def _update_lens_model(self, kwargs_special):
"""
updates lens model instance of this class (and all class instances related to it) when an update to the
modeled redshifts of the deflector and/or source planes are made
:param kwargs_special: keyword arguments from SpecialParam() class return of sampling arguments
:return: None, internal calls instance updated
"""
kwargs_model, update_bool = self.update_kwargs_model(kwargs_special)
if update_bool is True:
# TODO: this class instances are effectively duplicated in the likelihood module and may cause a lot of overhead
# in the calculation as the instances are re-generated every step, and even so doing it twice!
self._lens_model_class, self._source_model_class, _, _, _ = class_creator.create_class_instances(
all_models=True, **kwargs_model)
self._image2SourceMapping = Image2SourceMapping(lensModel=self._lens_model_class,
sourceModel=self._source_model_class)
def __init__(self,
z_lens,
z_source,
kwargs_model,
kwargs_aperture,
kwargs_seeing,
anisotropy_model,
cosmo=None,
lens_model_kinematics_bool=None,
light_model_kinematics_bool=None,
multi_observations=False,
kwargs_numerics_galkin=None,
analytic_kinematics=False,
Hernquist_approx=False,
MGE_light=False,
MGE_mass=False,
kwargs_mge_light=None,
kwargs_mge_mass=None,
sampling_number=1000,
num_kin_sampling=1000,
num_psf_sampling=100):
"""
:param z_lens: redshift of lens
:param z_source: redshift of source
:param kwargs_model: model keyword arguments, needs 'lens_model_list', 'lens_light_model_list'
:param kwargs_aperture: spectroscopic aperture keyword arguments, see lenstronomy.Galkin.aperture for options
:param kwargs_seeing: seeing condition of spectroscopic observation, corresponds to kwargs_psf in the GalKin module specified in lenstronomy.GalKin.psf
:param cosmo: astropy.cosmology instance, if None then will be set to the default cosmology
:param lens_model_kinematics_bool: bool list of length of the lens model. Only takes a subset of all the models
as part of the kinematics computation (can be used to ignore substructure, shear etc that do not describe the
main deflector potential
:param light_model_kinematics_bool: bool list of length of the light model. Only takes a subset of all the models
as part of the kinematics computation (can be used to ignore light components that do not describe the main
deflector
:param multi_observations: bool, if True uses multi-observation to predict a set of different observations with
the GalkinMultiObservation() class. kwargs_aperture and kwargs_seeing require to be lists of the individual
observations.
:param anisotropy_model: type of stellar anisotropy model. See details in MamonLokasAnisotropy() class of lenstronomy.GalKin.anisotropy
:param analytic_kinematics: boolean, if True, used the analytic JAM modeling for a power-law profile on top of a Hernquist light profile
ATTENTION: This may not be accurate for your specific problem!
:param Hernquist_approx: bool, if True, uses a Hernquist light profile matched to the half light radius of the deflector light profile to compute the kinematics
:param MGE_light: bool, if true performs the MGE for the light distribution
:param MGE_mass: bool, if true performs the MGE for the mass distribution
:param kwargs_numerics_galkin: numerical settings for the integrated line-of-sight velocity dispersion
:param kwargs_mge_mass: keyword arguments that go into the MGE decomposition routine
:param kwargs_mge_light: keyword arguments that go into the MGE decomposition routine
:param sampling_number: int, number of spectral rendering to compute the light weighted integrated LOS
dispersion within the aperture. This keyword should be chosen high enough to result in converged results within the tolerance.
