{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmap: Visualizing a Graph\n",
    "\n",
    "This example shows how to visualize graphs using a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graspologic\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Simple Graphs using heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A 2-block Stochastic Block Model is defined as below:\n",
    "\n",
    "\\begin{align*}\n",
    "P = \\begin{bmatrix}0.8 & 0.2 \\\\\n",
    "0.2 & 0.8 \n",
    "\\end{bmatrix}\n",
    "\\end{align*}\n",
    "\n",
    "In simple cases, the model is unweighted. Below, we plot an unweighted SBM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graspologic.simulations import sbm\n",
    "from graspologic.plot import heatmap\n",
    "\n",
    "n_communities = [50, 50]\n",
    "p = [[0.8, 0.2], \n",
    "     [0.2, 0.8]]\n",
    "\n",
    "A, labels = sbm(n_communities, p, return_labels=True)\n",
    "heatmap(A, title=\"Basic Heatmap function\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with Hierarchy Labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we have labels, we can use them to show communities on a Heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap(A, inner_hier_labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot outer hierarchy labels in addition to inner hierarchy labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outer_labels = [\"Outer Labels\"] * 100\n",
    "heatmap(A, inner_hier_labels=labels,\n",
    "        outer_hier_labels=outer_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighted SBMs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use heatmap when our graph is weighted. Here, we generate two weighted SBMs where the weights are distributed from a Poisson(3) and Normal(5, 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw weights from a Poisson(3) distribution\n",
    "wt = np.random.poisson\n",
    "wtargs = dict(lam=3)\n",
    "A_poisson= sbm(n_communities, p, wt=wt, wtargs=wtargs)\n",
    "\n",
    "# Plot\n",
    "title = 'Weighted Stochastic Block Model with \\n weights drawn from a Poisson(3) distribution'\n",
    "fig= heatmap(A_poisson, title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw weights from a Normal(5, 1) distribution\n",
    "wt = np.random.normal\n",
    "wtargs = dict(loc=5, scale=1)\n",
    "A_normal = sbm(n_communities, p, wt=wt, wtargs=wtargs)\n",
    "\n",
    "# Plot\n",
    "title = 'Weighted Stochastic Block Model with \\n weights drawn from a Normal(5, 1) distribution'\n",
    "fig = heatmap(A_normal, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colormaps\n",
    "\n",
    "You can change colormaps. See [here](https://matplotlib.org/tutorials/colors/colormaps.html) for a list of colormaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Weighted Stochastic Block Model with Poisson(3)'\n",
    "fig = heatmap(A_poisson, title=title, transform=None, cmap=\"binary\", center=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data transformations\n",
    "\n",
    "When your graphs have values that span a large range, it is often useful to transform the data in order to visualize it properly. Below, we use a real graph which is estimated from the a structural MRI scan. The data is provided by [HNU1](http://dx.doi.org/10.15387/fcp_indi.corr.hnu1). \n",
    "\n",
    "The data ranges from 0 to 44813, and visualizing without a transformation will emphasize the large weights. Both log and pass-to-ranks transforms provide better visualizations of the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "G = np.load('./data/sub-0025427_ses-1_dwi_desikan.npy')\n",
    "\n",
    "print((np.min(G), np.max(G)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Without transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Without Transformation'\n",
    "fig= heatmap(G, title=title, transform=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With log transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Log Transform'\n",
    "fig= heatmap(G, title=title, transform='log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With pass to ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title = 'Pass-to-ranks (zero-boost) Transform'\n",
    "fig= heatmap(G, title=title, transform='zero-boost')"
   ]
  }
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