notebooks/response_reviewers_similarity_taskmovie.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Response to Reviewer 1: similarity of the two MDS solutions\n",
    "\n",
    "Reviewer 1 asked to quantify the similarity of the two MDS solutions found with the task data and the movie data.\n",
    "\n",
    "We will do the following:\n",
    "\n",
    "1. Given the distances between pairs of ROIs, used to compute the MDS solution, we will correlate the ranks of their upper-triangular matrix (Spearman correlation). We will then bootstrap the task data, maintaining the movie data fixed since it has been already cross-validated across subjects, to obtain 95% confidence intervals of the correlation. We will also compute $R_v$ (Abdi 2007, 2010; Robert & Escoufier, 1976). \n",
    "\n",
    "2. Perform procrustes alignment of the movie-data MDS solution in 3 dimensions to the task-data MDS solution, and then plot the change between the two.\n",
    "\n",
    "[Skip to the analyses](#Load-data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Import and define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context('notebook')\n",
    "sns.set_style('ticks')\n",
    "sns.set_palette('dark')\n",
    "\n",
    "import sys\n",
    "p = ['../']\n",
    "for pp in p:\n",
    "    if pp not in sys.path:\n",
    "        sys.path.insert(1, pp)\n",
    "\n",
    "import expdir\n",
    "from mvpa2.suite import *\n",
    "from mds_rois import xboost\n",
    "fns = expdir.expdir()\n",
    "from scipy.spatial import distance as dist\n",
    "import seaborn as sns\n",
    "from os.path import join as pjoin\n",
    "import pandas as pd\n",
    "from scipy.spatial.distance import cdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "img_dir = './img_response_reviewers'\n",
    "try:\n",
    "    os.makedirs(img_dir)\n",
    "except OSError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def make_ds_distance(ds, make_symmetric=False, rank=False):\n",
    "    if rank:\n",
    "        ds_ = np.apply_along_axis(rankdata, 1, ds)\n",
    "    else:\n",
    "        ds_ = ds.samples\n",
    "    if make_symmetric:\n",
    "        # add transpose and take only upper triangular with diagonal\n",
    "        ds_new = []\n",
    "        for row in ds_:\n",
    "            tmp = row.reshape(8, 8)\n",
    "            tmp += tmp.T\n",
    "            tmp /= 2\n",
    "            ds_new.append(tmp[np.triu_indices_from(tmp)])\n",
    "        ds_ = np.array(ds_new)\n",
    "        \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": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def get_average_distance_ds(roi_rdm):\n",
    "    \"\"\"Generate a group distance matrix dataset\"\"\"\n",
    "    dsms_avg = np.mean([pdist(ds, 'correlation') for ds in roi_rdm], axis=0)\n",
    "    dsms_avg = squareform(dsms_avg)\n",
    "    dsms_avg = Dataset(dsms_avg,\n",
    "                       sa=roi_rdm[0].sa,\n",
    "                       fa={'roi': roi_rdm[0].sa.targets})\n",
    "    return dsms_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def reorder_dataset(ds, labels_ord):\n",
    "    labels_orig = ds.sa.targets\n",
    "    ds_ord = ds.copy()\n",
    "    idx_reorder = [np.where(lbl == labels_orig)[0][0] for lbl in labels_ord]\n",
    "\n",
    "    ds_ord = ds_ord[:, idx_reorder][idx_reorder, :]\n",
    "    return ds_ord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def get_labels_color(labels):\n",
    "    #assign different colors depending on system\n",
    "    # using colorbrewer2.org qualitative 6-class palette\n",
    "    early_visual = '#0000FF'\n",
    "    dorsal_core_system = '#b1551c'#'#e4761a'\n",
    "    ventral_core_system = '#e41a1a'\n",
    "    anterior_core_system = '#e4a11a'#'#4daf4a'\n",
    "    theory_of_mind = '#008100'\n",
    "    precuneus = '#099997'\n",
    "\n",
    "    label2color = {\n",