:param num_kin_sampling: number of kinematic renderings on a total IFU
:param num_psf_sampling: number of PSF displacements for each kinematic rendering on the IFU
"""
self.z_d = z_lens
self.z_s = z_source
self._kwargs_aperture_kin = kwargs_aperture
self._kwargs_psf_kin = kwargs_seeing
self.lensCosmo = LensCosmo(z_lens, z_source, cosmo=cosmo)
self.LensModel, self.SourceModel, self.LensLightModel, self.PointSource, extinction_class = class_creator.create_class_instances(
all_models=True, **kwargs_model)
self._lensLightProfile = LightProfileAnalysis(
light_model=self.LensLightModel)
self._lensMassProfile = LensProfileAnalysis(lens_model=self.LensModel)
self._lens_light_model_list = self.LensLightModel.profile_type_list
self._lens_model_list = self.LensModel.lens_model_list
self._kwargs_cosmo = {
'd_d': self.lensCosmo.dd,
'd_s': self.lensCosmo.ds,
'd_ds': self.lensCosmo.dds
}
self._lens_model_kinematics_bool = lens_model_kinematics_bool
self._light_model_kinematics_bool = light_model_kinematics_bool
self._sampling_number = sampling_number
self._num_kin_sampling = num_kin_sampling
self._num_psf_sampling = num_psf_sampling
if kwargs_mge_mass is None:
self._kwargs_mge_mass = {'n_comp': 20}
else:
self._kwargs_mge_mass = kwargs_mge_mass
if kwargs_mge_light is None:
self._kwargs_mge_light = {
'grid_spacing': 0.01,
'grid_num': 100,
'n_comp': 20,
'center_x': None,
'center_y': None
}
else:
self._kwargs_mge_light = kwargs_mge_light
if kwargs_numerics_galkin is None:
kwargs_numerics_galkin = {
'interpol_grid_num':
1000, # numerical interpolation, should converge -> infinity
'log_integration':
True, # log or linear interpolation of surface brightness and mass models
'max_integrate': 100,
'min_integrate': 0.001
} # lower/upper bound of numerical integrals
self._kwargs_numerics_kin = kwargs_numerics_galkin
self._anisotropy_model = anisotropy_model
self._analytic_kinematics = analytic_kinematics
self._Hernquist_approx = Hernquist_approx
self._MGE_light = MGE_light
self._MGE_mass = MGE_mass
self._multi_observations = multi_observations
def __init__(self,
multi_band_list,
kwargs_model,
likelihood_mask_list=None,
band_index=0,
kwargs_pixelbased=None):
"""
:param multi_band_list: list of imaging band configurations [[kwargs_data, kwargs_psf, kwargs_numerics],[...], ...]
:param kwargs_model: model option keyword arguments
:param likelihood_mask_list: list of likelihood masks (booleans with size of the individual images
:param band_index: integer, index of the imaging band to model
:param kwargs_pixelbased: keyword arguments with various settings related to the pixel-based solver (see SLITronomy documentation)
"""
self.type = 'single-band-multi-model'
if likelihood_mask_list is None:
likelihood_mask_list = [None for i in range(len(multi_band_list))]
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
band_index=band_index, **kwargs_model)
kwargs_data = multi_band_list[band_index][0]
kwargs_psf = multi_band_list[band_index][1]
kwargs_numerics = multi_band_list[band_index][2]
data_i = ImageData(**kwargs_data)
psf_i = PSF(**kwargs_psf)
index_lens_model_list = kwargs_model.get(
'index_lens_model_list',
[None for i in range(len(multi_band_list))])
self._index_lens_model = index_lens_model_list[band_index]
index_source_list = kwargs_model.get(
'index_source_light_model_list',
[None for i in range(len(multi_band_list))])
self._index_source = index_source_list[band_index]
index_lens_light_list = kwargs_model.get(
'index_lens_light_model_list',
[None for i in range(len(multi_band_list))])
self._index_lens_light = index_lens_light_list[band_index]
index_point_source_list = kwargs_model.get(
'index_point_source_model_list',
[None for i in range(len(multi_band_list))])
self._index_point_source = index_point_source_list[band_index]
index_optical_depth = kwargs_model.get(
'index_optical_depth_model_list',
[None for i in range(len(multi_band_list))])
self._index_optical_depth = index_optical_depth[band_index]
super(SingleBandMultiModel,
self).__init__(data_i,
psf_i,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
extinction_class,
kwargs_numerics=kwargs_numerics,
likelihood_mask=likelihood_mask_list[band_index],
kwargs_pixelbased=kwargs_pixelbased)
def __init__(self,
kwargs_model,
kwargs_fixed_lens=None,
kwargs_fixed_source=None,
kwargs_fixed_lens_light=None,
kwargs_fixed_ps=None,
kwargs_fixed_special=None,
kwargs_fixed_extinction=None,
kwargs_lower_lens=None,
kwargs_lower_source=None,
kwargs_lower_lens_light=None,
kwargs_lower_ps=None,
kwargs_lower_special=None,
kwargs_lower_extinction=None,
kwargs_upper_lens=None,
kwargs_upper_source=None,
kwargs_upper_lens_light=None,
kwargs_upper_ps=None,
kwargs_upper_special=None,
kwargs_upper_extinction=None,
kwargs_lens_init=None,
linear_solver=True,
joint_lens_with_lens=[],
joint_lens_light_with_lens_light=[],
