def test_get_data_dir(tmpdir):
# testing folder creation under different environments, enforcing
# a custom clean install
os.environ.pop('NILEARN_DATA', None)
os.environ.pop('NILEARN_SHARED_DATA', None)
expected_base_dir = os.path.expanduser('~/modl_data')
data_dir = get_data_dirs()[0]
assert_equal(data_dir, expected_base_dir)
expected_base_dir = os.path.join(tmpdir, 'modl_data')
os.environ['MODL_DATA'] = expected_base_dir
data_dir = get_data_dirs()[0]
assert_equal(data_dir, expected_base_dir)
expected_base_dir = os.path.join(tmpdir, 'mdoln_shared_data')
os.environ['MODL_SHARED_DATA'] = expected_base_dir
data_dir = get_data_dirs()[0]
assert_equal(data_dir, expected_base_dir)
expected_base_dir = os.path.join(tmpdir, 'modl_data')
os.environ.pop('MODL_DATA', None)
os.environ.pop('MODL_SHARED_DATA', None)
data_dir = get_data_dirs(expected_base_dir)[0]
assert_equal(data_dir, expected_base_dir)
def get_hcp_data(raw=False, data_dir=None):
data_dir = get_data_dirs(data_dir)[0]
if not os.path.exists(join(data_dir, 'HCP_extra')):
raise ValueError(
'Please download HCP_extra folder using make download-hcp_extra'
' first.')
if raw:
mask = join(data_dir, 'HCP_extra/mask_img.nii.gz')
try:
mapping = json.load(
open(join(data_dir, 'HCP_unmasked/mapping.json'), 'r'))
except FileNotFoundError:
raise IOError('Please unmask the data using hcp_prepare.py first.')
func_filenames = sorted(list(mapping.values()))
else:
hcp_dataset = fetch_hcp_rest(data_dir=data_dir, n_subjects=500)
mask = hcp_dataset.mask
# list of 4D nifti files for each subject
func_filenames = hcp_dataset.func
# Flatten it
func_filenames = [
record for subject in func_filenames for record in subject
]
# print basic information on the dataset
print('First functional nifti image (4D) is at: %s' %
hcp_dataset.func[0]) # 4D data
return mask, func_filenames
def prepare_hcp_raw_data(data_dir=None,
dest_data_dir='HCP_unmasked',
n_jobs=1,
smoothing_fwhm=3,
n_subject=500):
data_dir = get_data_dirs(data_dir)[0]
source_dir = join(data_dir, 'HCP')
dest_data_dir = join(data_dir, dest_data_dir)
dataset = fetch_hcp_rest(data_dir=data_dir, n_subjects=n_subject)
imgs = dataset.func
imgs = [img for subject_imgs in imgs for img in subject_imgs]
try:
os.mkdir(dest_data_dir)
except OSError:
raise ValueError('%s already exist,'
'please delete manually before proceeding' %
dest_data_dir)
mask = join(data_dir, 'HCP_extra', 'mask_img.nii.gz')
masker = NiftiMasker(mask_img=mask,
smoothing_fwhm=smoothing_fwhm,
standardize=True).fit()
Parallel(n_jobs=n_jobs)(
delayed(_single_mask)(masker, img, dest_data_dir, source_dir)
for img in imgs)
_gather(dest_data_dir)
def load_image(source, scale=1, gray=False, memory=Memory(cachedir=None)):
data_dir = get_data_dirs()[0]
if source == 'face':
image = face(gray=gray)
image = image.astype(np.float32) / 255
if image.ndim == 2:
image = image[..., np.newaxis]
if scale != 1:
image = memory.cache(rescale)(image, scale=scale)
return image
elif source == 'lisboa':
image = imread(join(data_dir, 'images', 'lisboa.jpg'), as_grey=gray)
image = image.astype(np.float32) / 255
if image.ndim == 2:
image = image[..., np.newaxis]
if scale != 1:
image = memory.cache(rescale)(image, scale=scale)
return image
elif source == 'aviris':
image = open_image(
join(
data_dir, 'aviris', 'f100826t01p00r05rdn_b/'
'f100826t01p00r05rdn_b_sc01_ort_img.hdr'))
image = np.array(image.open_memmap(), dtype=np.float32)
good_bands = list(range(image.shape[2]))
good_bands.remove(110)
image = image[:, :, good_bands]
indices = image == -50
image[indices] = -1
image[~indices] -= np.min(image[~indices])
image[~indices] /= np.max(image[~indices])
return image
else:
raise ValueError('Data source is not known')
def fetch_hcp_task(data_dir=None, n_subjects=500):
"""Nilearn like fetcher"""
data_dir = get_data_dirs(data_dir)[0]
source_dir = join(data_dir, 'HCP')
extra_dir = join(data_dir, 'HCP_extra')
mask = join(extra_dir, 'mask_img.nii.gz')
func = []
meta = []
contrasts = []
ids = []
list_dir = sorted(glob.glob(join(source_dir, '*/*/MNINonLinear/Results')))
for dirpath in list_dir[:n_subjects]:
dirpath_split = dirpath.split(os.sep)
subject_id = dirpath_split[-3]
