"bold.nii.gz",

"labels.txt",

"mask4_vt.nii.gz",

"mask8b_face_vt.nii.gz",

"mask8b_house_vt.nii.gz",

"mask8_face_vt.nii.gz",

"""

Helper function for extracting .nii.gz to .nii.

"""

for filename in glob.glob('%s/*.nii.gz' % dirname):

if not os.path.exists(re.sub("\.gz", "", filename)):

f_in = gzip.open(filename, 'rb')

f_out = open(filename[:-3], 'wb')

f_out.writelines(f_in)

f_out.close()

f_in.close()

if not os.path.exists(subject_dir):

return None

subject_data = dict()

subject_data["subject_dir"] = subject_dir

for file_name in SPM_AUDITORY_DATA_FILES:

file_path = os.path.join(subject_dir, file_name)

if os.path.exists(file_path) or os.path.exists(

file_path.rstrip(".gz")):

file_name = re.sub("(?:\.nii\.gz|\.txt)", "", file_name)

subject_data[file_name] = file_path

else:

print "%s missing from filelist!" % file_name

return None

_subject_data = {}

_subject_data["func"] = [subject_data[x] for x in subject_data.keys()

if re.match("^fM00223_0\d\d\.img$",

os.path.basename(x))]

_subject_data["func"].sort()

_subject_data["anat"] = [subject_data[x] for x in subject_data.keys()

if re.match("^sM00223_002\.img$",

os.path.basename(x))][0]

if not os.path.exists(subject_dir):

return None

subject_data = dict()

subject_data["subject_dir"] = subject_dir

for file_name in FSL_FEEDS_DATA_FILES:

file_path = os.path.join(subject_dir, file_name)

if os.path.exists(file_path) or os.path.exists(

file_path.rstrip(".gz")):

file_name = re.sub("(?:\.nii\.gz|\.txt)", "", file_name)

subject_data[file_name] = file_path

else:

if not os.path.basename(subject_dir) == 'data':

return _glob_fsl_feeds_data(os.path.join(subject_dir,

'feeds/data'))

else:

print "%s missing from filelist!" % file_name

return None

_subject_data = {"func": os.path.join(subject_dir,

"fmri.nii"),

"anat": os.path.join(subject_dir,

"structural_brain.nii"),

}

archive_name = os.path.basename(url)

archive_path = os.path.join(data_dir, archive_name)

subject_dir = os.path.join(data_dir, subject_id)

if redownload:

try:

print "Zapping all old downloads .."

# shutil.rmtree(subject_dir)

# os.remove(archive_path)

except OSError:

pass

finally:

print "Done."

if os.path.exists(subject_dir):

subject_data = _glob_haxby_subject_data(subject_dir)

if subject_data is None:

# shutil.rmtree(subject_dir)

return fetch_haxby_subject_data(data_dir, subject_id, url)

else:

return subject_id, subject_data

elif os.path.exists(archive_path):

try:

_uncompress_file(archive_path)

except:

print "Archive corrupted, trying to download it again."

os.remove(archive_path)

return fetch_haxby_subject_data(data_dir, subject_id, url)

else:

_fetch_file(url, data_dir)

try:

_uncompress_file(archive_path)

except:

print "Archive corrupted, trying to download it again."

os.remove(archive_path)

return fetch_haxby_subject_data(data_dir, subject_id, url)

if not os.path.exists(subject_dir):

return None

subject_data = dict()

subject_data["subject_dir"] = subject_dir

for file_name in HAXBY_SUBJECT_FILES:

file_path = os.path.join(subject_dir, file_name)

if os.path.exists(file_path) or os.path.exists(

file_path.rstrip(".gz")):

file_name = re.sub("(?:\.nii\.gz|\.txt)", "", file_name)

subject_data[file_name] = file_path

else:

print "%s missing from filelist!" % file_name

return None

n_jobs=1):

"""

Download and loads the haxby dataset

Parameters

----------

data_dir: string, optional

Path of the data directory. Used to force data storage in a specified

location. Default: None

subject_ids: list of string, option

Only download data for these subjects

redownload: bool, optional

Delete all local file copies on disk and re-download

Returns

-------

data: dictionary, keys are subject ids (subj1, subj2, etc.)

