example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BrainMatrix Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "sns.set_context('notebook', font_scale=1.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialzing the matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by initializing the `BrainMatrix` object. If the neurosynth database has not\n",
    "already been downloaded, it will be downloaded automatically. This took 5 minutes\n",
    "with a 13 Mbps connection. Loading the dataset into memory takes around 15 seconds.\n",
    "We are using a fairly large `downsample` value here to speed up the computation. You would\n",
    "likely want to use sharper images (a lower `downsample`) in your research."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from brain_matrix import BrainMatrix\n",
    "matrix = BrainMatrix(metric='emd', downsample=10, name='example', auto_save=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll compute a full distance matrix for 6 features, a total of 15 distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [00:31<00:00,  2.11s/it]\n"
     ]
    }
   ],
   "source": [
    "matrix.compute_distances(['syntactic', 'speech', 'semantic', 'music', 'motor', 'hand'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the matrix\n",
    "\n",
    "We'll visualize the resulting matrix in several ways. First, a heatmap is\n",
    "just the matrix with distances encoded by color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112b13f98>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xidrQIUhCXdfCWyoOuRMREUkAK3QiIjIq/LY1IiIiCZBoPueQOxERkRSwQiciIqMi1e9D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\nX1Xe7rqK9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a756d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(matrix.to_dataframe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Multidimensional scaling plot shows the best embedding of the\n",
    "features in a two dimensional space. This allows us to see how the\n",
    "different features cluster in a reduced dimension fMRI space. Features\n",
    "near each other have relatively similiar associated fMRI images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WVqaMjAxt3bpVS5YsUWBgoIYMGaLu3bvrvffe07FjxxQWFiZbW9t6b+vjjz/W\n8uXLFR0drcmTJ6tjx46SpICAAPM8Tk5Ounz5co3lbty4oczMTPn7+//bbTg6OurKlSs1pp0/f17/\n+te/NHjwYFlZWdV7/ACAlq3eR5gZGRkaNmyYCgoKZGdnp+DgYEVHR6uqqkrffvutrKysNHbsWL3z\nzjvKzMzUgw8+eEvrb9OmTY3XJ0+eVNu2bTV79mxzLHNycnT58mXzUemAAQN06tQpFRQUmJf76KOP\nNHv2bF27du2mdf6cr6+vjhw5UmPazp07tXjxYmIJALe5egfTy8tLVlZWioqK0pEjR3TkyBH98Y9/\nlLu7u+69915J0vjx4/X555+rU6dOGjRo0C2t38nJSZJ0+PBh5efny9vbW6WlpVq9erU+/vhj7dq1\nS3PmzJGVlZX5e89JkybJyclJc+bM0aFDh3TgwAGtWLFCY8aMkZub203r/LnZs2crJydHCxYs0Icf\nfqjt27frjTfe0O9+97v6fkwAgFai3qdknZyclJSUpDVr1ig6OlqVlZUaPHiwtm7daj712rt3b7m5\nuWnMmDG3fIR2zz33aMyYMYqPj9eFCxe0bNkyLViwQMnJyfrTn/6k7t27a/r06frqq6+UnZ0tSXJ2\ndlZycrJWrVql+fPny8HBQeHh4Zo3b16t6/zP//zPGtscNGiQNm/erLi4OEVGRsrNzU0vvPCCpk6d\nWt+PCQDQSlhV13Hvufz8fIWGhurw4cNyd3ev18pzc3M1evRopaWl6e677/5VAwUAoLH9Uvsa5dZ4\neXl5SktLU1pamgICAoglAKDFa