examples/get_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Get the data\n",
    "\n",
    "This is a simple guide on how to download the data using [this API](https://github.com/individual-brain-charting/api). You can also find the reference for the API [here](https://individual-brain-charting.github.io/docs/ibc_api.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the fetcher as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[siibra:INFO] Version: 0.4a47\n",
      "[siibra:WARNING] This is a development release. Use at your own risk.\n",
      "[siibra:INFO] Please file bugs and issues at https://github.com/FZJ-INM1-BDA/siibra-python.\n",
      "[siibra:INFO] Clearing siibra cache at /home/himanshu/.cache/siibra.retrieval\n"
     ]
    }
   ],
   "source": [
    "import ibc_api.utils as ibc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see what is available for a given data type on IBC, we need fetch the file that contains that information.\n",
    "The following loads a CSV file with all that info as a pandas dataframe and\n",
    "saves it as ``ibc_data/available_{data_type}.csv``.\n",
    "\n",
    "Let's do that for IBC volumetric contrast maps.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = ibc.get_info(data_type=\"volume_maps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what's in the database\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
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       "      <th>contrast</th>\n",
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       "      <th>0</th>\n",
       "      <td>01</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>ArchiSocial</td>\n",
       "      <td>ap</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>false_belief-mechanistic</td>\n",
       "      <td>0.000552</td>\n",
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       "      <td>sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.000543</td>\n",
       "      <td>volume_maps</td>\n",
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       "      <td>.nii.gz</td>\n",
       "      <td>false_belief-mechanistic_audio</td>\n",
       "      <td>2.893414</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...</td>\n",
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       "      <th>53219</th>\n",
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       "      <td>0.000570</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...</td>\n",
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       "      <th>53220</th>\n",
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       "      <td>ffx</td>\n",
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       "      <td>volume_maps</td>\n",
       "      <td>sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...</td>\n",
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       "      <th>53221</th>\n",
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       "      <td>0.000614</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53222</th>\n",
       "      <td>15</td>\n",
       "      <td>40</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Scene</td>\n",
       "      <td>ffx</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>correct</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.json</td>\n",
       "      <td>scene_possible_correct-scene_impossible_correct</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53223</th>\n",
       "      <td>15</td>\n",
       "      <td>40</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scene</td>\n",
       "      <td>ffx</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>.json</td>\n",
       "      <td>scene_possible_correct</td>\n",
       "      <td>0.000597</td>\n",
       "      <td>volume_maps</td>\n",
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       "</table>\n",
       "<p>53224 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject session     desc  hemi         task direction run  \\\n",
       "0          01      00  preproc   NaN  ArchiSocial        ap       \n",
       "1          01      00  preproc   NaN  ArchiSocial        ap       \n",
       "2          01      00  preproc   NaN  ArchiSocial        ap       \n",
       "3          01      00  preproc   NaN  ArchiSocial        ap       \n",
       "4          01      00  preproc   NaN  ArchiSocial        ap       \n",
       "...       ...     ...      ...   ...          ...       ...  ..   \n",
       "53219      15      40  preproc   NaN        Scene       ffx       \n",
       "53220      15      40  preproc   NaN        Scene       ffx       \n",
       "53221      15      40  preproc   NaN        Scene       ffx       \n",
       "53222      15      40  preproc   NaN        Scene       ffx       \n",
       "53223      15      40  preproc   NaN        Scene       ffx       \n",
       "\n",
       "                     space     suffix  datatype extension  \\\n",
       "0      MNI152NLin2009cAsym        NaN       NaN     .json   \n",
       "1      MNI152NLin2009cAsym        NaN       NaN   .nii.gz   \n",
       "2      MNI152NLin2009cAsym      audio       NaN     .json   \n",
       "3      MNI152NLin2009cAsym      audio       NaN   .nii.gz   \n",
       "4      MNI152NLin2009cAsym      video       NaN     .json   \n",
       "...                    ...        ...       ...       ...   \n",
       "53219  MNI152NLin2009cAsym    correct       NaN     .json   \n",
       "53220  MNI152NLin2009cAsym    correct       NaN     .json   \n",
       "53221  MNI152NLin2009cAsym  incorrect       NaN     .json   \n",
       "53222  MNI152NLin2009cAsym    correct       NaN     .json   \n",
       "53223  MNI152NLin2009cAsym    correct       NaN     .json   \n",
       "\n",
