def _prep_coords_cormat(self):
cor_mat_files = self.inputs.cor_mat_files
nb_cormats = len(cor_mat_files)
coords_files = self.inputs.coords_files
gm_mask_coords_file = self.inputs.gm_mask_coords_file
# loading gm mask corres
gm_mask_coords = np.array(np.loadtxt(gm_mask_coords_file), dtype='int')
nb_nodes = gm_mask_coords.shape[0]
# defining return matrices
sum_cormat = np.zeros((nb_nodes, nb_nodes), dtype=float)
group_cormat = np.zeros((nb_nodes, nb_nodes, nb_cormats), dtype=float)
group_vect = np.zeros((nb_nodes, nb_cormats), dtype=float)
assert nb_cormats == len(coords_files), \
("Error, nb_cormats and coords_files are imcompatible {} \
{}".format(nb_cormats, len(coords_files)))
for index_file in range(nb_cormats):
assert os.path.exists(cor_mat_files[index_file])
assert os.path.exists(coords_files[index_file])
Z_cor_mat = np.load(cor_mat_files[index_file])
print(Z_cor_mat.shape)
coords = np.array(np.loadtxt(
coords_files[index_file]), dtype='int')
print(coords.shape)
corres_cor_mat, possible_edge_mat = \
return_corres_correl_mat(Z_cor_mat, coords, gm_mask_coords)
corres_cor_mat = corres_cor_mat + np.transpose(corres_cor_mat)
sum_cormat += corres_cor_mat
group_cormat[:, :, index_file] = corres_cor_mat
group_vect[:, index_file] = np.sum(corres_cor_mat, axis=0)
return sum_cormat, group_cormat, group_vect
def _run_interface(self, runtime):
print('in prepare_cormat')
cor_mat_files = self.inputs.cor_mat_files
if isdefined(self.inputs.gm_mask_coords_file) and isdefined(self.inputs.coords_files):
coords_files = self.inputs.coords_files
gm_mask_coords_file = self.inputs.gm_mask_coords_file
print('loading gm mask corres')
gm_mask_coords = np.array(np.loadtxt(gm_mask_coords_file), dtype = 'int')
print(gm_mask_coords.shape)
#### read matrix from the first group
#print Z_cor_mat_files
sum_cormat = np.zeros((gm_mask_coords.shape[0],gm_mask_coords.shape[0]),dtype = float)
print(sum_cormat.shape)
group_cormat = np.zeros((gm_mask_coords.shape[0],gm_mask_coords.shape[0],len(cor_mat_files)),dtype = float)
print(group_cormat.shape)
group_vect = np.zeros((gm_mask_coords.shape[0],len(cor_mat_files)),dtype = float)
print(group_vect.shape)
if len(cor_mat_files) != len(coords_files):
print("warning, length of cor_mat_files, coords_files are imcompatible {} {} {}".format(len(cor_mat_files),len(coords_files)))
for index_file in range(len(cor_mat_files)):
print(cor_mat_files[index_file])
if os.path.exists(cor_mat_files[index_file]) and os.path.exists(coords_files[index_file]):
Z_cor_mat = np.load(cor_mat_files[index_file])
print(Z_cor_mat.shape)
coords = np.array(np.loadtxt(coords_files[index_file]),dtype = 'int')
print(coords.shape)
corres_cor_mat,possible_edge_mat = return_corres_correl_mat(Z_cor_mat,coords,gm_mask_coords)
corres_cor_mat = corres_cor_mat + np.transpose(corres_cor_mat)
print(corres_cor_mat.shape)
print(group_cormat.shape)
sum_cormat += corres_cor_mat
group_cormat[:,:,index_file] = corres_cor_mat
group_vect[:,index_file] = np.sum(corres_cor_mat,axis = 0)
else:
print("Warning, one or more files between " + cor_mat_files[index_file] + ', ' + coords_files[index_file] + " do not exists")
elif isdefined(self.inputs.gm_mask_labels_file) and isdefined(self.inputs.labels_files):
labels_files = self.inputs.labels_files
gm_mask_labels_file = self.inputs.gm_mask_labels_file
print('loading gm mask labels')
#gm_mask_labels = [line.strip() for line in open(gm_mask_labels_file)]
#print len(gm_mask_labels)
gm_mask_labels = np.array([line.strip() for line in open(gm_mask_labels_file)],dtype = 'str')
print(gm_mask_labels.shape)
#### read matrix from the first group
#print Z_cor_mat_files
sum_cormat = np.zeros((gm_mask_labels.shape[0],gm_mask_labels.shape[0]),dtype = float)
print(sum_cormat.shape)
group_cormat = np.zeros((gm_mask_labels.shape[0],gm_mask_labels.shape[0],len(cor_mat_files)),dtype = float)
print(group_cormat.shape)
group_vect = np.zeros((gm_mask_labels.shape[0],len(cor_mat_files)),dtype = float)
print(group_vect.shape)
if len(cor_mat_files) != len(labels_files):
print("warning, length of cor_mat_files, labels_files are imcompatible {} {} {}".format(len(cor_mat_files),len(labels_files)))
print(cor_mat_files)
for index_file in range(len(cor_mat_files)):
print(cor_mat_files[index_file])
if os.path.exists(cor_mat_files[index_file]) and os.path.exists(labels_files[index_file]):
Z_cor_mat = np.load(cor_mat_files[index_file])
