def _run_interface(self, runtime):
rada_lol_file = self.inputs.rada_lol_file
Pajek_net_file = self.inputs.Pajek_net_file
conmat_file = self.inputs.conmat_file
export_excel = self.inputs.export_excel
community_vect = read_lol_file(rada_lol_file)
corres_nodes, sparse_mat = \
read_Pajek_corres_nodes_and_sparse_matrix(Pajek_net_file)
# density
conmat = np.load(conmat_file)
corres_mat = conmat[:, corres_nodes][corres_nodes, :]
# intermodule
if not is_symetrical(corres_mat):
corres_mat = corres_mat + np.transpose(corres_mat)
df_avgmat = _inter_module_avgmat(corres_mat, community_vect)
df_avgmat_file = os.path.abspath("res_avgmat.csv")
df_avgmat.to_csv(df_avgmat_file)
if export_excel:
try:
import xlwt # noqa
df_avgmat_excel_file = os.path.abspath("res_avgmat.xls")
df_avgmat.to_excel(df_avgmat_excel_file)
except ImportError:
print("Error, xlwt not installed, cannot export Excel file")
return runtime
def test_read_lol_file():
"""
Test reading modular partition as radatools representation lol_file
"""
# TODO explicit assert test
community_vect = read_lol_file(lol_file)
print(community_vect)
def test_compute_modular_matrix():
"""
Test computing modular matrix where edges between nodes
belonging to the same module are given the same value
"""
# TODO explicit assert test
corres, sp = read_Pajek_corres_nodes_and_sparse_matrix(Pajek_net_file)
community_vect = read_lol_file(lol_file)
mod_mat = compute_modular_matrix(sp, community_vect)
print(mod_mat)
def test_compute_roles():
"""
test_node_roles
"""
# TODO explicit assert test
community_vect = read_lol_file(lol_file)
node_corres, sparse_matrix = read_Pajek_corres_nodes_and_sparse_matrix(
Pajek_net_file)
val = compute_roles(community_vect, sparse_matrix, role_type='4roles')
print(val)
def _run_interface(self, runtime):
rada_lol_file = self.inputs.rada_lol_file
Pajek_net_file = self.inputs.Pajek_net_file
print('Loading Pajek_net_file for reading node_corres')
node_corres, sparse_mat = read_Pajek_corres_nodes_and_sparse_matrix(
Pajek_net_file)
print(sparse_mat.todense())
print(node_corres.shape, sparse_mat.todense().shape)
print("Loading community belonging file " + rada_lol_file)
community_vect = read_lol_file(rada_lol_file)
print(community_vect)
print("Computing node roles")
node_roles, all_Z_com_degree, all_participation_coeff = compute_roles(
community_vect, sparse_mat, role_type=self.inputs.role_type)
node_roles_file = os.path.abspath('node_roles.txt')
np.savetxt(node_roles_file, node_roles, fmt='%d')
all_Z_com_degree_file = os.path.abspath('all_Z_com_degree.txt')
np.savetxt(all_Z_com_degree_file, all_Z_com_degree, fmt='%f')
all_participation_coeff_file = os.path.abspath(
'all_participation_coeff.txt')
np.savetxt(all_participation_coeff_file,
all_participation_coeff,
fmt='%f')
if self.inputs.compute_ndi:
ndi_values = compute_node_dissociation_index(
community_vect, sparse_mat)
ndi_values_file = os.path.abspath('ndi_values.txt')
np.savetxt(ndi_values_file, ndi_values, fmt='%f')
return runtime
