def fetch_bids_langloc_dataset(data_dir=None, verbose=1):
"""Download language localizer example bids dataset.
Parameters
----------
data_dir: string, optional
Path to store the downloaded dataset. if None employ nilearn
datasets default download directory.
verbose: int, optional
verbosity level (0 means no message).
Returns
-------
data_dir: string
Path to downloaded dataset
downloaded_files: list of string
Absolute paths of downloaded files on disk
"""
url = 'https://files.osf.io/v1/resources/9q7dv/providers/osfstorage/5888d9a76c613b01fc6acc4e'
dataset_name = 'bids_langloc_example'
main_folder = 'bids_langloc_dataset'
data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
verbose=verbose)
# The files_spec needed for _fetch_files
files_spec = [(main_folder + '.zip', url, {'move': main_folder + '.zip'})]
if not os.path.exists(os.path.join(data_dir, main_folder)):
downloaded_files = _fetch_files(data_dir, files_spec, resume=True,
verbose=verbose)
_uncompress_file(downloaded_files[0])
main_path = os.path.join(data_dir, main_folder)
file_list = [os.path.join(path, f) for
path, dirs, files in os.walk(main_path) for f in files]
return os.path.join(data_dir, main_folder), sorted(file_list)
def fetch_adni(data_dir=None):
"""Fetch ADNI timeseries data from Open Science Framework (OSF)
Parameters
----------
data_dir : string
Path where data should be downloaded
Returns
-------
data_dir : string
Path to the downloaded timeseries directory
"""
if data_dir is None:
warnings.warn('Data downloading is requested but data_dir is not '
'provided. Downloading to the current directory with '
'folder name ADNI', stacklevel=2)
data_dir = './ADNI'
url = 'https://osf.io/xhrcs/download'
# Download the zip file, first
dl_file = _fetch_file(url, data_dir=data_dir)
# Second, uncompress the downloaded zip file
_uncompress_file(dl_file, verbose=2)
return data_dir
def fetch_fiac_first_level(data_dir=None, verbose=1):
""" Download a first-level fiac fMRI dataset (2 sessions)
Parameters
----------
data_dir: string
directory where data should be downloaded and unpacked.
"""
data_dir = _get_dataset_dir('fiac_nistats',
data_dir=data_dir,
verbose=verbose)
def _glob_fiac_data():
"""glob data from subject_dir."""
_subject_data = {}
subject_dir = os.path.join(data_dir, 'nipy-data-0.2/data/fiac/fiac0')
for session in [1, 2]:
# glob func data for session session + 1
session_func = os.path.join(subject_dir, 'run%i.nii.gz' % session)
if not os.path.isfile(session_func):
print('Missing functional scan for session %i.' % session)
return None
_subject_data['func%i' % session] = session_func
# glob design matrix .npz file
sess_dmtx = os.path.join(subject_dir, 'run%i_design.npz' % session)
if not os.path.isfile(sess_dmtx):
print('Missing session file: %s' % sess_dmtx)
return None
_subject_data['design_matrix%i' % session] = sess_dmtx
# glob for mask data
mask = os.path.join(subject_dir, 'mask.nii.gz')
if not os.path.isfile(mask):
print('Missing mask image.')
return None
_subject_data['mask'] = mask
return Bunch(**_subject_data)
# maybe data_dir already contains the data ?
data = _glob_fiac_data()
if data is not None:
return data
# No. Download the data
print('Data absent, downloading...')
url = 'http://nipy.sourceforge.net/data-packages/nipy-data-0.2.tar.gz'
archive_path = os.path.join(data_dir, os.path.basename(url))
_fetch_file(url, data_dir)
try:
_uncompress_file(archive_path)
except:
print('Archive corrupted, trying to download it again.')
return fetch_fiac_first_level(data_dir=data_dir)
return _glob_fiac_data()
def fetch_fiac_first_level(data_dir=None, verbose=1):
""" Download a first-level fiac fMRI dataset (2 sessions)
Parameters
----------
data_dir: string
directory where data should be downloaded and unpacked.
"""
data_dir = _get_dataset_dir('fiac_nistats', data_dir=data_dir,
verbose=verbose)
def _glob_fiac_data():
"""glob data from subject_dir."""