       "                                              contrast  megabytes  \\\n",
       "0                             false_belief-mechanistic   0.000552   \n",
       "1                             false_belief-mechanistic   2.896178   \n",
       "2                       false_belief-mechanistic_audio   0.000543   \n",
       "3                       false_belief-mechanistic_audio   2.893414   \n",
       "4                       false_belief-mechanistic_video   0.000543   \n",
       "...                                                ...        ...   \n",
       "53219                        scene_correct-dot_correct   0.000570   \n",
       "53220                         scene_impossible_correct   0.000618   \n",
       "53221                       scene_impossible_incorrect   0.000614   \n",
       "53222  scene_possible_correct-scene_impossible_correct   0.000598   \n",
       "53223                           scene_possible_correct   0.000597   \n",
       "\n",
       "           dataset                                               path  \n",
       "0      volume_maps  sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...  \n",
       "1      volume_maps  sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...  \n",
       "2      volume_maps  sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...  \n",
       "3      volume_maps  sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...  \n",
       "4      volume_maps  sub-01/ses-00/sub-01_ses-00_task-ArchiSocial_d...  \n",
       "...            ...                                                ...  \n",
       "53219  volume_maps  sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...  \n",
       "53220  volume_maps  sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...  \n",
       "53221  volume_maps  sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...  \n",
       "53222  volume_maps  sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...  \n",
       "53223  volume_maps  sub-15/ses-40/sub-15_ses-40_task-Scene_dir-ffx...  \n",
       "\n",
       "[53224 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are over 26000 statistic maps (half of the rows because there are .json files corresponding to each map) available for download.\n",
    "But since it's a pandas dataframe, we can filter it to get just what we want.\n",
    "Let's see how many statistic maps are available for each task.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "task\n",
       "Audio                  5852\n",
       "MathLanguage           5760\n",
       "ArchiStandard          3588\n",
       "RSVPLanguage           3458\n",
       "MTTNS                  1824\n",
       "MTTWE                  1824\n",
       "Audi                   1800\n",
       "SpatialNavigation      1728\n",
       "ArchiSocial            1404\n",
       "Self                   1320\n",
       "Visu                   1152\n",
       "BiologicalMotion2      1100\n",
       "VSTMC                  1100\n",
       "BiologicalMotion1      1100\n",
       "HcpWm                  1092\n",
       "ArchiSpatial           1092\n",
       "ArchiEmotional         1092\n",
       "FaceBody                945\n",
       "RewProc                 918\n",
       "HcpMotor                858\n",
       "MVEB                    792\n",
       "DotPatterns             726\n",
       "NARPS                   720\n",
       "Scene                   693\n",
       "Attention               660\n",
       "EmoReco                 660\n",
       "WardAndAllport          660\n",
       "TwoByTwo                660\n",
       "MCSE                    648\n",
       "Moto                    648\n",
       "SelectiveStopSignal     528\n",
       "StopNogo                462\n",
       "Lec1                    432\n",
       "MVIS                    432\n",
       "EmoMem                  396\n",
       "VSTM                    360\n",
       "FingerTapping           330\n",
       "HcpEmotion              312\n",
       "HcpGambling             312\n",
       "HcpLanguage             312\n",
       "HcpRelational           234\n",
       "HcpSocial               234\n",
       "PreferenceFaces         222\n",
       "EmotionalPain           216\n",
       "Enumeration             216\n",
       "PreferenceHouses        216\n",
       "PainMovie               216\n",
       "Lec2                    216\n",
       "TheoryOfMind            216\n",
       "PreferenceFood          216\n",
       "PreferencePaintings     210\n",
       "Stroop                  198\n",
       "Catell                  198\n",
       "StopSignal              198\n",
       "ColumbiaCards           192\n",
       "Bang                    144\n",
       "Discount                132\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[\"task\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find the descriptions of all these tasks [here](https://individual-brain-charting.github.io/docs/tasks.html).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, let's just download the maps from Discount task, only for sub-08. You can filter the maps for tasks and subjects like this.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 12 files for subjects ['08'] and tasks ['Discount'].\n"
     ]
    },
    {
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       "      <th>25624</th>\n",
       "      <td>08</td>\n",
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       "      <th>25625</th>\n",
       "      <td>08</td>\n",
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       "      <td>2.921305</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
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       "    <tr>\n",
