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('', 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 test_uncompress():
# Create dummy file
fd, temp = mkstemp()
os.close(fd)
# Create a zipfile
dtemp = mkdtemp()
ztemp = os.path.join(dtemp, 'test.zip')
with contextlib.closing(zipfile.ZipFile(ztemp, 'w')) as testzip:
testzip.write(temp)
datasets._uncompress_file(ztemp, verbose=0)
assert(os.path.exists(os.path.join(dtemp, temp)))
shutil.rmtree(dtemp)
dtemp = mkdtemp()
ztemp = os.path.join(dtemp, 'test.tar')
with contextlib.closing(tarfile.open(ztemp, 'w')) as tar:
tar.add(temp)
datasets._uncompress_file(ztemp, verbose=0)
assert(os.path.exists(os.path.join(dtemp, temp)))
shutil.rmtree(dtemp)
dtemp = mkdtemp()
ztemp = os.path.join(dtemp, 'test.gz')
f = gzip.open(ztemp, 'wb')
f.close()
datasets._uncompress_file(ztemp, verbose=0)
assert(os.path.exists(os.path.join(dtemp, temp)))
shutil.rmtree(dtemp)
os.remove(temp)
def test_uncompress():
# Create dummy file
fd, temp = mkstemp()
os.close(fd)
# Create a zipfile
dtemp = mkdtemp()
ztemp = os.path.join(dtemp, 'test.zip')
with contextlib.closing(zipfile.ZipFile(ztemp, 'w')) as testzip:
testzip.write(temp)
datasets._uncompress_file(ztemp, verbose=0)
assert (os.path.exists(os.path.join(dtemp, temp)))
shutil.rmtree(dtemp)
dtemp = mkdtemp()
ztemp = os.path.join(dtemp, 'test.tar')
with contextlib.closing(tarfile.open(ztemp, 'w')) as tar:
tar.add(temp)
datasets._uncompress_file(ztemp, verbose=0)
assert (os.path.exists(os.path.join(dtemp, temp)))
shutil.rmtree(dtemp)
dtemp = mkdtemp()
ztemp = os.path.join(dtemp, 'test.gz')
f = gzip.open(ztemp, 'wb')
f.close()
datasets._uncompress_file(ztemp, verbose=0)
assert (os.path.exists(os.path.join(dtemp, temp)))
shutil.rmtree(dtemp)
os.remove(temp)
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()