def test_fetch_cobre(tmp_path, request_mocker):
metadata, ids = _cobre_metadata()
request_mocker.url_mapping["*phenotypic_data.tsv.gz"] = _cobre_data(ids)
request_mocker.url_mapping["*articles/4197885"] = metadata
# All subjects
cobre_data = check_deprecation(func.fetch_cobre,
"'fetch_cobre' has been deprecated")(
n_subjects=None, data_dir=str(tmp_path))
phenotypic_names = [
'func', 'confounds', 'phenotypic', 'description', 'desc_con',
'desc_phenotypic'
]
# test length of functional filenames to max 146
assert len(cobre_data.func) == 146
# test length of corresponding confounds files of same length to max 146
assert len(cobre_data.confounds) == 146
# test return type variables
assert sorted(cobre_data) == sorted(phenotypic_names)
# test functional filenames in a list
assert isinstance(cobre_data.func, list)
# test confounds files in a list
assert isinstance(cobre_data.confounds, list)
assert isinstance(cobre_data.func[0], str)
# returned phenotypic data will be an array
assert isinstance(cobre_data.phenotypic, np.recarray)
# Fetch only 30 subjects
data_30_subjects = func.fetch_cobre(n_subjects=30, data_dir=str(tmp_path))
assert len(data_30_subjects.func) == 30
assert len(data_30_subjects.confounds) == 30
# Test more than maximum subjects
test_150_subjects = func.fetch_cobre(n_subjects=150,
data_dir=str(tmp_path))
assert len(test_150_subjects.func) == 146
def test_fetch_cobre(tmp_path, request_mocker):
ids_n = [
40000, 40001, 40002, 40003, 40004, 40005, 40006, 40007, 40008, 40009,
40010, 40011, 40012, 40013, 40014, 40015, 40016, 40017, 40018, 40019,
40020, 40021, 40022, 40023, 40024, 40025, 40026, 40027, 40028, 40029,
40030, 40031, 40032, 40033, 40034, 40035, 40036, 40037, 40038, 40039,
40040, 40041, 40042, 40043, 40044, 40045, 40046, 40047, 40048, 40049,
40050, 40051, 40052, 40053, 40054, 40055, 40056, 40057, 40058, 40059,
40060, 40061, 40062, 40063, 40064, 40065, 40066, 40067, 40068, 40069,
40071, 40072, 40073, 40074, 40075, 40076, 40077, 40078, 40079, 40080,
40081, 40082, 40084, 40085, 40086, 40087, 40088, 40089, 40090, 40091,
40092, 40093, 40094, 40095, 40096, 40097, 40098, 40099, 40100, 40101,
40102, 40103, 40104, 40105, 40106, 40107, 40108, 40109, 40110, 40111,
40112, 40113, 40114, 40115, 40116, 40117, 40118, 40119, 40120, 40121,
40122, 40123, 40124, 40125, 40126, 40127, 40128, 40129, 40130, 40131,
40132, 40133, 40134, 40135, 40136, 40137, 40138, 40139, 40140, 40141,
40142, 40143, 40144, 40145, 40146, 40147
]
ids = np.asarray(ids_n, dtype='|U17')
current_age = np.ones(len(ids), dtype='<f8')
gender = np.ones(len(ids), dtype='<f8')
handedness = np.ones(len(ids), dtype='<f8')
subject_type = np.empty(len(ids), dtype="S10")
subject_type[0:74] = 'Control'
subject_type[74:146] = 'Patient'
diagnosis = np.ones(len(ids), dtype='<f8')
frames_ok = np.ones(len(ids), dtype='<f8')
fd = np.ones(len(ids), dtype='<f8')
fd_scrubbed = np.ones(len(ids), dtype='<f8')
csv = np.rec.array([
ids, current_age, gender, handedness, subject_type, diagnosis,
frames_ok, fd, fd_scrubbed
],
dtype=[('ID', '|U17'), ('Current Age', '<f8'),
('Gender', '<f8'), ('Handedness', '<f8'),
('Subject Type', '|U17'), ('Diagnosis', '<f8'),
('Frames OK', '<f8'), ('FD', '<f8'),
('FD Scrubbed', '<f8')])
# Create a dummy 'files'
cobre_dir = str(tmp_path / 'cobre')
os.mkdir(cobre_dir)
# Create the tsv
name_f = os.path.join(cobre_dir, 'phenotypic_data.tsv')
with open(name_f, 'wb') as f:
header = '# {0}\n'.format('\t'.join(csv.dtype.names))
f.write(header.encode())
np.savetxt(f, csv, delimiter='\t', fmt='%s')
# create an empty gz file
f_in = open(name_f)
name_f_gz = os.path.join(cobre_dir, 'phenotypic_data.tsv.gz')
f_out = gzip.open(name_f_gz, 'wb')
f_out.close()
f_in.close()
dummy = os.path.join(cobre_dir, '4197885')
dummy_data = []
for i in np.hstack(ids_n):
# Func file
f = 'fmri_00' + str(i) + '.nii.gz'
m = 'fmri_00' + str(i) + '.tsv.gz'
dummy_data.append({'download_url': 'whatever', 'name': f})
dummy_data.append({'download_url': 'whatever', 'name': m})
# Add the TSV file
dummy_data.append({
'download_url': 'whatever',
'name': 'phenotypic_data.tsv.gz'
})
# Add JSON files
dummy_data.append({
'download_url': 'whatever',
'name': 'keys_confounds.json'
})
dummy_data.append({
'download_url': 'whatever',
'name': 'keys_phenotypic_data.json'
})
dummy_data = {'files': dummy_data}
json.dump(dummy_data, open(dummy, 'w'))
local_url = "file://" + dummy
# All subjects
cobre_data = check_deprecation(func.fetch_cobre,
"'fetch_cobre' has been deprecated")(
n_subjects=None,
data_dir=str(tmp_path),
url=local_url)
phenotypic_names = [
'func', 'confounds', 'phenotypic', 'description', 'desc_con',
'desc_phenotypic'
]
# test length of functional filenames to max 146
assert len(cobre_data.func) == 146
# test length of corresponding confounds files of same length to max 146
assert len(cobre_data.confounds) == 146
# test return type variables
assert sorted(cobre_data) == sorted(phenotypic_names)
# test functional filenames in a list
assert isinstance(cobre_data.func, list)
# test confounds files in a list
assert isinstance(cobre_data.confounds, list)
assert isinstance(cobre_data.func[0], str)
# returned phenotypic data will be an array
assert isinstance(cobre_data.phenotypic, np.recarray)
# Fetch only 30 subjects
data_30_subjects = func.fetch_cobre(n_subjects=30,
url=local_url,
data_dir=str(tmp_path))
assert len(data_30_subjects.func) == 30
assert len(data_30_subjects.confounds) == 30
# Test more than maximum subjects
test_150_subjects = func.fetch_cobre(n_subjects=150,
url=local_url,
data_dir=str(tmp_path))
assert len(test_150_subjects.func) == 146
os.remove(dummy)