}),\n",
       " ('H4N6', 'H10N2', {'count': 1}),\n",
       " ('H4N6', 'H2N6', {'count': 1}),\n",
       " ('H4N6', 'H7N3', {'count': 1}),\n",
       " ('H4N6', 'H2N2', {'count': 1}),\n",
       " ('H4N6', 'H2N3', {'count': 1}),\n",
       " ('H4N6', 'H1N3', {'count': 1}),\n",
       " ('H4N6', 'H1N6', {'count': 1}),\n",
       " ('H4N6', 'H3N1', {'count': 1}),\n",
       " ('H4N6', 'H9N2', {'count': 1}),\n",
       " ('H4N6', 'H9N1', {'count': 1}),\n",
       " ('H4N6', 'H9N6', {'count': 1}),\n",
       " ('H4N6', 'H5N6', {'count': 1}),\n",
       " ('H4N6', 'H8N4', {'count': 1}),\n",
       " ('H5N2', 'H12N8', {'count': 1}),\n",
       " ('H5N2', 'H11N3', {'count': 1}),\n",
       " ('H5N2', 'H11N2', {'count': 1}),\n",
       " ('H5N2', 'H4N6', {'count': 1}),\n",
       " ('H5N2', 'H11N9', {'count': 1}),\n",
       " ('H5N2', 'H4N7', {'count': 1}),\n",
       " ('H5N2', 'H3N8', {'count': 1}),\n",
       " ('H5N2', 'H3N1', {'count': 1}),\n",
       " ('H5N2', 'H3N2', {'count': 1}),\n",
       " ('H5N2', 'H3N6', {'count': 1}),\n",
       " ('H5N2', 'H5N9', {'count': 1}),\n",
       " ('H5N2', 'H5N8', {'count': 1}),\n",
       " ('H5N2', 'H4N2', {'count': 1}),\n",
       " ('H5N2', 'H4N3', {'count': 1}),\n",
       " ('H5N2', 'H5N5', {'count': 1}),\n",
       " ('H5N2', 'H5N3', {'count': 1}),\n",
       " ('H5N2', 'H5N2', {'count': 1}),\n",
       " ('H5N2', 'H5N1', {'count': 1}),\n",
       " ('H5N2', 'H6N8', {'count': 1}),\n",
       " ('H5N2', 'H10N9', {'count': 1}),\n",
       " ('H5N2', 'H12N3', {'count': 1}),\n",
       " ('H5N2', 'H6N1', {'count': 1}),\n",
       " ('H5N2', 'H6N2', {'count': 1}),\n",
       " ('H5N2', 'H7N1', {'count': 1}),\n",
       " ('H5N2', 'H10N5', {'count': 1}),\n",
       " ('H5N2', 'H10N6', {'count': 1}),\n",
       " ('H5N2', 'H10N7', {'count': 1}),\n",
       " ('H5N2', 'H9N2', {'count': 1}),\n",
       " ('H5N2', 'Mixed', {'count': 1}),\n",
       " ('H5N2', 'H12N5', {'count': 1}),\n",
       " ('H5N2', 'H10N2', {'count': 1}),\n",
       " ('H5N2', 'H14N2', {'count': 1}),\n",
       " ('H5N2', 'H2N7', {'count': 1}),\n",
       " ('H5N2', 'H2N2', {'count': 1}),\n",
       " ('H5N2', 'H1N2', {'count': 1}),\n",
       " ('H5N2', 'H1N1', {'count': 1}),\n",
       " ('H5N2', 'H5N7', {'count': 1}),\n",
       " ('H5N2', 'H7N3', {'count': 1}),\n",
       " ('H5N2', 'H8N4', {'count': 1}),\n",
       " ('H5N1', 'H10N8', {'count': 1}),\n",
       " ('H5N1', 'H12N8', {'count': 1}),\n",
       " ('H5N1', 'H12N1', {'count': 1}),\n",
       " ('H5N1', 'H11N9', {'count': 1}),\n",
       " ('H5N1', 'H8N4', {'count': 1}),\n",
       " ('H5N1', 'H5N6', {'count': 1}),\n",
       " ('H5N1', 'H1N1', {'count': 1}),\n",
       " ('H5N1', 'H4N1', {'count': 1}),\n",
       " ('H5N1', 'H5N3', {'count': 1}),\n",
       " ('H5N1', 'H5N2', {'count': 1}),\n",
       " ('H5N1', 'H5N1', {'count': 1}),\n",
       " ('H5N1', 'H10N5', {'count': 1}),\n",
       " ('H5N1', 'H6N8', {'count': 1}),\n",
       " ('H5N1', 'H9N2', {'count': 1}),\n",
       " ('H5N1', 'H9N1', {'count': 1}),\n",
       " ('H5N1', 'H4N8', {'count': 1}),\n",
       " ('H5N1', 'H7N8', {'count': 1}),\n",
       " ('H5N1', 'H12N3', {'count': 1}),\n",
       " ('H5N1', 'H6N1', {'count': 1}),\n",
       " ('H5N1', 'H6N2', {'count': 1}),\n",
       " ('H5N1', 'H5N5', {'count': 1}),\n",
       " ('H4N5', 'H10N5', {'count': 1}),\n",
       " ('H6N8', 'H11N7', {'count': 1}),\n",
       " ('H6N8', 'H11N8', {'count': 1}),\n",
       " ('H6N8', 'H12N5', {'count': 1}),\n",
       " ('H6N8', 'H3N8', {'count': 1}),\n",
       " ('H6N8', 'H3N2', {'count': 1}),\n",
       " ('H6N8', 'H3N6', {'count': 1}),\n",
       " ('H6N8', 'H6N8', {'count': 1}),\n",
       " ('H6N8', 'H4N8', {'count': 1}),\n",
       " ('H6N8', 'H5N9', {'count': 1}),\n",
       " ('H6N8', 'H5N8', {'count': 1}),\n",
       " ('H6N8', 'H4N6', {'count': 1}),\n",
       " ('H6N8', 'H5N2', {'count': 1}),\n",
       " ('H6N8', 'H10N8', {'count': 1}),\n",
       " ('H6N8', 'H7N8', {'count': 1}),\n",
       " ('H6N8', 'H6N1', {'count': 1}),\n",
       " ('H6N8', 'H6N2', {'count': 1}),\n",
       " ('H6N8', 'H6N3', {'count': 1}),\n",
       " ('H6N8', 'H6N5', {'count': 1}),\n",
       " ('H6N8', 'H10N6', {'count': 1}),\n",
       " ('H6N8', 'H1N8', {'count': 1}),\n",
       " ('H6N8', 'H2N1', {'count': 1}),\n",
       " ('H6N8', 'H1N1', {'count': 1}),\n",
       " ('H6N8', 'H2N8', {'count': 1}),\n",
       " ('H6N8', 'H9N2', {'count': 1}),\n",
       " ('H6N8', 'H9N7', {'count': 1}),\n",
       " ('H6N8', 'H6N6', {'count': 1}),\n",
       " ('H6N8', 'H8N8', {'count': 1}),\n",
       " ('H6N8', 'H13N8', {'count': 1}),\n",
       " ('H6N9', 'H3N9', {'count': 1}),\n",
       " ('H6N9', 'H6N9', {'count': 1}),\n",
       " ('H6N9', 'H10N9', {'count': 1}),\n",
       " ('H7N9', 'H5N9', {'count': 1}),\n",
       " ('H7N9', 'H7N7', {'count': 1}),\n",
       " ('H7N9', 'H11N9', {'count': 1}),\n",
       " ('H7N9', 'H2N9', {'count': 1}),\n",
       " ('H7N9', 'H3N9', {'count': 1}),\n",
       " ('H7N9', 'H7N9', {'count': 1}),\n",
       " ('H7N9', 'H6N6', {'count': 1}),\n",
       " ('H7N9', 'H9N7', {'count': 1}),\n",
       " ('H7N9', 'H3N3', {'count': 1}),\n",
       " ('H7N9', 'H9N9', {'count': 1}),\n",
       " ('H7N9', 'H7N3', {'count': 1}),\n",
       " ('H7N9', 'H3N6', {'count': 1}),\n",
       " ('H7N8', 'H4N8', {'count': 1}),\n",
       " ('H7N8', 'H12N3', {'count': 1}),\n",
       " ('H7N8', 'H4N6', {'count': 1}),\n",
       " ('H7N8', 'H12N7', {'count': 1}),\n",
       " ('H7N8', 'H5N1', {'count': 1}),\n",
       " ('H7N8', 'H11N8', {'count': 1}),\n",
       " ('H7N8', 'H3N8', {'count': 1}),\n",
       " ('H7N8', 'H7N9', {'count': 1}),\n",
       " ('H7N8', 'H7N8', {'count': 1}),\n",
       " ('H7N8', 'H3N3', {'count': 1}),\n",
       " ('H7N8', 'H7N6', {'count': 1}),\n",
       " ('H7N8', 'H7N7', {'count': 1}),\n",
       " ('H7N5', 'H5N3', {'count': 1}),\n",
       " ('H7N5', 'H11N2', {'count': 1}),\n",
       " ('H6N1', 'H11N1', {'count': 1}),\n",
       " ('H6N1', 'H11N2', {'count': 1}),\n",
       " ('H6N1', 'H5N3', {'count': 1}),\n",
       " ('H6N1', 'H12N7', {'count': 1}),\n",
       " ('H6N1', 'H3N8', {'count': 1}),\n",
       " ('H6N1', 'H3N1', {'count': 1}),\n",
       " ('H6N1', 'H4N2', {'count': 1}),\n",
       " ('H6N1', 'H4N6', {'count': 1}),\n",
       " ('H6N1', 'H5N2', {'count': 1}),\n",
       " ('H6N1', 'H5N1', {'count': 1}),\n",
       " ('H6N1', 'H6N8', {'count': 1}),\n",
       " ('H6N1', 'H6N9', {'count': 1}),\n",
       " ('H6N1', 'H7N8', {'count': 1}),\n",
       " ('H6N1', 'H12N3', {'count': 1}),\n",
       " ('H6N1', 'H6N1', {'count': 1}),\n",
       " ('H6N1', 'H7N7', {'count': 1}),\n",
       " ('H6N1', 'H6N4', {'count': 1}),\n",
       " ('H6N1', 'H6N5', {'count': 1}),\n",
       " ('H6N1', 'H10N7', {'count': 1}),\n",
       " ('H6N1', 'H6N2', {'count': 1}),\n",
       " ('H6N1', 'H2N7', {'count': 1}),\n",
       " ('H6N1', 'H2N1', {'count': 1}),\n",
       " ('H6N1', 'H7N1', {'count': 1}),\n",
       " ('H6N1', 'H1N1', {'count': 1}),\n",
       " ('H6N1', 'H2N9', {'count': 1}),\n",
       " ('H6N1', 'H9N2', {'count': 1}),\n",
       " ('H6N1', 'H8N4', {'count': 1}),\n",
       " ('H6N1', 'H6N7', {'count': 1}),\n",
       " ('H6N2', 'H11N2', {'count': 1}),\n",
       " ('H6N2', 'H11N9', {'count': 1}),\n",
       " ('H6N2', 'H11N8', {'count': 1}),\n",
       " ('H6N2', 'H12N5', {'count': 1}),\n",
       " ('H6N2', 'H3N8', {'count': 1}),\n",
       " ('H6N2', 'H3N2', {'count': 1}),\n",
       " ('H6N2', 'H10N8', {'count': 1}),\n",
       " ('H6N2', 'H6N9', {'count': 1}),\n",
       " ('H6N2', 'H4N2', {'count': 1}),\n",
       " ('H6N2', 'H4N6', {'count': 1}),\n",
       " ('H6N2', 'H5N2', {'count': 1}),\n",
       " ('H6N2', 'H6N8', {'count': 1}),\n",
       " ('H6N2', 'H10N9', {'count': 1}),\n",