    "    'EV1 - L': early_visual,\n",
    "    'EV1 - R': early_visual,\n",
    "    'EV2 - L': early_visual,\n",
    "    'EV2 - R': early_visual,\n",
    "    'IFG1 - L': anterior_core_system,\n",
    "    'IFG2 - L': anterior_core_system,\n",
    "    'IFG2 - R': anterior_core_system,\n",
    "    'MPFC - L': theory_of_mind,\n",
    "    'MPFC - R': theory_of_mind,\n",
    "    'OccFus - L': ventral_core_system,\n",
    "    'OccFus - R': ventral_core_system,\n",
    "    'TPJ - L': theory_of_mind,\n",
    "    'TPJ - R': theory_of_mind,\n",
    "    'aFus - R': ventral_core_system,\n",
    "    'aMTG - L': dorsal_core_system,\n",
    "    'aMTG - R': dorsal_core_system,\n",
    "    'dPreCun - L': precuneus,\n",
    "    'dPreCun - R': precuneus,\n",
    "    'mFus - L': ventral_core_system,\n",
    "    'mFus - R': ventral_core_system,\n",
    "    'mMTG - L': dorsal_core_system,\n",
    "    'mMTG - R': dorsal_core_system,\n",
    "    'mPreCun - L': precuneus,\n",
    "    'mPreCun - R': precuneus,\n",
    "    'pFus - L': ventral_core_system,\n",
    "    'pFus - R': ventral_core_system,\n",
    "    'pMTG - L': dorsal_core_system,\n",
    "    'pMTG - R': dorsal_core_system,\n",
    "    'vPreCun - L': precuneus,\n",
    "    'vPreCun - R': precuneus,\n",
    "    }\n",
    "\n",
    "    labels_color = [label2color[lbl] for lbl in labels]\n",
    "    assert(len(labels) == len(labels_color))\n",
    "    return labels_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_mds_together(coordinates1, coordinates2, labels, labels_color=None):\n",
    "    \"\"\"Plot the two MDS together with lines going from coordinates2 to coordinates2\"\"\"\n",
    "    fig = plt.figure(figsize=(16, 12))\n",
    "    nd = coordinates1.shape[1]\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    # I should receive a set of coordinates, a set of labels, and an adjacency matrix\n",
    "    assert(len(coordinates1) == len(labels))\n",
    "    assert(len(coordinates2) == len(labels))\n",
    "    ax.scatter(coordinates1[:, 0], coordinates1[:, 1], s=1, c='k')\n",
    "    ax.scatter(coordinates2[:, 0], coordinates2[:, 1], s=1, c='k')\n",
    "\n",
    "    \n",
    "    # Add lines between the two points\n",
    "    common_kwargs = dict(\n",
    "        alpha=0.7,\n",
    "        #color=m.to_rgba(value),\n",
    "        #alpha=norm_alpha(value),\n",
    "        #linewidth=norm_size(value)#value*linewidth_mult\n",
    "        #linewidth=4\n",
    "    )\n",
    "    for (xs, ys), (xe, ye), color in zip(coordinates2, coordinates1, labels_color):\n",
    "        #ax.plot([xs, xe], [ys, ye], color=color, **common_kwargs)\n",
    "        ax.arrow(xs, ys, xe - xs, ye - ys, color=color, \n",
    "                 width=0.0002, **common_kwargs)\n",
    "\n",
    "\n",
    "    # Add the labels only for the first dataset\n",
    "    text_kwargs = dict(fontsize=16,\n",
    "                    horizontalalignment='center',\n",
    "                    verticalalignment='center',\n",
    "                    weight='bold')\n",
    "    \n",
    "    if labels_color is None:\n",
    "        labels_color = ['black'] * len(labels)\n",
    "\n",
    "    for ((x, y), label, color) in zip(coordinates2, labels, labels_color):\n",
    "        ax.text(x, y, label, color='white', #color, \n",
    "                bbox=dict(facecolor=color, alpha=0.6, boxstyle=\"round,pad=0.3\"), **text_kwargs)\n",
    "\n",
    "        \n",
    "    #pretty_plot(ax)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.yaxis.set_visible(False)\n",
    "    \n",
    "    return fig#, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Task data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# this contains the first level RDMs\n",
    "roi_rdm = [load_dsm(i) for i in range(1, 34)]\n",
    "# this contains the second level RDMs (between ROIs) for each subject\n",