joint_source_with_source=[],
joint_lens_with_light=[],
joint_source_with_point_source=[],
joint_lens_light_with_point_source=[],
joint_extinction_with_lens_light=[],
joint_lens_with_source_light=[],
mass_scaling_list=None,
point_source_offset=False,
num_point_source_list=None,
image_plane_source_list=None,
solver_type='NONE',
Ddt_sampling=None,
source_size=False,
num_tau0=0,
lens_redshift_sampling_indexes=None,
source_redshift_sampling_indexes=None,
source_grid_offset=False,
num_shapelet_lens=0,
log_sampling_lens=[]):
"""
:param kwargs_model: keyword arguments to describe all model components used in class_creator.create_class_instances()
:param kwargs_fixed_lens: fixed parameters for lens model (keyword argument list)
:param kwargs_fixed_source: fixed parameters for source model (keyword argument list)
:param kwargs_fixed_lens_light: fixed parameters for lens light model (keyword argument list)
:param kwargs_fixed_ps: fixed parameters for point source model (keyword argument list)
:param kwargs_fixed_special: fixed parameters for special model parameters (keyword arguments)
:param kwargs_fixed_extinction: fixed parameters for extinction model parameters (keyword argument list)
:param kwargs_lower_lens: lower limits for parameters of lens model (keyword argument list)
:param kwargs_lower_source: lower limits for parameters of source model (keyword argument list)
:param kwargs_lower_lens_light: lower limits for parameters of lens light model (keyword argument list)
:param kwargs_lower_ps: lower limits for parameters of point source model (keyword argument list)
:param kwargs_lower_special: lower limits for parameters of special model parameters (keyword arguments)
:param kwargs_lower_extinction: lower limits for parameters of extinction model (keyword argument list)
:param kwargs_upper_lens: upper limits for parameters of lens model (keyword argument list)
:param kwargs_upper_source: upper limits for parameters of source model (keyword argument list)
:param kwargs_upper_lens_light: upper limits for parameters of lens light model (keyword argument list)
:param kwargs_upper_ps: upper limits for parameters of point source model (keyword argument list)
:param kwargs_upper_special: upper limits for parameters of special model parameters (keyword arguments)
:param kwargs_upper_extinction: upper limits for parameters of extinction model (keyword argument list)
:param kwargs_lens_init: initial guess of lens model keyword arguments (only relevant as the starting point of
the non-linear solver)
:param linear_solver: bool, if True fixes the linear amplitude parameters 'amp' (avoid sampling) such that they
get overwritten by the linear solver solution.
:param joint_lens_with_lens: list [[i_lens, k_lens, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between two lens models
:param joint_lens_light_with_lens_light: list [[i_lens_light, k_lens_light, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between two lens light models, the second adopts the value of the first
:param joint_source_with_source: [[i_source, k_source, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between two source surface brightness models, the second adopts the value of the first
:param joint_lens_with_light: list [[i_light, k_lens, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between lens model and lens light model
:param joint_source_with_point_source: list [[i_point_source, k_source], [...], ...],
joint position parameter between lens model and source light model
:param joint_lens_light_with_point_source: list [[i_point_source, k_lens_light], [...], ...],
joint position parameter between lens model and lens light model
:param joint_extinction_with_lens_light: list [[i_lens_light, k_extinction, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameters between the lens surface brightness and the optical depth models
:param joint_lens_with_source_light: [[i_source, k_lens, ['param_name1', 'param_name2', ...]], [...], ...],
joint parameter between lens model and source light model. Samples light model parameter only.
:param mass_scaling_list: boolean list of length of lens model list (optional) models with identical integers
will be scaled with the same additional scaling factor. First integer starts with 1 (not 0)
:param point_source_offset: bool, if True, adds relative offsets ot the modeled image positions relative to the
time-delay and lens equation solver
:param num_point_source_list: list of number of point sources per point source model class
:param image_plane_source_list: optional, list of booleans for the source_light components.
If a component is set =True it will parameterized the positions in the image plane and ray-trace the
parameters back to the source position on the fly during the fitting.