def _run_interface(self, runtime):
rada_lol_file = self.inputs.rada_lol_file
Pajek_net_file = self.inputs.Pajek_net_file
group_conmat_file = self.inputs.group_conmat_file
export_excel = self.inputs.export_excel
community_vect = read_lol_file(rada_lol_file)
corres_nodes, sparse_mat = \
read_Pajek_corres_nodes_and_sparse_matrix(Pajek_net_file)
# density
group_conmats = np.load(group_conmat_file)
if len(group_conmats.shape) == 2:
group_conmats = group_conmats.reshape(group_conmats.shape[0],
group_conmats.shape[1], 1)
print(group_conmats.shape)
for i in range(group_conmats.shape[2]):
conmat = group_conmats[:, :, i]
corres_mat = conmat[:, corres_nodes][corres_nodes, :]
# intermodule
if not is_symetrical(corres_mat):
corres_mat = corres_mat + np.transpose(corres_mat)
df_avgmat = _inter_module_avgmat(corres_mat, community_vect)
df_avgmat_file = os.path.abspath("res_avgmat_" + str(i) + ".csv")
df_avgmat.to_csv(df_avgmat_file)
if export_excel:
try:
import xlwt # noqa
df_avgmat.to_excel(
os.path.abspath("res_avgmat_" + str(i) + ".xls"))
except ImportError:
print("Error xlwt not installed, cannot export Excel file")
return runtime
def visu_graph_modules_roles(net_file,
lol_file,
roles_file,
coords_file,
inter_modules=True,
modality_type="",
s_textcolor="white",
c_colval=c_colval_modules,
umin=0,
umax=50,
x_offset=0,
y_offset=0,
z_offset=0,
default_size=10,
hub_to_non_hub=3):
# coords
coords = np.loadtxt(coords_file)
if modality_type == "MEG":
coords = 1000 * coords
temp = np.copy(coords)
coords[:, 1] = coords[:, 0]
coords[:, 0] = temp[:, 1]
coords[:, 2] += z_offset
coords[:, 1] += y_offset
coords[:, 0] += x_offset
# net file
node_corres, sparse_matrix = read_Pajek_corres_nodes_and_sparse_matrix(
net_file)
corres_coords = coords[node_corres, :]
corres_coords = coords[node_corres, :]
# lol file
community_vect = read_lol_file(lol_file)
max_col = np.array([val for val in c_colval.keys()]).max()
community_vect[community_vect > max_col] = max_col
"""Create the connectivity object :"""
c_connect = np.array(compute_modular_matrix(sparse_matrix, community_vect),
dtype='float64')
c_connect = np.ma.masked_array(c_connect, mask=True)
c_connect.mask[-1.0 <= c_connect] = False
if inter_modules:
c_colval[-1] = "grey"
else:
c_connect.mask[-1.0 == c_connect] = True
c_obj = ConnectObj('ConnectObj1',
corres_coords,
c_connect,
color_by='strength',
custom_colors=c_colval)
"""Create the source object :"""
node_roles = np.array(np.loadtxt(roles_file), dtype='int64')
# prov_hubs
prov_hubs = (node_roles[:, 0] == 2) & (node_roles[:, 1] == 1)
coords_prov_hubs = corres_coords[prov_hubs]
colors_prov_hubs = [c_colval[i] for i in community_vect[prov_hubs]]
# prov_no_hubs
prov_no_hubs = (node_roles[:, 0] == 1) & (node_roles[:, 1] == 1)
coords_prov_no_hubs = corres_coords[prov_no_hubs]
colors_prov_no_hubs = [c_colval[i] for i in community_vect[prov_no_hubs]]
# connec_hubs
connec_hubs = (node_roles[:, 0] == 2) & (node_roles[:, 1] == 2)
coords_connec_hubs = corres_coords[connec_hubs]