'bold': string

Path to nifti file with bold data

'session_target': string

Path to text file containing session and target data

'mask*': string

Path to correspoding nifti mask file

'labels': string

Path to text file containing labels (can be used for LeaveOneLabelOut

cross validation for example)

References

----------

`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J.,

and Pietrini, P. (2001). Distributed and overlapping representations of

faces and objects in ventral temporal cortex. Science 293, 2425-2430.`

Notes

-----

PyMVPA provides a tutorial using this dataset :

http://www.pymvpa.org/tutorial.html

More informations about its structure :

http://dev.pymvpa.org/datadb/haxby2001.html

See `additional information

<http://www.sciencemag.org/content/293/5539/2425>`_

"""

data_dir = os.path.join(data_dir, "haxby2001")

subjects = dict()

if subject_ids is None:

subject_ids = HAXBY_SUBJECT_IDS

else:

subject_ids = [subject_id for subject_id in subject_ids \

if subject_id in HAXBY_SUBJECT_IDS]

# url spitter

def url_factory():

for subject_id in subject_ids:

url = ('http://data.pymvpa.org'

'/datasets/haxby2001/%s-2010.01.14.tar.gz') % subject_id

yield data_dir, subject_id, url, redownload

# parallel fetch

pairs = joblib.Parallel(n_jobs=n_jobs, verbose=100)(

joblib.delayed(fetch_haxby_subject_data)(x, y, z, w)\

for x, y, z, w in url_factory())

# pack pairs in to a dict

for subject_id, subject_data in pairs:

subjects[subject_id] = subject_data

'''

Function to fetch SPM auditory data.

'''

url = "ftp://ftp.fil.ion.ucl.ac.uk/spm/data/MoAEpilot/MoAEpilot.zip"

subject_dir = data_dir

archive_path = os.path.join(subject_dir, os.path.basename(url))

if redownload:

try:

print "Zapping all old downloads .."

# shutil.rmtree(subject_dir)

# os.remove(archive_path)

except OSError:

pass

finally:

print "Done."

if os.path.exists(subject_dir):

subject_data = _glob_spm_auditory_data(subject_dir)

if subject_data is None:

# shutil.rmtree(subject_dir)

return fetch_spm_auditory_data(data_dir)

else:

return subject_data

elif os.path.exists(archive_path):

try:

_uncompress_file(archive_path)

except:

print "Archive corrupted, trying to download it again."

# os.remove(archive_path)

return fetch_spm_auditory_data(data_dir)

else:

_fetch_file(url, data_dir)

try:

_uncompress_file(archive_path)

except:

print "Archive corrupted, trying to download it again."

# os.remove(archive_path)

return fetch_spm_auditory_data(data_dir)

'''

Function to fetch SPM auditory data.

'''

url = ("http://fsl.fmrib.ox.ac.uk/fsldownloads/oldversions/"

"fsl-4.1.0-feeds.tar.gz")

subject_dir = data_dir

archive_path = os.path.join(subject_dir, os.path.basename(url))

if redownload:

try:

print "Zapping all old downloads .."

# shutil.rmtree(subject_dir)

# os.remove(archive_path)

except OSError:

pass

finally:

print "Done."

if os.path.exists(subject_dir):

subject_data = _glob_fsl_feeds_data(subject_dir)

if subject_data is None:

# shutil.rmtree(subject_dir)

return fetch_fsl_feeds_data(data_dir)

else:

return subject_data

elif os.path.exists(archive_path):

try:

_uncompress_file(archive_path)

except:

print "Archive corrupted, trying to download it again."

os.remove(archive_path)

return fetch_fsl_feeds_data(data_dir)

else:

_fetch_file(url, data_dir)

try:

_uncompress_file(archive_path)

except:

print "Archive corrupted, trying to download it again."

os.remove(archive_path)

return fetch_fsl_feeds_data(data_dir)

"""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

""" 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)