def fetch_fsl_feeds_data(data_dir, redownload=False):
'''
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)
return _glob_fsl_feeds_data(subject_dir)
def fetch_spm_auditory_data(data_dir, redownload=False):
'''
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)
return _glob_spm_auditory_data(subject_dir)
def fetch_haxby_subject_data(data_dir, subject_id, url, redownload=False):
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)
return subject_id, _glob_haxby_subject_data(subject_dir)
if __name__ == '__main__':
if len(sys.argv) < 4:
print ("Usage: python %s <data_root_dir> "
"<preproc_root_dir> <glm_root_dir>" % sys.argv[0])
print ("Example:\r\npython %s ~/datasets/raw"
" ~/datasets/preproc ~/datasets/glm") % sys.argv[0]
sys.exit(1)
root_dir, preproc_dir, glm_dir = sys.argv[1:]
# download data
data_dir = fetch_openfmri(FULL_ID, root_dir)
# condition_key file in tarball is incomplete
_fetch_file('https://openfmri.org/system/files/condition_key.txt',
os.path.join(data_dir, SHORT_ID, 'models', MODEL_ID))
# this dataset does not contain contrast definitions
contrasts_file = '%s_task_contrasts.txt' % SHORT_ID
assert os.path.isfile(contrasts_file), \
"No contrasts file: %s" % contrasts_file
dest = os.path.join(data_dir, SHORT_ID,
'models', MODEL_ID, 'task_contrasts.txt')
shutil.copy(contrasts_file, dest)
# apply SPM preprocessing
apply_preproc(SHORT_ID, data_dir, preproc_dir, ignore_list,
dataset_description=DESCRIPTION)
# prepare GLM (get data and design)