:param solver_type: string, option for specific solver type
see detailed instruction of the Solver4Point and Solver2Point classes
:param Ddt_sampling: bool, if True, samples the time-delay distance D_dt (in units of Mpc)
:param source_size: bool, if True, samples a source size parameters to be evaluated in the flux ratio likelihood
:param num_tau0: integer, number of different optical depth re-normalization factors
:param lens_redshift_sampling_indexes: list of integers corresponding to the lens model components whose redshifts
are a free parameter (only has an effect in multi-plane lensing) with same indexes indicating joint redshift,
in ascending numbering e.g. [-1, 0, 0, 1, 0, 2], -1 indicating not sampled fixed indexes
:param source_redshift_sampling_indexes: list of integers corresponding to the source model components whose redshifts
are a free parameter (only has an effect in multi-plane lensing) with same indexes indicating joint redshift,
in ascending numbering e.g. [-1, 0, 0, 1, 0, 2], -1 indicating not sampled fixed indexes. These indexes are
the sample as for the lens
:param source_grid_offset: optional, if True when using a pixel-based modelling (e.g. with STARLETS-like profiles),
adds two additional sampled parameters describing RA/Dec offsets between data coordinate grid and pixelated source plane coordinate grid.
:param num_shapelet_lens: number of shapelet coefficients in the 'SHAPELETS_CART' or 'SHAPELETS_POLAR' mass profile.
:param log_sampling_lens: Sample the log10 of the lens model parameters. Format : [[i_lens, ['param_name1', 'param_name2', ...]], [...], ...],
"""
self._lens_model_list = kwargs_model.get('lens_model_list', [])
self._lens_redshift_list = kwargs_model.get('lens_redshift_list', None)
self._source_light_model_list = kwargs_model.get(
'source_light_model_list', [])
self._source_redshift_list = kwargs_model.get('source_redshift_list',
None)
self._lens_light_model_list = kwargs_model.get('lens_light_model_list',
[])
self._point_source_model_list = kwargs_model.get(
'point_source_model_list', [])
self._optical_depth_model_list = kwargs_model.get(
'optical_depth_model_list', [])
self._kwargs_model = kwargs_model
# check how many redshifts need to be sampled
num_z_sampling = 0
if lens_redshift_sampling_indexes is not None:
num_z_sampling = int(np.max(lens_redshift_sampling_indexes) + 1)
if source_redshift_sampling_indexes is not None:
num_z_source = int(np.max(source_redshift_sampling_indexes) + 1)
num_z_sampling = max(num_z_sampling, num_z_source)
self._num_z_sampling, self._lens_redshift_sampling_indexes, self._source_redshift_sampling_indexes = num_z_sampling, lens_redshift_sampling_indexes, source_redshift_sampling_indexes
self._lens_model_class, self._source_model_class, _, _, _ = class_creator.create_class_instances(
all_models=True, **kwargs_model)
self._image2SourceMapping = Image2SourceMapping(
lensModel=self._lens_model_class,
sourceModel=self._source_model_class)
if kwargs_fixed_lens is None:
kwargs_fixed_lens = [{} for i in range(len(self._lens_model_list))]
if kwargs_fixed_source is None:
kwargs_fixed_source = [
{} for i in range(len(self._source_light_model_list))
]
if kwargs_fixed_lens_light is None:
kwargs_fixed_lens_light = [
{} for i in range(len(self._lens_light_model_list))
]
if kwargs_fixed_ps is None:
kwargs_fixed_ps = [
{} for i in range(len(self._point_source_model_list))
]
if kwargs_fixed_special is None:
kwargs_fixed_special = {}
self._joint_lens_with_lens = joint_lens_with_lens
self._joint_lens_light_with_lens_light = joint_lens_light_with_lens_light
self._joint_source_with_source = joint_source_with_source
self._joint_lens_with_light = joint_lens_with_light
self._joint_lens_with_source_light = joint_lens_with_source_light
self._joint_source_with_point_source = copy.deepcopy(
joint_source_with_point_source)