colors_connec_hubs = [c_colval[i] for i in community_vect[connec_hubs]]
# connec_no_hubs
connec_no_hubs = (node_roles[:, 0] == 1) & (node_roles[:, 1] == 2)
coords_connec_no_hubs = corres_coords[connec_no_hubs]
colors_connec_no_hubs = [
c_colval[i] for i in community_vect[connec_no_hubs]
]
list_sources = []
if len(coords_prov_no_hubs != 0):
s_obj1 = SourceObj('prov_no_hubs',
coords_prov_no_hubs,
color=colors_prov_no_hubs,
alpha=.5,
edge_width=2.,
radius_min=default_size,
radius_max=default_size)
list_sources.append(s_obj1)
if len(coords_connec_no_hubs != 0):
s_obj2 = SourceObj('connec_no_hubs',
coords_connec_no_hubs,
color=colors_connec_no_hubs,
alpha=.5,
edge_width=2.,
radius_min=default_size,
radius_max=default_size,
symbol='square')
list_sources.append(s_obj2)
if len(coords_prov_hubs != 0):
s_obj3 = SourceObj('prov_hubs',
coords_prov_hubs,
color=colors_prov_hubs,
alpha=.5,
edge_width=2.,
radius_min=default_size * hub_to_non_hub,
radius_max=default_size * hub_to_non_hub)
list_sources.append(s_obj3)
if len(coords_connec_hubs != 0):
s_obj4 = SourceObj('connec_hubs',
coords_connec_hubs,
color=colors_connec_hubs,
alpha=.5,
edge_width=2.,
radius_min=default_size * hub_to_non_hub,
radius_max=default_size * hub_to_non_hub,
symbol='square')
list_sources.append(s_obj4)
return c_obj, list_sources
def visu_graph_modules(net_file,
lol_file,
coords_file,
labels_file=0,
inter_modules=True,
modality_type="",
s_textcolor="white",
c_colval=c_colval_modules,
umin=0,
umax=50,
x_offset=0,
y_offset=0,
z_offset=0):
# coords
coords = np.loadtxt(coords_file)
if modality_type == "MEG":
coords = 1000 * coords
temp = np.copy(coords)
coords[:, 1] = coords[:, 0]
coords[:, 0] = temp[:, 1]
coords[:, 2] += z_offset
coords[:, 1] += y_offset
coords[:, 0] += x_offset
# labels
if labels_file:
labels = [line.strip() for line in open(labels_file)]
np_labels = np.array(labels)
# net file
node_corres, sparse_matrix = read_Pajek_corres_nodes_and_sparse_matrix(
net_file)
# lol file
community_vect = read_lol_file(lol_file)
c_connect = np.array(compute_modular_matrix(sparse_matrix, community_vect),
dtype='float64')
c_connect = np.ma.masked_array(c_connect, mask=True)
c_connect.mask[c_connect > -1.0] = False
corres_coords = coords[node_corres, :]
if inter_modules:
c_colval[-1] = "grey"
"""Create the connectivity object :"""
c_obj = ConnectObj('ConnectObj1',
corres_coords,
c_connect,
color_by='strength',
custom_colors=c_colval)
# source object
colors_nodes = []
for i in community_vect:
if i in c_colval.keys():
colors_nodes.append(c_colval[i])
else:
colors_nodes.append("black")
if labels_file:
corres_labels = np_labels[node_corres]
s_obj = SourceObj('SourceObj1',
corres_coords,
text=corres_labels,
text_color=s_textcolor,
text_size=10,
color=colors_nodes,
alpha=.5,
edge_width=2.,
radius_min=10.,
radius_max=10.)
else:
s_obj = SourceObj('SourceObj1',
corres_coords,
text_color=s_textcolor,
text_size=10,
color=colors_nodes,
alpha=.5,
edge_width=2.,
radius_min=10.,
radius_max=10.)
