searchlightsrm_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "import nibabel as nib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "run download-data.sh before executing the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# run searchlight SRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "    0.          0.          0.        ]]]\r\n"
     ]
    }
   ],
   "source": [
    "! mpirun -n 16 python3 searchlightsrm_time_segment_matching.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Get Whole Brain Mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wbmask_fname = 'data/mask/MNI152_T1_3mm_brain_mask.nii'\n",
    "mask = nib.load(wbmask_fname)\n",
    "affine = mask.affine\n",
    "mask = mask.get_data()\n",
    "zeromaskidx = mask <= 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Accuracy Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "acc_path = 'data/sherlock/searchlight_srm_tsm_acc.npz'\n",
    "acc_file = np.load(acc_path)\n",
    "acc_map = acc_file['acc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map to MNI Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "ds = 6 #downsampled by 6\n",
    "mni_shape = [61,73,61]\n",
    "min_size  = [1,7,0] # min and max of nonzeros\n",
    "max_size  = [59,65,58]\n",
    "\n",
    "acc_mni = np.zeros_like(mask)\n",
    "\n",
    "# mapping (make sure length of each dimension is a multiple of 'ds')\n",
    "acc_mni[min_size[0]-1:max_size[0]+1, min_size[1]-1:max_size[1]+1, min_size[2]:max_size[2]+2] = \\\n",
    "acc_map.repeat(ds, axis=0).repeat(ds, axis=1).repeat(ds, axis=2)\n",
    "\n",
    "# if no downsampling\n",
    "#acc_mni[min_size[0]:max_size[0], min_size[1]:max_size[1], min_size[2]:max_size[2]] = acc_map\n",
    "\n",
    "# mask\n",
    "acc_mni[zeromaskidx] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save accuracy map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out_path = 'data/sherlock/searchlight_srm_tsm_acc_mni.nii'\n",
    "acc_img = nib.Nifti1Image(acc_mni, affine)\n",
    "nib.save(acc_img, out_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x114cf6ba8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dDgAASDjM2QCSWv/+/dWnTx/dd999mjlzJilVAGLaihUr7HLnzp2jGAkSyZnVRi9fYwW6\nhEVnoxqv16vHH39czz//fI37Zs6c6bOXRSStX7/eLrdr186RNpyyYcMG7d69W5JUWVkZ5WjCt2rV\nKo0bNy6i1/QG+M/z+uuv19ixY/WPf/xD06ZNi2ibyeQ///mPJGnXrl0+t5F4Dh06pFWrVtm3rb06\nzP2Dhg8f7npcABB7mLMR0zwejxYsWOD3vqlTp0a0sxHOvhmx6N9DhmjfT+V3r7lGbaIaTfjWjx+v\n9ePHB7x/aRjfungC/I4vuugidenSRXPnztXll18esB6Cm4Nhvf52zpgRt68/1G7mr39d72uwdwcA\nuI/OhmHKlCmaMmVKtMNAgqjt9eTxeOzRIACIZc2aNbPLqampUYwEQHCsORvB1HMenQ2XWStNAQAQ\nD/5ywQXRDgFJgDkciYvOBgAAAJAwmLORtBJljgbgFPbIAGKDuQIVgHhTruA6EuVOByKJfTZcs3z5\n8miHgHrKy8uLdggA6mHt2rXRDgEAkg4jGwAAwEebNqzrBsQv0qiShjmaYa7mgfiUlZWldevW2bdH\njhwZxWgSywcffBDtEJAEDh8+rNWrV0uSz2aaI0aMiFZIAJDw6GxECPMxEt+SwYN9bv+z2v3h7MOR\n7PJ438BFv5swwe/xK3/690vewwASAkvfAgCAGLRmzRpJ0oknnhjlSAAkCjobAABIeuqppzRq1Kho\nhxF1fxgzxud2RxfaTHOhjRIX2milo1nw6ZIaudCmE5q70MYPLrSRvGJrZIPVqAAAAAA4gpENAAAg\nSTr22GOjHQKAeitTcOOFrEYV08w9F1giEJI0yc9kZyaNB/bGG29EOwTAx4wLL9QM4zYTxgGg/uhs\nAAAAAAkjtuZs0NkAAACSpMzMzGiHAKDeyhTctGzSqGLS0qVLJUnt27e3jzVt2jRa4QBx5c0337TL\n33//fRQjAeq2du1au5yWdiT/ediwYdEKBwDiEp2NIP2lWj7+F9XuD2dpQDeWlnNjOcGTJH0kaaGk\nMySd7EAbbixZeKoD16z+urk52XLAR/o+/tOr319t4+Z/rQ+vmVBff8vDayYkbgxOf+NCGw3rrhI3\nWoVYf85FF9U4Nqva7R3J9p4GEAcOSwpm01yWvgUAAA5bunSp/WON4ADR9s9//jPaIYRk/vz56ty5\ns9LT03XmmWdq27ZtAetu3bpVffv2VVZWljIyMtStWzfdd999PnXWrl2r3r17q0WLFmrSpIl69uxp\nZ9fEG0Y2AAAAEFPuGDdOdxi3Y3kUceXKlbruuuv06KOPqk+fPpo3b54GDx6sjz76SFlZWTXqZ2Zm\nasaMGfrFL36hzMxMbdmyRdOmTVOTJk30m9/8RpLUsmVL3XLLLeratasaNmyop59+WlOnTlV2drbO\nO++8OiIKdsSCkQ0AAOCwtLQ0++fQoWBWsAFgmjdvnq644gpNnjxZXbt21YIFC5SRkaGFCxf6rd+j\nRw+NHz9e3bp1U4cOHTRx4kQNHjxY//73v+06/fr108iRI3XKKaeoc+fOuvrqq/WLX/xCW7Zsceth\nRQydjSBs2LAh2iEgQTzzzDPRDgFABPXyeHx+ACSX8vJybd++XQMHDrSPeTweDRo0SK+++mpQ19i5\nc6deffVVnXPOOQHr5Ofn66OPPlL//v2DuKK19G1dPyx9CwAAAMSsoqIiVVZWKjs72+d4dna2du3a\nVeu57du31zfffKPKykrdeuutmjp1qs/9P/74o9q1a6eysjI1aNBADz/8sAYMGBDxx+A0OhsAACSx\nlJSjSQ6kUQHu2bJliw4ePKjXXntNN954o0488USNHz/evv+YY47R22+/rYMHDyo/P1/XXHONTjjh\nBPXr16+OK5dJqggigsr6hB80OhsBrFu3zi63adMmipEgkTRr1kyrV6+2b48ZMyaK0QCItGeeeYa9\nOIAkkpWVpdTUVBUWFvocLywsVOvWrWs9t2PHIxsn/OxnP1NBQYFuvfVWn86Gx+PRCSecIEn6xS9+\noffff1933HFHEJ2N2EJnI4D/jhp1tBzFOJBYXq72H8SYGF5dAwAA1C4tLU05OTnKz8/XiBFHNo/y\ner3Kz8/X1VdfHfR1KisrVVZW+47eVVVVddY5Ith9Ntz5DEJnAwCAJNa8+dEtZps1axbFSID4dO21\n1yo3N1c5OTn20rclJSXKzc2VJN10003au3evFi9eLEl6+OGH1aFDB3Xt2lWStHnzZt1zzz2aOXOm\nfc0777xTp59+urp06aKysjI9++yzWrp0qRYsWOD646svOhsAHPHMM8+IZBIkm/Lycp9V50ipAhLf\nuHHjVFRUpFmzZqmwsFA9evTQhg0b1KpVK0lSQUGBvvzyS7t+VVWVbrrpJv3vf/9TgwYN1KVLF919\n992aNm2aXae4uFhXXnmlvvrqK6Wnp6tr165atmxZcOnXlVXBTcdwZ8oGnQ0AAACgPqZPn67p06f7\nvW/RokU+t6+66ipdddVVtV5vzpw5mjNnTnjBlCu4VW3Lw7t8qOhs/GR4tfXRfxmlOJBcZld73f2J\nORwAXHbMMcfY5czMzChGAiAR0dkA4IjS0tJohwC4rrLSNy+BlCoArqtQcKMWwayOGwF0NgAASGK5\nv2QsPxjzlyxRRkaGfbtdu3Y6n+fONb2qZQLsIBMgbtDZAAAgySxfvjzaIcQdj8ejffv22bc3b94c\nxWiAWjCyASAhjKx9De+xLoUBxJKUlBRVVVXZtysqjv41f/755yVJQ4cOdT0uAIgWOhsAAAB1aNmy\npd54441ohwHUjZGN2LN69epohwBIktasWaPRo0dHOwwAYUpNTfWZJG6WrZ1/N27caB8bNGiQe8EF\ncFYd97dyIYb3XWgjrZ7nT7rgghrHcmfM8Lm96sEH7c94pZIu8nN/fbmxXliJC22c5EIbiA10NgAA\nAIBEwT4bAAAgmlJSUqIdAoAkQWdDUrNmzaIdAiDJd3OtWLZp0yadG+0ggBiUmprqc7u8vLxGubi4\n2NWYACQZ5mwAAAAAcASdDQAAEE2tWrkx7RsA6GxIkho3bhztEABJUnp6erRDCIqZGgLgqOr7bHiM\nXY+t4+b754UXXrDLQ4YMcSFCAAmPkY3Y8F/jD8Cxku6sdv83LsTQ34U2nnehjTQdfSE1UP2XF/Tn\ndAeuWd2JLrRR13eJ77kQQ9g+OPqeOb+9ar5pIiBtfXjnhfr6ywivmZA0d6ENN/6fcuL9XJ0bS3lK\n0n6X2gEAHJW0nQ0AAJLJ+vVHe/PHH398FCMB4KgKBbf0LSMbQPJp2LChVqxYYd+eMGFCFKMBEKqK\nigpVVPj/C26lVJkb/ZWWltrlLVu22OW+ffs6Et9TI0c6cl0EZ9yMGRo6dKh9e6qfjQKBRENnAwAA\nAEgUzNkAEMjJZ56pk80DjGwAcaWiosJngnj1fTck+dxvThY/ePCgo7FlZro1Owa1CTTyBSQqOhsA\nAABAoihXcCMbLi0sSWcDAIAk0KABf/LdturBB31uj5sxQyUlJVGKBogO/ucBUKuNGzdKkga1i3Ig\nQBzwer0+qVPmPhtm2WJOFudDKICIYM4GAAAAAEew9C0AAHCbORkdkTVgwABJNdOm/Nm6davT4QAx\nhc4GgFqZaR4Aald99Smv11trnUArU1npi5I0aNCgSIaICFr2U+fC+jetjvrBdEYQnPF+0hJX+nm/\nJaUYS6NKcacZAAAAAMmGkQ0AABLU0qVL7fLxxx8fxUgAuCbGRjbobACo4cUXX4x2CEBcatCggU9q\nlFlOSUnx+bf6/WbKYmlpqZNhAoBr6GwAAAAAiYKRDQAA4IZmzZrZ5aZNm0YxEgDJis4GgFo1bNgw\n2iEAcSMlJSVgmpS1MpW5QpVZ10yjOnw4mEXyAcCPcgW3z4ZLq2GzGhUAAAAARzCyAUCS9NJLL9ll\ncx+ARo0aRSMcIC55PB55jPX//Y1smCMY5iiHWa6oOJpMbe25Ec5+G2YaVXp6uvqHfAXp/TDOCVUr\nF9r4xoU2JMn6LTZQ3ftuhCPDgWtWF87rJFTFLrSRtJizAQAAAMARdDYAAIAbMjKOfg/eoAF/8gG4\nLyn+5/mvny3tgXhgbsg1adKkiF9/w4YNdtlMnUpLS/NbBlC7iooKnzQqM2WqvLy8xjEzdSrQ/hxM\nFgcQkhgb2WCCOAAAAABHJMXIBgAAyWTlypWSpEMTJkQ5EsA9V1XLZHnIGDlMKjE2skFnA4hhTq8E\nZa7xb6ZRmbnd5nEAtSsvLw+4GpW1wpQ3iA9AZh0r/QoA4lFSdDZKwjjnHDfS1E93von+rzrfRmZ3\nKb1E0m6px0lSLyfW5WvrwDWrOW6z8208FuqLcdy4o+dKujya39J02+R4E7/qfm5Y54X6+lv9dljN\nhCQzAtfoPGOGz+3PHnwwAlcNzQ8utBGJ5yoYLLUJICnE2KZ+SdHZAAAgmVgLOxyKchwAQGcDSDJP\nP/20XTaXxTTTpcyymWoFoHaVlZUB3zPW+yqYNCpzxSpzgz8AqFOMzdngUwQAAAAARzCyAQBR8Lc9\ne/T+++/bt9evXx/FaAAACSPGRjbobABJxtykz0z3CLQyFatROePgwYMhdTB+9atfRWWCOELj9XoD\nvq8s5gpVgVKqzDQqq/5TTz1lHxs1alS9YwWQoGKss0EaFQAAAABHMLIBJIklS5ZIklq3bm0fCzSa\nYR73VNskCZHxww8/6LTTTrNvv/vuu5KkX/7yl/YxcxQqmEnFiL6UlJSA7yV/v0NzlMN8r5nnWXVC\nmSjOwg5AEmPp2/rbsWNHSPU/DKONVDf+rh90volS55tQeon0wU/rK37g1DqL+x26rqmq7ir19Xk9\nzw/1tW/63//+J0k6cOCAfaxhw4Z22fxg6698+LALOy6EsymOQn/9fR1eMyGp6yX7uzPO8Hv8rRBS\npYL5W1Jfboyyl7nQhuTO39VPP/3U5/3jr7NudjDMcmVlpdq1a+f4Zp4A4Ka47Gzk5OQ434gbf2Hf\ndaENN+w+Wpz0pfNtJLPb3XjtxzHHXn9AkG688cZ6nX/nnXfqhBNOqHccTZo0keTO9zQAYkyMzdmI\ny87G9u3bQ6r/3zA+oP3MjWemm/NNlLrQoUk/6cg3ypO+lJa2l7o1dqCRVg5cs7rwBw2C9mQ9R34u\nCvG1b9qwYYMkqUWLFvYx8xtYc5TDLFspISec8F3YbQdtwhVhnRbq6+8eFzqvbvR7drnQhht/i9Jd\naENyZ6T3lnvvDZh6aI1iBJogXllZqTZt2qiqqspvepV5LJDFpD0CUTF//nzNnTtXBQUF6t69ux58\n8EH17t3bb921a9fqkUce0VtvvaWysjL97Gc/06233qrzzz/frvP3v/9dS5Ys0XvvvSfpyBftf/nL\nXwJeM5bFZWejV69eIdWvrLtKzTbc+P+6ifNNFDvfhDKP7gunbo2lXhmB64atmQPXrM6FFOfwuwpH\nhPraN3300UeSpFatjvbczHSNQOUGDY78N3HqqS4kH9XztRPs6++4+jUTlB9daKNh3VXqzY3/Ct1K\nGnKj49SlS5eA8yWsFaYCpVGxeR8Qn1auXKnrrrtOjz76qPr06aN58+Zp8ODB+uijj5SVlVWj/iuv\nvKLzzz9fd9xxh5o3b66FCxdq+PDheuONN9S9e3dJ0ubNmzVx4kSdddZZaty4se68806df/75ev/9\n99WmTZvaA2JkAwCi69x16+zyxo0ba63br18/bR071umQgHpbunRptEMAktK8efN0xRVXaPLkyZKk\nBQsW6Nlnn9XChQt1ww03+K1vuv3227Vu3To9/fTTdmfjiSee8Knz97//XWvWrFF+fr4mTZrk0CNx\nBp0NIElYIxSB9gCw7q9+PJjUjXhTVwfDZO53ANQlJSXF5/1jjlZY5drSqGo7nojvRSDelZeXa/v2\n7br55pvtYx6PR4MGDdKrr74a1DW8Xq8OHDigY489NmCd4uJilZeX11rHFmMjG6yNBwAAAIShqKhI\nlZWVys7O9jmenZ2tgoKCoK5x9913q7i4WOPGjQtY58Ybb1S7du00aNCgesUbDYxsAACQAJo1ayYX\nlnEAEEF5eXmaM2eO1q9f73d+h3RklbpVq1Zp8+bNPgu4BMQ+GwCiwUrtMFfKCbSJWKJLrWMvi8oZ\nM+zy4cOcMLgYAAAgAElEQVRu7GaBRGKmO5WXH/1rXtdrKVB6lcXKBwcQO7KyspSamqrCwkKf44WF\nhT6b6PqzYsUKTZs2TatXr9a5557rt87cuXN11113KT8/Xz/72c+CC4o0KgAAACD+paWlKScnR/n5\n+fYxr9er/Px8nXXWWQHPW758uS677DKtWLFCQ4YM8Vvnrrvu0u23364NGzaoZ8+eEY/dLYxsAACQ\nADIzM3XAget2dOCa1XVyoY12LrQhHdlI8WtJp8iZVdvdWAl+hAttuJTB44prr71Wubm5ysnJsZe+\nLSkpUW5uriTppptu0t69e7V48WJJR1KncnNz9cADD6h37972qEh6erqaNm0qSfrrX/+qP/3pT1q+\nfLk6dOhg12nSpIkyMzNrD4iRDQDR5PV6/f5YG4kF+klW27Zti3YIiCNer1eHDx/2+1NZWVnr6mbW\nSlbVfwDEtnHjxmnu3LmaNWuWevbsqXfeeUcbNmyw97UqKCjQl18e3er1scceU2Vlpa688kq1bdvW\n/pk5c6ZdZ8GCBSovL9eYMWN86txzzz2uP776YmQDAAAAqIfp06dr+vTpfu9btGiRz+1NmzbVeb3P\nPvss/GBibGSDzgaQJKzRCfObVbNsTl5N9H026nLOOefoX//6l33bnDAetDomoSNxBdovIzU11edf\nyXeRhkB7bgS7SEFaWlrowQKAw+hsAAAAAIkixpa+JRkUAIAEYI6SAECsYGQDSBLWev8VFf6TNM39\nAMw0D7OcLLKysnTmmWdKkl577bUoR4N4Uv39ZaY2WZ0BM03R7CCYKYtmubZJ5QBQA3M23Lc5jHNa\nuTC01OlN59t43/km1Oxt6fOfyp/vlpo40MZJbztw0WpcaCK+l/pb4X/DoUj6KMxfQqivv1frur9/\nf5/bfcKYs9HZOKdXr156YOrUkK9RFzdeT4k0C6COxSIBAA5Iis4GAACJrkED/qQDECMbAKKjrKxM\nkm+6lJny0ahRI7vcuHFju9ywYUMXoktsLVq0iHYIcElVVVXAD/3malNmfX9ls+6ll14awQgBJLwY\n62wwQRwAAACAIxjZAIA6vFFtz4xg5nBsMc7Zwp4bcAG7jQOQFHNL39LZAJJEbm6uJGnt2rX2MXOl\nHHPVqUCr5QCoXfUUKjNV0VpVKlC6lIkVqAAkCjobAAAAQKJgzgYAAIg00qgAxCJGNoAkY6Z1mGlU\nZmqHWYc0qpqqz+EALCkpKT7vHzNNyl/KVKAOgrlqHACEhJENAAAAAMmAkQ0gyZSWltrlJk2O7rdt\nfutqfjMLIHiBJnxLRxdhCLQAgzkpvLbrBGKOTgJIYoxsAAAAAEgGSTGy8Ts/3xDNJQ8dceCYJUvs\n8iWXXBLFSADEOpbLBSDpyKhGMMMJ7LMBwIkOxuTJk+3yM888Y5fNyeJ8aAHCU1lZGTCdyUqfMve0\nMdOozPN4DwIIW4WC62yQRgUAAAAgnjGyAQBAAjh8+HC0QwAQCyokBTNbwKWRDTobQBI7cOCAXU5P\nT7fLZmoHK1MBoQm0t4aVJhUojcqse+mllzoZIgC4hs4GAAAAkCiC/Y6QORsAACBY5kglAMQKRjaA\nJPbrX//aLpsrU2VmZtrlcDYXA5KVx+Pxec+Yq0pZKVNmGpVZNxJzLk6q9xVq6n2iAxetpvvHzrfx\nuPNNqJmkr38qt5d0nANtTHHgmtX1yHK+jT1FzreRtCokBbPHp0v7gDKyAQAAAMARSTuykW1sltax\nY0e90b9/FKMBjuj5n//oq6++inYYiFN//cc/JEnbt2/3e/+gQYM0beRIn2P/fOUVO/2mqqpKvxk+\nPOR2L5wxwy7n5OToltzckK+B+vvxxx+VHe0gAERfsKtRuZS4kLSdDXOztI0bN0YxEuCoQ4cOaezY\nsVFpe9iwYXbZfE+Yq+UgtgXqZFi++eabGsd2795tr3yUl5dX7xhYftU3Ncp8/1ib+gXayK+0tNSF\n6ADAXUnb2QAAAAASToytWE9nQ0e+TQZiQXFxcbRDkHQk3aaGFe7Hgchq1KhRjWPmfg4TJ07UtRdf\nHPJ1J06caJc//fTT8IJLECkpKfYIhuQ7ymEet5j72Fx22WX1anvixInaFsbvD0BiKZcUzBhzudOB\n/ITOxk9eqnb7LRfabO7Wb9kFVnLGCkmtHLy+k9z4dWTUcX8HF2II24S6kzu31jPlanmY5zn9+gtH\nZt1V6i2t2u21Dz5Ya/21fs6prqDa6mNdgvidXvzLX/rcbljnGdHxQ7QDAIAkRGcDAAAASBAVCu4L\nVLeyrVj6Vr4TY4Fo4rUIxDePx+Pz4/V67Z/KykpVVlaqoqLC/jl8+LD9AwCJiJENAAAAIEHE2sgG\nnY2fbPB69fzzz9u3l11wQRSjQaIav369z1KXlpHV9j4AYsUnXq+eeuop+/Z1F15Yo87T//2vXd63\nb5+mVVtg4I+LFqlBg6N/bv5kLD0OAEhsdDYMQ4cOtcvLohgHEldFRYXS0o5O0SVtCvFg1KhRdvk6\nP/efeuqpdtnfXh+51Tb5S+TORnl5ucrKyuzbZnpUZWWlJN9VqaZOnepecACSAiMbAAAA9bDyp13r\n+/TpYx/r1KmTXW7fvr2+Mm4DiB46GwAAIK6cfvrpknw7GB06HF08vHHjxm6HBMQM9tkAktiaiy7y\nuT3MW/feFbGqvntqxLObnnpK+fn5PsfeqGOPi3jRvtrvNdQ9My655BJdUkea1Cd1vO7z8vL0xzjd\nnG7//v0+G8X+9re/jWI0ABB9dDYAAACABMGcDQAA4IjeP40aPfHEE5KkrpMnRzMcx3SZMqXGsS+j\nEAcQi+hsAIh7zzzzjFpEO4goqp5ChciaOHFi3KZRsboUAPiiswEA1fz1X/9SUVGRJGnz5s1Rjiay\n7l21SpI0duzYiF2zSx3zd+qaoxHOOatXr5YkNWrUyD7WsOHRGSbThwwJuc26LFq3Tj/++KN9e9Kk\nSRFvAwDqi5ENAHFv2LBh2hrtIBz09ddf69///ne0w3BEbZ0Mc/O+WDdmzBjX2xwxYoTrbYbLmqS/\nLUHTqADEDzobAAAAiHvt4niFx0iKtaVvU+quAgAAAAChY2TDsGbNmmiHgCQzqVqu+1K+lYkJ94wb\nV+v9fX7avdi+3aeP3nAyIAdV31cjUdQ152PJkiW6xs+KRgAQ72JtzgYjGwAAAAAcwcgGAAAAkCBi\nbWSDzoahosKtpx1AImnevHm0Q0CIJk+eTBpVsD53volf/dL5Nnq86nwbmd2lHSVS3m7pupOkXhkO\nNNLWgWtW58KK38/V8/zTXj3yCz3zzDPrH0wEzJ8/X3PnzlVBQYG6d++uBx98UL179/Zbt6CgQNdd\nd53efPNNffzxx/q///s/3XvvvT513n//fc2aNUvbt2/X559/rvvuu09XX321Gw8l4kijAgAAAMK0\ncuVKXXfddZo9e7Z27typ7t27a/DgwfZ+TdWVlZXpuOOO0x//+Ef16NHDb52SkhJ16dJFf/3rX9Wm\nTZuQ4rFGNur6YWQDSALxPCH8Vz/FvmTJEvtYlyT5pviNBx+s9XasWPTSSxo0aFBI5/x54UJJUnp6\nun2sY8eOdtnfJnplZWX2sZycnLBijYZv4/j9ByS7AwcORDsE27x583TFFVdo8k/72ixYsEDPPvus\nFi5cqBtuuKFG/Y4dO2revHmSpMcff9zvNU8//XSdfvrpkqQbb7wxpHhibelbOhuG8ePH2+WnJ0yI\nYiRA/JhsbBq2NUk6G/Hi4MGDIZ8zdepUByIBgMRUXl6u7du36+abb7aPeTweDRo0SK++6kLuXhwg\njQoAgATV2+u1fwqffjra4QAJp6ioSJWVlcrOzvY5np2drYKCgqjERBoVAMAVJSUlAe/Ly8tzMRLE\ngmHDhmlbtIMAIqSwsDDaISBIdDYCeN6FNtxYv+YHF9poJenQT+XXJTV2oI1iB65ZXeCPZQjWr4wc\n+JUrV2peiOmI4b5eQ3397Q2znVC0cqGNcPJtW1fbxO/eZcs0ceLEyAQEAEkmKytLqampNTo/hYWF\nat26dVRiirWlb0mjAgAAAMKQlpamnJwc5efn28e8Xq/y8/N11llnRTGy2MHIBgBHjB8/PuSRDURW\nMCMWVVVVLkQCAJE1adKkaIdgu/baa5Wbm6ucnBz16dNH8+bNU0lJiXJzcyVJN910k/bu3avFixfb\n57z99tvyer06ePCgvvnmG7399ttq2LChunXrJunIxPP3339fXq9Xhw8f1p49e/T222+rSZMm6tKl\nS63xxNrIBp0NAAAAIEzjxo1TUVGRZs2apcLCQvXo0UMbNmxQq1ZHEmoLCgr05Zdf+pzTs2dPeX5K\na92xY4fy8vLUsWNHffrpp5KkvXv3+tSZO3eu5s6dq/79++vll1928dHVH52NAFYbv8jWrVur76mn\nRjEaAIiMgp/m1SxdujTKkSASduzYEVL9D8NoI9WN7UhCX6U5ZKXON6H0EumDnyaRWf9G3H6Hrmty\nYcCzvhvTB/Pa79WrVz1bCd706dM1ffp0v/ctWrSoxrG6RpU7duwY9sgz+2zEiX379tnlZs2aRTES\nAIi8WEpBQPhc2UTRjVyLd11oww27jxYnfRm4WqTaSGa3B/Ha97