def __init__(self,
multi_band_list,
kwargs_model,
kwargs_params,
arrow_size=0.02,
cmap_string="gist_heat",
likelihood_mask_list=None,
bands_compute=None,
multi_band_type='multi-linear',
band_index=0,
source_marg=False,
linear_prior=None):
"""
:param multi_band_list:
:param kwargs_model:
:param kwargs_params:
:param arrow_size:
:param cmap_string:
:param likelihood_mask_list:
:param bands_compute:
:param multi_band_type:
:param band_index:
:param source_marg:
:param linear_prior:
"""
if bands_compute is None:
bands_compute = [True] * len(multi_band_list)
if multi_band_type == 'single-band':
multi_band_type = 'multi-linear' # this makes sure that the linear inversion outputs are coming in a list
self._imageModel = class_creator.create_im_sim(
multi_band_list,
multi_band_type,
kwargs_model,
bands_compute=bands_compute,
likelihood_mask_list=likelihood_mask_list,
band_index=band_index)
model, error_map, cov_param, param = self._imageModel.image_linear_solve(
inv_bool=True, **kwargs_params)
logL = self._imageModel.likelihood_data_given_model(
source_marg=source_marg,
linear_prior=linear_prior,
**kwargs_params)
n_data = self._imageModel.num_data_evaluate
if n_data > 0:
print(logL * 2 / n_data,
'reduced X^2 of all evaluated imaging data combined.')
self._band_plot_list = []
self._index_list = []
index = 0
for i in range(len(multi_band_list)):
if bands_compute[i] is True:
if multi_band_type == 'joint-linear':
param_i = param
cov_param_i = cov_param
else:
param_i = param[index]
cov_param_i = cov_param[index]
bandplot = ModelBandPlot(
multi_band_list,
kwargs_model,
model[index],
error_map[index],
cov_param_i,
param_i,
copy.deepcopy(kwargs_params),
likelihood_mask_list=likelihood_mask_list,
band_index=i,
arrow_size=arrow_size,
cmap_string=cmap_string)
self._band_plot_list.append(bandplot)
self._index_list.append(index)
else:
self._index_list.append(-1)
index += 1
def __init__(self,
multi_band_list,
kwargs_model,
kwargs_params,
multi_band_type='multi-linear',
kwargs_likelihood=None,
verbose=True):
"""
:param multi_band_list: list of imaging data configuration [[kwargs_data, kwargs_psf, kwargs_numerics], [...]]
:param kwargs_model: model keyword argument list
:param kwargs_params: keyword arguments of the model parameters, same as output of FittingSequence() 'kwargs_result'
:param multi_band_type: string, option when having multiple imaging data sets modelled simultaneously. Options are:
- 'multi-linear': linear amplitudes are inferred on single data set
- 'linear-joint': linear amplitudes ae jointly inferred
- 'single-band': single band
:param kwargs_likelihood: likelihood keyword arguments as supported by the Likelihood() class
:param verbose: if True (default), computes and prints the total log-likelihood.
This can deactivated for speedup purposes (does not run linear inversion again), and reduces the number of prints.
"""
# here we retrieve those settings in the likelihood keyword arguments that are relevant for the image reconstruction
if kwargs_likelihood is None:
kwargs_likelihood = {}
image_likelihood_mask_list = kwargs_likelihood.get(
'image_likelihood_mask_list', None)
source_marg = kwargs_likelihood.get('source_marg', False)
linear_prior = kwargs_likelihood.get('linear_prior', None)
bands_compute = kwargs_likelihood.get('bands_compute', None)
if bands_compute is None:
bands_compute = [True] * len(multi_band_list)
if multi_band_type == 'single-band':
multi_band_type = 'multi-linear' # this makes sure that the linear inversion outputs are coming in a list
self._imageModel = class_creator.create_im_sim(
multi_band_list,
multi_band_type,
kwargs_model,
bands_compute=bands_compute,
likelihood_mask_list=image_likelihood_mask_list)
# here we perform the (joint) linear inversion with all data
model, error_map, cov_param, param = self._imageModel.image_linear_solve(
inv_bool=True, **kwargs_params)
check_solver_error(param)
if verbose:
logL = self._imageModel.likelihood_data_given_model(
source_marg=source_marg,
linear_prior=linear_prior,
**kwargs_params)
n_data = self._imageModel.num_data_evaluate
if n_data > 0:
print(logL * 2 / n_data,
'reduced X^2 of all evaluated imaging data combined.')
