def fetch_poldrack_mixed_gambles(data_dir=None):
"""Download and loads the Poldrack Mixed Gambles dataset
For the moment, only bold images are loaded
"""
# definition of dataset files
file_names = ["ds005/sub0%02i/BOLD/task001_run00%s/bold.nii.gz" % (s, r)
for s in range(1, 17)
for r in range(1, 4)]
# load the dataset
try:
# Try to load the dataset
files = _get_dataset("poldrack_mixed_gambles",
file_names, data_dir=data_dir)
except IOError:
# If the dataset does not exists, we download it
url = 'http://openfmri.org/system/files/ds005_raw.tgz'
_fetch_dataset('poldrack_mixed_gambles', [url], data_dir=data_dir)
files = _get_dataset("poldrack_mixed_gambles",
file_names, data_dir=data_dir)
files = np.asarray(np.split(np.asarray(files), 16))
# return the data
return Bunch(data=files)
def fetch_openfmri(accession_number, data_dir, redownload=False):
""" Downloads and extract datasets from www.openfmri.org
Parameters
----------
accession_number: str
Dataset identifier, as displayed on https://openfmri.org/data-sets
data_dir: str
Destination directory.
redownload: boolean
Set to True to force redownload of already available data.
Defaults to False.
Datasets
--------
{accession_number}: {dataset name}
ds000001: Balloon Analog Risk-taking Task
ds000002: Classification learning
ds000003: Rhyme judgment
ds000005: Mixed-gambles task
ds000007: Stop-signal task with spoken & manual responses
ds000008: Stop-signal task with unselective and selective stopping
ds000011: Classification learning and tone-counting
ds000017: Classification learning and stop-signal (1 year test-retest)
ds000051: Cross-language repetition priming
ds000052: Classification learning and reversal
ds000101: Simon task dataset
ds000102: Flanker task (event-related)
ds000105: Visual object recognition
ds000107: Word and object processing
Returns
-------
ds_path: str
Path of the dataset.
"""
datasets = {
'ds000001': 'Balloon Analog Risk-taking Task',
'ds000002': 'Classification learning',
'ds000003': 'Rhyme judgment',
'ds000005': 'Mixed-gambles task',
'ds000007': 'Stop-signal task with spoken & manual responses',
'ds000008': 'Stop-signal task with unselective and selective stopping',
'ds000011': 'Classification learning and tone-counting',
'ds000017': ('Classification learning and '
'stop-signal (1 year test-retest)'),
'ds000051': 'Cross-language repetition priming',
'ds000052': 'Classification learning and reversal',
'ds000101': 'Simon task dataset',
'ds000102': 'Flanker task (event-related)',
'ds000105': 'Visual object recognition',
'ds000107': 'Word and object processing',
}
files = {
'ds000001': ['ds001_raw_fixed_1'],
'ds000002': ['ds002_raw_0'],
'ds000003': ['ds003_raw'],
'ds000005': ['ds005_raw'],
'ds000007': ['ds007_raw'],
'ds000008': ['ds008_raw'],
'ds000011': ['ds011_raw'],
'ds000017': ['ds017A_raw', 'ds017B_raw'],
'ds000051': ['ds051_raw'],
'ds000052': ['ds052_raw'],
'ds000101': ['ds101_raw'],
'ds000102': ['ds102_raw'],
'ds000105': ['ds105_raw'],
'ds000107': ['ds107_raw'],
}
ds_url = 'https://openfmri.org/system/files/%s.tgz'
ds_name = datasets[accession_number].lower().replace(' ', '_')
ds_urls = [ds_url % name for name in files[accession_number]]
ds_path = os.path.join(data_dir, ds_name)
if not os.path.exists(ds_path) or redownload:
if os.path.exists(ds_path):
shutil.rmtree(ds_path)
_fetch_dataset(ds_name, ds_urls, data_dir, verbose=1)
return ds_path