       " ('H6N2', 'H6N1', {'count': 1}),\n",
       " ('H6N2', 'H10N2', {'count': 1}),\n",
       " ('H6N2', 'H6N3', {'count': 1}),\n",
       " ('H6N2', 'H6N5', {'count': 1}),\n",
       " ('H6N2', 'H6N6', {'count': 1}),\n",
       " ('H6N2', 'H10N7', {'count': 1}),\n",
       " ('H6N2', 'Mixed', {'count': 1}),\n",
       " ('H6N2', 'H6N2', {'count': 1}),\n",
       " ('H6N2', 'H2N7', {'count': 1}),\n",
       " ('H6N2', 'H2N2', {'count': 1}),\n",
       " ('H6N2', 'H2N3', {'count': 1}),\n",
       " ('H6N2', 'H1N2', {'count': 1}),\n",
       " ('H6N2', 'H1N1', {'count': 1}),\n",
       " ('H6N2', 'H7N7', {'count': 1}),\n",
       " ('H6N2', 'H9N2', {'count': 1}),\n",
       " ('H6N2', 'H7N3', {'count': 1}),\n",
       " ('H6N2', 'H8N4', {'count': 1}),\n",
       " ('H7N6', 'H2N7', {'count': 1}),\n",
       " ('H7N6', 'H13N6', {'count': 1}),\n",
       " ('H7N6', 'H7N7', {'count': 1}),\n",
       " ('H7N6', 'H7N6', {'count': 1}),\n",
       " ('H7N6', 'H3N5', {'count': 1}),\n",
       " ('H7N6', 'H3N6', {'count': 1}),\n",
       " ('H7N1', 'H12N3', {'count': 1}),\n",
       " ('H7N1', 'H11N9', {'count': 1}),\n",
       " ('H7N1', 'H12N7', {'count': 1}),\n",
       " ('H7N1', 'H3N9', {'count': 1}),\n",
       " ('H7N1', 'H3N8', {'count': 1}),\n",
       " ('H7N1', 'H10N8', {'count': 1}),\n",
       " ('H7N1', 'H5N9', {'count': 1}),\n",
       " ('H7N1', 'H4N2', {'count': 1}),\n",
       " ('H7N1', 'H5N1', {'count': 1}),\n",
       " ('H7N1', 'H6N8', {'count': 1}),\n",
       " ('H7N1', 'H7N8', {'count': 1}),\n",
       " ('H7N1', 'H7N5', {'count': 1}),\n",
       " ('H7N1', 'H10N1', {'count': 1}),\n",
       " ('H7N1', 'H7N7', {'count': 1}),\n",
       " ('H7N1', 'H7N6', {'count': 1}),\n",
       " ('H7N1', 'H7N1', {'count': 1}),\n",
       " ('H7N1', 'H7N3', {'count': 1}),\n",
       " ('H7N1', 'H7N2', {'count': 1}),\n",
       " ('H7N1', 'H6N1', {'count': 1}),\n",
       " ('H7N1', 'H6N2', {'count': 1}),\n",
       " ('H7N1', 'H2N7', {'count': 1}),\n",
       " ('H7N1', 'H2N1', {'count': 1}),\n",
       " ('H7N1', 'H1N1', {'count': 1}),\n",
       " ('H7N1', 'H9N2', {'count': 1}),\n",
       " ('H7N1', 'H8N4', {'count': 1}),\n",
       " ('H6N5', 'H2N5', {'count': 1}),\n",
       " ('H6N5', 'H4N8', {'count': 1}),\n",
       " ('H6N5', 'H10N9', {'count': 1}),\n",
       " ('H6N5', 'H8N4', {'count': 1}),\n",
       " ('H6N5', 'H3N8', {'count': 1}),\n",
       " ('H6N5', 'H4N6', {'count': 1}),\n",
       " ('H6N5', 'H5N5', {'count': 1}),\n",
       " ('H6N5', 'H11N9', {'count': 1}),\n",
       " ('H6N5', 'H5N2', {'count': 1}),\n",
       " ('H6N5', 'H7N2', {'count': 1}),\n",
       " ('H6N5', 'H6N8', {'count': 1}),\n",
       " ('H6N5', 'H6N9', {'count': 1}),\n",
       " ('H6N5', 'H7N8', {'count': 1}),\n",
       " ('H6N5', 'H12N3', {'count': 1}),\n",
       " ('H6N5', 'H7N5', {'count': 1}),\n",
       " ('H6N5', 'H6N1', {'count': 1}),\n",
       " ('H6N5', 'H6N2', {'count': 1}),\n",
       " ('H6N5', 'H10N7', {'count': 1}),\n",
       " ('H6N5', 'H3N5', {'count': 1}),\n",
       " ('H6N5', 'H6N5', {'count': 1}),\n",
       " ('H6N5', 'H3N6', {'count': 1}),\n",
       " ('H6N6', 'H2N3', {'count': 1}),\n",
       " ('H6N6', 'H5N6', {'count': 1}),\n",
       " ('H6N6', 'H4N6', {'count': 1}),\n",
       " ('H6N6', 'H5N2', {'count': 1}),\n",
       " ('H6N6', 'H6N8', {'count': 1}),\n",
       " ('H6N6', 'H3N8', {'count': 1}),\n",
       " ('H6N6', 'H7N3', {'count': 1}),\n",
       " ('H6N6', 'H6N1', {'count': 1}),\n",
       " ('H6N6', 'H6N2', {'count': 1}),\n",
       " ('H6N6', 'H6N4', {'count': 1}),\n",
       " ('H6N6', 'H6N5', {'count': 1}),\n",
       " ('H6N6', 'H6N6', {'count': 1}),\n",
       " ('H6N6', 'H3N6', {'count': 1}),\n",
       " ('H6N7', 'H6N8', {'count': 1}),\n",
       " ('H6N7', 'H6N1', {'count': 1}),\n",
       " ('H6N7', 'H6N7', {'count': 1}),\n",
       " ('H6N7', 'H6N5', {'count': 1}),\n",
       " ('H6N7', 'H12N5', {'count': 1}),\n",
       " ('H12N6', 'H12N2', {'count': 1}),\n",
       " ('H12N7', 'H9N7', {'count': 1}),\n",
       " ('H7N4', 'H4N8', {'count': 1}),\n",
       " ('H7N4', 'H7N6', {'count': 1}),\n",
       " ('H7N4', 'H11N8', {'count': 1}),\n",
       " ('H7N4', 'H1N4', {'count': 1}),\n",
       " ('H7N4', 'H7N4', {'count': 1}),\n",
       " ('H7N4', 'H3N2', {'count': 1}),\n",
       " ('H7N4', 'H7N3', {'count': 1}),\n",
       " ('H7N7', 'H11N7', {'count': 1}),\n",
       " ('H7N7', 'H4N6', {'count': 1}),\n",
       " ('H7N7', 'H12N7', {'count': 1}),\n",
       " ('H7N7', 'H12N5', {'count': 1}),\n",
       " ('H7N7', 'H3N8', {'count': 1}),\n",
       " ('H7N7', 'H3N7', {'count': 1}),\n",
       " ('H7N7', 'H3N6', {'count': 1}),\n",
       " ('H7N7', 'H4N2', {'count': 1}),\n",
       " ('H7N7', 'H5N3', {'count': 1}),\n",
       " ('H7N7', 'H4N7', {'count': 1}),\n",
       " ('H7N7', 'H10N8', {'count': 1}),\n",
       " ('H7N7', 'H7N9', {'count': 1}),\n",
       " ('H7N7', 'H7N8', {'count': 1}),\n",
       " ('H7N7', 'H7N5', {'count': 1}),\n",
       " ('H7N7', 'H7N7', {'count': 1}),\n",
       " ('H7N7', 'H7N6', {'count': 1}),\n",
       " ('H7N7', 'H7N1', {'count': 1}),\n",
       " ('H7N7', 'H7N3', {'count': 1}),\n",
       " ('H7N7', 'H7N2', {'count': 1}),\n",
       " ('H7N7', 'H10N7', {'count': 1}),\n",
       " ('H7N7', 'Mixed', {'count': 1}),\n",
       " ('H7N7', 'H12N8', {'count': 1}),\n",
       " ('H7N7', 'H1N2', {'count': 1}),\n",
       " ('H7N7', 'H9N7', {'count': 1}),\n",
       " ('H7N7', 'H8N4', {'count': 1}),\n",
       " ('H7N7', 'H6N7', {'count': 1}),\n",
       " ('H1N9', 'H1N9', {'count': 1}),\n",
       " ('H1N9', 'H1N8', {'count': 1}),\n",
       " ('H1N9', 'H5N9', {'count': 1}),\n",
       " ('H1N9', 'H11N2', {'count': 1}),\n",
       " ('H1N9', 'H1N2', {'count': 1}),\n",
       " ('H1N9', 'H1N1', {'count': 1}),\n",
       " ('H1N9', 'H11N9', {'count': 1}),\n",
       " ('H1N9', 'H6N9', {'count': 1}),\n",
       " ('H1N9', 'H6N8', {'count': 1}),\n",
       " ('H1N9', 'H3N8', {'count': 1}),\n",
       " ('H1N9', 'H4N9', {'count': 1}),\n",
       " ('H1N9', 'H7N9', {'count': 1}),\n",
       " ('H1N9', 'H13N9', {'count': 1}),\n",
       " ('H6N3', 'H6N5', {'count': 1}),\n",
       " ('H6N3', 'H9N2', {'count': 1}),\n",
       " ('H6N3', 'H7N5', {'count': 1}),\n",
       " ('H6N3', 'H6N2', {'count': 1}),\n",
       " ('H6N3', 'H6N7', {'count': 1}),\n",
       " ('H6N3', 'H7N7', {'count': 1}),\n",
       " ('H6N3', 'H7N2', {'count': 1}),\n",
       " ('H1N3', 'H10N3', {'count': 1}),\n",
       " ('H1N3', 'H1N3', {'count': 1}),\n",
       " ('H1N3', 'H4N6', {'count': 1}),\n",
       " ('H1N3', 'H1N1', {'count': 1}),\n",
       " ('H1N3', 'H5N3', {'count': 1}),\n",
       " ('H1N3', 'H4N7', {'count': 1}),\n",
       " ('H1N3', 'H12N5', {'count': 1}),\n",
       " ('H1N3', 'H2N7', {'count': 1}),\n",
       " ('H1N3', 'H1N2', {'count': 1}),\n",
       " ('H1N3', 'H3N3', {'count': 1}),\n",
       " ('H1N3', 'H7N6', {'count': 1}),\n",
       " ('H1N3', 'H7N3', {'count': 1}),\n",
       " ('H1N2', 'H1N9', {'count': 1}),\n",
       " ('H1N2', 'H1N8', {'count': 1}),\n",
       " ('H1N2', 'H12N5', {'count': 1}),\n",
       " ('H1N2', 'H11N2', {'count': 1}),\n",
       " ('H1N2', 'H12N2', {'count': 1}),\n",
       " ('H1N2', 'H1N2', {'count': 1}),\n",
       " ('H1N2', 'H1N1', {'count': 1}),\n",
       " ('H1N2', 'H5N2', {'count': 1}),\n",
       " ('H1N2', 'H5N1', {'count': 1}),\n",
       " ('H1N2', 'H1N6', {'count': 1}),\n",
       " ('H1N2', 'H12N7', {'count': 1}),\n",
       " ('H1N2', 'H3N8', {'count': 1}),\n",
       " ('H1N2', 'H13N2', {'count': 1}),\n",
       " ('H1N2', 'H9N2', {'count': 1}),\n",
       " ('H1N2', 'H3N2', {'count': 1}),\n",
       " ('H1N2', 'H4N2', {'count': 1}),\n",
       " ('H1N1', 'H11N1', {'count': 1}),\n",
       " ('H1N1', 'H11N9', {'count': 1}),\n",
       " ('H1N1', 'H12N7', {'count': 1}),\n",