return c_obj, s_obj
def compute_nodes_rada_df(local_dir,
gm_coords=[],
coords_file="",
gm_labels=[],
labels_file="",
radatools_version="3.2",
mapflow=[],
mapflow_name=""):
"""node properties df"""
if radatools_version == "3.2":
net_prop_dir = "net_prop"
elif radatools_version == "4.0":
net_prop_dir = "prep_rada"
elif radatools_version == "5.0":
net_prop_dir = "net_prop"
elif radatools_version == "run":
net_prop_dir = ""
else:
print("Warning, could not find radatools_version {}".format(
radatools_version))
return
list_df = []
if len(mapflow) == 0:
Pajek_file = os.path.join(local_dir, net_prop_dir, "Z_List.net")
if os.path.exists(Pajek_file):
columns = []
columns_names = []
# nodes in the connected graph
node_corres = read_Pajek_corres_nodes(Pajek_file)
print(os.path.exists(coords_file))
if os.path.exists(coords_file) and len(gm_coords):
# MNI coordinates
coords = np.array(np.loadtxt(coords_file), dtype=int)
# node_coords
node_coords = coords[node_corres, :]
# where_in_gm_mask
where_in_gm_mask = where_in_coords(node_coords, gm_coords)
where_in_gm_mask = where_in_gm_mask.reshape(
where_in_gm_mask.shape[0], 1)
columns.append(where_in_gm_mask)
columns_names.append('Where_in_GM_mask')
if os.path.exists(labels_file):
labels = np.array(
[line.strip() for line in open(labels_file)],
dtype=str)
node_labels = labels[node_corres].reshape(-1, 1)
columns.append(node_coords)
columns_names.append('labels')
columns.append(node_coords)
columns_names.expend(['MNI_x', 'MNI_y', 'MNI_z'])
elif os.path.exists(labels_file) and len(gm_labels):
# TODO
labels = np.array([line.strip() for line in open(labels_file)],
dtype=str)
node_labels = labels[node_corres].reshape(-1, 1)
where_in_gm_mask = where_in_labels(node_labels, labels)
columns.append(where_in_gm_mask)
columns_names.append('Where_in_GM_mask')
columns.append(node_labels)
columns_names.append('labels')
0 / 0
elif len(gm_labels):
node_labels = np.array(gm_labels)[node_corres].reshape(-1, 1)
where_in_gm_mask = where_in_labels(node_labels,
gm_labels).reshape(-1, 1)
print(node_labels)
print(where_in_gm_mask)
columns.append(where_in_gm_mask)
columns_names.append('Where_in_GM_mask')
columns.append(node_labels)
columns_names.append('labels')
else:
print("No labels, no coords")
columns.append(node_corres)
columns_names.append('node_corres')
print(columns)
print(columns_names)
list_df.append(
pd.DataFrame(np.concatenate(tuple(columns), axis=1),
columns=columns_names))
else:
print("Missing {}".format(Pajek_file))
info_nodes_file = os.path.join(local_dir, net_prop_dir,
"Z_List-info_nodes.txt")
print(info_nodes_file)
if os.path.exists(info_nodes_file):
# loading info_nodes
df_node_info = pd.read_table(info_nodes_file)
list_df.append(df_node_info)
# modules /community_vect
partition_file = os.path.join(local_dir, "community_rada",
"Z_List.lol")
if os.path.exists(partition_file):
# loading partition_file
community_vect = read_lol_file(partition_file)
list_df.append(pd.DataFrame(community_vect, columns=['Module']))
# node roles
roles_file = os.path.join(local_dir, "node_roles", "node_roles.txt")
part_coeff_file = os.path.join(local_dir, "node_roles",
"all_participation_coeff.txt")
Z_com_degree_file = os.path.join(local_dir, "node_roles",
"all_Z_com_degree.txt")
if os.path.exists(roles_file) and os.path.exists(part_coeff_file) and \
os.path.exists(Z_com_degree_file):
# loding node roles
node_roles = np.array(np.loadtxt(roles_file), dtype=int)
part_coeff = np.loadtxt(part_coeff_file)
part_coeff = part_coeff.reshape(part_coeff.shape[0], 1)