self.model_band_list = []
for i in range(len(multi_band_list)):
if bands_compute[i] is True:
if multi_band_type == 'joint-linear':
param_i = param
cov_param_i = cov_param
else:
param_i = param[i]
cov_param_i = cov_param[i]
model_band = ModelBand(
multi_band_list,
kwargs_model,
model[i],
error_map[i],
cov_param_i,
param_i,
copy.deepcopy(kwargs_params),
image_likelihood_mask_list=image_likelihood_mask_list,
band_index=i,
verbose=verbose)
self.model_band_list.append(model_band)
else:
self.model_band_list.append(None)
def __init__(self,
multi_band_list,
kwargs_model,
kwargs_params,
image_likelihood_mask_list=None,
bands_compute=None,
multi_band_type='multi-linear',
source_marg=False,
linear_prior=None,
arrow_size=0.02,
cmap_string="gist_heat",
fast_caustic=True,
linear_solver=True):
"""
:param multi_band_list: list of [[kwargs_data, kwargs_psf, kwargs_numerics], [], ..]
:param multi_band_type: string, option when having multiple imaging data sets modelled simultaneously.
Options are:
- 'multi-linear': linear amplitudes are inferred on single data set
- 'linear-joint': linear amplitudes ae jointly inferred
- 'single-band': single band
:param kwargs_model: model keyword arguments
:param bands_compute: (optional), bool list to indicate which band to be included in the modeling
:param image_likelihood_mask_list: list of image likelihood mask
(same size as image_data with 1 indicating being evaluated and 0 being left out)
:param kwargs_params: keyword arguments of 'kwargs_lens', 'kwargs_source' etc as coming as kwargs_result from
FittingSequence class
:param source_marg:
:param linear_prior:
:param arrow_size:
:param cmap_string:
:param fast_caustic: boolean; if True, uses fast (but less accurate) caustic calculation method
:param linear_solver: bool, if True (default) fixes the linear amplitude parameters 'amp' (avoid sampling) such
that they get overwritten by the linear solver solution.
"""
if bands_compute is None:
bands_compute = [True] * len(multi_band_list)
if multi_band_type == 'single-band':
multi_band_type = 'multi-linear' # this makes sure that the linear inversion outputs are coming in a list
self._imageModel = class_creator.create_im_sim(
multi_band_list,
multi_band_type,
kwargs_model,
bands_compute=bands_compute,
linear_solver=linear_solver,
image_likelihood_mask_list=image_likelihood_mask_list)
model, error_map, cov_param, param = self._imageModel.image_linear_solve(
inv_bool=True, **kwargs_params)
check_solver_error(param)
log_l = self._imageModel.likelihood_data_given_model(
source_marg=source_marg,
linear_prior=linear_prior,
**kwargs_params)
n_data = self._imageModel.num_data_evaluate
if n_data > 0:
print(
log_l * 2 / n_data,
'reduced X^2 of all evaluated imaging data combined '
'(without degrees of freedom subtracted).')
self._band_plot_list = []
self._index_list = []
index = 0
for i in range(len(multi_band_list)):
if bands_compute[i] is True:
if multi_band_type == 'joint-linear':
param_i = param
cov_param_i = cov_param
else:
param_i = param[index]
cov_param_i = cov_param[index]
bandplot = ModelBandPlot(
multi_band_list,
kwargs_model,
model[index],
error_map[index],
cov_param_i,
param_i,
copy.deepcopy(kwargs_params),
likelihood_mask_list=image_likelihood_mask_list,
band_index=i,
arrow_size=arrow_size,
cmap_string=cmap_string,
fast_caustic=fast_caustic)
self._band_plot_list.append(bandplot)
self._index_list.append(index)
else:
self._index_list.append(-1)
index += 1