_subject_data = {}
subject_dir = os.path.join(data_dir, 'nipy-data-0.2/data/fiac/fiac0')
for session in [1, 2]:
# glob func data for session
session_func = os.path.join(subject_dir, 'run%i.nii.gz' % session)
if not os.path.isfile(session_func):
print('Missing functional scan for session %i.' % session)
return None
_subject_data['func%i' % session] = session_func
# glob design matrix .npz file
sess_dmtx = os.path.join(subject_dir, 'run%i_design.npz' % session)
if not os.path.isfile(sess_dmtx):
print('Missing session file: %s' % sess_dmtx)
return None
_subject_data['design_matrix%i' % session] = sess_dmtx
# glob for mask data
mask = os.path.join(subject_dir, 'mask.nii.gz')
if not os.path.isfile(mask):
print('Missing mask image.')
return None
_subject_data['mask'] = mask
return Bunch(**_subject_data)
# maybe data_dir already contains the data ?
data = _glob_fiac_data()
if data is not None:
return data
# No. Download the data
print('Data absent, downloading...')
url = 'http://nipy.sourceforge.net/data-packages/nipy-data-0.2.tar.gz'
archive_path = os.path.join(data_dir, os.path.basename(url))
_fetch_file(url, data_dir)
try:
_uncompress_file(archive_path)
except:
print('Archive corrupted, trying to download it again.')
return fetch_fiac_first_level(data_dir=data_dir)
return _glob_fiac_data()
def _download_spm_auditory_data(data_dir, subject_dir, subject_id):
print("Data absent, downloading...")
url = ("http://www.fil.ion.ucl.ac.uk/spm/download/data/MoAEpilot/"
"MoAEpilot.zip")
archive_path = os.path.join(subject_dir, os.path.basename(url))
_fetch_file(url, subject_dir)
try:
_uncompress_file(archive_path)
except:
print("Archive corrupted, trying to download it again.")
return fetch_spm_auditory(data_dir=data_dir, data_name="",
subject_id=subject_id)
def fetch_language_localizer_demo_dataset(data_dir=None, verbose=1):
"""Download language localizer demo dataset.
Parameters
----------
data_dir: string, optional
Path to store the downloaded dataset. if None employ nilearn
datasets default download directory.
verbose: int, optional
verbosity level (0 means no message).
Returns
-------
data_dir: string
Path to downloaded dataset
downloaded_files: list of string
Absolute paths of downloaded files on disk
"""
url = 'https://osf.io/nh987/download'
main_folder = 'fMRI-language-localizer-demo-dataset'
data_dir = _get_dataset_dir(main_folder,
data_dir=data_dir,
verbose=verbose)
# The files_spec needed for _fetch_files
files_spec = [(main_folder + '.zip', url, {'move': main_folder + '.zip'})]
# Only download if directory is empty
# Directory will have been created by the call to _get_dataset_dir above
if not os.listdir(data_dir):
downloaded_files = _fetch_files(data_dir,
files_spec,
resume=True,
verbose=verbose)
_uncompress_file(downloaded_files[0])
file_list = [
os.path.join(path, f) for path, dirs, files in os.walk(data_dir)
for f in files
]
return data_dir, sorted(file_list)
def _download_data_spm_multimodal(data_dir, subject_dir, subject_id):
print('Data absent, downloading...')
urls = [
# fmri
('http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/'
'multimodal_fmri.zip'),
# structural
('http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/'
'multimodal_smri.zip')
]
for url in urls:
archive_path = os.path.join(subject_dir, os.path.basename(url))
_fetch_file(url, subject_dir)
try:
_uncompress_file(archive_path)
except: # noqa:E722
print('Archive corrupted, trying to download it again.')
return fetch_spm_multimodal_fmri(data_dir=data_dir,
data_name='',
subject_id=subject_id)
return _glob_spm_multimodal_fmri_data(subject_dir)
def _download_data_spm_multimodal(data_dir, subject_dir, subject_id):
print("Data absent, downloading...")
urls = [
# fmri
("http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/"
"multimodal_fmri.zip"),
# structural
("http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/"
"multimodal_smri.zip")
]
for url in urls:
archive_path = os.path.join(subject_dir, os.path.basename(url))
_fetch_file(url, subject_dir)
try:
_uncompress_file(archive_path)
except:
print("Archive corrupted, trying to download it again.")
return fetch_spm_multimodal_fmri(data_dir=data_dir,
data_name="",
subject_id=subject_id)
return _glob_spm_multimodal_fmri_data(subject_dir)
def fetch_spm_auditory(data_dir=None,
data_name='spm_auditory',
subject_id="sub001",
verbose=1):
"""Function to fetch SPM auditory single-subject data.
Parameters
----------
data_dir: string
Path of the data directory. Used to force data storage in a specified
location. If the data is already present there, then will simply
glob it.
