def test_get_conn_matrix(conn_model_in, n_features):
""" Test computing a functional connectivity matrix."""
network = 'Default'
ID = '002'
smooth = 2
coords = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
node_size = 8
extract_strategy = 'zscore'
roi = None
atlas = None
uatlas = None
labels = [1, 2, 3]
time_series = generate_signals(n_features=n_features,
n_confounds=5,
length=n_features,
same_variance=False)[0]
outs = get_conn_matrix(
time_series,
conn_model_in,
tempfile.TemporaryDirectory(),
node_size,
smooth,
False,
network,
ID,
roi,
False,
False,
True,
True,
atlas,
uatlas,
labels,
coords,
3,
False,
0,
extract_strategy,
)
conn_matrix_out, conn_model_out = outs[0], outs[1]
assert isinstance(conn_matrix_out, np.ndarray)
assert np.shape(conn_matrix_out) == np.shape(time_series)
assert conn_model_in == conn_model_out
if "corr" in conn_model_in:
assert (is_spd(conn_matrix_out, decimal=7))
d = np.sqrt(np.diag(np.diag(conn_matrix_out)))
assert_array_almost_equal(np.diag(conn_matrix_out),
np.ones((n_features)))
elif "partcorr" in conn_model_in:
prec = np.linalg.inv(conn_matrix_out)
d = np.sqrt(np.diag(np.diag(prec)))
assert_array_almost_equal(
d.dot(conn_matrix_out).dot(d), -prec + 2 * np.diag(np.diag(prec)))
def test_get_conn_matrix_cov(conn_model):
"""
Test for get_conn_matrix functionality
"""
base_dir = str(Path(__file__).parent/"examples")
dir_path = base_dir + '/002/fmri'
time_series_file = dir_path + '/whole_brain_cluster_labels_PCA200/002_Default_rsn_net_ts.npy'
time_series = np.load(time_series_file)
node_size = 2
smooth = 2
c_boot = 0
dens_thresh = False
network = 'Default'
ID = '002'
roi = None
min_span_tree = False
disp_filt = False
hpass = None
parc = None
prune = 1
norm = 1
binary = False
atlas = 'whole_brain_cluster_labels_PCA200'
uatlas = None
labels_file_path = dir_path + '/whole_brain_cluster_labels_PCA200/Default_func_labelnames_wb.pkl'
labels_file = open(labels_file_path, 'rb')
labels = pickle.load(labels_file)
coord_file_path = dir_path + '/whole_brain_cluster_labels_PCA200/Default_func_coords_wb.pkl'
coord_file = open(coord_file_path, 'rb')
coords = pickle.load(coord_file)
start_time = time.time()
[conn_matrix, conn_model, dir_path, node_size, smooth, dens_thresh, network,
ID, roi, min_span_tree, disp_filt, parc, prune, atlas, uatlas,
labels, coords, c_boot, norm, binary, hpass] = fmri_estimation.get_conn_matrix(time_series, conn_model,
dir_path, node_size, smooth, dens_thresh, network, ID, roi, min_span_tree,
disp_filt, parc, prune, atlas, uatlas, labels, coords, c_boot, norm, binary, hpass)
print("%s%s%s" %
('get_conn_matrix --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))
assert conn_matrix is not None
assert conn_model is not None
assert dir_path is not None
assert node_size is not None
assert smooth is not None
assert c_boot is not None
assert dens_thresh is not None
assert network is not None
assert ID is not None
#assert roi is not None
assert min_span_tree is not None
assert disp_filt is not None
#assert parc is not None
assert prune is not None
assert atlas is not None
#assert uatlas is not None
#assert labels is not None
assert coords is not None
def test_get_conn_matrix(conn_model_in, time_series):
""" Test computing a functional connectivity matrix."""
pass_args = np.random.rand(len(getargspec(get_conn_matrix).args) - 2)
outs = get_conn_matrix(time_series, conn_model_in, *pass_args)
conn_matrix_out, conn_model_out = outs[0], outs[1]
assert isinstance(conn_matrix_out, np.ndarray)
assert np.shape(conn_matrix_out) == np.shape(time_series)
assert conn_model_in == conn_model_out
assert (pass_args == outs[2:]).all()
def test_get_conn_matrix(conn_model_in, time_series):
""" Test computing a functional connectivity matrix."""
network = 'Default'
ID = '002'
smooth = 2
coords = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
node_size = 8
extract_strategy = 'zscore'
roi = None
atlas = None
uatlas = None
labels = [1, 2, 3]
outs = get_conn_matrix(
time_series,
conn_model_in,
tempfile.TemporaryDirectory(),
node_size,
smooth,
False,
network,
ID,
roi,
False,
False,
True,
True,
atlas,
uatlas,
labels,
coords,
3,
False,
0,
extract_strategy,
)
conn_matrix_out, conn_model_out = outs[0], outs[1]
assert isinstance(conn_matrix_out, np.ndarray)
assert np.shape(conn_matrix_out) == np.shape(time_series)
assert conn_model_in == conn_model_out
def test_get_conn_matrix(conn_model_in, n_features):
""" Test computing a functional connectivity matrix."""
tmp = tempfile.TemporaryDirectory()
dir_path = str(tmp.name)
os.makedirs(dir_path, exist_ok=True)
subnet = 'Default'
ID = '002'
smooth = 2
coords = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]
node_radius = 8
signal = 'zscore'
roi = None
atlas = None
parcellation = None
labels = [1, 2, 3]
time_series = generate_signals(n_features=n_features,
n_confounds=5,
length=n_features,
same_variance=False)[0]
outs = get_conn_matrix(
time_series,
conn_model_in,
dir_path,
node_radius,
smooth,
False,
subnet,
ID,
roi,
False,
False,
True,
True,
atlas,
parcellation,
labels,
coords,
3,
False,
0,
signal,
)
conn_matrix_out, conn_model_out = outs[0], outs[1]
assert isinstance(conn_matrix_out, np.ndarray)
assert np.shape(conn_matrix_out) == np.shape(time_series)
assert conn_model_in == conn_model_out
if "corr" in conn_model_in:
assert (is_spd(conn_matrix_out, decimal=7))
d = np.sqrt(np.diag(np.diag(conn_matrix_out)))
assert_array_almost_equal(np.diag(conn_matrix_out),
np.ones(n_features))
elif "partcorr" in conn_model_in:
prec = np.linalg.inv(conn_matrix_out)
d = np.sqrt(np.diag(np.diag(prec)))
assert_array_almost_equal(
d.dot(conn_matrix_out).dot(d), -prec + 2 * np.diag(np.diag(prec)))
tmp.cleanup()