JxZ0ygswEAQJzavn17va/x3zo+\ntP3MjU8K3ZxvotSFDk36SUdGNCZ9KS1tL3VzYnlGN5a6C23ALCxPhjjyc1EEXuvJgjkbAAAgIiKR\nJlJZVxueOipEQhPnm3BjCfXMjKPlbo2lXhmB64bNjWQLF9YqDbXr4GZKVLyjsxEnzFVcnn/ejV03\nkAwWrFqlsWPHRjsM17xWbQj7TI8bn1qSVwEpAwCAGENnAwAAAEgQjGwAAICY0dsYEXvhhRfUcujQ\nKEYDHNHt3/9W3759ox0GIoDORhC+/fbbaIeABJFMKVQAAMB9LH0LIGmZczieeOIJzZ88OYrRxL9F\nzz2nH3/8UdKRHdsBAIg1Lqw3EP9Yjx6IvEsuuSTaIcS94uJijR8/no4GImbIkCHRDgGQ5LvfGUJj\nzdmo68etORt0NgAAAAA4gjQqAAAAIEGwGlWc+rba+vUt2S8AQaj+uoGv6vtwdPXzvvqwjufQ3zmJ\n4hNePwCSFAuqJA46GwAAAECCYGQDAAAAgCNY+hYAAMSsZtUPtHWh0TXON5HZxo20xH9JOz6Scq6Q\nVvxN6nVy5Jt459zIX7O63zvfxOWbfW8/sWCJJFYqTER0NgAAAIAEQRpVgvj7k0/a5ZNOOkn9Tzst\nitEgFmzYtk2fffaZfZvJbaGrazJ4ImMyOIBkxohG4qKzEaYLL7zQLr/88stRjASxYs+ePXQwAABA\nVDGyAQAAYtbJ1UbZVqxYYZe7du2qHqN6uh0SEtA7r76tgoIC+/b5UYwFzqKzAQAAApowYYJdfuaZ\nZ9QjirEgcZSWlur88+liOIGRjQQ0YMAAe/M2M6WqTZs2drlBgyNPdWVlpX2svPzoSyE1NdUuN2zY\nsEYbXuObppSUFLtcVVVll8vKyuxyRUXNl5B5nsfYCM0sW+eZ56elpfmNee/evZKkESNGaMeOHcrJ\nydHMefPUpUsXSVLr1q39XsOKuaSkxD5mPmbzuTBjto5bz2X1+83nyN/jC/Tcm4/VbNtqx3yOzbrm\nNzIXXHCBEB0fer326+/J7dvVq1cvrV69WpKUkZFh1zN/t+bv/+DBg3b50KFDdjk9PV2SlJWVZR9r\n165djfsl/68RT4DNBs15Peedd15dDw8AgLhGZwMAAABIEBUKbp8NRjbi1IABA+zy008/bZetb0QD\nfYNvfvNvfkNvfRtvHjPP8wZYwcb6xt+83/z2NdAogNWeWffw4aMvWfNbYLNs6devn3r16iXJd5Qn\nMzOzRtvmKIH5TXOjRo3ssvm8WHGasQV6TCbz+fJ3LND1rJhKS0vtY/v27bPLo0eP9tseoq+4uFiS\n72iW+Ts3X3vmaIb5/rTes+boiDmqZvL32jPfV1Y8kvT999/X/QCAGDVs2DDpqmhHgURg/j1FYqOz\n4aBmzY5ujWSlXJgfnlNSUlRRUeE35QkAAAAIVbmCm7PBDuIJoF+/fnXWqaqq0scff+xCNAAAAEh0\nTBCHj5SUFJ8UC8l/2o/JHAkx037MlBEr9clMETHToczzzNQQK/XDTAsxz/vxxx/t8sSJE2uN05pA\nLklNmzat0YY5od2M03z85iTcJk2aSPJNazHTXgJNgPc3cuQvXUryTZn64YcfJPnuqYL4MGXKlBrH\nFi5caJcvvfRSv+etX7/eLrds2VJS4JRD87i/BRzMY0VFRXZ53LhxdT8AAEhwo0aNinYIcElK3VVg\n8nq9euSRR9SzZ09lZGQoKytLAwcO1LvvvmvX+eqrrzR79mydccYZOvbYY9WqVSude+65ys/Pj2Lk\nQGTt3btX48aNU4sWLdSsWTONGjXKZ6UlAAnqf17fHyAIL87coBdnbpCe8x75gWOskY26fhjZiFFT\np07V8uXLNXnyZM2YMUPFxcXauXOnvv76a7vOunXrdPfdd2vUqFHKzc1VRUWFlixZovPOO0+LFi3y\n+60rEE+Ki4t1zjnn6MCBA7rlllvUoEED3XvvvTrnnHP01ltvqUWLFtEOEQAQQ9hTI3nR2QjBqlWr\ntGTJEj311FMaMWJEwHoDBgzQF198oWOPPdY+dsUVV6hHjx6aNWtWjc5Gz56Bd2Ndvny5JN8UIZOZ\nDvTrX/86qMfhlkmTJrna3tKlS+2yv+errrQvBG/+/Pn65JNPtG3bNnv1sSFDhujnP/+57rnnHt12\n221RjrCmQKlTpsaNG9tlazGHQAs4BFoJzXrtmSmH33zzTWjBAgAQplhb+jYu06j+9a9/KSUlRevW\nratxX15enlJSUvT6669HvN158+bpjDPO0IgRI+T1en02pTN169bNp6MhHZlbcMEFF+irr76qMUcD\niLRDhw6pW7du6tatm8/cmO+//15t2rRR3759Ay4VHIw1a9aod+/edkdDkk455RQNHDhQq1atqlfs\nAAAgccRlZ+Occ85R+/bttWzZshr3LVu2TCeeeKLOOOMMHT58WN9++21QP3U5cOCA3njjDfXu3Vt/\n+MMf1KxZMzVp0kRdunTRP//5z6Di3rdvnzIyMnwmOANOaNy4sRYvXqyPP/5Yf/jDH+zj06dP14ED\nB7R48WJ5PJ6w3iNer1fvvPOOTj/99Brt9unTR5988