Z_com_degree = np.loadtxt(Z_com_degree_file)
Z_com_degree = Z_com_degree.reshape(Z_com_degree.shape[0], 1)
list_df.append(
pd.DataFrame(np.concatenate(
(node_roles, part_coeff, Z_com_degree), axis=1),
columns=[
'Role_quality', 'Role_quantity',
'Participation_coefficient',
'Z_community_degree'
]))
# ndi values
ndi_values_file = os.path.join(local_dir, "node_roles",
"ndi_values.txt")
if os.path.exists(ndi_values_file):
# loding node roles
ndi_values = np.array(np.loadtxt(ndi_values_file))
list_df.append(
pd.DataFrame(ndi_values, columns=['Node_Dissociation_Index']))
else:
# Multiple files (mapflow)
for i, cond in enumerate(mapflow):
list_strip_df = []
Pajek_file = os.path.join(local_dir, "prep_rada", "mapflow",
"_prep_rada" + str(i), "Z_List.net")
if os.path.exists(coords_file) and os.path.exists(Pajek_file) and \
os.path.exists(labels_file):
# labels
labels = np.array([line.strip() for line in open(labels_file)],
dtype=str)
# MNI coordinates
coords = np.array(np.loadtxt(coords_file), dtype=int)
# nodes in the connected graph
node_corres = read_Pajek_corres_nodes(Pajek_file)
# node_coords
node_coords = coords[node_corres, :]
node_labels = labels[node_corres].reshape(-1, 1)
# where_in_gm_mask
where_in_gm_mask = where_in_coords(node_coords, gm_coords)
where_in_gm_mask = where_in_gm_mask.reshape(
where_in_gm_mask.shape[0], 1)
# print where_in_gm_mask
print(where_in_gm_mask.shape)
list_strip_df.append(
pd.DataFrame(np.concatenate(
(where_in_gm_mask, node_labels, node_coords), axis=1),
columns=[
'Where_in_GM_mask', 'labels', 'MNI_x',
'MNI_y', 'MNI_z'
]))
else:
if not os.path.exists(coords_file):
print("Missing {}".format(coords_file))
if not os.path.exists(Pajek_file):
print("Missing {}".format(Pajek_file))
if not os.path.exists(labels_file):
print("Missing {}".format(labels_file))
info_nodes_file = os.path.join(local_dir, net_prop_dir,
"Z_List-info_nodes.txt")
print(info_nodes_file)
if os.path.exists(info_nodes_file):
# loading info_nodes
df_node_info = pd.read_table(info_nodes_file)
list_strip_df.append(df_node_info)
# modules /community_vect
partition_file = os.path.join(local_dir, "community_rada",
"mapflow",
"_community_rada" + str(i),
"Z_List.lol")
if os.path.exists(partition_file):
# loading partition_file
community_vect = read_lol_file(partition_file)
list_strip_df.append(
pd.DataFrame(community_vect, columns=['Module']))
# node roles
roles_file = os.path.join(local_dir, "node_roles", "mapflow",
"_node_roles" + str(i), "node_roles.txt")
part_coeff_file = os.path.join(local_dir, "node_roles", "mapflow",
"_node_roles" + str(i),
"all_participation_coeff.txt")
Z_com_degree_file = os.path.join(local_dir, "node_roles",
"mapflow", "_node_roles" + str(i),
"all_Z_com_degree.txt")
if os.path.exists(roles_file) and os.path.exists(part_coeff_file) \
and os.path.exists(Z_com_degree_file):
node_roles = np.array(np.loadtxt(roles_file), dtype=int)
part_coeff = np.loadtxt(part_coeff_file)
part_coeff = part_coeff.reshape(part_coeff.shape[0], 1)
Z_com_degree = np.loadtxt(Z_com_degree_file)
Z_com_degree = Z_com_degree.reshape(Z_com_degree.shape[0], 1)
list_strip_df.append(
pd.DataFrame(np.concatenate(
(node_roles, part_coeff, Z_com_degree), axis=1),
columns=[
'Role_quality', 'Role_quantity',
'Participation_coefficient',
'Z_community_degree'
]))
# ndi values
ndi_values_file = os.path.join(local_dir, "node_roles", "mapflow",
"_node_roles" + str(i),
"ndi_values.txt")
if os.path.exists(ndi_values_file):
ndi_values = np.array(np.loadtxt(ndi_values_file))
