def check_neuron(neuron):
"""
Check the features of the neurons.
Parameters:
-----------
neuron: Neuron
The neuron to be checked.
Returns:
warnings if some features are not correct.
"""
cor = 1
n = Neuron(input_file=get_swc_matrix(neuron),
input_format='Matrix of swc without Node class')
n.fit()
list_features = neuron.features.keys()
for f in range(len(list_features)):
# if list_features[f] == 'curvature':
# print(n.features[list_features[f]])
# print(neuron.features[list_features[f]])
# print(n.features[list_features[f]] - neuron.features[list_features[f]])
if len(n.features[list_features[f]]) - \
len(neuron.features[list_features[f]]) != 0:
print("The size of feature " + list_features[f] +
" is not calculated correctly.")
cor = 0
else:
a = n.features[list_features[f]] - neuron.features[
list_features[f]]
if list_features[f] == 'branch_angle' or list_features[
f] == 'side_branch_angle':
a = np.sort(n.features[list_features[f]], axis=0) - \
np.sort(neuron.features[list_features[f]], axis=0)
a = a**2
# if list_features[f] == 'curvature':
# print(n.features[list_features[f]])
# print(neuron.features[list_features[f]])
# print(a.sum())
if (a.sum() > 0.):
print("The feature " + list_features[f] +
" is not calculated correctly.")
cor = 0
def read_file(self, input_format, input_file):
if(input_format == 'Matrix of swc'):
self.index = np.array([])
index = -1
for neuron in input_file:
try:
index += 1
m = subsample.fast_straigthen_subsample_swc(neuron, self.len_subsampling)
n = Neuron(input_format=input_format, input_file=m)
n.fit()
self.index = np.append(self.index, index)
self.database.append(n)
print index
except:
print('ERROR IN:')
print index
if(input_format == 'Matrix of swc without Node class'):
self.index = np.array([])
index = -1
for neuron in input_file:
try:
index += 1
m = subsample.fast_straigthen_subsample_swc(neuron, self.len_subsampling)
n = Neuron(input_format=input_format, input_file=m)
self.index = np.append(self.index, index)
self.database.append(n)
print index
except:
print('ERROR IN:')
print index
if(input_format == 'swc'):
for neuron in input_file:
n = Neuron(input_format=input_format, input_file=neuron)
purne_n, dis = subsample.straight_subsample_with_fixed_number(n, self.n_subsampling)
self.database.append(purne_n)