import numpy as np
from lenstronomy.Util import class_creator
__all__ = ["ImageLikelihood"]
[docs]
class ImageLikelihood(object):
"""Manages imaging data likelihoods."""
[docs]
def __init__(
self,
multi_band_list,
multi_band_type,
kwargs_model,
bands_compute=None,
image_likelihood_mask_list=None,
source_marg=False,
linear_prior=None,
check_positive_flux=False,
kwargs_pixelbased=None,
linear_solver=True,
):
"""
:param bands_compute: list of bools with same length as data objects, indicates which "band" to include in the
fitting
:param image_likelihood_mask_list: list of boolean 2d arrays of size of images marking the pixels to be
evaluated in the likelihood
:param source_marg: marginalization addition on the imaging likelihood based on the covariance of the inferred
linear coefficients
:param linear_prior: float or list of floats (when multi-linear setting is chosen) indicating the range of
linear amplitude priors when computing the marginalization term.
:param check_positive_flux: bool, option to punish models that do not have all positive linear amplitude
parameters
:param kwargs_pixelbased: keyword arguments with various settings related to the pixel-based solver
(see SLITronomy documentation)
: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.
"""
self.imSim = class_creator.create_im_sim(
multi_band_list,
multi_band_type,
kwargs_model,
bands_compute=bands_compute,
image_likelihood_mask_list=image_likelihood_mask_list,
kwargs_pixelbased=kwargs_pixelbased,
linear_solver=linear_solver,
)
self._model_type = self.imSim.type
self._source_marg = source_marg
self._linear_prior = linear_prior
self._check_positive_flux = check_positive_flux
[docs]
def logL(
self,
kwargs_lens=None,
kwargs_source=None,
kwargs_lens_light=None,
kwargs_ps=None,
kwargs_special=None,
kwargs_extinction=None,
**kwargs,
):
"""
:param kwargs_lens: lens model keyword argument list according to LensModel module
:param kwargs_source: source light keyword argument list according to LightModel module
:param kwargs_lens_light: deflector light (not lensed) keyword argument list according to LightModel module
:param kwargs_ps: point source keyword argument list according to PointSource module
:param kwargs_special: special keyword argument list as part of the Param module
:param kwargs_extinction: extinction parameter keyword argument list according to LightModel module
:return: log likelihood of the data given the model, linear parameter inversion list
"""
logL, param = self.imSim.likelihood_data_given_model(
kwargs_lens,
kwargs_source,
kwargs_lens_light,
kwargs_ps,
kwargs_extinction=kwargs_extinction,
kwargs_special=kwargs_special,
source_marg=self._source_marg,
linear_prior=self._linear_prior,
check_positive_flux=self._check_positive_flux,
)
if np.isnan(logL) is True:
return -(10**15), param
return logL, param
@property
def num_data(self):
"""
:return: number of image data points
"""
return self.imSim.num_data_evaluate
[docs]
def num_param_linear(
self,
kwargs_lens=None,
kwargs_source=None,
kwargs_lens_light=None,
kwargs_ps=None,
kwargs_special=None,
kwargs_extinction=None,
kwargs_tracer_source=None,
):
"""
:return: number of linear parameters solved for during the image reconstruction process
"""
return self.imSim.num_param_linear(
kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps
)
[docs]
def reset_point_source_cache(self, cache=True):
"""
:param cache: boolean
:return: None
"""
self.imSim.reset_point_source_cache(cache=cache)