       " ('H1N1', 'H3N8', {'count': 1}),\n",
       " ('H1N1', 'H3N1', {'count': 1}),\n",
       " ('H1N1', 'H3N2', {'count': 1}),\n",
       " ('H1N1', 'H3N6', {'count': 1}),\n",
       " ('H1N1', 'H5N9', {'count': 1}),\n",
       " ('H1N1', 'H4N2', {'count': 1}),\n",
       " ('H1N1', 'H4N3', {'count': 1}),\n",
       " ('H1N1', 'H4N1', {'count': 1}),\n",
       " ('H1N1', 'H4N6', {'count': 1}),\n",
       " ('H1N1', 'H5N2', {'count': 1}),\n",
       " ('H1N1', 'H5N1', {'count': 1}),\n",
       " ('H1N1', 'H6N1', {'count': 1}),\n",
       " ('H1N1', 'H6N2', {'count': 1}),\n",
       " ('H1N1', 'H7N1', {'count': 1}),\n",
       " ('H1N1', 'H7N3', {'count': 1}),\n",
       " ('H1N1', 'H10N7', {'count': 1}),\n",
       " ('H1N1', 'H10N1', {'count': 1}),\n",
       " ('H1N1', 'Mixed', {'count': 1}),\n",
       " ('H1N1', 'H1N9', {'count': 1}),\n",
       " ('H1N1', 'H1N8', {'count': 1}),\n",
       " ('H1N1', 'H2N1', {'count': 1}),\n",
       " ('H1N1', 'H2N2', {'count': 1}),\n",
       " ('H1N1', 'H2N3', {'count': 1}),\n",
       " ('H1N1', 'H1N3', {'count': 1}),\n",
       " ('H1N1', 'H1N2', {'count': 1}),\n",
       " ('H1N1', 'H1N1', {'count': 1}),\n",
       " ('H1N1', 'H1N7', {'count': 1}),\n",
       " ('H1N1', 'H1N6', {'count': 1}),\n",
       " ('H1N1', 'H1N5', {'count': 1}),\n",
       " ('H1N1', 'H1N4', {'count': 1}),\n",
       " ('H1N1', 'H9N2', {'count': 1}),\n",
       " ('H1N1', 'H9N1', {'count': 1}),\n",
       " ('H1N1', 'H9N5', {'count': 1}),\n",
       " ('H1N1', 'H8N4', {'count': 1}),\n",
       " ('H1N6', 'H1N3', {'count': 1}),\n",
       " ('H1N6', 'H1N1', {'count': 1}),\n",
       " ('H1N6', 'H4N7', {'count': 1}),\n",
       " ('H1N5', 'H1N1', {'count': 1}),\n",
       " ('H1N5', 'H5N3', {'count': 1}),\n",
       " ('H1N5', 'H6N5', {'count': 1}),\n",
       " ('H1N4', 'H1N3', {'count': 1}),\n",
       " ('H1N4', 'H1N2', {'count': 1}),\n",
       " ('H1N4', 'H1N5', {'count': 1}),\n",
       " ('H9N2', 'H12N8', {'count': 1}),\n",
       " ('H9N2', 'H5N3', {'count': 1}),\n",
       " ('H9N2', 'H11N9', {'count': 1}),\n",
       " ('H9N2', 'H12N7', {'count': 1}),\n",
       " ('H9N2', 'H3N8', {'count': 1}),\n",
       " ('H9N2', 'H3N2', {'count': 1}),\n",
       " ('H9N2', 'H10N8', {'count': 1}),\n",
       " ('H9N2', 'H4N2', {'count': 1}),\n",
       " ('H9N2', 'H4N6', {'count': 1}),\n",
       " ('H9N2', 'H5N2', {'count': 1}),\n",
       " ('H9N2', 'H5N1', {'count': 1}),\n",
       " ('H9N2', 'H6N8', {'count': 1}),\n",
       " ('H9N2', 'H7N9', {'count': 1}),\n",
       " ('H9N2', 'H7N7', {'count': 1}),\n",
       " ('H9N2', 'H7N3', {'count': 1}),\n",
       " ('H9N2', 'H7N2', {'count': 1}),\n",
       " ('H9N2', 'H6N2', {'count': 1}),\n",
       " ('H9N2', 'H2N3', {'count': 1}),\n",
       " ('H9N2', 'H1N2', {'count': 1}),\n",
       " ('H9N2', 'H1N1', {'count': 1}),\n",
       " ('H9N2', 'H2N9', {'count': 1}),\n",
       " ('H9N2', 'H9N2', {'count': 1}),\n",
       " ('H9N2', 'H9N1', {'count': 1}),\n",
       " ('H9N2', 'H9N7', {'count': 1}),\n",
       " ('H9N2', 'H6N6', {'count': 1}),\n",
       " ('H9N2', 'H9N9', {'count': 1}),\n",
       " ('H9N2', 'H10N7', {'count': 1}),\n",
       " ('H9N1', 'H2N7', {'count': 1}),\n",
       " ('H9N1', 'H12N5', {'count': 1}),\n",
       " ('H9N1', 'H9N3', {'count': 1}),\n",
       " ('H9N1', 'H9N2', {'count': 1}),\n",
       " ('H9N1', 'H9N1', {'count': 1}),\n",
       " ('H9N1', 'H9N7', {'count': 1}),\n",
       " ('H9N1', 'H9N6', {'count': 1}),\n",
       " ('H9N1', 'H9N5', {'count': 1}),\n",
       " ('H9N1', 'H3N1', {'count': 1}),\n",
       " ('H9N1', 'H9N8', {'count': 1}),\n",
       " ('H9N7', 'H9N2', {'count': 1}),\n",
       " ('H9N7', 'H9N9', {'count': 1}),\n",
       " ('H9N7', 'H2N7', {'count': 1}),\n",
       " ('H9N7', 'H10N7', {'count': 1}),\n",
       " ('H7N3', 'H11N3', {'count': 1}),\n",
       " ('H7N3', 'H11N2', {'count': 1}),\n",
       " ('H7N3', 'H4N6', {'count': 1}),\n",
       " ('H7N3', 'H11N9', {'count': 1}),\n",
       " ('H7N3', 'H12N7', {'count': 1}),\n",
       " ('H7N3', 'H5N2', {'count': 1}),\n",
       " ('H7N3', 'H3N8', {'count': 1}),\n",
       " ('H7N3', 'H3N3', {'count': 1}),\n",
       " ('H7N3', 'H3N2', {'count': 1}),\n",
       " ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(subtypeG.edges(data=True), key=lambda x:x[2]['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('H2N6', 0),\n",
       " ('H8N8', 0),\n",
       " ('H8N2', 0),\n",
       " ('H5N6', 0),\n",
       " ('H5N4', 0),\n",
       " ('H1N7', 0),\n",
       " ('H9N3', 0),\n",
       " ('H12N2', 0),\n",
       " ('H12N3', 0),\n",
       " ('H12N1', 0),\n",
       " ('H15N4', 0),\n",
       " ('H6N4', 1),\n",
       " ('H8N1', 1),\n",
       " ('H9N6', 1),\n",
       " ('H9N8', 1),\n",
       " ('H5N7', 1),\n",
       " ('H4N7', 1),\n",
       " ('H11N6', 1),\n",
       " ('H4N4', 1),\n",
       " ('H3N7', 1),\n",
       " ('H5N8', 1),\n",
       " ('H4N5', 1),\n",
       " ('H12N6', 1),\n",
       " ('H12N7', 1),\n",
       " ('H11N7', 1),\n",
       " ('H12N9', 1),\n",
       " ('H14N6', 1),\n",
       " ('H2N4', 2),\n",
       " ('H7N5', 2),\n",
       " ('H15N9', 2),\n",
       " ('H14N2', 2),\n",
       " ('H1N8', 3),\n",
       " ('H2N8', 3),\n",
       " ('H4N1', 3),\n",
       " ('H6N9', 3),\n",
       " ('H1N6', 3),\n",
       " ('H1N5', 3),\n",
       " ('H1N4', 3),\n",
       " ('H9N9', 3),\n",
       " ('H12N4', 3),\n",
       " ('H13N6', 3),\n",
       " ('H13N2', 3),\n",
       " ('H13N9', 3),\n",
       " ('H2N5', 4),\n",
       " ('H9N7', 4),\n",
       " ('H7N2', 4),\n",
       " ('H16N3', 4),\n",
       " ('H13N8', 4),\n",
       " ('H10N5', 5),\n",
       " ('H6N7', 5),\n",
       " ('H10N8', 6),\n",
       " ('H10N9', 6),\n",
       " ('H10N1', 6),\n",
       " ('H7N6', 6),\n",
       " ('H9N5', 6),\n",
       " ('H12N8', 7),\n",
       " ('H3N3', 7),\n",
       " ('H4N9', 7),\n",
       " ('H5N9', 7),\n",
       " ('H5N5', 7),\n",
       " ('H7N4', 7),\n",
       " ('H6N3', 7),\n",
       " ('H10N6', 8),\n",
       " ('H2N7', 8),\n",
       " ('H3N5', 8),\n",
       " ('H11N8', 8),\n",
       " ('H3N9', 9),\n",
       " ('H11N1', 9),\n",
       " ('H9N1', 10),\n",
       " ('H10N4', 11),\n",
       " ('H3N1', 11),\n",
       " ('H10N3', 12),\n",
       " ('H4N3', 12),\n",
       " ('H7N9', 12),\n",
       " ('H7N8', 12),\n",
       " ('H1N3', 12),\n",
       " ('H6N6', 13),\n",
       " ('H1N9', 13),\n",
       " ('H11N3', 13),\n",
       " ('H10N2', 14),\n",
       " ('H2N2', 15),\n",
       " ('H1N2', 16),\n",
       " ('H5N3', 19),\n",
       " ('H11N2', 19),\n",
       " ('H8N4', 21),\n",
       " ('Mixed', 21),\n",
       " ('H5N1', 21),\n",
       " ('H6N5', 21),\n",
       " ('H3N6', 22),\n",
       " ('H2N9', 24),\n",
       " ('H2N1', 25),\n",
       " ('H4N8', 25),\n",
       " ('H7N1', 25),\n",
       " ('H7N7', 26),\n",
       " ('H12N5', 26),\n",
       " ('H3N2', 27),\n",
       " ('H9N2', 27),\n",
       " ('H4N2', 28),\n",
       " ('H6N8', 28),\n",
       " ('H6N1', 28),\n",
       " ('H6N2', 30),\n",
       " ('H7N3', 34),\n",
       " ('H10N7', 36),\n",
       " ('H2N3', 37),\n",
       " ('H1N1', 37),\n",
       " ('H5N2', 39),\n",
       " ('H11N9', 47),\n",
       " ('H3N8', 49),\n",
       " ('H4N6', 50)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([(node, len(subtypeG.successors(node))) for node in subtypeG.nodes()], key=lambda x:x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('H12N9', 0),\n",
       " ('H15N9', 0),\n",
       " ('H2N4', 2),\n",
       " ('H2N6', 2),\n",
       " ('H8N8', 2),\n",
       " ('H8N2', 2),\n",
       " ('H8N1', 2),\n",
       " ('H5N4', 2),\n",
       " ('H4N4', 2),\n",
       " ('H12N6', 2),\n",
       " ('H1N7', 2),\n",
       " ('H9N3', 2),\n",
       " ('H12N2', 2),\n",