    "roi_dist = [make_ds_distance(ds, make_symmetric=True) for ds in roi_rdm]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Dataset: 30x30@float64, <sa: centers,targets>, <fa: roi>>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roi_dist[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Movie data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "res_hpal1 = h5load('roi_rdm_hpal_part1.hdf5')\n",
    "res_hpal2 = h5load('roi_rdm_hpal_part2.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# average across parts\n",
    "hpal_distance = np.tanh((np.arctanh(res_hpal1) + np.arctanh(res_hpal2))/2)\n",
    "# normalize by noise values, as in Guntupalli et al., 2016\n",
    "diag = np.diag(hpal_distance).reshape((1, -1))\n",
    "sqrt_diag = np.sqrt((diag * diag.T))\n",
    "# normalize by noise and make distance\n",
    "hpal_distance_norm = 1. - hpal_distance / sqrt_diag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# get the right order of labels for the movie data\n",
    "roi_coord = pd.read_csv('new_roi_jim.csv')\n",
    "labels_hpal = list(roi_coord['Abbreviation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# make it a ds so we can reorder it\n",
    "hpal_distance_norm_ds = Dataset(hpal_distance_norm.copy())\n",
    "hpal_distance_norm_ds.sa['targets'] = labels_hpal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1. Compute similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "First we reorder all datasets in the same way, so we have correspondence between task and movie data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "task_dist_ord = map(lambda x: reorder_dataset(x, labels_hpal), roi_dist)\n",
    "hpal_dist_ord = hpal_distance_norm_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for task_dist in task_dist_ord:\n",
    "    assert(np.array_equal(task_dist.targets, hpal_dist_ord.targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The matrix is symmetric, so we'll take only the upper triangular matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hpal_dist_ord_triu = squareform(hpal_dist_ord.samples)\n",
    "task_dist_ord_triu = np.array(map(lambda x: squareform(x.samples), task_dist_ord))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Compute the correlation $\\rho_0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def correlate(x, y):\n",
    "    return spearmanr(x, y)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "r0 = correlate(\n",
    "    hpal_dist_ord_triu,\n",
    "    task_dist_ord_triu.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Now we can bootstrap\n",
    "nbs = 10000\n",
    "corr_bs = []\n",
    "# set seed for reproducibility\n",
    "np.random.seed(345)\n",
    "for idx in xboost(task_dist_ord_triu.shape[0], nbs):\n",
    "    this_bs = task_dist_ord_triu[idx].mean(axis=0)\n",
    "    corr_bs.append(correlate(this_bs, hpal_dist_ord_triu))\n",
    "corr_bs = np.array(corr_bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cis = np.percentile(corr_bs, [2.5, 97.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Movie and Task: 0.48 [0.34, 0.49]\n"
     ]
    }
   ],
   "source": [
    "print(\"Correlation between Movie and Task: {0} [{1}, {2}]\".format(\n",
    "    np.round(r0, 2), *np.round(cis, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute $R_v$ (Abdi 2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def normalize_matrix(X):\n",
    "    # define centering matrix\n",
    "    C = np.eye(X.shape[0]) - 1./X.shape[0]\n",
    "    sX = -np.dot(C, X).dot(C.T)/2.\n",
    "    # compute first eigval\n",
    "    eval1 = np.linalg.eigvals(sX)[0].real\n",
    "    sX /= eval1\n",
    "    return sX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rv(X, Y, normalized=False):\n",
    "    X_ = X if normalized else normalize_matrix(X)\n",
    "    Y_ = Y if normalized else normalize_matrix(Y)\n",