# Set up the parameters being sampled in log space in a similar way than the parameters being fixed.
self._log_sampling_lens = log_sampling_lens
kwargs_logsampling_lens = [[]
for i in range(len(self._lens_model_list))]
kwargs_logsampling_lens = self._update_log_sampling(
kwargs_logsampling_lens, log_sampling_lens)
for param_list in self._joint_source_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
self._joint_lens_light_with_point_source = copy.deepcopy(
joint_lens_light_with_point_source)
for param_list in self._joint_lens_light_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
if mass_scaling_list is None:
mass_scaling_list = [False] * len(self._lens_model_list)
self._mass_scaling_list = mass_scaling_list
if 1 in self._mass_scaling_list:
self._num_scale_factor = np.max(self._mass_scaling_list)
self._mass_scaling = True
else:
self._num_scale_factor = 0
self._mass_scaling = False
self._point_source_offset = point_source_offset
if num_point_source_list is None:
num_point_source_list = [1] * len(self._point_source_model_list)
# Attention: if joint coordinates with other source profiles, only indicate one as bool
if image_plane_source_list is None:
image_plane_source_list = [False] * len(
self._source_light_model_list)
self._image_plane_source_list = image_plane_source_list
try:
self._num_images = num_point_source_list[0]
except:
self._num_images = 0
self._solver_type = solver_type
if self._solver_type == 'NONE':
self._solver = False
else:
self._solver = True
self._solver_module = Solver(solver_type=self._solver_type,
lensModel=self._lens_model_class,
num_images=self._num_images)
source_model_list = self._source_light_model_list
if len(source_model_list) != 1 or source_model_list[0] not in [
'SLIT_STARLETS', 'SLIT_STARLETS_GEN2'
]:
# source_grid_offset only defined for source profiles compatible with pixel-based solver
source_grid_offset = False
self._joint_extinction_with_lens_light = joint_extinction_with_lens_light
# fix parameters joint within the same model types
kwargs_fixed_lens_updated = self._add_fixed_lens(
kwargs_fixed_lens, kwargs_lens_init)
kwargs_fixed_lens_updated = self._fix_joint_param(
kwargs_fixed_lens_updated, self._joint_lens_with_lens)
kwargs_fixed_lens_updated = self._fix_joint_param(
kwargs_fixed_lens_updated, self._joint_lens_with_source_light)
kwargs_fixed_lens_light_updated = self._fix_joint_param(
kwargs_fixed_lens_light, self._joint_lens_light_with_lens_light)
kwargs_fixed_source_updated = self._fix_joint_param(
kwargs_fixed_source, self._joint_source_with_source)
kwargs_fixed_ps_updated = copy.deepcopy(kwargs_fixed_ps)
kwargs_fixed_extinction_updated = self._fix_joint_param(
kwargs_fixed_extinction, self._joint_extinction_with_lens_light)
# fix parameters joint with other model types
kwargs_fixed_lens_updated = self._fix_joint_param(
kwargs_fixed_lens_updated, self._joint_lens_with_light)
kwargs_fixed_source_updated = self._fix_joint_param(
kwargs_fixed_source_updated, self._joint_source_with_point_source)
kwargs_fixed_lens_light_updated = self._fix_joint_param(
kwargs_fixed_lens_light_updated,
self._joint_lens_light_with_point_source)
self.lensParams = LensParam(self._lens_model_list,
kwargs_fixed_lens_updated,
kwargs_logsampling=kwargs_logsampling_lens,
num_images=self._num_images,
solver_type=self._solver_type,
kwargs_lower=kwargs_lower_lens,
kwargs_upper=kwargs_upper_lens,
num_shapelet_lens=num_shapelet_lens)
self.lensLightParams = LightParam(self._lens_light_model_list,
kwargs_fixed_lens_light_updated,
param_type='lens_light',
linear_solver=linear_solver,
kwargs_lower=kwargs_lower_lens_light,
kwargs_upper=kwargs_upper_lens_light)
self.souceParams = LightParam(self._source_light_model_list,
kwargs_fixed_source_updated,
param_type='source_light',
linear_solver=linear_solver,
kwargs_lower=kwargs_lower_source,
kwargs_upper=kwargs_upper_source)
self.pointSourceParams = PointSourceParam(
self._point_source_model_list,
kwargs_fixed_ps_updated,
num_point_source_list=num_point_source_list,
linear_solver=linear_solver,
kwargs_lower=kwargs_lower_ps,
kwargs_upper=kwargs_upper_ps)
self.extinctionParams = LightParam(
self._optical_depth_model_list,
kwargs_fixed_extinction_updated,
kwargs_lower=kwargs_lower_extinction,
kwargs_upper=kwargs_upper_extinction,
linear_solver=False)
self.specialParams = SpecialParam(
Ddt_sampling=Ddt_sampling,
mass_scaling=self._mass_scaling,
kwargs_fixed=kwargs_fixed_special,
num_scale_factor=self._num_scale_factor,
kwargs_lower=kwargs_lower_special,
kwargs_upper=kwargs_upper_special,
point_source_offset=self._point_source_offset,
num_images=self._num_images,
source_size=source_size,
num_tau0=num_tau0,
num_z_sampling=num_z_sampling,
source_grid_offset=source_grid_offset)
for lens_source_joint in self._joint_lens_with_source_light:
i_source = lens_source_joint[0]
if i_source in self._image_plane_source_list:
raise ValueError(
"linking a source light model with a lens model AND simultaneously parameterizing the"
" source position in the image plane is not valid!")