       " ('H12N1', 2),\n",
       " ('H14N6', 2),\n",
       " ('H15N4', 2),\n",
       " ('H9N8', 3),\n",
       " ('H3N7', 3),\n",
       " ('H1N5', 3),\n",
       " ('H1N4', 3),\n",
       " ('H6N4', 4),\n",
       " ('H9N6', 4),\n",
       " ('H5N6', 4),\n",
       " ('H11N6', 4),\n",
       " ('H1N6', 4),\n",
       " ('H14N2', 4),\n",
       " ('H13N2', 4),\n",
       " ('H10N4', 5),\n",
       " ('H5N7', 5),\n",
       " ('H5N5', 5),\n",
       " ('H4N5', 5),\n",
       " ('H9N9', 5),\n",
       " ('H12N4', 5),\n",
       " ('H13N9', 5),\n",
       " ('H2N5', 6),\n",
       " ('H2N8', 6),\n",
       " ('H7N5', 6),\n",
       " ('H6N7', 6),\n",
       " ('H7N4', 6),\n",
       " ('H16N3', 6),\n",
       " ('H13N6', 6),\n",
       " ('H1N8', 7),\n",
       " ('H5N8', 7),\n",
       " ('H1N9', 7),\n",
       " ('H6N3', 7),\n",
       " ('H9N5', 7),\n",
       " ('H11N1', 7),\n",
       " ('H13N8', 7),\n",
       " ('H12N8', 8),\n",
       " ('H4N9', 8),\n",
       " ('H7N9', 8),\n",
       " ('H6N6', 8),\n",
       " ('H10N9', 9),\n",
       " ('H10N3', 9),\n",
       " ('H3N9', 9),\n",
       " ('H6N9', 9),\n",
       " ('H2N9', 10),\n",
       " ('H3N5', 10),\n",
       " ('H7N8', 10),\n",
       " ('H7N6', 10),\n",
       " ('H1N3', 10),\n",
       " ('H9N7', 10),\n",
       " ('H11N7', 10),\n",
       " ('H12N3', 10),\n",
       " ('H2N1', 11),\n",
       " ('H2N2', 11),\n",
       " ('H4N7', 11),\n",
       " ('H5N1', 11),\n",
       " ('H3N1', 12),\n",
       " ('H4N3', 12),\n",
       " ('H4N1', 12),\n",
       " ('H7N1', 12),\n",
       " ('H9N1', 12),\n",
       " ('H10N1', 13),\n",
       " ('H5N9', 13),\n",
       " ('H6N5', 13),\n",
       " ('H10N5', 14),\n",
       " ('H12N7', 14),\n",
       " ('H7N7', 15),\n",
       " ('H1N2', 15),\n",
       " ('H11N8', 15),\n",
       " ('H10N2', 16),\n",
       " ('H10N6', 16),\n",
       " ('H5N3', 16),\n",
       " ('H3N3', 16),\n",
       " ('H7N2', 16),\n",
       " ('H11N3', 16),\n",
       " ('H10N8', 17),\n",
       " ('Mixed', 17),\n",
       " ('H11N2', 18),\n",
       " ('H4N2', 19),\n",
       " ('H6N1', 19),\n",
       " ('H4N8', 20),\n",
       " ('H3N6', 21),\n",
       " ('H2N7', 22),\n",
       " ('H10N7', 23),\n",
       " ('H8N4', 23),\n",
       " ('H7N3', 23),\n",
       " ('H3N2', 24),\n",
       " ('H2N3', 25),\n",
       " ('H6N8', 25),\n",
       " ('H11N9', 25),\n",
       " ('H12N5', 25),\n",
       " ('H6N2', 28),\n",
       " ('H1N1', 29),\n",
       " ('H4N6', 30),\n",
       " ('H5N2', 31),\n",
       " ('H9N2', 33),\n",
       " ('H3N8', 36)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([(node, len(subtypeG.predecessors(node))) for node in subtypeG.nodes()], key=lambda x:x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "centralities = pd.DataFrame(sorted(nx.degree_centrality(subtypeG).items(), key=lambda x:x[1]))\n",
    "centralities.columns = ['Subtype', 'Degree Centrality']\n",
    "centralities.to_csv('Subtype Reassortment Centralities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def clean_axis(ax):\n",
    "    \"\"\"Remove ticks, tick labels, and frame from axis\"\"\"\n",
    "    ax.get_xaxis().set_ticks([])\n",
    "    ax.get_yaxis().set_ticks([])\n",
    "    for sp in ax.spines.values():\n",
    "        sp.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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d5FCSW8cHd/dbR3FPJ7lyinkCAAAAAAzORFfejuypqp1JtiZ5ZLWAqnpJkjcl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\nDGpleSkry0vriq3uHiyRybIJX+3u01e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XNjltAAAAAIDNtbK8lJXlpXXFzqJ4\nW6/YqFpIsrIqppM8keR8kpNV9eeSvDeXZ+s+udagi48c2uw8AQAAAAAGtXoi6uNHDl8zdtDibXef\nmmqfT3Ilk6Wp/n1TL7lvqv1LQ+YGAAAAADBmr7vRCQAAAAAA8GqKtwAAAAAAI6R4CwAAAAAwQoq3\nAAAAAAAjVN19o3PYsKrqSy/ffHkDcPO7+/6DG37NxbPHB8jk+m2FY7geGz3urXDMYzSL87AVzvX1\nfE83Yqsc8xiPY2i+Q8CtaIy/I8aYEzevO7dVurvW2mfmLQAAAADACCneAgAAAACM0O1DDl5Ve5M8\n191PVdWuJA9O7f6eJG9L8mSSF7r7oaran+TpJP8qyY8nuZjkX3b3sSHzBAAAAAAYm0GLt6tUkq90\n9+GqenOSO7r7parqJNurameSKwvZfmeSX+juv1dVPz/DHAEAAAAARmEWxdsDVbU7yY5cnlWbJPuS\nnJqKOZZkMcmZyfavJvlcVX0wyc+uNejRxw5dbc/NL2RufmFTkwYAAAAA2Gwry0tZWV5aV+wsircn\nuvv0ZNmEPZO+he7++FTMs0kuJHkgybkkH0yy2N2/UlWfSXJy9aCLjxwaNGkAAAAAgM22eiLq40cO\nXzN2Fg8sq1dsVC0kWVkV00meSPKeSfuXk/xgVf1Ekt+cQY4AAAAAAKMy6Mzb7j411T6f5EoZeWmq\nf9/US+6bav+FIXMDAAAAABizWcy8BQAAAABggxRvAQAAAABGSPEWAAAAAGCEBl3zFtga7r7/4Ibi\nL549PlAmsPlc3zevsZ27jeaT3JrX0yyO2ed6cxrjMYztPjOr94Ab5Xp+l27URr9DY7wP3Ipm8bk6\n14yVmbcAAAAAACOkeAsAAAAAMEKDLptQVXuTPNfdT1XVriQPTu3+niRvS/Jkkhe6+6Gq2p/k6SRf\nTfJokueT/JPu/oUh8wQAAAAAGJtZrnlbSb7S3Yer6s1J7ujul6qqk2yvqp1JehL7p5M82d2fr6rP\nJlG8BQAAAABuKbMo3h6oqt1JduTyrNok2Zfk1FTMsSSLSc5Mtn82yaNV9X1J3rTWoEcfO3S1PTe/\nkLn5hU1NGgAAAABgs60sL2VleWldsbMo3p7o7tOTZRP2TPoWuvvjUzHPJrmQ5IEk57r7q0kOVtVt\nucas28VHDg2YMgAAAADA5ls9EfXxI4evGTuL4m29YqNqIcnKqphO8kSS80lOVtW3J3k4yeuTfDwA\nAAAAALeYQYu33X1qqn0+yZUy8tJU/76pl9w31f7wkLkBAAAAAIzZ6250AgAAAAAAvJriLQAAAADA\nCCneAgAAAACMUHX3jc5hw6qqL728/rzvvv/ghsa/ePb4RlMC4CbldwQwC+41AABcy53bKt1da+37\nujNvq+o9VfXwpP2XhkgOAAAAAIBXe61lE/6rJNsm7f984FwAAAAAAJi4/TX2/4ckXVW3JfnWjQ5e\nVXuTPNfdT1XVriR7knwxybuSvNjdH6uqk0le6O6Hqmp/kqeTfDXJo0meT/JPuvsXNvreAAAAAAA3\ns9eaeftULs+4/WySn/sG36uSvCXJB5K8lOSZSX8n2V5VOyftJPnTSZ7s7gNJ/uo3+L4AAAAAADed\n15p5+6tJ3jRp/8p1vseBqtqdZMfk5/nufriqjlXVWycxx5IsJjkz2f7ZJI9W1fdNvT8AAAAAwC3j\ntYq3/1Muz779piT/S5Lvv473ONHdpyfLJnw0yaVJ/4tJ7pq0n01yIckDSc5191eTHJws17DmkglH\nHzt0tT03v5C5+YXrSA0AAAAAYHZWlpeysry0rtjXKt7+3939vyVJVf2x68ynptpfSnJbVX0iye3d\n/YWqSi4vl/BEkvNJTlbVtyd5OMnrk3x8rUEXHzl0nekAAAAAANwYqyeiPn7k8DVjX6t4u7+qvncS\n96aq+o+7+y+uN5HuPjXVPp/kVZl0976pzfum2h9e7/sAAAAAAGw1r1W8/c+6+9JrxAAAAAAAsMle\n9xr7f6Kq/tZkGQMAAAAAAGbk68687e6/VlV/KMmJqvr/khzt7l+bRWIAAAAAALey6u5r76x6f5Lv\nTfLrSf5+ko939389o9yuqar6jnd8ZN3xF88eHzCbrePu+w9uKH6jn+vQ44/V2I57o/kkzjVMm8V3\naIzG+L2+nnOxEUPf+67HrXgtXY+t8Lt0o2bxHR3bd+56bIVj2Aq2wu+UW/Vcj+3/jNfzHmPk+rs5\n+b81X89Gz91/+I0T6e5aa99rrXl7Z5K/1t1fq6rXJ/noht4ZAAAAAIDr8lpr3r61u782aS9291eG\nTggAAAAAgK8z87aq/kGS/6Sq3pakk/zWzLICAAAAALjFXbN4291/oar+0yTPT7Y3POu2qvYmea67\nn6qqXUn2JPliknclebG7P1ZVJ5O80N0PVdX+JE8n+VdJfjzJxST/sruPbfS9AQAAAABuZq+15u2+\nJN+cJFV1qbt/6Bt4r0ryliR/PJcfgPbMpL+TbK+qnZN2knxnkl/o7r9XVT//DbwnAAAAAMBN6bWK\nt7/T3f9tklTV37rO9zhQVbuT7Jj8PN/dD1fVsap66yTmWJLFJGcm27+a5HNV9cEkP7vWoC8/c+Zq\n+3VvuDe33XXvdaYHAAAAADAbv/vSv87X/t2/Xlfs11vz9iNJ/lhVPTLpevt15nOiu09Plk34aJJL\nk/4Xk9w1aT+b5EKSB5KcS/LBXH5A2q9U1WeSnFw96LZ7vus60wEA+P/Zu/9gv+7yPvDvR1hbyySp\n3LR0A7hpnRLVmU0JnXU83W51bydlkl0rP0lbF9oKQ6gVoaEMs4Et0diybG+jkNpZkIJbSiOX7bYO\nizMsWKHtBO69mJ0dq9PNdjq1VBZns86QqXFRgDQe4sHP/qFj8UVcSfde3/PV0dXrNaPRc895vuc8\n58f3+733mc98DgAAwOXxkm/9xoGoX/uPJy+Ye7GRt/9u+PeC5Q3WUzPxU0leUlV/P8k13f1vqyo5\nO13CA0lO52yj9pNJ7qyq1yf5zQ3uFwAAAADginWxB5YtV9Vds4uyzgZudz80E59OcvcqObfP/HjD\nTPyT69kXAAAAAMBWcqk5bx/O2abttyT5a+OXAwAAAABAconmbXefeiGuqp8YvxwAAAAAAJJLNG+H\nh4UlyfNJ/sX45QAAAAAAkCTV3RdPqHpJkr+Q5P/q7q/MpapLqKp+9rmL1321u/7mA+t+zZmTR0eo\nBAAAAAC4kB3bK91dq63bdqEXVdU/HMK/l+TPJ/mlEWoDAAAAAGAVF2zeJvn94f/ru/tIkmfmUA8A\nAAAAALl48/bbq+rnkvzG8PNL17vxqtpbVbcO8a6qumuIH6yq9wzx8aq6f4jvqKqFqlqsqk9X1fur\namG9+wUAAAAAuNJd7IFldyT57u5+oXn7Cy9yX5UkVfW6JI8nuWlY3kmuq6obZ3KfT/KVJH8oyW+/\nyP0CAAAAAFxxLti87e7fz9dH3aa7/8MG97G/qvYk2ZnkTJLtST6Qrzdvk+RIkoM529RNkk9390pV\nvSzJ/Un+xvkbvffwoXPx7oXF7F5Y3GB5AAAAAADzsbK8lJXlpTXlVndfeGXVtu5+fqOFVNXeJF/o\n7hNVtSvJI0key9mm8auT/PUk705yIMnbk3xXkoe6e3l4/X+R5J929185b7v97HMXrpvk+psPTQtQ\nEgAAIABJREFUrPs1Z04eHaESAAAAAOBCdmyvdHettu5ic94myTs3Yf+zO364u+9IcjjJJ7v7s8Py\nTvJAktcm6ar68ap6MMk/SfK+TagBAAAAAOCKcqmRt48m+UySLyVJdx+bU10XZeTtpRl5CwAAAADT\nd7GRtxd7YFmS/PwI9QAAAAAAcAmXmjbhlUn2DHPQfu8c6gEAAAAAIJdu3v75JL83xH9y3FIAAAAA\nAHjBpaZN+FrOPkBsZ5I/Pod61mw9c7qayxW2nvXO6+xzYG3GPq8bmY97vVxrLsTnxtqM/T7dCp8b\nU3y2wDzu76ndG7CZfEesjc8BLpd5/D6wXu5X5uVSI29/OcmrkjyY5P7xywEAAAAAILn0yNsfS3K4\nuz87j2IAAAAAADjrUiNvH07yN6vqn1bVuseoV9Xeqrp1iHdV1aGq+lxVvb+q3jIsP15V9w/xvqpa\nqKo/VVX/qKo+vN59AgAAAABsBRdt3nb3E0n+1yT/b5Iff5H7qiSd5MtJrk3y1Au7SXJdVd04xOnu\n3+zun3qR+wMAAAAAuGJddNqEqno4yRNJjnf3z25wH/urak+SnUlOdfdrqmpbko8n+cSQcyTJwSSP\nb3AfAAAAAABbygWbt1V1bZI3zfx8XXf//gb2cay7T1TVriS3JUl3P19VX62qGnKeTvJkkluSnFrL\nRp/7na/3ebd9yyvykm99xQZKAwAAAACYn5XlpawsL60p92Ijb/9ukj9x3rLbN1BPzcS7quqDQ/yp\n7u6hf9tJHkhyOsnxqvojSf6nJN9XVe/q7iPnb3T7d3z/BkoBAAAAALh8di8sZvfC4rmf77vn7gvm\nXrB52913VdUrkvy57v5YVb1mvYV090Mz8ekkr18lZ7YhfMNMvG+9+wMAAAAA2Cou+sCyJPckWRzi\nN10kDwAAAACATXSp5u2Xhn/J2akNAAAAAACYg0s1b/9Dkh+qqg8neWoO9QAAAAAAkIs/sCxJ/mGS\nD+XsQ8d+f/xy1u7MyaNrzr3+5gOjbXsj29/IPuZhI8exHlM85vWax7Ue+35lOnw2jeNqPK/z+NyY\n4mfT1Goa+3t0qubxHuLStsLn90ZM7XOA6fBZMw1X4+8DW8VW6A9shd9Rrsb7e4p/b11ulxp5+wvd\n/Xvd/ZUkPzePggAAAAAAuHTzdscFYgAAAAAARnSpaRP+j6p6ZIj/9/VuvKr2Jnmmux+tql1J/nqS\nv5nkXyb5N939gao6nuSL3f2OqrojyakkO5PcmuTbknywu//VevcNAAAAAHAlu1Tz9pEkT3T3yap6\n5YvcVyXpJF9Ocm2+/gC0TnJdVd34QmJ3fzTJR6tqZ5JfSKJ5CwAAAABcVS7VvH0gyR8kOZnk3Un2\nb2Af+6tqT86Opj3V3a+pqm1JPp7kE0POkSQHkzx+3msPJtnasw4DAAAAAKziUs3b30tyZoif3eA+\njnX3iWHahNuSpLufr6qvVlUNOU8neTLJLUlODct/LsmvdfdvrLbRew8fOhfvXljM7oXFDZYHAAAA\nADAfK8tLWVleWlPupZq3zyT5i1X195M8v8F6aibeVVUfHOJPdXcP/dvO2VG+p5McT3IgyQ8k+baq\n+tPd/Q/O3+jBOw9tsBwAAAAAgMvj/IGo991z9wVzL9q87e77qurPJNmWZNd6C+nuh2bi00lev0rO\n7TM/3jD8v5LkfevdHwAAAADAVnHB5m1VvTvJf5Xk15P8WJJ/Ma+iAAAAAACudhcbefvd3f36qvpM\nkr/c3Rud8xYAAAAAgHXadpF1315Vtyb5QpK/VFX//ZxqAgAAAAC46l1s5O1HkvzRJL+a5I/NpxwA\nAAAAAJKLNG+7+/gc61i3628+sObcMyePjljJNG3kmNdzTje6j6lZ7zFP0dV43aZoivfSVvgcmMcx\nzMPUappaPfNytR73eq33fTfF8zrFY5ja5+tGjH1eN3Idpnatp/i9NcV7ab3mcW/Mw1b4HFivq/GY\nN2IrnKcp/l0wtq3y2bReY9+vW+Hv2M12sWkTAAAAAAC4TDRvAQAAAAAmaNTmbVXtHR56lqraVVV3\nDfGDVfWeIT5eVfcP8b6qWqiz7quq91bV3xqzRgAAAACAKZrnyNtKkqp6XZLHZ5Z3kuuq6sYhTpIf\nS/KKJH+Q5LfnWCMAAAAAwCRc8IFlm2h/Ve1JsjPJmSTbk3wgyU0zOUeSHMzXm7rfneQz3f2Bqvpw\nkk+ev9Hnfufr/d9t3/KKvORbXzFO9QAAAAAAm2RleSkry0tryp1H8/ZYd5+oql1JHknyWJI7k7y6\nql415Dyd5MkktyQ5lbOjbf9gWPf8ahvd/h3fP2rRAAAAAACbbffCYnYvLJ77+b577r5g7jymTaiZ\n+OHuviPJ4SSf7O7PDss7yQNJXjvEjyT5wap6b5KlOdQIAAAAADApo4687e6HZuLTOdu0TXf/VpJ3\nDvHtMy+5YSb+qTFrAwAAAACYsnk+sAwAAAAAgDXSvAUAAAAAmCDNWwAAAACACaruvtw1rFtV9bPP\njVf39TcfWFf+mZNHR6oEANgsvt8B4MXxXQowjh3bK91dq60z8hYAAAAAYII0bwEAAAAAJmjU5m1V\n7a2qW4d4V1XdNcQPVtV7hvh4Vd0/xPuqaqGq/lRV/aOq+vCY9QEAAAAATNU8R95WklTV65I8PrO8\nk1xXVTcOcbr7N7v7p+ZYGwAAAADApFwzh33sr6o9SXYmOZNke5IPJLlpJudIkoP5xqbuRd17+NC5\nePfCYnYvLG5CqQAAAAAA41lZXsrK8tKacufRvD3W3SeqaleSR5I8luTOJK+uqlcNOU8neTLJLUlO\nrWWjB+88NEKpAAAAAADjOX8g6n333H3B3HlMm1Az8cPdfUeSw0k+2d2fHZZ3kgeSvDZJV9UfqaoH\nk3xfVb1rDjUCAAAAAEzKqCNvu/uhmfh0zjZt092/leSdQ3z7zEtumIn3jVkbAAAAAMCUzfOBZQAA\nAAAArJHmLQAAAADABGneAgAAAABMUHX35a5h3aqqn33uyqsbYJ6uv/nA6Ps4c/Lo6PuArWzs9+lW\neI/O47NsvbbCeQUAYDp2bK90d622zshbAAAAAIAJ0rwFAAAAAJigUZu3VbW3qm4d4l1VdaiqPldV\n76+qtwzLj1fV/UN8R1UtVNViVX16yFsYs0YAAAAAgCma58jbStJJvpzk2iRPDcs7yXVVdeNM7vNJ\nvpLkDyX57TnWCAAAAAAwCdfMYR/7q2pPkp1JTnX3a6pqW5KPJ/nEkHMkycEkjw8/f7q7V6rqZUnu\nT/I35lAnAAAAAMBkzKN5e6y7T1TVriS3JUl3P19VX62qF56i9nSSJ5PckrMN3h6W/27Ojr79Jvce\nPnQu3r2wmN0Li+NUDwAAAACwSVaWl7KyvLSm3Hk0b2sm3lVVHxziT3V3D/3bTvJAktNJjlfVjyf5\nwZwdrfu+1TZ68M5DoxUMAAAAADCG8wei3nfP3RfMHbV5290PzcSnk7x+lZzbZ368YSb+1RFLAwAA\nAACYtHk+sAwAAAAAgDXSvAUAAAAAmCDNWwAAAACACaruvtw1rFtV9bPPXXl1wxiuv/nAuvLPnDw6\nUiXzdbUeN1wu3nNsFvfS1WMrXOutcAxTNI/z6tqNY4rndYo1rdcUj2GKNcFWtmN7pbtrtXVG3gIA\nAAAATJDmLQAAAADABGneAgAAAABM0KjN26raW1W3DvGuqrpriB+sqvcM8fGqun+I91XVQlX9map6\nf1X9SlW9ecwaAQAAAACmaJ4jbytJqup1SR6fWd5JrquqG4c43X2qu386yW1JfnCONQIAAAAATMI1\nc9jH/qrak2RnkjNJtif5QJKbZnKOJDmYmaZuVf1wkv1D7je59/Chc/HuhcXsXljc5LIBAAAAADbX\nyvJSVpaX1pQ7j+btse4+UVW7kjyS5LEkdyZ5dVW9ash5OsmTSW5JcipJuvtjST5WVR8dXvcNDt55\naA6lAwAAAABsnvMHot53z90XzJ1H87Zm4oe7+3BVfWeSt3b3Z6sqOTtdwgNJTic5XlULSX4iybVJ\nPjWHGgEAAAAAJmXU5m13PzQTn05yeIh/K8k7h/j2mZfcMBMvj1kbAAAAAMCUzfOBZQAAAAAArJHm\nLQAAAADABGneAgAAAABM0DweWMYqrr/5wLryz5w8OlIlrMd6r1uy/mvn3rh6uNZXpnl8DjCOrfCe\n28j9t15jf2+t1xTfc1OsaR7mcf9NzVa4blM0xfO6Fb4jpuhqPK9b5Ttian/HTvFv8Y2Y2nm9Wl1p\n59XIWwAAAACACdK8BQAAAACYoFGbt1W1t6puHeJdVXXXED9YVe8Z4uNVdf8Q31FVC1X1o1X1D6vq\nn1fVa8esEQAAAABgiuY58raSpKpel+TxmeWd5LqquvHcgu6PdvffTrIvyV+bY40AAAAAAJMwjweW\n7a+qPUl2JjmTZHuSDyS5aSbnSJKD+camboZlq84KfO/hQ+fi3QuL2b2wuGkFAwAAAACMYWV5KSvL\nS2vKnUfz9lh3n6iqXUkeSfJYkjuTvLqqXjXkPJ3kySS3JDlVVZXk55L8Wnf/xmobPXjnodELBwAA\nAADYTOcPRL3vnrsvmDuPaRNqJn64u+9IcjjJJ7v7s8PyTvJAktcO8YEkP5DkJ6vqjjnUCAAAAAAw\nKaOOvO3uh2bi0znbtE13/1aSdw7x7TMvuWH4fyXJ+8asDQAAAABgyub5wDIAAAAAANZI8xYAAAAA\nYII0bwEAAAAAJqi6+3LXsG5V1c8+d+XVDQAw6/qbD6wr/8zJoyNVAgAAXC47tle6u1ZbZ+QtAAAA\nAMAEad4CAAAAAEzQqM3bqtpbVbcO8a6qOlRVn6uq91fVW4blx6vq/iHeV1ULVfXKqnqkqj5YVe8a\ns0YAAAAAgCma58jbStJJvpzk2iRPDcs7yXVVdeMQJ8n3JvlId785yWvmWCMAAAAAwCRcM4d97K+q\nPUl2JjnV3a+pqm1JPp7kE0POkSQHkzw+/PyZJB+rqjcl+dAcagQAAAAAmJR5NG+PdfeJqtqV5LYk\n6e7nq+qrVfXCU9SeTvJkkluSnErypiQHu/vTVfXhJMfP3+i9hw+di3cvLGb3wuKYxwAAAAAA8KKt\nLC9lZXlpTbnV3ZfO2qCq2pvkme5+dGje3pXk2WH1/93d762qX07y1mHZ6SRvSPK7Se5M8oUkX+nu\nd5633X72ufHqBgCYh+tvPrCu/DMnj45UCQAAcLns2F7p7lpt3ajN27Fo3gIAW4HmLQAAcLHm7Twf\nWAYAAAAAwBpp3gIAAAAATJDmLQAAAADABF1zuQsA2AjzRE7Deq/DerluXEk28n5wj1+a8wrT5/cy\nYCvwWcZUGXkLAAAAADBBmrcAAAAAABM0avO2qvZW1a1DvKuqDlXV56rq/VX1lmH58aq6f4j3VdVC\nVX1PVT1cVb9UVa8bs0YAAAAAgCma58jbStJJvpzk2iRPDcs7yXVVdeMQJ8kPJXlfd+9P8rfmWCMA\nAAAAwCTM44Fl+6tqT5KdSU5192uqaluSjyf5xJBzJMnBJI8PP38oyV1V9SNJvn0ONQIAAAAATMo8\nmrfHuvtEVe1KcluSdPfzVfXVqqoh5+kkTya5JWcbvF9IcqCqXpLkI6tt9N7Dh87FuxcWs3thcbwj\nAAAAAADYBCvLS1lZXlpT7jyatzUT76qqDw7xp7q7h/5tJ3kgyekkx6vqO5O8O8lLk/z8ahs9eOeh\n0QoGAAAAABjD+QNR77vn7gvmjtq87e6HZuLTSV6/Ss7tMz/eMBPfMWJpAAAAAACTNs8HlgEAAAAA\nsEaatwAAAAAAE6R5CwAAAAAwQZq3AAAAAAATVN19uWtYt6rqZ59be93X33xgXds/c/LoekuanPUe\nc7I1jhsANsvV+PvDRmzkd4712Crn1f0EANPmu5rLacf2SnfXauuMvAUAAAAAmCDNWwAAAACACRq1\neVtVe6vq1iHeVVV3DfGDVfWeIT5eVfcP8b6qWqiq76mqh6vql6rqdWPWCAAAAAAwRfMceVtJMjRj\nH59Z3kmuq6obhzhJfijJ+7p7f5K/NccaAQAAAAAm4Zo57GN/Ve1JsjPJmSTbk3w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0l+pbt/uqoOJXm2u++sqvckOZrkD5K8a8jv+7r7T4+ZJwAAAADA1MzzhmWVpJM8n+Q1Sb429HeS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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e2f6510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This cell doesn't do much better compared to the one above it.\n",
    "A = nx.to_numpy_matrix(subtypeG, weight='count')\n",
    "A = pd.DataFrame(A)\n",
    "\n",
    "A.columns = subtypeG.nodes()\n",
    "A.index = subtypeG.nodes()\n",
    "fig = plt.figure(figsize=(24,24))\n",
    "heatmap = plt.pcolor(A, cmap='Blues')\n",
    "# heatmap.set_clim(vmin=0, vmax=10)\n",
    "plt.axes().set_aspect('equal')\n",
    "plt.xticks(range(0, len(A.index)), A.index, rotation=90)\n",
    "plt.yticks(range(0, len(A.columns)), A.columns)\n",
    "plt.axes().set_xticks(np.arange(len(A.columns))+0.5)\n",
    "plt.axes().set_yticks(np.arange(len(A.index))+0.5)\n",
    "plt.xlim(0,len(subtypeG.nodes()))\n",
    "plt.ylim(0,len(subtypeG.nodes()))\n",
    "plt.xlabel('Donor Subtype')\n",
    "plt.ylabel('Receiver Subtype')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 109\n",
      "Size of distance matrix: (109, 109)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a650190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairwise_dists = distance.squareform(distance.pdist(A))\n",
    "print 'Number of rows: {0}'.format(A.shape[0])\n",
    "print 'Size of distance matrix: {0}'.format(pairwise_dists.shape) \n",
    "row_clusters = sch.linkage(pairwise_dists,method='complete')\n",
    "den = sch.dendrogram(row_clusters) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x115a7a410>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zQm1jN+evN3OruZen926iomjhxI9dfaN09I7gtBpJS07cUGHOcpkUjVqJWqmI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\nQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119628d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(A.ix[den['leaves']], cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ericmjl/anaconda/envs/py27/lib/python2.7/site-packages/matplotlib/backends/backend_pdf.py:2264: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  different = bool(ours != theirs)\n"