       "      <th>25626</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>ap</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.json</td>\n",
       "      <td>delay</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25627</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>ap</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.nii.gz</td>\n",
       "      <td>delay</td>\n",
       "      <td>2.923846</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25628</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>ffx</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.json</td>\n",
       "      <td>amount</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25629</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>ffx</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.nii.gz</td>\n",
       "      <td>amount</td>\n",
       "      <td>2.925251</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25630</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>ffx</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.json</td>\n",
       "      <td>delay</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25631</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>ffx</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.nii.gz</td>\n",
       "      <td>delay</td>\n",
       "      <td>2.925747</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25632</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>pa</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.json</td>\n",
       "      <td>amount</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25633</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>pa</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.nii.gz</td>\n",
       "      <td>amount</td>\n",
       "      <td>2.921803</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25634</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>pa</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.json</td>\n",
       "      <td>delay</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25635</th>\n",
       "      <td>08</td>\n",
       "      <td>27</td>\n",
       "      <td>preproc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discount</td>\n",
       "      <td>pa</td>\n",
       "      <td></td>\n",
       "      <td>MNI152NLin2009cAsym</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.nii.gz</td>\n",
       "      <td>delay</td>\n",
       "      <td>2.920833</td>\n",
       "      <td>volume_maps</td>\n",
       "      <td>sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject session     desc  hemi      task direction run  \\\n",
       "25624      08      27  preproc   NaN  Discount        ap       \n",
       "25625      08      27  preproc   NaN  Discount        ap       \n",
       "25626      08      27  preproc   NaN  Discount        ap       \n",
       "25627      08      27  preproc   NaN  Discount        ap       \n",
       "25628      08      27  preproc   NaN  Discount       ffx       \n",
       "25629      08      27  preproc   NaN  Discount       ffx       \n",
       "25630      08      27  preproc   NaN  Discount       ffx       \n",
       "25631      08      27  preproc   NaN  Discount       ffx       \n",
       "25632      08      27  preproc   NaN  Discount        pa       \n",
       "25633      08      27  preproc   NaN  Discount        pa       \n",
       "25634      08      27  preproc   NaN  Discount        pa       \n",
       "25635      08      27  preproc   NaN  Discount        pa       \n",
       "\n",
       "                     space suffix  datatype extension contrast  megabytes  \\\n",
       "25624  MNI152NLin2009cAsym    NaN       NaN     .json   amount   0.000503   \n",
       "25625  MNI152NLin2009cAsym    NaN       NaN   .nii.gz   amount   2.921305   \n",
       "25626  MNI152NLin2009cAsym    NaN       NaN     .json    delay   0.000505   \n",
       "25627  MNI152NLin2009cAsym    NaN       NaN   .nii.gz    delay   2.923846   \n",
       "25628  MNI152NLin2009cAsym    NaN       NaN     .json   amount   0.000504   \n",
       "25629  MNI152NLin2009cAsym    NaN       NaN   .nii.gz   amount   2.925251   \n",
       "25630  MNI152NLin2009cAsym    NaN       NaN     .json    delay   0.000506   \n",
       "25631  MNI152NLin2009cAsym    NaN       NaN   .nii.gz    delay   2.925747   \n",
       "25632  MNI152NLin2009cAsym    NaN       NaN     .json   amount   0.000503   \n",
       "25633  MNI152NLin2009cAsym    NaN       NaN   .nii.gz   amount   2.921803   \n",
       "25634  MNI152NLin2009cAsym    NaN       NaN     .json    delay   0.000505   \n",
       "25635  MNI152NLin2009cAsym    NaN       NaN   .nii.gz    delay   2.920833   \n",
       "\n",
       "           dataset                                               path  \n",
       "25624  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25625  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25626  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25627  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25628  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25629  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25630  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25631  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25632  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25633  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25634  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  \n",
       "25635  volume_maps  sub-08/ses-27/sub-08_ses-27_task-Discount_dir-...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_db = ibc.filter_data(db, task_list=[\"Discount\"], subject_list=[\"08\"])\n",
    "filtered_db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to download the few selected maps that we filtered.\n",
    "\n",
