def align_images(self,
n_particles=10,
n_iterations=10,
lowerLimit=-0.2,
upperLimit=0.2,
threadCount=1,
compute_bands=None):
"""
aligns the coordinate systems of different exposures within a fixed model parameterisation by executing a PSO
with relative coordinate shifts as free parameters
:param n_particles: number of particles in the Particle Swarm Optimization
:param n_iterations: number of iterations in the optimization process
:param lowerLimit: lower limit of relative shift
:param upperLimit: upper limit of relative shift
:param verbose: bool, print statements
:param compute_bands: bool list, if multiple bands, this process can be limited to a subset of bands
:return:
"""
kwargs_model = self._updateManager.kwargs_model
kwargs_likelihood = self._updateManager.kwargs_likelihood
likelihood_mask_list = kwargs_likelihood.get(
'image_likelihood_mask_list', None)
kwargs_temp = self.best_fit(bijective=False)
if compute_bands is None:
compute_bands = [True] * len(self.multi_band_list)
for i in range(len(self.multi_band_list)):
if compute_bands[i] is True:
alignmentFitting = AlignmentFitting(
self.multi_band_list,
kwargs_model,
kwargs_temp,
band_index=i,
likelihood_mask_list=likelihood_mask_list)
kwargs_data, chain = alignmentFitting.pso(
n_particles=n_particles,
n_iterations=n_iterations,
lowerLimit=lowerLimit,
upperLimit=upperLimit,
threadCount=threadCount,
mpi=self._mpi,
print_key='Alignment fitting for band %s ...' % i)
print('Align completed for band %s.' % i)
print('ra_shift: %s, dec_shift: %s' %
(kwargs_data['ra_shift'], kwargs_data['dec_shift']))
self.multi_band_list[i][0] = kwargs_data
return 0
def align_images(self, fitting_kwargs, lens_input, source_input,
lens_light_input, ps_input, cosmo_input):
lens_updated = self._param.update_lens_scaling(cosmo_input, lens_input)
source_updated = self._param.image2source_plane(
source_input, lens_updated)
mpi = fitting_kwargs.get('mpi', False)
compute_bool = fitting_kwargs.get('compute_bands',
[True] * len(self.multi_band_list))
n_particles = fitting_kwargs.get('n_particles', 10)
n_iterations = fitting_kwargs.get('n_iterations', 10)
lowerLimit = fitting_kwargs.get('lower_limit_shift', -0.2)
upperLimit = fitting_kwargs.get('upper_limit_shift', 0.2)
for i in range(len(self.multi_band_list)):
if compute_bool[i] is True:
kwargs_data = self.multi_band_list[i][0]
kwargs_psf = self.multi_band_list[i][1]
kwargs_numerics = self.multi_band_list[i][2]
alignmentFitting = AlignmentFitting(kwargs_data,
kwargs_psf,
kwargs_numerics,
self.kwargs_model,
lens_updated,
source_updated,
lens_light_input,
ps_input,
compute_bool=compute_bool)
kwargs_data, chain = alignmentFitting.pso(
n_particles,
n_iterations,
lowerLimit,
upperLimit,
threadCount=1,
mpi=mpi,
print_key='Alignment fitting for band %s ...' % i)
print('Align completed for band %s.' % i)
self.multi_band_list[i][0] = kwargs_data
return 0