     ]
    },
    {
     "data": {
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H3r0jN65rbQAFqjpwh/RMbqBRNWeggMU5aCA9DwV3JmYoBarCH5zj\n+/fDD1jie6+V+TREQhBF2t8BNgAAgNCEpAAAAABAaDZugnUYcp/L0p14xOW46e4DAAAAgQlJYQVK\nV56X7sOjrrckJQUAAAA2yXJ7AAAAACA0M0nhbZtf/g4AAABAHSEpvGGu5e+CWAAAAIDl5VLGzWhy\nn0vpSh71oLBTe/q+1NQkbQ7tHF3ZpeF8qmq3hzGufa8AW1Zzvx773r/U8dZ6zq+cd0yec9OLdA1z\nn7G/+2u+N+3h74ilxjfS9zXS8/DpW3o3g1GTFAAAAAAITUgKAAAAAIQmJAUAAAAAQhOSAgAAAACh\n2d0eVij3+SWl1Czdj6+4HMfdBA4AAABgLkJSWKdma7ve1+xuDwAAALBGltsDAAAAAKEJSQEAAACA\n0Cy3h2UNuc+WqQMAAAAsSEgKCypdeX7rv28xOG0O7WjHGs6n2c8JY6q9NmuvdfbJdRLbEp/r2M/N\nJZ7Da372j/37y5q/+2t/D0ucd+xzrn2Muc+aP9c1X0trHrevqHkfS/0tvNQ9bG0stwcAAAAAQhOS\nAgAAAAChCUkBAAAAgNDUJIV12tyGTk/px9JdAAAAALiLkBRW6L0Nndbs+892U6EuAAAAwCvL7QEA\nAACA0ISkAAAAAEBoQlIAAAAAIDQhKQAAAAAQWi5l3L1Wcp9L6Uoe9aDA6l1vabUbNzWHtqrdcD5N\n3BOIq/Z7WMv3lQg8v2Lz+d/P2DE31xxsx9O39G5maSYpAAAAABCakBQAAAAACE1ICgAAAACE9m3p\nDgDTyH1+SSk1c53vclxtSVIAAACADwlJYb+aOTdRW/PGTQAAAAAfsdweAAAAAAhNSAoAAAAAhCYk\nBQAAAABCU5MUdmLujZoAAAAA9iKXMu5eK7nPZc7NYoB//Pndm/u7uOaNm5pDW9VuOJ8m7sn0at9r\njT2MBzCtSPdXYqu51l3nRLH2e7/vK/CRp2/p3ZzEcnsAAAAAIDQhKQAAAAAQmpAUAAAAAAjNxk2w\nX0Pu82x1Qi/H1ZYkBQAAAPiQkBR2qnTlec7zrXnjJgAAAICPWG4PAAAAAIQmJAUAAAAAQhOSAgAA\nAACh5VLGLSOY+1xKV/KoBwXelfv8klJqUkppye/e9/+0n95MhvNpjq7sUnNoq9oZYz7jWgIA5jD2\n7xx+h/md8YD7PH1L7+YmZpLC9jX+xwQAAADA/YSkAAAAAEBoQlIAAAAAIDQhKQAAAAAQ2relOwBM\n49cNnebwlH7MdSoAAACAUQlJYb9m3dDp+8/Pd7cHAAAAWCPL7QEAAACA0ISkAAAAAEBoQlIAAAAA\nIDQ1SWE/htznxeqCDufTUqeGDzWH9tM2ka7fSO8VmNYS99eac05xXuJyzd1v7DExxr8zHqzZVv8G\nE5LCTpSuPP/685KBKQAAAMCWWG4PAAAAAIQmJAUAAAAAQhOSAgAAAAChqUkK+zXrRk6XoxKoAAAA\nwDYJSWGn/tzIaWrXW5KSAgAAAJtkuT0AAAAAEJqQFAAAAAAITUgKAAAAAISWSxm3jGDucyldyaMe\nFPhL7vNLSqlJKaU1fOe+/6edvSbpcD7NfUoe0BzaqnY+V2BO7k2x+fzvY9wAeNRSz5Knb+nd/MRM\nUtiuZg3hKAAAAMDWCUkBAAAAgNCEpAAAAABAaN+W7gAwjV9rls7hKf2Y61QAAAAAoxKSwn7NWrP0\n+8/5N24CAAAAGIPl9gAAAABAaEJSAAAAACA0ISkAAAAAEJqapLAjc2/WBAAAALAHQlLYl/9t1pT7\nPOtGSsP59Gmb5tCOdqy1i/Rex2bsgDm5l6zDUvf+2uPV9C/StRTpvbJPft+E5a3x+2W5PQAAAAAQ\nmpAUAAAAAAhNSAoAAAAAhKYmKezXMGdd0stx1hKoAAAAAKMRksJOla48z3m+6y1JSQEAAIBNstwe\nAAAAAAhNSAoAAAAAhCYkBQAAAABCU5MUtm/WDZoAAAAA9kZIChv36wZNwtJ5NId2tecczqeJe3K/\npfpWM3ZrHjeAz3hGTG/t/YMt28M9DNgHy+0BAAAAgNCEpAAAAABAaEJSAAAAACA0NUlh53KfX1JK\nzdTnuRyVQwUAAAC2SUgK+9eUruSpT3K9JSkpAAAAsEmW2wMAAAAAoQlJAQAAAIDQhKQAAAAAQGhq\nksK+DLnPaoMCAAAAfIGQFHakdOX5z/8mNB3fcD6FOCcA2+AZEVtzaKva7eE6ifRemdZS10jtNVzD\ndQ7js9weAAAAAAjNTFLYiNznl5RSs3Q/AAAAAPZGSArb0ZSu5NcfLKMHAAAAGIeQFPZvls2cLkeZ\nLQAAALBNQlLYubc2c5rC9ZakpAAAAMAm2bgJAAAAAAhNSAoAAAAAhCYkBQAAAABCU5MUmM1wPi3d\nBSbQHNpRj+c6AcZQe29yz5mWz2F6kcZuze/VtX6/SGMS6b2yLe5h/zCTFAAAAAAITUgKAAAAAIQm\nJAUAAAAAQhOSAgAAAACh2bgJdir3+SWl1Mx1vsuxzHUqAAAAgFEJSWG/mtKVPNfJrrckJQUAAAA2\nyXJ7AAAAACA0ISkAAAAAEJqQFAAAAAAITU1S2KF/N22ChzSHtqrdcD6t+rxj9w/YBt/9dYj0OSz1\n3GQdlvpcXXfAVq3x/mUmKezTbLvaAwAAAGydkBQAAAAACE1ICgAAAACEJiQFAAAAAEKzcRPs15D7\nXOY62eU426kAAAAARiUkhZ0qXXme83zXW5KSAgAAAJskJIUdyX1+SXa2BwAAAPgSNUlhX5rSlbx0\nJwAAAAC2REgKAAAAAIRmuT0AD2kObVW74Xwatd3Y5wUYg3vT78YeD+O7Dj6H9TDGwBiWuJes8f5l\nJikAAAAAEJqQFAAAAAAITUgKAAAAAISmJins05D7XOY84eU46+kAAAAARiMkhRnlPr+klJqpz1O6\n8jz1Of50vSUpKQAAALBJQlKYV1O6ku954RszQ2efLQoAAACwR0JS2Ki3ZosKTQEAAAC+zsZNAAAA\nAEBoQlIAAAAAIDTL7YFRNIf20zbD+TRDT+5X8x5SWv/7GMva3+fa+wdsw9j3/j3cm8Yck7HHYw/j\nu5Taz7WGzwGAPTKTFAAAAAAITUgKAAAAAIQmJAUAAAAAQlOTFPZlyH0uS5z4Kf1Y4rQAAAAADxOS\nwo6Urjwvde7vP9tFwlkAAACAR1luDwAAAACEJiQFAAAAAEITkgIAAAAAoQlJAQAAAIDQcinj7rWS\n+1xKV/KoB4WdeOT7sfbv1vWWwmzc1BzaT9sM59MMPflbTd9SWq5/AHvnPgzAmngu3a927GoZ42nV\nfl6X/57ezVXMJAUAAAAAQhOSAgAAAAChCUkBAAAAgNC+Ld0BYBq5zy8ppWau812OYUqSAgAAADsj\nJIX9aubc6CnSxk0AAADAvlhuDwAAAACEJiQFAAAAAEITkgIAAAAAoalJCjsy92ZNAAAAAHsgJIV9\n+d9mTbnPm91IqTm0Ve2G82ninmzPHsak9vOvtYcxAWLyPLyfsQP4h/vc/cYeu5pnk8/rfmOMneX2\nAAAAAEBoQlIAAAAAIDQhKQAAAAAQmpqksF/DnHVJL8fNlkAFAAAAghOSwk6VrjzPeb7rLUlJAQAA\ngE0SksJ2zDozFAAAACAKISlsRM3MUCEqAAAAwNfZuAkAAAAACM1MUmA2zaGtajecT6Mer1bteWvb\nLWHsMR7TUp8XwJzGvDe5z93P2AHwqCX+tlrz33MRmEkKAAAAAIQmJAUAAAAAQhOSAgAAAAChCUkB\nAAAAgNBs3AT7MuQ+lyVOfDkucloAAACAhwlJYUdKV56XOvf1lqSkAAAAwCZZbg8AAAAAhCYkBQAA\nAABCE5ICAAAAAKHlUsYtI5j7XEpX8qgHhZ3Y8/dDTdLfNYd2kfMO59Mi5wXYqtr7tfsrUdR8J3wf\nANiqp2/p3UzGTFIAAAAAIDQhKQAAAAAQmpAUAAAAAAhNSAoAAAAAhPZt6Q4A88h9fkkpNVMd/3K0\nbxMAAACwTUJSiKMpXXl3F7dH2d0eAAAA2CrL7QEAAACA0ISkAAAAAEBoQlIAAAAAIDQ1SWHHpt6s\niccN59PSXQAIzX0Yfuc7wVo1h7aqnWsYpjPm93CN32kzSWHfmtKVPOWGTQAAAABbJyQFAAAAAEIT\nkgIAAAAAoalJCnEMuc9lqoNfjpMdGgAAAGBSQlIIonTlecrjX29JSgoAAABskuX2AAAAAEBoQlIA\nAAAAIDQhKQAAAAAQmpqksCO5zy8ppWbpfgAAAABsiZAU9qUpXcmvP0y5m/1fJz60n7YZzqcZerIO\ne3ivNZ9pSsu917X3DwBYN7+/bovPApY35vdwjd9py+0BAAAAgNCEpAAAAABAaJbbE4qanQAAAAD8\nSUhKNL/V7JzbnDVCPzN2YPyUfox1KAAAAIBZCUkhrlED4+8/29UEwAAAAABfoSYpAAAAABCakBQA\nAAAACE1ICgAAAACEpiYp7Nuwps2iAAAAANZISAo7Vrry/N6/CU/5zHA+Ld0FgMk1h7aqXe09cezj\nwZat/fvgewjAryy3BwAAAABCE5ICAAAAAKEJSQEAAACA0NQkhY3JfX5JKTUjHGrUTZ2e0o+xDgUA\nAAAwKyEpbE9TupLf+oevhJ4fbep0j+8/WxtBAQAAAJtkuT0AAAAAEJqQFAAAAAAITUgKAAAAAIQm\nJAUAAAAAQrNxEzCK4Xz6tE1zaGc/JwB8pPZZUvsM82yC/+f7ADANv5f8rXZMLv99f0zMJAUAAAAA\nQhOSAgAAAAChCUkBAAAAgNDUJIUgcp9fUkrNVMe/HMtUhwYAAACYlJAU4mhKV/JUB7/ekpQUAAAA\n2CTL7QEAAACA0ISkAAAAAEBoQlIAAAA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11595ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_pairwise_dists = distance.squareform(distance.pdist(A.T))\n",
    "# cluster\n",
    "col_clusters = sch.linkage(col_pairwise_dists,method='complete')\n",
    "\n",
    "fig = plt.figure(figsize=(24,24))\n",
    "heatmapGS = gridspec.GridSpec(2,2,wspace=0.0,hspace=0.0,width_ratios=[0.25,1],height_ratios=[0.25,1])\n",
    "\n",
    "### col dendrogram ####\n",
    "col_denAX = fig.add_subplot(heatmapGS[0,1])\n",
    "col_denD = sch.dendrogram(col_clusters,color_threshold=np.inf)\n",
    "clean_axis(col_denAX)\n",
    "\n",
    "### row dendrogram ###\n",
    "row_denAX = fig.add_subplot(heatmapGS[1,0])\n",
    "row_denD = sch.dendrogram(row_clusters,color_threshold=np.inf,orientation='right')\n",
    "clean_axis(row_denAX)\n",
    "\n",
    "### heatmap ###\n",
    "heatmapAX = fig.add_subplot(heatmapGS[1,1])\n",
    "axi = heatmapAX.pcolor(A.ix[den['leaves'],col_denD['leaves']],cmap='Blues')\n",
    "plt.xlim(0, len(A.index))\n",
    "plt.ylim(0, len(A.columns))\n",
    "clean_axis(heatmapAX)\n",
    "\n",
    "\n",
    "### set labels ###\n",
    "# heatmapAX.set_xticks(np.arange(A.shape[1])+0.5)\n",
    "# xlabels = A.columns[col_denD['leaves']]\n",
    "# heatmapAX.set_xticklabels(xlabels, rotation=90)\n",
    "\n",
    "# heatmapAX.set_yticks(np.arange(A.shape[0])+0.5)\n",
    "# ylabels = A.columns[row_denD['leaves']]\n",
    "# heatmapAX.yaxis.set_ticks_position('right')\n",
    "# heatmapAX.set_yticklabels(ylabels)\n",
    "\n",
    "\n",
    "### remove tick lines ###\n",
    "for l in heatmapAX.get_xticklines() + heatmapAX.get_yticklines(): \n",
    "    l.set_markersize(0)\n",
    " \n",
    "\n",
    "plt.savefig('Inter-Subtype Connectivity.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x121557790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Programatically create the snail shell plot.\n",
    "\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "ax = fig.add_subplot(111, polar=True)\n",
    "node_fs = 10\n",
    "\n",
    "\n",
    "# circle = plt.Circle((0, 0), 1, transform=ax.transData._b, color=\"red\", alpha=0.4)\n",
    "# ax.add_artist(circle) \n",
    "\n",
    "# Add the \"spoke\" circle\n",
    "circle = plt.Circle((0,0), radius=1, transform=ax.transData._b, linewidth=0, color='green', alpha=0.05)\n",
    "\n",
    "hub_thresh = 0.5\n",
    "\n",
    "ax.add_artist(circle)\n",
    "# Add the \"hub\" circle\n",