    "The following will save the requested maps under\n",
    "``ibc_data/resulting_smooth_maps/sub-08/task-Discount`` \n",
    "(or whatever subject you chose). And will also create a local CSV file ``ibc_data/downloaded_volume_maps.csv`` to track the downloaded files. This will contain local file paths and the time they were downloaded at, and is updated everytime you download new files.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 12 files to download.\n",
      "***\n",
      "To continue, please go to https://iam.ebrains.eu/auth/realms/hbp/device?user_code=UFKZ-XXQU\n",
      "***\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[siibra:INFO] 139625 objects found for dataset ad04f919-7dcc-48d9-864a-d7b62af3d49d returned.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ebrains token successfuly set.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Overall Progress:   0%|\u001b[32m                                                                                                                                                                                                                                                                       \u001b[0m| 0/12 [00:00<?, ?it/s]\u001b[0m\u001b[A\n",
      "Overall Progress:  17%|\u001b[32m██████████████████████████████████████████▌                                                                                                                                                                                                                    \u001b[0m| 2/12 [00:00<00:00, 12.46it/s]\u001b[0m\u001b[A\n",
      "Overall Progress:  33%|\u001b[32m█████████████████████████████████████████████████████████████████████████████████████                                                                                                                                                                          \u001b[0m| 4/12 [00:00<00:00, 12.26it/s]\u001b[0m\u001b[A\n",
      "Overall Progress:  50%|\u001b[32m███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                                                                               \u001b[0m| 6/12 [00:00<00:00, 11.73it/s]\u001b[0m\u001b[A\n",
      "Overall Progress:  67%|\u001b[32m██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████                                                                                     \u001b[0m| 8/12 [00:00<00:00, 11.80it/s]\u001b[0m\u001b[A\n",
      "Overall Progress:  83%|\u001b[32m███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                          \u001b[0m| 10/12 [00:00<00:00, 11.94it/s]\u001b[0m\u001b[A\n",
      "Overall Progress: 100%|\u001b[32m██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\u001b[0m| 12/12 [00:01<00:00, 11.97it/s]\u001b[0m\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded requested files from IBC volume_maps dataset. See ibc_data/downloaded_volume_maps.csv for details.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>local_path</th>\n",
       "      <th>downloaded_on</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:53.472528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:53.628380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:53.634523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:53.793226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:53.799418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:53.972341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:53.979429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:54.140314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:54.146809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:54.304385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:54.310566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...</td>\n",
       "      <td>2023-10-05 17:23:54.468429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           local_path  \\\n",
       "0   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "1   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "2   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "3   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "4   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "5   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "6   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "7   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "8   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "9   ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "10  ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "11  ibc_data/volume_maps/sub-08/ses-27/sub-08_ses-...   \n",
       "\n",
       "                 downloaded_on  \n",
       "0   2023-10-05 17:23:53.472528  \n",
       "1   2023-10-05 17:23:53.628380  \n",
       "2   2023-10-05 17:23:53.634523  \n",
       "3   2023-10-05 17:23:53.793226  \n",
       "4   2023-10-05 17:23:53.799418  \n",
       "5   2023-10-05 17:23:53.972341  \n",
       "6   2023-10-05 17:23:53.979429  \n",
       "7   2023-10-05 17:23:54.140314  \n",
       "8   2023-10-05 17:23:54.146809  \n",
       "9   2023-10-05 17:23:54.304385  \n",
       "10  2023-10-05 17:23:54.310566  \n",
       "11  2023-10-05 17:23:54.468429  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloaded_db = ibc.download_data(filtered_db)\n",
    "downloaded_db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try plotting one of these contrast maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays._slicers.OrthoSlicer at 0x7f7bdd436bb0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 525.6x187.2 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn.plotting import plot_stat_map\n",
    "\n",
    "map_path = downloaded_db[\"local_path\"][1]\n",
    "plot_stat_map(map_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}