Returns
-------
data: sklearn.datasets.base.Bunch
Dictionary-like object, the interest attributes are:
- 'func': string list. Paths to functional images
- 'anat': string list. Path to anat image
References
----------
:download:
http://www.fil.ion.ucl.ac.uk/spm/data/auditory/
"""
data_dir = _get_dataset_dir(data_name, data_dir=data_dir, verbose=verbose)
subject_dir = os.path.join(data_dir, subject_id)
def _glob_spm_auditory_data():
"""glob data from subject_dir.
"""
if not os.path.exists(subject_dir):
return None
subject_data = {}
for file_name in SPM_AUDITORY_DATA_FILES:
file_path = os.path.join(subject_dir, file_name)
if os.path.exists(file_path):
subject_data[file_name] = file_path
else:
print("%s missing from filelist!" % file_name)
return None
_subject_data = {}
_subject_data["func"] = sorted([
subject_data[x] for x in subject_data.keys()
if re.match("^fM00223_0\d\d\.img$", os.path.basename(x))
])
# volumes for this dataset of shape (64, 64, 64, 1); let's fix this
for x in _subject_data["func"]:
vol = nibabel.load(x)
if len(vol.shape) == 4:
vol = nibabel.Nifti1Image(vol.get_data()[:, :, :, 0],
vol.get_affine())
nibabel.save(vol, x)
_subject_data["anat"] = [
subject_data[x] for x in subject_data.keys()
if re.match("^sM00223_002\.img$", os.path.basename(x))
][0]
# ... same thing for anat
vol = nibabel.load(_subject_data["anat"])
if len(vol.shape) == 4:
vol = nibabel.Nifti1Image(vol.get_data()[:, :, :, 0],
vol.get_affine())
nibabel.save(vol, _subject_data["anat"])
return Bunch(**_subject_data)
# maybe data_dir already contains the data ?
data = _glob_spm_auditory_data()
if data is not None:
return data
# No. Download the data
print("Data absent, downloading...")
url = ("http://www.fil.ion.ucl.ac.uk/spm/download/data/MoAEpilot/"
"MoAEpilot.zip")
archive_path = os.path.join(subject_dir, os.path.basename(url))
_fetch_file(url, subject_dir)
try:
_uncompress_file(archive_path)
except:
print("Archive corrupted, trying to download it again.")
return fetch_spm_auditory(data_dir=data_dir,
data_name="",
subject_id=subject_id)
return _glob_spm_auditory_data()
def fetch_spm_multimodal_fmri(data_dir=None,
data_name="spm_multimodal_fmri",
subject_id="sub001",
verbose=1):
"""Fetcher for Multi-modal Face Dataset.
Parameters
----------
data_dir: string
path of the data directory. Used to force data storage in a specified
location. If the data is already present there, then will simply
glob it.
Returns
-------
data: sklearn.datasets.base.Bunch
Dictionary-like object, the interest attributes are:
- 'func1': string list. Paths to functional images for session 1
- 'func2': string list. Paths to functional images for session 2
- 'trials_ses1': string list. Path to onsets file for session 1
- 'trials_ses2': string list. Path to onsets file for session 2
- 'anat': string. Path to anat file
References
----------
:download:
http://www.fil.ion.ucl.ac.uk/spm/data/mmfaces/
"""
data_dir = _get_dataset_dir(data_name, data_dir=data_dir, verbose=verbose)
subject_dir = os.path.join(data_dir, subject_id)
def _glob_spm_multimodal_fmri_data():
"""glob data from subject_dir."""
_subject_data = {'slice_order': 'descending'}
for session in range(2):
# glob func data for session s + 1
session_func = sorted(
glob.glob(
os.path.join(subject_dir,
("fMRI/Session%i/fMETHODS-000%i-*-01.img" %
(session + 1, session + 5)))))
if len(session_func) < 390:
print("Missing %i functional scans for session %i." %
(390 - len(session_func), session))
return None
_subject_data['func%i' % (session + 1)] = session_func
# glob trials .mat file
sess_trials = os.path.join(subject_dir,
"fMRI/trials_ses%i.mat" % (session + 1))
if not os.path.isfile(sess_trials):
print("Missing session file: %s" % sess_trials)
return None
_subject_data['trials_ses%i' % (session + 1)] = sess_trials
# glob for anat data
anat = os.path.join(subject_dir, "sMRI/smri.img")
if not os.path.isfile(anat):
print("Missing structural image.")
