def test_timseries_extraction_extract(conf):
"""Test timeseries extraction and save methods of the TimeseriesExtraction class."""
dir_path_tmp = tempfile.TemporaryDirectory()
dir_path = dir_path_tmp.name
func_file = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
img_data = np.random.rand(50, 50, 50, 20)
img = nib.Nifti1Image(img_data, np.eye(4))
img.to_filename(func_file.name)
# Create a temp parcel file
parcels_tmp = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
parcels = np.zeros((50, 50, 50))
parcels[10:20, 0, 0], parcels[0, 10:20, 0], parcels[0, 0, 10:20] = 1, 2, 3
nib.Nifti1Image(parcels, np.eye(4)).to_filename(parcels_tmp.name)
net_parcels_map_nifti_file = parcels_tmp.name
# Create empty mask file
mask_tmp = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
mask = np.zeros((50, 50, 50))
nib.Nifti1Image(parcels, np.eye(4)).to_filename(mask_tmp.name)
mask = mask_tmp.name
if conf:
conf_file = tempfile.NamedTemporaryFile(mode='w+', suffix='.tsv')
conf_mat = np.random.rand(20)
conf_df = pd.DataFrame({
'Conf1': conf_mat,
"Conf2": [np.nan] * len(conf_mat)
})
conf_df.to_csv(conf_file.name, sep='\t', index=False)
conf = conf_file.name
smooth = 1
network = 'Default'
ID = '002'
smooth = 2
hpass = 100
coords = [[10] * 3, [15] * 3, [20] * 3]
node_size = 2
extract_strategy = 'mean'
roi, labels, atlas, uatlas = [None] * 4
te = TimeseriesExtraction(net_parcels_nii_path=net_parcels_map_nifti_file,
node_size=node_size,
conf=conf,
func_file=func_file.name,
roi=roi,
dir_path=dir_path,
ID=ID,
network=network,
smooth=smooth,
hpass=hpass,
mask=mask,
extract_strategy=extract_strategy)
te.prepare_inputs()
# Test parc extraction
te.extract_ts_parc()
assert np.shape(te.ts_within_nodes) == (np.shape(img_data)[-1],
len(np.unique(parcels)) - 1)
# Test save and clean up
te._mask_path = te._mask_img
te.save_and_cleanup()
assert '_parcel_masker' not in te.__dict__.keys()
func_file.close()
parcels_tmp.close()
mask_tmp.close()
if conf:
conf_file.close()
def test_timseries_extraction_prepare_inputs(conf, hpass, mask, func_file,
dim):
""" Test preparing inputs method of the TimeseriesExtraction class."""
base_dir = str(Path(__file__).parent / "examples")
net_parcels_map_nifti_file = f"{base_dir}/miscellaneous/002_parcels_Default.nii.gz"
dir_path = f"{base_dir}/BIDS/sub-25659/ses-1/func"
if func_file:
func_tmp = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
if dim == 4:
img_data = np.random.rand(50, 50, 50, 20)
else:
img_data = np.random.rand(50, 50, 50)
img = nib.Nifti1Image(img_data, np.eye(4))
img.to_filename(func_tmp.name)
func_file = func_tmp.name
else:
func_file = "missing/file"
if mask:
mask_tmp = tempfile.NamedTemporaryFile(mode='w+', suffix='.nii.gz')
mask_data = np.zeros(np.shape(img_data))
mask_img = nib.Nifti1Image(mask_data, np.eye(4))
mask_img.to_filename(mask_tmp.name)
mask = mask_tmp.name
if conf:
conf_mat = np.random.rand(20)
conf_tmp = tempfile.NamedTemporaryFile(mode='w+', suffix='.tsv')
conf_df = pd.DataFrame({'Column1': conf_mat})
conf_df.to_csv(conf_tmp.name)
conf = conf_tmp.name
else:
conf = "missing/file"
smooth = 1
network = 'Default'
ID = '002'
smooth = 2
coords = [[10] * 3, [15] * 3, [20] * 3]
node_size = 8
extract_strategy = 'zscore'
roi, labels, atlas, uatlas = [None] * 4
te = TimeseriesExtraction(net_parcels_nii_path=net_parcels_map_nifti_file,
node_size=node_size,
conf=conf,
func_file=func_file,
roi=roi,
dir_path=dir_path,
ID=ID,
network=network,
smooth=smooth,
hpass=hpass,
mask=mask,
extract_strategy=extract_strategy)
te.prepare_inputs()
assert np.shape(te._func_img) == np.shape(img_data)
if hpass and hpass > 0:
assert te.hpass == hpass
assert te._detrending is False
else:
assert te.hpass is None
assert te._detrending is True
if hpass and dim == 4:
assert te._t_r == img.header.get_zooms()[-1]
else:
assert te._t_r == None
if mask:
assert np.shape(te._mask_img) == np.shape(img_data)
if func_file:
func_tmp.close()
if conf:
conf_tmp.close()
if mask:
mask_tmp.close()