list_strip_df.append(
pd.DataFrame(ndi_values,
columns=['Node_Dissociation_Index']))
# Converting list_strip_df to df, and adding it to list_df
if len(list_strip_df):
nb_nodes = len(list_strip_df[0].index)
list_strip_df.append(
pd.DataFrame([i] * nb_nodes, columns=[mapflow_name]))
strip_df = pd.concat(list_strip_df, axis=1)
list_df.append(strip_df)
return list_df
def compute_nodes_rada_df(local_dir,
gm_coords,
coords_file,
labels_file,
radatools_version="3.2"):
"""node properties df"""
if radatools_version == "3.2":
net_prop_dir = "net_prop"
elif radatools_version == "4.0":
net_prop_dir = "prep_rada"
elif radatools_version == "5.0":
net_prop_dir = "net_prop"
else:
print("Warning, could not find radatools_version {}".format(
radatools_version))
return
list_df = []
Pajek_file = os.path.join(local_dir, "prep_rada", "Z_List.net")
if os.path.exists(coords_file) and os.path.exists(Pajek_file) and \
os.path.exists(labels_file):
# labels
labels = np.array([line.strip() for line in open(labels_file)],
dtype=str)
# MNI coordinates
coords = np.array(np.loadtxt(coords_file), dtype=int)
# nodes in the connected graph
node_corres = read_Pajek_corres_nodes(Pajek_file)
# node_coords
node_coords = coords[node_corres, :]
node_labels = labels[node_corres].reshape(-1, 1)
# where_in_gm_mask
where_in_gm_mask = where_in_coords(node_coords, gm_coords)
where_in_gm_mask = where_in_gm_mask.reshape(where_in_gm_mask.shape[0],
1)
# print where_in_gm_mask
print(where_in_gm_mask.shape)
list_df.append(
pd.DataFrame(np.concatenate(
(where_in_gm_mask, node_labels, node_coords), axis=1),
columns=[
'Where_in_GM_mask', 'labels', 'MNI_x', 'MNI_y',
'MNI_z'
]))
else:
if not os.path.exists(coords_file):
print("Missing {}".format(coords_file))
if not os.path.exists(Pajek_file):
print("Missing {}".format(Pajek_file))
if not os.path.exists(labels_file):
print("Missing {}".format(labels_file))
info_nodes_file = os.path.join(local_dir, net_prop_dir,
"Z_List-info_nodes.txt")
print(info_nodes_file)
if os.path.exists(info_nodes_file):
# loading info_nodes
df_node_info = pd.read_table(info_nodes_file)
list_df.append(df_node_info)
# modules /community_vect
partition_file = os.path.join(local_dir, "community_rada", "Z_List.lol")
if os.path.exists(partition_file):
# loading partition_file
community_vect = read_lol_file(partition_file)
list_df.append(pd.DataFrame(community_vect, columns=['Module']))
# node roles
roles_file = os.path.join(local_dir, "node_roles", "node_roles.txt")
part_coeff_file = os.path.join(local_dir, "node_roles",
"all_participation_coeff.txt")
Z_com_degree_file = os.path.join(local_dir, "node_roles",
"all_Z_com_degree.txt")
if os.path.exists(roles_file) and os.path.exists(part_coeff_file) and \
os.path.exists(Z_com_degree_file):
# loding node roles
node_roles = np.array(np.loadtxt(roles_file), dtype=int)
part_coeff = np.loadtxt(part_coeff_file)
part_coeff = part_coeff.reshape(part_coeff.shape[0], 1)
Z_com_degree = np.loadtxt(Z_com_degree_file)
Z_com_degree = Z_com_degree.reshape(Z_com_degree.shape[0], 1)
list_df.append(
pd.DataFrame(np.concatenate((node_roles, part_coeff, Z_com_degree),
axis=1),
columns=[
'Role_quality', 'Role_quantity',
'Participation_coefficient', 'Z_community_degree'
]))
# ndi values
ndi_values_file = os.path.join(local_dir, "node_roles", "ndi_values.txt")
if os.path.exists(ndi_values_file):
# loding node roles
ndi_values = np.array(np.loadtxt(ndi_values_file))
list_df.append(
pd.DataFrame(ndi_values, columns=['Node_Dissociation_Index']))
return list_df