return None
_subject_data["anat"] = anat
return Bunch(**_subject_data)
# maybe data_dir already contains the data ?
data = _glob_spm_multimodal_fmri_data()
if data is not None:
return data
# No. Download the data
print("Data absent, downloading...")
urls = [
# fmri
("http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/"
"multimodal_fmri.zip"),
# structural
("http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/"
"multimodal_smri.zip")
]
for url in urls:
archive_path = os.path.join(subject_dir, os.path.basename(url))
_fetch_file(url, subject_dir)
try:
_uncompress_file(archive_path)
except:
print("Archive corrupted, trying to download it again.")
return fetch_spm_multimodal_fmri(data_dir=data_dir,
data_name="",
subject_id=subject_id)
return _glob_spm_multimodal_fmri_data()
def fetch_fsl_feeds(data_dir=None, data_name="fsl_feeds", verbose=1):
"""Function to fetch FSL FEEDS dataset (single-subject)
Parameters
----------
data_dir: string
path of the data directory. Used to force data storage in a specified
location. If the data is already present there, then will simply
glob it.
Returns
-------
data: sklearn.datasets.base.Bunch
Dictionary-like object, the interest attributes are:
- 'func': string list. Paths to functional images
- 'anat': string list. Path to anat image
"""
data_dir = _get_dataset_dir(data_name, data_dir=data_dir,
verbose=verbose)
def _glob_fsl_feeds_data(subject_dir):
"""glob data from subject_dir.
"""
if not os.path.exists(subject_dir):
return None
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)
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
return Bunch(data_dir=data_dir,
func=os.path.join(subject_dir, "fmri.nii.gz"),
anat=os.path.join(
subject_dir, "structural_brain.nii.gz"))
# maybe data_dir already contents the data ?
data = _glob_fsl_feeds_data(data_dir)
if not data is None:
return data
# download the data
print("Data absent, downloading...")
url = ("http://fsl.fmrib.ox.ac.uk/fsldownloads/oldversions/"
"fsl-4.1.0-feeds.tar.gz")
archive_path = os.path.join(data_dir, os.path.basename(url))
_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_dir=data_dir, data_name="")
return _glob_fsl_feeds_data(data_dir)
def fetch_spm_auditory(data_dir=None, data_name='spm_auditory',
subject_id="sub001", verbose=1):
"""Function to fetch SPM auditory single-subject data.
Parameters
----------
data_dir: string
path of the data directory. Used to force data storage in a specified
location. If the data is already present there, then will simply
glob it.
Returns
-------
data: sklearn.datasets.base.Bunch
Dictionary-like object, the interest attributes are:
- 'func': string list. Paths to functional images
- 'anat': string list. Path to anat image
References
----------
:download:
http://www.fil.ion.ucl.ac.uk/spm/data/auditory/
"""
data_dir = _get_dataset_dir(data_name, data_dir=data_dir,
verbose=verbose)
subject_dir = os.path.join(data_dir, subject_id)
def _glob_spm_auditory_data():
"""glob data from subject_dir.
"""
if not os.path.exists(subject_dir):
return None
subject_data = {}
for file_name in SPM_AUDITORY_DATA_FILES:
file_path = os.path.join(subject_dir, file_name)
if os.path.exists(file_path):
subject_data[file_name] = file_path
else:
print("%s missing from filelist!" % file_name)
return None
_subject_data = {}
_subject_data["func"] = sorted([subject_data[x]
for x in subject_data.keys()
if re.match("^fM00223_0\d\d\.img$",
os.path.basename(x))])
# volumes for this dataset of shape (64, 64, 64, 1); let's fix this
for x in _subject_data["func"]:
vol = nibabel.load(x)
if len(vol.shape) == 4:
vol = nibabel.Nifti1Image(vol.get_data()[:, :, :, 0],
vol.get_affine())
nibabel.save(vol, x)
_subject_data["anat"] = [subject_data[x] for x in subject_data.keys()
if re.match("^sM00223_002\.img$",
os.path.basename(x))][0]
# ... same thing for anat
vol = nibabel.load(_subject_data["anat"])
if len(vol.shape) == 4:
vol = nibabel.Nifti1Image(vol.get_data()[:, :, :, 0],
vol.get_affine())
nibabel.save(vol, _subject_data["anat"])
return Bunch(**_subject_data)
# maybe data_dir already contains the data ?
data = _glob_spm_auditory_data()
if not data is None:
return data
# No. Download the data
print("Data absent, downloading...")
url = ("http://www.fil.ion.ucl.ac.uk/spm/download/data/MoAEpilot/"
"MoAEpilot.zip")
archive_path = os.path.join(subject_dir, os.path.basename(url))
_fetch_file(url, subject_dir)
try:
_uncompress_file(archive_path)
except:
print("Archive corrupted, trying to download it again.")
return fetch_spm_auditory(data_dir=data_dir, data_name="",
subject_id=subject_id)
return _glob_spm_auditory_data()
def fetch_spm_multimodal_fmri(data_dir=None, data_name="spm_multimodal_fmri",
subject_id="sub001", verbose=1):
"""Fetcher for Multi-modal Face Dataset.
