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