def test_plot_all_nonet_no_mask(plotting_data):
"""
Test plot_all_nonet_no_mask functionality
"""
base_dir = str(Path(__file__).parent/"examples")
temp_dir = tempfile.TemporaryDirectory()
dir_path = str(temp_dir.name)
network = None
ID = '002'
thr = 0.95
node_size = 2
smooth = 2
conn_model = 'sps'
parlistfile = None
atlas = 'whole_brain_cluster_labels_PCA200'
roi = f"{base_dir}/miscellaneous/pDMN_3_bin.nii.gz"
prune = 1
norm = 1
hpass = 0.1
binary = False
extract_strategy = 'mean'
edge_threshold = '99%'
conn_matrix = plotting_data['conn_matrix']
labels = plotting_data['labels']
coords = plotting_data['coords']
start_time = time.time()
plot_gen.plot_all_func(conn_matrix, conn_model, atlas, dir_path, ID, network, labels, roi,
coords, thr, node_size, edge_threshold, smooth, prune,
parlistfile, norm, binary, hpass, extract_strategy)
print("%s%s%s" % ('plot_all --> finished: ', str(np.round(time.time() - start_time, 1)), 's'))
temp_dir.cleanup()
def test_plot_all_nonet_with_mask(plotting_data):
"""
Test plot_all_nonet_with_mask functionality
"""
temp_dir = tempfile.TemporaryDirectory()
dir_path = str(temp_dir.name)
network = None
ID = '002'
thr = 0.95
node_size = 'None'
smooth = 2
prune = False
norm = 1
hpass = 0.1
binary = False
conn_model = 'sps'
atlas = b'whole_brain_cluster_labels_PCA200'
parlistfile = None
roi = None
extract_strategy = 'mean'
edge_threshold = '99%'
conn_matrix = plotting_data['conn_matrix']
labels = plotting_data['labels']
coords = plotting_data['coords']
# Force an isolate in the matrix
conn_matrix[:, 0] = 0
conn_matrix[0, :] = 0
# Adds coverage
coords = np.array(coords)
labels = np.array(labels)
start_time = time.time()
plot_gen.plot_all_func(conn_matrix,
conn_model,
atlas,
dir_path,
ID,
network,
labels,
roi,
coords,
thr,
node_size,
edge_threshold,
smooth,
prune,
parlistfile,
norm,
binary,
hpass,
extract_strategy,
edge_color_override=True)
print("%s%s%s" % ('plot_all (Masking version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
temp_dir.cleanup()
def _run_interface(self, runtime):
from pynets.plotting import plot_gen
if self.inputs.coords.ndim == 1:
print('Only 1 node detected. Plotting is not applicable...')
else:
plot_gen.plot_all_func(self.inputs.conn_matrix,
self.inputs.conn_model,
self.inputs.atlas,
self.inputs.dir_path,
self.inputs.ID,
self.inputs.network,
self.inputs.labels.tolist(),
self.inputs.roi,
[tuple(coord) for coord in self.inputs.coords.tolist()],
self.inputs.thr,
self.inputs.node_size,
self.inputs.edge_threshold,
self.inputs.smooth,
self.inputs.prune,
self.inputs.uatlas,
self.inputs.c_boot,
self.inputs.norm,
self.inputs.binary,
self.inputs.hpass)
self._results['out'] = 'None'
return runtime
def test_plot_all_nonet_with_mask():
"""
Test plot_all_nonet_with_mask functionality
"""
import tempfile
base_dir = str(Path(__file__).parent / "examples")
dir_path = str(tempfile.TemporaryDirectory().name)
os.makedirs(dir_path)
network = None
ID = '002'
thr = 0.95
node_size = 2
smooth = 2
prune = 1
norm = 1
hpass = 0.1
binary = False
conn_model = 'sps'
atlas = 'whole_brain_cluster_labels_PCA200'
parlistfile = None
roi = None
extract_strategy = 'mean'
conn_matrix = np.genfromtxt(
f"{base_dir}/miscellaneous/002_rsn-Default_nodetype-parc_est-sps_thrtype-PROP_thr-0.94.txt"
)
edge_threshold = '99%'
labels_file_path = f"{base_dir}/miscellaneous/Default_func_labelnames_wb.pkl"
labels_file = open(labels_file_path, 'rb')
labels = pickle.load(labels_file)
coord_file_path = f"{base_dir}/miscellaneous/Default_func_coords_wb.pkl"
coord_file = open(coord_file_path, 'rb')
coords = pickle.load(coord_file)
start_time = time.time()
#coords already a list
plot_gen.plot_all_func(conn_matrix, conn_model, atlas, dir_path, ID,
network, labels, roi, coords, edge_threshold, thr,
node_size, smooth, prune, parlistfile, norm, binary,
hpass, extract_strategy)
print("%s%s%s" % ('plot_all (Masking version) --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
def test_plot_all_nonet_no_mask():
"""
Test plot_all_nonet_no_mask functionality
"""
# Set example inputs
base_dir = str(Path(__file__).parent / "examples")
dir_path = base_dir + '/002/fmri'
network = None
ID = '002'
thr = 0.95
node_size = 2
smooth = 2
c_boot = 3
conn_model = 'sps'
parlistfile = None
atlas = 'whole_brain_cluster_labels_PCA200'
roi = None
prune = 1
norm = 1
hpass = 0.1
binary = False
conn_matrix = np.genfromtxt(
dir_path +
'/whole_brain_cluster_labels_PCA200/002_Default_est_sps_0.94.txt')
edge_threshold = '99%'
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()
#coords already a list
plot_gen.plot_all_func(conn_matrix, conn_model, atlas, dir_path, ID,
network, labels, roi, coords, edge_threshold, thr,
node_size, smooth, prune, parlistfile, c_boot, norm,
binary, hpass)
print("%s%s%s" % ('plot_all --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))