serie_id = dirpath_split[-4]
subject_id = int(subject_id)
ids.append(subject_id)
kwargs = {'subject_id': subject_id, 'serie_id': serie_id}
subject_func = []
subject_contrasts = []
for i, task_id in enumerate(task_ids):
task = tasks[task_id]
task_name = task[0]
contrast_idx = task[1]
contrast = task[2]
this_func = join(
dirpath, "tfMRI_%s/tfMRI_%s_hp200_s4_"
"level2vol.feat/cope%i.feat/"
"stats/zstat1.nii.gz" % (task_name, task_name, contrast_idx))
if os.path.exists(this_func):
subject_contrasts.append(contrast)
subject_func.append(this_func)
meta.append(kwargs)
contrasts.append(subject_contrasts)
func.append(subject_func)
results = {
'func': func,
'contrast': contrasts,
'meta': meta,
'mask': mask,
'description': "Human connectome project",
'contrasts_description': contrasts_description
}
return Bunch(**results)
def fetch_hcp_behavioral(data_dir=None, n_subjects=500):
import pandas as pd
data_dir = get_data_dirs(data_dir)[0]
source_dir = join(data_dir, 'HCP')
df = pd.read_csv(join(source_dir, 'restricted_scores.csv'))
list_dir = sorted(glob.glob(join(source_dir, '*/*/MNINonLinear/Results')))
subjects = []
for dirpath in list_dir[:n_subjects]:
dirpath_split = dirpath.split(os.sep)
subject_id = int(dirpath_split[-3])
subjects.append(subject_id)
indices = [subject in subjects for subject in df['Subject']]
df = df.loc[indices]
return df
def load_movielens(version):
data_home = get_data_dirs()[0]
if version == "100k":
path = os.path.join(data_home, "movielens100k", "movielens100k.pkl")
elif version == "1m":
path = os.path.join(data_home, "movielens1m", "movielens1m.pkl")
elif version == "10m":
path = os.path.join(data_home, "movielens10m", "movielens10m.pkl")
else:
raise ValueError("Invalid version of movielens.")
# FIXME: make downloader
if not os.path.exists(path):
raise ValueError("Dowload dataset using 'make download-movielens%s' at"
" project root." % version)
X = skjoblib.load(path)
return X
def fetch_hcp_rest(data_dir=None, n_subjects=500):
"""Nilearn like fetcher"""
data_dir = get_data_dirs(data_dir)[0]
source_dir = join(data_dir, 'HCP')
extra_dir = join(data_dir, 'HCP_extra')
mask = join(extra_dir, 'mask_img.nii.gz')
func = []
meta = []
ids = []
list_dir = sorted(glob.glob(join(source_dir, '*/*/MNINonLinear/Results')))
for dirpath in list_dir[:n_subjects]:
dirpath_split = dirpath.split(os.sep)
subject_id = dirpath_split[-3]
serie_id = dirpath_split[-4]
subject_id = int(subject_id)
ids.append(subject_id)
kwargs = {'subject_id': subject_id, 'serie_id': serie_id}
meta.append(kwargs)
subject_func = []
for filename in os.listdir(dirpath):
name, ext = os.path.splitext(filename)
if name in ('rfMRI_REST1_RL', 'rfMRI_REST1_LR', 'rfMRI_REST2_RL',
'rfMRI_REST2_LR'):
filename = join(dirpath, filename, filename + '.nii.gz')
subject_func.append(filename)
func.append(subject_func)
results = {
'func': func,
'meta': meta,
'mask': mask,
'description': "'Human connectome project"
}
return Bunch(**results)
def load_rest_func(dataset='adhd',
n_subjects=40, test_size=0.1, raw=False, random_state=None):
data_dir = get_data_dirs()[0]
if dataset == 'adhd':
adhd_dataset = datasets.fetch_adhd(n_subjects=n_subjects)
mask = join(data_dir, 'ADHD_mask', 'mask_img.nii.gz')
data = adhd_dataset.func # list of 4D nifti files for each subject
elif dataset == 'hcp':
if not os.path.exists(join(data_dir, 'HCP_extra')):
raise ValueError(
'Please download HCP_extra folder using make '
'download-hcp_extra '
' first.')
if raw:
mask = join(data_dir, 'HCP_extra/mask_img.nii.gz')
try:
mapping = json.load(
open(join(data_dir, 'HCP_unmasked/mapping.json'), 'r'))
except FileNotFoundError:
raise ValueError(
'Please unmask the data using hcp_prepare.py first.')
data = sorted(list(mapping.values()))
else:
hcp_dataset = fetch_hcp_rest(data_dir=data_dir,
n_subjects=n_subjects)
mask = hcp_dataset.mask
# list of 4D nifti files for each subject
data = hcp_dataset.func
# Flatten it
data = [(record for record in subject) for subject in data]
else:
raise NotImplementedError
train_data, test_data = train_test_split(data,
test_size=test_size,
random_state=random_state)
return train_data, test_data, mask