tda-networks/usa_roads.ipynb

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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
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    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import graph_tool.all as gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
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   "outputs": [],
   "source": [
    "gt.show_config()"
   ]
  },
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   "outputs": [],
   "source": [
    "# G = gt.load_graph_from_csv(\"data/usa_roads/usa_roads_ny.csv\", eprop_types=[\"int\"], eprop_names=[\"distance\"], string_vals=False)\n",
    "# G.save(\"data/usa_roads/usa_roads_ny.gt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "autoscroll": false,
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   },
   "outputs": [],
   "source": [
    "G = gt.load_graph(\"data/usa_roads/usa_roads_ny.gt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
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    "ein.tags": "worksheet-0",
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   },
   "outputs": [],
   "source": [
    "print(G)\n",
    "G.list_properties()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
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   },
   "outputs": [],
   "source": [
    "dist = G.ep.get(\"distance\")\n",
    "dist.get_array()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
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    }
   },
   "outputs": [],
   "source": [
    "filt = G.new_edge_property(\"bool\")\n",
    "filt.a = dist.a > 800\n",
    "print(filt.a.mean())\n",
    "G.set_edge_filter(filt)\n",
    "print(G.num_edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
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   },
   "outputs": [],
   "source": [
    "ordered_dist = dist.get_array()\n",
    "ordered_dist = np.unique(np.sort(ordered_dist))\n",
    "ordered_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import clique as cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "condmat = gt.collection.data[\"cond-mat-2005\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clique_sizes = []\n",
    "for c in cl.find_cliques(condmat):\n",
    "    clique_sizes.append(len(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(clique_sizes, bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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