Force-directed graphs in Lightning.ipynb

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   "source": [
    "# <img style='float: left' src=\"http://lightning-viz.github.io/images/logo.png\"> <br> <br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Force-directed graphs in <a href='http://lightning-viz.github.io/'><font color='#9175f0'>Lightning</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <hr> Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "ename": "ModuleNotFoundError",
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      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
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      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'lightning'"
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   ],
   "source": [
    "import os\n",
    "from lightning import Lightning\n",
    "\n",
    "from numpy import random, asarray, linspace, corrcoef\n",
    "from colorsys import hsv_to_rgb\n",
    "from sklearn import datasets\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgn = Lightning(ipython=True, host='http://public.lightning-viz.org')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <hr> Random binary network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A random graph where every node has the same degree, with default styling.\n",
    "<br>\n",
    "This and other graph plots take matrices (e.g. adjacency matrices) as inputs.\n",
    "<br>\n",
    "We use the `networkx` library to generate the graph, then get its adjancency matrix.\n",
    "<br>\n",
    "Double click on a point to see its neighbors (all should have 3).\n",
    "<br>\n",
    "Drag points to try and move the graph, it should remain tight because of the degree structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 100\n",
    "G = nx.random_regular_graph(3,n)\n",
    "mat = nx.adjacency_matrix(G).todense()\n",
    "lgn.force(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <hr> Random weighted network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous network was binary (all links either 0 or 1).\n",
    "Here the links are weighted, which is reflected in the line widths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.random_regular_graph(3,100)\n",
    "mat = asarray(nx.adjacency_matrix(G).todense()) * (((random.rand(100,100))*5) ** 2)\n",
    "lgn.force(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <hr> Lobster network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lobster graph, a backbone with some leaves, is colored here by node ordering.\n",
    "<br>\n",
    "We also set all nodes with degree less than 2 to gray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.random_lobster(60, 0.5, 0.0, seed=44)\n",
    "mat = nx.adjacency_matrix(G).todense()\n",
    "n = len(G.nodes())\n",
    "c = [list(asarray(hsv_to_rgb(float(x) / n , 0.8, 1.0))*255) if y > 1 else [150,150,150] for (x,y) in G.degree_iter()]\n",
    "lgn.force(mat, color=c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <hr> Coloring by degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we color points (and also change their size) to indicate their degree.\n",
    "<br>\n",
    "Click to confirm bigger points have more connections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.random_geometric_graph(50, 0.1)\n",
    "mat = nx.adjacency_matrix(G).todense()\n",
    "g = G.degree().values()\n",
    "lgn.force(mat, group=g, size=(asarray(g) + 1.5)*3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <hr> Graph of clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphs can be a useful way to look at data that doesn't neccessarily come from a graph.\n",
    "<br>\n",
    "Here we create a graph from a thresholded correlation matrix on data drawn from a set of clusters.\n",
    "<br>\n",
    "The cluster identities are clear in the resulting graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d, g = datasets.make_blobs(n_features=5, n_samples=50, centers=5, cluster_std=2.0, random_state=100)\n",
    "c = corrcoef(d)\n",
    "c[c<0.9] = 0\n",
    "lgn.force(c, group=g)"
   ]
  }
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