    "    vecX = X_.copy().flatten()\n",
    "    vecY = Y_.copy().flatten()\n",
    "    return np.sum(vecX*vecY)/sqrt(np.sum(vecX*vecX)*np.sum(vecY*vecY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from numpy.testing import assert_almost_equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_rv():\n",
    "    x = np.random.randn(10, 10)\n",
    "    y = np.random.randn(10, 10)\n",
    "    Dx = squareform(pdist(x))\n",
    "    Dy = squareform(pdist(y))\n",
    "    assert(rv(Dx, Dx) == 1.)\n",
    "    assert(rv(Dx, Dy) >= 0.)\n",
    "\n",
    "    # make very different distance matrices\n",
    "    x1 = np.random.randn(5, 10)\n",
    "    x2 = np.random.randn(5, 10) + 10**8\n",
    "    x = np.vstack((x1, x2))\n",
    "    Dx = squareform(pdist(x))\n",
    "    y = np.vstack((x1, x1))\n",
    "    Dy = squareform(pdist(y))\n",
    "    assert_almost_equal(rv(Dx, Dy), 0)\n",
    "test_rv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "task_dist_ord_samples = np.array(map(lambda x: x.samples, task_dist_ord))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Now we can bootstrap\n",
    "nbs = 10000\n",
    "rv_bs = []\n",
    "# set seed for reproducibility\n",
    "np.random.seed(345)\n",
    "for idx in xboost(task_dist_ord_samples.shape[0], nbs):\n",
    "    this_bs = task_dist_ord_samples[idx].mean(axis=0)\n",
    "    rv_bs.append(rv(this_bs, hpal_dist_ord.samples))\n",
    "rv_bs = np.array(rv_bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rv0 = rv(task_dist_ord_samples.mean(axis=0), hpal_dist_ord.samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cis_rv = np.percentile(rv_bs, [2.5, 97.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rv between Movie and Task: 0.755 [0.7254, 0.7612]\n"
     ]
    }
   ],
   "source": [
    "print(\"Rv between Movie and Task: {0} [{1}, {2}]\".format(\n",
    "    np.round(rv0, 4), *np.round(cis_rv, 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plot a scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S8vKbVs+sQQwzCoIgCOWTZZnCwsIqvScnz1JHrWmYGl0CiCAIQlNhNptJS88j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\nGGNM6/FkxhhjTOvxZMYYY0zr/Q+XYIxEe12ZHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21a3f450d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.jointplot(hpal_dist_ord_triu[None, :], \n",
    "              task_dist_ord_triu.mean(axis=0)[None, :], \n",
    "              kind='reg', stat_func=spearmanr)\n",
    "ax.set_axis_labels(xlabel='Movie data correlation distances', ylabel='Task data correlation distances');\n",
    "#ax.savefig(pjoin(img_dir, 'scatterplot_mds.png'), dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2. Perform Procrustes alignment to plot one MDS solution onto the other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from mds_rois import mdsf\n",
    "from scipy.spatial import procrustes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# task solution\n",
    "task_coordinate = mdsf(squareform(task_dist_ord_triu.mean(axis=0)), k=3)\n",
    "# movie solution\n",
    "movie_coordinate = mdsf(squareform(hpal_dist_ord_triu), k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "task_pr, movie_pr, disparity = procrustes(task_coordinate, movie_coordinate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "labels_color = get_labels_color(labels_hpal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BoW3T3NONwzTpSSSzz+vq6c5O7bzZwPCWGD2xOC7TJGFZRHt7sq+Pd3xknZZt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fZA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f219d96ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_mds_together(task_pr[:, [0, 1]], movie_pr[:, [0, 1]], \n",
    "                        labels=labels_hpal, labels_color=labels_color);\n",
    "sns.despine(left=True, bottom=True)\n",
    "#fig.savefig(pjoin(img_dir, 'mds_movie2task.png'), dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}