print(Z_cor_mat)
print(Z_cor_mat.shape)
labels = np.array([line.strip() for line in open(labels_files[index_file])],dtype = 'str')
print("labels_subj:")
print(labels.shape)
corres_cor_mat,possible_edge_mat = return_corres_correl_mat_labels(Z_cor_mat,labels,gm_mask_labels)
corres_cor_mat = corres_cor_mat + np.transpose(corres_cor_mat)
print(corres_cor_mat)
print(group_cormat.shape)
sum_cormat += corres_cor_mat
group_cormat[:,:,index_file] = corres_cor_mat
group_vect[:,index_file] = np.sum(corres_cor_mat,axis = 0)
else:
print("Warning, one or more files between " + cor_mat_files[index_file] + ', ' + coords_files[index_file] + " do not exists")
print(group_cormat)
else:
print("Error, neither coords nor labels are defined properly")
print(self.inputs.gm_mask_coords_file)
print(self.inputs.gm_mask_labels_file)
print(self.inputs.coords_files)
0/0
group_cormat_file= os.path.abspath('group_cormat.npy')
np.save(group_cormat_file,group_cormat)
group_vect_file= os.path.abspath('group_vect.npy')
np.save(group_vect_file,group_vect)
print('saving cor_mat matrix')
avg_cormat_file = os.path.abspath('avg_cormat.npy')
if (len(cor_mat_files) != 0):
avg_cormat = sum_cormat /len(cor_mat_files)
np.save(avg_cormat_file,avg_cormat)
return runtime
def return_all_iter_cormats(cormat_path,
iterables,
iternames,
gm_mask_coords_file=0,
gm_mask_labels_file=0,
mapflow_iterables=0,
mapflow_iternames=0,
export_df=False):
"""
gm_mask_coords_file is the coords commun to all analyses
"""
print(product(*iterables))
all_iter_cormats = []
all_descriptors = []
assert isInAlphabeticalOrder(iternames), \
("Warning, iternames are not in alphabetical oroder, check the \
iterables order as well" )
if gm_mask_coords_file != 0:
gm_mask_coords = np.loadtxt(gm_mask_coords_file)
print(gm_mask_coords)
if export_df:
writer = pd.ExcelWriter(os.path.join(cormat_path, "all_cormats.xls"))
for iter_obj in product(*iterables):
print(iter_obj)
assert len(iter_obj) == len(
iternames), "Error, different number of iternames and iterables"
iter_dir = "".join([
"_" + zip_iter[0].strip() + "_" + zip_iter[1].strip()
for zip_iter in zip(iternames, iter_obj)
])
print(iter_dir)
if mapflow_iterables == 0:
cormat_file = os.path.join(cormat_path, iter_dir,
"compute_conf_cor_mat",
"Z_cor_mat_resid_ts.npy")
assert os.path.exists(cormat_file), \
("Warning, file {} could not be found".format(cormat_file))
cormat = np.load(cormat_file)
print(cormat.shape)
if gm_mask_coords_file != 0:
coords_file = os.path.join(cormat_path, iter_dir,
"extract_mean_ROI_ts",
"subj_coord_rois.txt")
coords = np.loadtxt(coords_file)
cormat, _ = return_corres_correl_mat(cormat, coords,
gm_mask_coords)
if export_df:
if gm_mask_labels_file:
labels = [
line.strip() for line in open(gm_mask_labels_file)
]
else:
labels = list(range(cormat.shape[0]))
df = pd.DataFrame(cormat, columns=labels, index=labels)
df.to_excel(writer, "_".join(iter_obj))
all_iter_cormats.append(cormat)
all_descriptors.append(iter_obj)
else:
for i, map_iter in enumerate(mapflow_iterables):
cormat_file = os.path.join(cormat_path, iter_dir,
"compute_conf_cor_mat", "mapflow",
"_compute_conf_cor_mat" + str(i),
"Z_cor_mat_resid_ts.npy")
new_iter_obj = list(iter_obj)
new_iter_obj.append(str(map_iter))
assert os.path.exists(cormat_file), \
("Warning, file {} could not be found".format(cormat_file))
cormat = np.load(cormat_file)
if gm_mask_coords_file:
coords_file = os.path.join(cormat_path, iter_dir,
"extract_mean_ROI_ts",
"subj_coord_rois.txt")
coords = np.loadtxt(coords_file)
cormat, _ = return_corres_correl_mat(
cormat, coords, gm_mask_coords)
if export_df:
if gm_mask_labels_file:
labels = [
line.strip() for line in open(gm_mask_labels_file)
]
else:
labels = list(range(cormat.shape[0]))
df = pd.DataFrame(cormat, columns=labels, index=labels)
df.to_excel(writer, "_".join(new_iter_obj))
all_iter_cormats.append(cormat)
all_descriptors.append(new_iter_obj)
if mapflow_iternames != 0:
iternames.append(mapflow_iternames)
pd_all_descriptors = pd.DataFrame(all_descriptors, columns=iternames)
if export_df:
pd_all_descriptors.to_excel(
os.path.join(cormat_path, "all_descriptors.xls"))
return np.array(all_iter_cormats), pd_all_descriptors
def test_return_corres_correl_mat():
"""test corres matrix based on coords"""
# TODO find a real assert test...
ref_mat = return_corres_correl_mat(mat, coords, ref_coords)
print(ref_mat)