    "circle = plt.Circle((0,0), radius=1 - hub_thresh, transform=ax.transData._b, linewidth=0, color='green', alpha=0.2)\n",
    "ax.add_artist(circle)\n",
    "\n",
    "\n",
    "\n",
    "for group, data in centralities.groupby(centralities['Degree Centrality'] > hub_thresh):\n",
    "    for row, series in data.iterrows():\n",
    "        theta = float(row) / len(data) * 2 * math.pi\n",
    "        if series['Degree Centrality'] > hub_thresh:\n",
    "            radius = 1 - series['Degree Centrality'] #preserves scalings\n",
    "            # radius = 1 - math.sqrt(series['Degree Centrality']) #accentuates hub/spoke status\n",
    "            # radius = 0.3\n",
    "        if series['Degree Centrality'] <= hub_thresh:\n",
    "            # radius = 1 - series['Degree Centrality'] ** 2 #accentuates hub/spoke status\n",
    "            radius = 1 - series['Degree Centrality'] #preserves scalings\n",
    "            # radius = 1\n",
    "            # ax.plot([theta, theta], [0.5, 1], color='black', linestyle=':')\n",
    "\n",
    "        ax.scatter(theta, radius)\n",
    "    \n",
    "# ax.set_yticks(np.arange(1,0)) \n",
    "# ax.set_xticks(ax.get_xticks(), ['' * len(ax.get_xticks())])\n",
    "ax.annotate('Hub \\n Subtypes', xy=(0,0), ha='center', va='center')\n",
    "ax.annotate('Spoke \\n Subtypes', xy=(math.pi/2, hub_thresh+(1-hub_thresh)/2), ha='center', va='center')\n",
    "ax.axis('off')\n",
    "plt.savefig('Hub and Spoke.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Degree Centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H12N9</td>\n",
       "      <td>0.009259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H2N6</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H8N8</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H8N2</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H5N4</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H1N7</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H9N3</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H12N2</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>H12N1</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>H15N9</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>H15N4</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>H8N1</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>H4N4</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>H12N6</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>H14N6</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>H2N4</td>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>H3N7</td>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>H5N6</td>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>H9N8</td>\n",
       "      <td>0.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>H9N6</td>\n",
       "      <td>0.046296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>H6N4</td>\n",
       "      <td>0.046296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>H11N6</td>\n",
       "      <td>0.046296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>H5N7</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>H4N5</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>H1N5</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>H1N4</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>H14N2</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>H1N6</td>\n",
       "      <td>0.064815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>H13N2</td>\n",
       "      <td>0.064815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>H5N8</td>\n",
       "      <td>0.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>H10N2</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>H2N7</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>H1N2</td>\n",
       "      <td>0.287037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>H5N1</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>H2N9</td>\n",
       "      <td>0.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>H6N5</td>\n",
       "      <td>0.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>H5N3</td>\n",
       "      <td>0.324074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>H2N1</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>H7N1</td>\n",
       "      <td>0.342593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>H11N2</td>\n",
       "      <td>0.342593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Mixed</td>\n",
       "      <td>0.351852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>H7N7</td>\n",
       "      <td>0.379630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>H3N6</td>\n",
       "      <td>0.398148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>H8N4</td>\n",
       "      <td>0.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>H4N8</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>H4N2</td>\n",
       "      <td>0.435185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>H6N1</td>\n",
       "      <td>0.435185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>H3N2</td>\n",
       "      <td>0.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>H12N5</td>\n",
       "      <td>0.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>H6N8</td>\n",
       "      <td>0.490741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>H7N3</td>\n",
       "      <td>0.527778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>H6N2</td>\n",
       "      <td>0.537037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>H10N7</td>\n",
       "      <td>0.546296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>H9N2</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>H2N3</td>\n",
       "      <td>0.574074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>H1N1</td>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>H5N2</td>\n",
       "      <td>0.648148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>H11N9</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>H4N6</td>\n",
       "      <td>0.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>H3N8</td>\n",
       "      <td>0.787037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Subtype  Degree Centrality\n",
       "0     H12N9           0.009259\n",
       "1      H2N6           0.018519\n",
       "2      H8N8           0.018519\n",
       "3      H8N2           0.018519\n",
       "4      H5N4           0.018519\n",
       "5      H1N7           0.018519\n",
       "6      H9N3           0.018519\n",
       "7     H12N2           0.018519\n",
       "8     H12N1           0.018519\n",
       "9     H15N9           0.018519\n",
       "10    H15N4           0.018519\n",
       "11     H8N1           0.027778\n",
       "12     H4N4           0.027778\n",
       "13    H12N6           0.027778\n",
       "14    H14N6           0.027778\n",
       "15     H2N4           0.037037\n",
       "16     H3N7           0.037037\n",
       "17     H5N6           0.037037\n",
       "18     H9N8           0.037037\n",
       "19     H9N6           0.046296\n",
       "20     H6N4           0.046296\n",
       "21    H11N6           0.046296\n",
       "22     H5N7           0.055556\n",
       "23     H4N5           0.055556\n",
       "24     H1N5           0.055556\n",
       "25     H1N4           0.055556\n",
       "26    H14N2           0.055556\n",
       "27     H1N6           0.064815\n",
       "28    H13N2           0.064815\n",
       "29     H5N8           0.074074\n",
       "..      ...                ...\n",
       "79    H10N2           0.277778\n",
       "80     H2N7           0.277778\n",
       "81     H1N2           0.287037\n",
       "82     H5N1           0.296296\n",
       "83     H2N9           0.314815\n",
       "84     H6N5           0.314815\n",