gkdaiCZMIcD1fzrhk1H52YwR8N1zNmIkEmT\nJmnevHk6cOCAjjnmGElHVnx56aWX9Mc//lHSkRSkqVOn1nktj8fjs5KTP5988om8Xq+WL1+utLQ0\nzZ07V02bNtX999+vCRMmqFmzZrXmI3788cdau3atxo8f77OiTV1iLTUqlrmdthXr+vTpoxtuuEF3\n3XWXLrzwQu3bt08rV67UAw88oC5dukgK7z3y3XffqaysTG3atKlRzzq2d+9enXTSSRF8NO4ItLpZ\nKKzV2woKCuxj48ePr19gAADEqbjtbEyePFl33HGHVq9ebX9YWrFihSorK3XxxRdLOpJDvnHjxoi0\nZ+2W/d133+n111+3v9UdPny4OnfurNtuuy1gZ6O0tFRjx45VRkaG7rjjjojEE2u6du2q7du3q2vX\nrtEOBYZbb71Vzz77rCZPnqyDBw/q3HPP1VVXHd3+N5z3iLU8sLnTu8Wa82AuIewGXn8AABzBPhsR\ncsopp6h3795atmyZ3dnIy8vTmWeeqRNOOEGSlJ2drezs7JCuW1xcbHcspCN7PmRlZdn7PXTu3Nkn\nfSQzM1PDhw/XsmXLVFVVVePb0KqqKo0fP14ffvihXnjhBbVu3TqsxxvrMjIyfPL3ERvS0tL0+OOP\nq3fv3kpPT/fZa0IK7z1ivRfMuSAWa78Uc38UN/D6A4DYcc4550Q7BMSQuO1sSEdGN2bOnKm9e/eq\ntLRUr732mh5++GH7/kOHDmn//v1BXcv6wDV37lzNnj3bPt6pUyd9+umnatu2rU8903HHHafy8nIV\nFxfbKV2W3/zmN3ruueeUl5en/v37h/wYgfp64YUXJB15P+zevVsdO3a07wvnPXLssceqUaNG2rdv\nX4061jHr/RJvzDQqK90x1HQqawO/oUOHRi4wAACCxMhGBE2YMEHXXnutli9frpKSEjVs2NBnd96V\nK1eGnI8+ZcoUnX322fZ91je0bdq0UevWrbVnz54a5+/Zs0eNGzeu0dG4/vrrtXjxYt1///3sGoyo\neOeddzRnzhxdeumleuutt/Sb3/xG7777rv1aDec94vF4dNppp+nNN9+sUe/111/XCSecoMzMzMg+\nEADxw98k8U7Bz1VE7Pvgnvd9lgU3l/ceMGBANEJCDIvrzkbLli01dOhQPfHEEzp06JCGDBnis+Rs\nOPnonTp1UqdOnfzeN378eD3wwAPKz8/XwIEDJR35FnP9+vX2bcvdd9+te+65R7fccotPjjzgloqK\nCuXm5ur444/X/fffr08//VS9e/fWNddco7///e+Swp/XNGbMGN10003asWOHnb60a9cuvfzyy7rh\nhhsi+jjcZI5sWIJZ0MFKH5PYUwNA4jt48KAKCwvt28OGDYtiNKiuXMHtsxHM6EckxHVnQzqSSjVm\nzBh5PJ4aG4mFk49em5tuukmrVq3S6NGjdc0116hp06b629/+poqKCt1+++12vbVr1+rGG2/UySef\nrFNOOaXGEr3nn3++WrVqFbG4AH/mzJmjd955Ry+//LIyMzN12mmnadasWbrllls0evRoDR06NOz3\nyPTp0/XYY4/pggsu0O9+9zs1aNBA8+bNU5s2bXTttdc68GgAAEAwSKOKsOHDh6tFixbyer217uod\nCccdd5y2bt2q3/3ud7rvvvtUXl6us846S3l5eTrttNPseu+88448Ho92796tyZMn17jOpk2b6GzA\nUTt37tSdd96pGTNmqF+/fvbx3//+91q3bp2mTZum//73v2ratGlY12/SpIk2b96sa665Rrfffruq\nqqp07rnn6t5771XLli0j9TAAAECc83jrs41wDKisrFTbtm01cuRIPfroo9EOB0Ace+utt+yy1RGr\nqqqyj5mTxc3jX3/9tV0+66yznAwRSBhPP/20XT7++OPtcosWLeyytZy2dGR1SEk++2KZ8wYaNDj6\n/am/zXPLy8v9ls2PQVYbkv/FIax9dCT5bHb61VdfSZLat2+v999/X+PHj9e6dev085//vMa1zGXD\n09LSatxvpm6a8ZiP21o101xm3LpW9bL5f5X5fPljPj5zc9YDBw5IOrKHkmXixIm1Xgvu27Fjh3Jy\nctRVUjDbR5dI+lDS9u3bHV3RMS53EDetXbtWRUVFfkcQAAAAAERP3KZRvfHGG3r77bd12223qVev\nXurbt2+0QwIAAEEaPny4XV6/fr1dNr+VN0crrG/8zW/nA41QmMf9LfxgXsPcM8jfCIR5zDzP/Obf\nau/w4cN2uby83B4p8DdCY8ZsxmA+5kCjqdYoh3nMvK75HJqsa5ujJOZzZY5smItdDBkyxO/1EJuY\nsxEhjzzyiJYtW6aePXtq0aJF0Q4HQALo0aNHWOedeOKJEY4EAIDEELedjUWLFtHJAAAAAAzlklLr\nrMXStwAAIEmYq0muXLnSLpujhs2bN5cUeNK0yUwN8jcJ2xQoLctK2zLP279/v10uKSmxy1aqVllZ\nmX2N1NRUO1YzNcqM2UpbMo8FSrky47Tqm8fMspk6Zl7DSrsy65qP6fPPP7fLo0aNEhAJcT9BHAAA\nRN62bds0ffp0nX766WrYsKHPh1Z/