gistfile1.txt

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TMs = {\n",
    "    \"TM1\": range(34,64),\n",
    "    \"TM2\": range(71,100),\n",
    "    \"TM3\": range(106,140),\n",
    "    \"TM4\": range(150,172),\n",
    "    \"TM5\": range(200,230),\n",
    "    \"TM6\": range(241,276),\n",
    "    \"TM7\": range(286,309)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TM_ecs = {tm:{} for tm in TMs.keys()}\n",
    "with open(\"gross_aln_gremlin_ecs.tsv\") as f:\n",
    "    for line in f.readlines()[1:]:\n",
    "        fields = line.rstrip().split()\n",
    "        i = int(fields[0])\n",
    "        j = int(fields[1])\n",
    "        score = float(fields[5])\n",
    "        if score>=1:\n",
    "            for tm1, tm1_ranges in TMs.iteritems():\n",
    "                if i in tm1_ranges:\n",
    "                    break\n",
    "\n",
    "            for tm2, tm2_ranges in TMs.iteritems():\n",
    "                if j in tm2_ranges:\n",
    "                    break\n",
    "            try:\n",
    "                TM_ecs[tm1][tm2]+=1\n",
    "            except:\n",
    "                TM_ecs[tm1][tm2]=1\n",
    "\n",
    "            try:\n",
    "                TM_ecs[tm2][tm1]+=1\n",
    "            except:\n",
    "                TM_ecs[tm2][tm1]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tm_graph = nx.Graph()\n",
    "for tm1, tm1_partners in TM_ecs.iteritems():\n",
    "    for tm2, tm1_tm2 in tm1_partners.iteritems():\n",
    "        if not tm_graph.has_edge(tm1, tm2):\n",
    "            tm_graph.add_edge(tm1, tm2, count=tm1_tm2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pos = nx.spring_layout(tm_graph)\n",
    "weights = [tm_graph[u][v]['count'] for u,v in tm_graph.edges()]\n",
    "labels = nx.get_edge_attributes(tm_graph,'count')\n",
    "nx.draw(tm_graph,pos=pos, with_labels=True, width=weights, node_size=1800, node_color=\"#D3D3D3\", edge_color=\"#688e3a\")\n",
    "nx.draw_networkx_edge_labels(tm_graph,pos,edge_labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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