example_jessi_rdm_distmat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here we load a bunch of stuff\n",
    "import sys\n",
    "p = ['../', '/home/contematto/github/seaborn/']\n",
    "for pp in p:\n",
    "    if pp not in sys.path:\n",
    "        sys.path.insert(1, pp)\n",
    "import expdir\n",
    "from mvpa2.suite import *\n",
    "fns = expdir.expdir()\n",
    "from scipy.spatial import distance as dist\n",
    "from scipy.stats import scoreatpercentile\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "from os.path import join as pjoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_dsm(subnr):\n",
    "    fn = pjoin(fns.mvpadir(subnr), 'roi_rsa_jimroi_5r_fisher_nooverlap/dsm_condition.hdf5')\n",
    "    ds = h5load(fn)\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_ds_distance(ds):\n",
    "    ds_ = ds.samples        \n",
    "    ds_dist = squareform(pdist(ds_, metric='correlation'))\n",
    "    return Dataset(ds_dist, sa=ds.sa, fa={'roi': ds.sa.targets})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# this contains the first level\n",
    "roi_rdm = [load_dsm(i) for i in range(1, 34)]\n",
    "# this contains the second level for each subject\n",
    "roi_dist = [make_ds_distance(ds) for ds in roi_rdm]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`roi_rdm` is a list containing PyMVPA datasets of RDMs within each ROI; each sample (row) is one ROI, and each feature (column) is a pairwise distance. We have 64 features because the original RDM was 8x8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Dataset: 30x64@float64, <sa: centers,targets>>\n",
      "['IFG1 - L' 'EV2 - L' 'EV1 - R' 'mFus - R' 'mFus - L' 'EV1 - L' 'EV2 - R'\n",
      " 'TPJ - L' 'mMTG - L' 'vPreCun - L' 'pMTG - L' 'pMTG - R' 'vPreCun - R'\n",
      " 'mMTG - R' 'TPJ - R' 'pFus - L' 'OccFus - R' 'MPFC - L' 'aFus - R'\n",
      " 'MPFC - R' 'OccFus - L' 'pFus - R' 'mPreCun - R' 'dPreCun - L' 'aMTG - L'\n",
      " 'IFG2 - R' 'aMTG - R' 'IFG2 - L' 'dPreCun - R' 'mPreCun - L']\n"
     ]
    }
   ],
   "source": [
    "print(roi_rdm[0])\n",
    "print(roi_rdm[0].sa.targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`roi_dist` contains the distance matrix across rois, computed using correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Dataset: 30x30@float64, <sa: centers,targets>, <fa: roi>>\n"
     ]
    }
   ],
   "source": [
    "print(roi_dist[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can just average the matrices and look at it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avg_dist = np.dstack(roi_dist).mean(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b981d1410>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS8AAAEACAYAAADiJPA7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8be2bf1950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(avg_dist, square=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}