Hn/8cZ166qlKT0/XySefrIceesilSAGYrDkbdf24NWeDzgYA\nAKjhueee08KFC5WSkqIuXbrUWvdvf/ubLr/8cp122ml66KGHdNZZZ+nqq6/W3Xff7VK0AGIVaVQA\nAKCG6dOn6/e//70aNWqkGTNm6KOPPvJb79ChQ7rllls0fPhwOwXqsssuU2VlpebMmaNp06apWbNm\nQbc7fvx4v8c3btwo6cgGuxYzXcjcn8JfypF5v3memcJkpkxZ6VDmSlFFRUV22RzpsVKfzNWoAjl0\n6JBdttKoAo0amStsmStP+bs/0D4iZvzWvhzm4zBX8+zZs2etscN98+fP19y5c1VQUKDu3bvrwQcf\nVO/evWs9p0LBjSYwsgEAAGr1+eefKyUlJeBPfbRq1cpnA7pANm3apO+++07Tp0/3OX7llVfq4MGD\nevbZZ+sVB5CsVq5cqeuuu06zZ8/Wzp071b17dw0ePNins+hPrKVRMbIBAECcatWqlZYuXepzrLy8\nXDNnzrS/bS8tLfWZzBxIamqqPQk7FDt37pQk5eTk+BzPyclRSkqKdu7cmVS7TXfo0EGPPvqoTjjh\nhGiHgjg3b948XXHFFfbG1QsWLNCzzz6rhQsX6oYbbohydMGjswEAQJzKyMio8UH+yiuvVHFxsdat\nWydJuuuuuzR79uw6r9WpUyd9+umnIcewb98+paamKisry+d4WlqaWrZsqb1794Z8TX8GDRpU49jL\nL79sl1u1amWXzVEdK43I3OjOvD89Pd0n5urnfffdd37j6d+/f41jL774ojwejzp27CiPx2OnTJnX\nNdOhrFWq/G1eWL2ume5l1THrmhvymWlU5uaDe/bskSRddNFFfh8TYkd5ebm2b9+um2++2T7m8Xg0\naNAgvfrqq7WfK8lba40jGNkAAAAhWbJkiR555BHNmzdP/fr1kyRNmTJFZ599dp3nmh+6Q1FaWup3\nl27pyFwGa54AgOAVFRWpsrJS2dnZPsezs7O1a9euWs/1vyB0+PXqi84GAAAJ4K233tJvf/tbXXzx\nxfq///s/+3inTp3UqVMnx9pNT0/3+VbddOjQobA7MQBCk5WVpYyMDBUHkTZpycjIqDEqGWl0NgAA\niHM//PCDRo8era5du+qxxx7zua+4uFgHDx6s8xr+UqGC0aZNG1VWVqqoqMjn/PLycn377bdq27Zt\nyEGtjA4AAANcSURBVNcM1oABA/wez8/Pt8tWTE2aNLGPmWlLgTYJ/OGHHyRJ3377rX3sggsuqDWe\ngoICv9c10538pW2Z95sxmGUzNcqag2OmSJmb85mpXxMmTKg1ZsSmrKwspaamqrCw0Od4YWGhWrdu\n7fecDh066IMPPqhzAnn1djp06FCvWOtCZwMAgDjm9Xo1ceJE/fjjj9q0aZPPDtSSNHfuXEfnbPTo\n0UNer1dvvvmmhgwZYh/ftm2bqqqq1KNHj5CvCSS7tLQ05eTkKD8/XyNGjJB05L2en5+vq6++OuB5\nHTp0cLzzECo6GwAAxLFbb71VL730kl544QW/HzKcnrMxYMAAHXvssXrkkUd8OhuPPPKIMjMz9f/+\n3/8L67r1MXDgwFrvtybPS/4nhUvShRdeKCm0vSesVYOqe/LJJ+2yuU9IZmamJAVcpthMTztw4IBd\n/v777yVJo0ePDjo2xJ9rr71Wubm5ysnJUZ8+fTRv3jyVlJQoNzc32qGFhM4GAABx6r333tNtt92m\n/v37q6CgQMuWLfO5/+KLLw57zsYXX3yhJ554QpL05ptvSpJuv/12SVLHjh01adIkSUcmgc+ZM0dX\nXXWVxo0bp8GDB+uVV15RXl6e/vKXv4S1nC4Aady4cSoqKtKsWbNUWFioHj16aMOGDT4rr8UDj9fc\nfhIAAMSNzZs3B5y3IPnm/Idz7XPPPddnPoGlf//+PsvOStLjjz+ue+65R5999pnat2+vGTNmaMaM\nGWG376RQRjYigZENJDM6GwAAAAAc4b8rDQAAAAD1RGcDAAAAgCPobAAAAABwBJ0NAAAAAI6gswEA\nAADAEXQ2AAAAADiCzgYAAAAAR9DZAAAAAOAIOhsAAAAAHEFnAwAAAIAj6GwAAAAAcASdDQAAAACO\noLMBAAAAwBF0NgAAAAA4gs4GAAAAAEfQ2QAAAADgCDobAAAAABxBZwMAAACAI+hsAAAAAHAEnQ0A\nAAAAjqCzAQAAAMARdDYAAAAAOILOBgAAAABH0NkAAAAA4Ag6GwAAAAAcQWcDAAAAgCPobAAAAABw\nBJ0NAAAAAI6gswEAAADAEXQ2AAAAADiCzgYAAAAAR9DZAAAAAOAIOhsAAAAAHEFnAwAAAIAj6GwA\nAAAAcASdDQAAAACO+P+iJdKFkh2rUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b5c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn import plotting\n",
    "plotting.plot_stat_map(out_path)"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}