def test_get_dataset_descr_warning():
"""Tests that function ``_get_dataset_descr()`` gives a warning
when no description is available.
"""
with pytest.warns(UserWarning,
match="Could not find dataset description."):
descr = _get_dataset_descr("")
assert descr == ""
def load_atlas_smith():
dataset_name = '/iexec_in/data'
files = [
# 'rsn20.nii.gz',
# 'PNAS_Smith09_rsn10.nii.gz',
# 'rsn70.nii.gz',
'bm20.nii.gz'
# 'PNAS_Smith09_bm10.nii.gz',
# 'bm70.nii.gz'
]
files = [dataset_name + '/' + f for f in files]
fdescr = _get_dataset_descr(dataset_name)
keys = ['bm20'] #['rsn20', 'rsn10', 'rsn70', 'bm20', 'bm10', 'bm70']
params = dict(zip(keys, files))
params['description'] = fdescr
return Bunch(**params)
def fetch_localizer(subject_ids=None,
get_anats=False,
data_type='raw',
data_dir=None,
url=None,
resume=True,
verbose=1):
"""Download and load Brainomics Localizer dataset (94 subjects).
"The Functional Localizer is a simple and fast acquisition
procedure based on a 5-minute functional magnetic resonance
imaging (fMRI) sequence that can be run as easily and as
systematically as an anatomical scan. This protocol captures the
cerebral bases of auditory and visual perception, motor actions,
reading, language comprehension and mental calculation at an
individual level. Individual functional maps are reliable and
quite precise. The procedure is decribed in more detail on the
Functional Localizer page." This code is modified from
`fetch_localizer_contrasts` from nilearn.datasets.funcs.py.
(see http://brainomics.cea.fr/localizer/)
"Scientific results obtained using this dataset are described in
Pinel et al., 2007" [1]
Notes:
It is better to perform several small requests than a big one because the
Brainomics server has no cache (can lead to timeout while the archive
is generated on the remote server). For example, download
n_subjects=np.array(1,10), then n_subjects=np.array(10,20), etc.
Parameters
----------
subject_ids: list
List of Subject IDs (e.g., ['S01','S02']. If None is given,
all 94 subjects are used.
get_anats: boolean
Whether individual structural images should be fetched or not.\
data_type: string
type of data to download. Valid values are ['raw','preprocessed']
data_dir: string, optional
Path of the data directory. Used to force data storage in a specified
location.
url: string, optional
Override download URL. Used for test only (or if you setup a mirror of
the data).
resume: bool
Whether to resume download of a partly-downloaded file.
verbose: int
Verbosity level (0 means no message).
Returns
-------
data: Bunch
Dictionary-like object, the interest attributes are :
- 'functional': string list
Paths to nifti contrast maps
- 'structural' string
Path to nifti files corresponding to the subjects structural images
References
----------
Pinel, Philippe, et al.
"Fast reproducible identification and large-scale databasing of
individual functional cognitive networks."
BMC neuroscience 8.1 (2007): 91.
"""
if subject_ids is None:
subject_ids = ['S%02d' % x for x in np.arange(1, 95)]
elif not isinstance(subject_ids, (list)):
raise ValueError(
"subject_ids must be a list of subject ids (e.g., ['S01','S02'])")
if data_type == 'raw':
dat_type = "raw fMRI"
dat_label = "raw bold"
anat_type = "raw T1"
anat_label = "raw anatomy"
elif data_type == 'preprocessed':
dat_type = "preprocessed fMRI"
dat_label = "bold"
anat_type = "normalized T1"
anat_label = "anatomy"
else:
raise ValueError(
"Only ['raw','preprocessed'] data_types are currently supported.")
root_url = "http://brainomics.cea.fr/localizer/"
dataset_name = 'brainomics_localizer'
data_dir = _get_dataset_dir(dataset_name,
data_dir=data_dir,
verbose=verbose)
fdescr = _get_dataset_descr(dataset_name)
opts = {'uncompress': True}
bold_files = []
anat_files = []
for subject_id in subject_ids:
base_query = ("Any X,XT,XL,XI,XF,XD WHERE X is Scan, X type XT, "
"X concerns S, "
"X label XL, X identifier XI, "
"X format XF, X description XD, "
'S identifier = "%s", ' % (subject_id, ) +
'X type IN(%(types)s), X label "%(label)s"')
file_tarball_url = "%sbrainomics_data.zip?rql=%s&vid=data-zip" % (
root_url,
_urllib.parse.quote(base_query % {
"types": "\"%s\"" % dat_type,
"label": dat_label
},
safe=',()'))
name_aux = str.replace(str.join('_', [dat_type, dat_label]), ' ', '_')
file_path = os.path.join("brainomics_data", subject_id,
"%s.nii.gz" % name_aux)
bold_files.append(
_fetch_files(data_dir, [(file_path, file_tarball_url, opts)],
verbose=verbose))
if get_anats:
file_tarball_url = "%sbrainomics_data_anats.zip?rql=%s&vid=data-zip" % (
root_url,
_urllib.parse.quote(base_query % {
"types": "\"%s\"" % anat_type,
"label": anat_label
},
safe=',()'))
if data_type == 'raw':
anat_name_aux = "raw_T1_raw_anat_defaced.nii.gz"
elif data_type == 'preprocessed':
anat_name_aux = "normalized_T1_anat_defaced.nii.gz"
file_path = os.path.join("brainomics_data", subject_id,
anat_name_aux)
anat_files.append(
_fetch_files(data_dir, [(file_path, file_tarball_url, opts)],
verbose=verbose))
# Fetch subject characteristics (separated in two files)
if url is None:
url_csv = ("%sdataset/cubicwebexport.csv?rql=%s&vid=csvexport" %
(root_url, _urllib.parse.quote("Any X WHERE X is Subject")))
url_csv2 = ("%sdataset/cubicwebexport2.csv?rql=%s&vid=csvexport" %
(root_url,
_urllib.parse.quote(
"Any X,XI,XD WHERE X is QuestionnaireRun, "
"X identifier XI, X datetime "
"XD",
safe=',')))
else:
url_csv = "%s/cubicwebexport.csv" % url
url_csv2 = "%s/cubicwebexport2.csv" % url
filenames = [("cubicwebexport.csv", url_csv, {}),
("cubicwebexport2.csv", url_csv2, {})]
csv_files = _fetch_files(data_dir, filenames, verbose=verbose)
metadata = pd.merge(pd.read_csv(csv_files[0], sep=';'),
pd.read_csv(csv_files[1], sep=';'),
on='"subject_id"')
metadata.to_csv(os.path.join(data_dir, 'metadata.csv'))
for x in ['cubicwebexport.csv', 'cubicwebexport2.csv']:
os.remove(os.path.join(data_dir, x))
if not get_anats:
anat_files = None
return Bunch(functional=bold_files,
structural=anat_files,
ext_vars=metadata,
description=fdescr)
def test_get_dataset_descr(name):
"""Test function ``_get_dataset_descr()``."""
descr = _get_dataset_descr(name)
assert isinstance(descr, str)
assert len(descr) > 0