Parameters
----------
data_dir: string
path of the data directory. Used to force data storage in a specified
location. If the data is already present there, then will simply
glob it.
Returns
-------
data: sklearn.datasets.base.Bunch
Dictionary-like object, the interest attributes are:
- 'func1': string list. Paths to functional images for session 1
- 'func2': string list. Paths to functional images for session 2
- 'trials_ses1': string list. Path to onsets file for session 1
- 'trials_ses2': string list. Path to onsets file for session 2
- 'anat': string. Path to anat file
References
----------
:download:
http://www.fil.ion.ucl.ac.uk/spm/data/mmfaces/
"""
data_dir = _get_dataset_dir(data_name, data_dir=data_dir,
verbose=verbose)
subject_dir = os.path.join(data_dir, subject_id)
def _glob_spm_multimodal_fmri_data():
"""glob data from subject_dir."""
_subject_data = {'slice_order': 'descending'}
for s in range(2):
# glob func data for session s + 1
session_func = sorted(glob.glob(
os.path.join(
subject_dir,
("fMRI/Session%i/fMETHODS-000%i-*-01.img" % (
s + 1, s + 5)))))
if len(session_func) < 390:
print "Missing %i functional scans for session %i." % (
390 - len(session_func), s)
return None
else:
_subject_data['func%i' % (s + 1)] = session_func
# glob trials .mat file
sess_trials = os.path.join(
subject_dir,
"fMRI/trials_ses%i.mat" % (s + 1))
if not os.path.isfile(sess_trials):
print "Missing session file: %s" % sess_trials
return None
else:
_subject_data['trials_ses%i' % (s + 1)] = sess_trials
# glob for anat data
anat = os.path.join(subject_dir, "sMRI/smri.img")
if not os.path.isfile(anat):
print "Missing structural image."
return None
else:
_subject_data["anat"] = anat
return Bunch(**_subject_data)
# maybe data_dir already contains the data ?
data = _glob_spm_multimodal_fmri_data()
if not data is None:
return data
# No. Download the data
print("Data absent, downloading...")
urls = [
# fmri
("http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/"
"multimodal_fmri.zip"),
# structural
("http://www.fil.ion.ucl.ac.uk/spm/download/data/mmfaces/"
"multimodal_smri.zip")
]
for url in urls:
archive_path = os.path.join(subject_dir, os.path.basename(url))
_fetch_file(url, subject_dir)
try:
_uncompress_file(archive_path)
except:
print("Archive corrupted, trying to download it again.")
return fetch_spm_multimodal_fmri_data(data_dir=data_dir,
data_name="",
subject_id=subject_id)
return _glob_spm_multimodal_fmri_data()
def fetch_fsl_feeds(data_dir=None, data_name="fsl_feeds", verbose=1):
"""Function to fetch FSL FEEDS dataset (single-subject)
Parameters
----------
data_dir: string
path of the data directory. Used to force data storage in a specified
location. If the data is already present there, then will simply
glob it.
Returns
-------
data: sklearn.datasets.base.Bunch
Dictionary-like object, the interest attributes are:
- 'func': string list. Paths to functional images
- 'anat': string list. Path to anat image
"""
data_dir = _get_dataset_dir(data_name, data_dir=data_dir,
verbose=verbose)
def _glob_fsl_feeds_data(subject_dir):
"""glob data from subject_dir.
"""
if not os.path.exists(subject_dir):
return None
subject_data = {}
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.gz"),
"anat": os.path.join(subject_dir,
"structural_brain.nii.gz")
}
return Bunch(**_subject_data)
# maybe data_dir already contents the data ?
data = _glob_fsl_feeds_data(data_dir)
if not data is None:
return data
# download the data
print("Data absent, downloading...")
url = ("http://fsl.fmrib.ox.ac.uk/fsldownloads/oldversions/"
"fsl-4.1.0-feeds.tar.gz")
archive_path = os.path.join(data_dir, os.path.basename(url))
_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_dir=data_dir, data_name="")
return _glob_fsl_feeds_data(data_dir)