5Sbr1dVVWn79u2ysbHR73//+8bYBAAATapRjjB79+6t48ePN8aq\nm43q6mqV5+TIyq6tbO/uZenhAAAaWbN9vFdzVvqPTF0aN0GXwsL17aj7dWnyVJXn5lp6WECLsGfP\nHnl6eurGjRuWHgpwSwjmLaq+cUOFLy5S+YkTUmWlVFamsvR0mZ5f2KDP7gRaq//4j/9Qamqq7Ozs\nLD0U4JYQzFtUsmevKr788qbpZZ+cUNkPv94CoG6urq7y9fXlZiBocQjmLaoqvlb7G+Xlqr5a1LSD\nAX4lT09P7d27V3PnzjU/AzYtLU1ffvml+bmwU6ZMUe5PvnLw9PTUm2++WWM9kydP1gsvvGB+/dZb\nbyk8PFze3t4KCQnR+vXrzXfkqu2U7M6dOxUWFqYBAwZo/Pjx+uCDDxp5z4FbRzBvUfuHHpJ11643\nTbfp21c6WB4KAAAEkUlEQVRtA4ZZYETArxMTE6N+/fpp8+bNuuuuu7R48WJFRUVpwoQJiouL0zff\nfKOYmBjD6zt27JiWLFmiCRMmaOvWrYqIiNCmTZv05z//udb5t2zZotWrVys8PFybNm2Sr6+vnnnm\nGZ06daqhdhFoEI1ylWxr1sbVRU7P/n8qWhurqu++kyRZu7urw4L5srLh40TLExgYqKioKEnf38M5\nIiJC48aNMz+rdtq0adqyZYvh9WVlZaldu3aaOXOm7OzsNGTIELVp00Z33HHHTfNWVVUpKSlJ06ZN\n07PPPivp+wcq5OXl6fjx4/Ly8mqAPQQaBn/D14PjExGyD3tAJbvfkpWtrRwenSrrH+5TC7Q0Pj4+\n5p87deokSerfv795mrOzs65dq+OriFr4+/urpKRE48eP15gxYxQaGlrnA9jPnj0rk8mkkSNH1pie\nnJx8K7sANAlOydaTTdeu6vD0U3KaM5tYokVzcHC4aZq9vX291zdo0CBt3LhRrq6uSkhI0IQJEzR2\n7Fh9+umnN81rMpkkSS4uLvXeHtBUCCaAW/bzB72XlJTUeB0aGqqUlBR99NFHWr16tUpLS7VgwYKb\n1vPjE4R+/hza06dP64svvmjgUQO/DsEEcEvat2+vCxcumF8XFBTo3Llz5tevv/66pkyZIun707kT\nJkzQ5MmTdfHixZvW5eHhoQ4dOujo0aM1pi9ZskQ7d+5snB0A6onvMAHckhEjRmjXrl3y9PSUg4OD\nEhISapzWHTx4sOLi4rRs2TKFh4fr8uXLSklJ0ahRo25al62trWbNmqX4+Hg5ODjI399fBw8e1Jkz\nZ7Ry5cqm3C3g3yKYAG7J4sWLtXz5ci1evFgdO3bU7373O3322Wfm9/39/bV27Vpt3rxZ+/btU/v2\n7fXAAw/UekpWkp588knZ2trqjTfeUGJiovr06aOkpCT17du3qXYJMKRRn4cJAEBL8kvt4ztMAAAM\nIJgAABhAMAEAMICLfgA0e9XV1brx97/rRnq6rF07yeHxabKu5YYLQGMimACaterqapmi56vk7b1S\nWZkkqeTNN+Wybp3sBvj8m6WBhsMpWQDN2vX9+1Xyl93mWEpSxZmvVBQba8FR4XZEMAE0a2XpGdLP\nbsUnSWWfnVJ1RYUFRoTbFcEE0Ly1b1frZKv27SRr/gpD0+FPG4BmzWHqFFn/8Nixn7IfESQrgokm\nxJ82AM2abZ8+6vhyjGwH+Eh2drLu2lXtp05Rx5eWWXpouM1wlSyAZq/9uAfVbuwYVZ4/L+uOHWXt\n6GjpIeE2RDABtAhW1tay6dHD0sPAbYxTsgAAGEAwAQAwgGACAGAAwQQAwACCCQCAAQQTAAADCCYA\nAAYQTAAADCCYAAAYQDABADCAYAIAYADBBADAgDpvvl5ZWSlJunDhQpMNBgAAS/qxeT828KfqDOal\nS5ckSdOmTWukYQEA0DxdunRJv/nNb2pMs6qurq6ubebS0lKdOnVKXbp0UZs2bZpkgAAAWFJlZaUu\nXbokLy8v2dvb13ivzmACAID/w0U/AAAYQDABADCAYAIAYADBBADAgP8Hnh7qechfjZ0AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1075e5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = matrix.plot_mds(clusters=2, dim=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, a dendrogram displays a hierarchical taxonomy. It is another\n",
    "way to see how the different features cluster together in\n",
    "fMRI space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fred/Projects/brain_matrix/plotting.py:88: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  clustering = scipy.cluster.hierarchy.linkage(df, method=method)\n"
     ]
    },
    {
     "data": {
      "image/png": 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xRfXo0cMLk7qfq2vx008/afDgwQoJCZGvr6969+6tw4cPV6r/jYyra5GWlqZu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\nsAAAAOMRLAAAwHgECwAAMN7/Aw3PHK7j7JZ3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120297da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = matrix.plot_dendrogram()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}