def compute(results):
cartesian = spherical_to_cartesian(np.squeeze(results['theta']), np.squeeze(results['phi']))
if len(cartesian.shape) == 3:
return {'vec0': np.mean(cartesian, axis=1), 'vec0.std': np.std(cartesian, axis=1)}
else:
return {'vec0': np.mean(cartesian, axis=0)[None, :], 'vec0.std': np.std(cartesian, axis=0)[None, :]}
def _get_extra_optimization_map_funcs(self, template, parameter_list):
extra_optimization_maps = copy(template.extra_optimization_maps)
if all(
map(lambda name: name in [p.name for p in parameter_list],
('theta', 'phi'))):
extra_optimization_maps.append(
lambda results: {
'vec0':
spherical_to_cartesian(np.squeeze(results['theta']),
np.squeeze(results['phi']))
})
return extra_optimization_maps
def _get_vector_result_maps(self, theta, phi, vector_name='vec0'):
"""Convert spherical coordinates to cartesian vector in 3d
Args:
theta (ndarray): the double array with the theta values
phi (ndarray): the double array with the phi values
vector_name (str): the name for this vector, the common naming scheme is:
<model_name>.<vector_name>[_{0,1,2}]
Returns:
dict: containing the cartesian vector with the main the fibre direction.
It returns an element .vec0 and elements vec0_
"""
cartesian = spherical_to_cartesian(theta, phi)
extra_dict = {'{}.{}'.format(self.name, vector_name): cartesian}
for ind in range(3):
extra_dict.update({
'{}.{}_{}'.format(self.name, vector_name, ind):
cartesian[:, ind]
})
return extra_dict
def compute(results):
cartesian = spherical_to_cartesian(np.squeeze(results['theta']), np.squeeze(results['phi']))
return {'vec0': np.mean(cartesian, axis=1), 'vec0.std': np.std(cartesian, axis=1)}