       "85     H5N3           0.324074\n",
       "86     H2N1           0.333333\n",
       "87     H7N1           0.342593\n",
       "88    H11N2           0.342593\n",
       "89    Mixed           0.351852\n",
       "90     H7N7           0.379630\n",
       "91     H3N6           0.398148\n",
       "92     H8N4           0.407407\n",
       "93     H4N8           0.416667\n",
       "94     H4N2           0.435185\n",
       "95     H6N1           0.435185\n",
       "96     H3N2           0.472222\n",
       "97    H12N5           0.472222\n",
       "98     H6N8           0.490741\n",
       "99     H7N3           0.527778\n",
       "100    H6N2           0.537037\n",
       "101   H10N7           0.546296\n",
       "102    H9N2           0.555556\n",
       "103    H2N3           0.574074\n",
       "104    H1N1           0.611111\n",
       "105    H5N2           0.648148\n",
       "106   H11N9           0.666667\n",
       "107    H4N6           0.740741\n",
       "108    H3N8           0.787037\n",
       "\n",
       "[109 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centralities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['H12N9',\n",
       " 'H2N6',\n",
       " 'H8N8',\n",
       " 'H8N2',\n",
       " 'H5N4',\n",
       " 'H1N7',\n",
       " 'H9N3',\n",
       " 'H12N2',\n",
       " 'H12N1',\n",
       " 'H15N9',\n",
       " 'H15N4',\n",
       " 'H8N1',\n",
       " 'H4N4',\n",
       " 'H12N6',\n",
       " 'H14N6',\n",
       " 'H2N4',\n",
       " 'H3N7',\n",
       " 'H5N6',\n",
       " 'H9N8',\n",
       " 'H9N6',\n",
       " 'H6N4',\n",
       " 'H11N6',\n",
       " 'H5N7',\n",
       " 'H4N5',\n",
       " 'H1N5',\n",
       " 'H1N4',\n",
       " 'H14N2',\n",
       " 'H1N6',\n",
       " 'H13N2',\n",
       " 'H5N8',\n",
       " 'H7N5',\n",
       " 'H9N9',\n",
       " 'H12N4',\n",
       " 'H13N9',\n",
       " 'H2N8',\n",
       " 'H13N6',\n",
       " 'H1N8',\n",
       " 'H2N5',\n",
       " 'H12N3',\n",
       " 'H16N3',\n",
       " 'H11N7',\n",
       " 'H6N7',\n",
       " 'H13N8',\n",
       " 'H4N7',\n",
       " 'H5N5',\n",
       " 'H6N9',\n",
       " 'H7N4',\n",
       " 'H9N5',\n",
       " 'H6N3',\n",
       " 'H9N7',\n",
       " 'H10N9',\n",
       " 'H4N1',\n",
       " 'H4N9',\n",
       " 'H12N8',\n",
       " 'H12N7',\n",
       " 'H10N4',\n",
       " 'H7N6',\n",
       " 'H11N1',\n",
       " 'H3N9',\n",
       " 'H3N5',\n",
       " 'H10N1',\n",
       " 'H10N5',\n",
       " 'H7N2',\n",
       " 'H5N9',\n",
       " 'H7N9',\n",
       " 'H1N9',\n",
       " 'H10N3',\n",
       " 'H6N6',\n",
       " 'H7N8',\n",
       " 'H1N3',\n",
       " 'H9N1',\n",
       " 'H10N8',\n",
       " 'H3N1',\n",
       " 'H3N3',\n",
       " 'H11N8',\n",
       " 'H10N6',\n",
       " 'H4N3',\n",
       " 'H2N2',\n",
       " 'H11N3',\n",
       " 'H10N2',\n",
       " 'H2N7',\n",
       " 'H1N2',\n",
       " 'H5N1',\n",
       " 'H2N9',\n",
       " 'H6N5',\n",
       " 'H5N3',\n",
       " 'H2N1',\n",
       " 'H7N1',\n",
       " 'H11N2',\n",
       " 'Mixed',\n",
       " 'H7N7',\n",
       " 'H3N6',\n",
       " 'H8N4',\n",
       " 'H4N8',\n",
       " 'H4N2',\n",
       " 'H6N1',\n",
       " 'H3N2',\n",
       " 'H12N5',\n",
       " 'H6N8',\n",
       " 'H7N3',\n",
       " 'H6N2',\n",
       " 'H10N7',\n",
       " 'H9N2',\n",
       " 'H2N3',\n",
       " 'H1N1',\n",
       " 'H5N2',\n",
       " 'H11N9',\n",
       " 'H4N6',\n",
       " 'H3N8']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = sorted(nx.degree_centrality(subtypeG).items(),  key=lambda x:x[1])\n",
    "nodes = [i[0] for i in nodes]\n",
    "nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bhNEtQM8k5qQ2wURoBru+De0ejX7OTCIOJ1y8Pa9QYpb5/vXOMOxJsg4SsJPO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ODJevEZ4vgp5/Am1efR7ebQna4ForXDArPLMjf958ZOa/ixaUiSg863Nk2u8I3Af8CB0eh8o4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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122138050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a Circos panel for all of the subtypes.\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from circos import CircosPlot\n",
    "\n",
    "fig = plt.figure(figsize=(4.5, 7.5))\n",
    "# Annotate the figure with NA subtype labels\n",
    "for na in range(1,11):\n",
    "    ax = plt.subplot2grid((18, 11), (0, na))\n",
    "    if na == 10:\n",
    "        ax.annotate('Mx'.format(na), xy=(0.5, 0), va='bottom', ha='center', xycoords='axes fraction', fontsize=8)\n",
    "    else:\n",
    "        ax.annotate('N{0}'.format(na), xy=(0.5, 0), va='bottom', ha='center', xycoords='axes fraction', fontsize=8)\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.yaxis.set_visible(False)\n",
    "    for k in ax.spines.keys():\n",
    "        ax.spines[k].set_visible(False)\n",
    "        \n",
    "    ax.set_aspect('equal')\n",
    "\n",
    "# Annotate the figure with HA subtype labels\n",
    "for ha in range(1,18):\n",
    "    ax = plt.subplot2grid((18, 11), (ha, 0))\n",
    "    if ha == 17:\n",
    "        ax.annotate('Mx'.format(ha), xy=(1, 0.5), va='center', ha='right', xycoords='axes fraction', fontsize=8)\n",
    "    else:\n",
    "        ax.annotate('H{0}'.format(ha), xy=(1, 0.5), va='center', ha='right', xycoords='axes fraction', fontsize=8)\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.yaxis.set_visible(False)\n",
    "    for k in ax.spines.keys():\n",
    "        ax.spines[k].set_visible(False)\n",
    "    ax.set_aspect('equal')\n",
    "        \n",
    "# Plot the circos plots for each subtype\n",
    "subtypes_considered = []\n",
    "for node in subtypeG.nodes():\n",
    "    if node != 'Mixed':\n",
    "        ha = int(node.split('N')[0].split('H')[1])\n",
    "        na = int(node.split('N')[1])\n",
    "        ax = plt.subplot2grid((18, 11), (ha, na))\n",
    "    if node == 'Mixed':\n",
    "        ax = plt.subplot2grid((18, 11), (17, 10))\n",
    "    \n",
    "    nodes = sorted(nx.degree_centrality(subtypeG).items(),  key=lambda x:x[1])\n",
    "    nodes = [i[0] for i in nodes]\n",
    "    edges = [(u, v) for u,v in subtypeG.edges() if node in [u, v] and u != v]\n",
    "    c = CircosPlot(nodes, edges, radius=1, nodecolor='blue', figsize=(1,1), fig=fig, ax=ax)\n",
    "    c.draw()\n",
    "    ax.set_aspect('equal')\n",
    "\n",
    "# Add in one final axes to indicate that this is figure panel (a).\n",
    "ax = plt.subplot2grid((18, 11), (0,0))\n",
    "ax.annotate('a.', xy=(0,1), xycoords='axes fraction', ha='left', va='top')\n",
    "ax.xaxis.set_visible(False)\n",
    "ax.yaxis.set_visible(False)\n",
    "for k in ax.spines.keys():\n",
    "    ax.spines[k].set_visible(False)\n",
    "# Save the figure\n",
    "plt.subplots_adjust(left=0, right=1.0, bottom=0, top=1.0)\n",
    "plt.savefig('Inter-Subtype Connectivity Circos.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3A5tL+l/c52mqkJhgxo/xlBjvUKJXaMlELTHXjAtxIf5FM0YBh0hFJBmpMsxs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11853c650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make one example figure with one circos plot.\n",
    "subtype_of_interest = 'H7N9'\n",
    "\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "ax = fig.add_subplot(111)\n",
    "nodes = sorted(nx.degree_centrality(subtypeG).items(),  key=lambda x:x[1])\n",
    "nodes = [i[0] for i in nodes]\n",
    "edges = [(u, v) for u,v in subtypeG.edges() if subtype_of_interest in [u, v] and u != v]\n",
    "c = CircosPlot(nodes, edges, radius=1, nodecolor='blue', figsize=(1,1), fig=fig, ax=ax)\n",
    "c.draw()\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "plt.savefig('Circos Example - H7N9.pdf')"
   ]
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}