def __init__(self,
kwargs_data_joint,
kwargs_model,
param_class,
image_likelihood=True,
check_bounds=True,
check_matched_source_position=False,
astrometric_likelihood=False,
image_position_likelihood=False,
source_position_likelihood=False,
image_position_uncertainty=0.004,
check_positive_flux=False,
source_position_tolerance=0.001,
source_position_sigma=0.001,
force_no_add_image=False,
source_marg=False,
linear_prior=None,
restrict_image_number=False,
max_num_images=None,
bands_compute=None,
time_delay_likelihood=False,
force_minimum_source_surface_brightness=False,
flux_min=0,
image_likelihood_mask_list=None,
flux_ratio_likelihood=False,
kwargs_flux_compute={},
prior_lens=[],
prior_source=[],
prior_extinction=[],
prior_lens_light=[],
prior_ps=[],
prior_special=[],
prior_lens_kde=[],
prior_source_kde=[],
prior_lens_light_kde=[],
prior_ps_kde=[],
prior_special_kde=[],
prior_extinction_kde=[],
prior_lens_lognormal=[],
prior_source_lognormal=[],
prior_extinction_lognormal=[],
prior_lens_light_lognormal=[],
prior_ps_lognormal=[],
prior_special_lognormal=[],
custom_logL_addition=None):
"""
initializing class
:param param_class: instance of a Param() class that can cast the sorted list of parameters that are sampled into the
conventions of the imSim_class
:param image_likelihood: bool, option to compute the imaging likelihood
:param source_position_likelihood: bool, if True, ray-traces image positions back to source plane and evaluates
relative errors in respect ot the position_uncertainties in the image plane
:param check_bounds: bool, option to punish the hard bounds in parameter space
:param check_matched_source_position: bool, option to check whether point source position solver finds a solution to match all
the image positions in the same source plane coordinate
:param astrometric_likelihood: bool, additional likelihood term of the predicted vs modelled point source position
:param flaot, image_position_uncertainty: 1-sigma Gaussian uncertainty on the point source position
(only used if point_source_likelihood=True)
:param check_positive_flux: bool, option to punish models that do not have all positive linear amplitude parameters
:param source_position_tolerance: float, punishment of check_solver occurs when image positions are predicted further
away than this number
:param image_likelihood_mask_list: list of boolean 2d arrays of size of images marking the pixels to be evaluated in the likelihood
:param force_no_add_image: bool, if True: computes ALL image positions of the point source. If there are more
images predicted than modelled, a punishment occures
:param source_marg: marginalization addition on the imaging likelihood based on the covariance of the infered
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 restrict_image_number: bool, if True: computes ALL image positions of the point source. If there are more
images predicted than indicated in max_num_images, a punishment occurs
:param max_num_images: int, see restrict_image_number
:param bands_compute: list of bools with same length as data objects, indicates which "band" to include in the fitting
:param time_delay_likelihood: bool, if True computes the time-delay likelihood of the FIRST point source
:param force_minimum_source_surface_brightness: bool, if True, evaluates the source surface brightness on a grid
and evaluates if all positions have positive flux
:param kwargs_flux_compute: keyword arguments of how to compute the image position fluxes (see FluxRatioLikeliood)
:param custom_logL_addition: a definition taking as arguments (kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps, kwargs_special, kwargs_extinction)
and returns a logL (punishing) value.
"""
multi_band_list, image_type, time_delays_measured, time_delays_uncertainties, flux_ratios, flux_ratio_errors, ra_image_list, dec_image_list = self._unpack_data(
**kwargs_data_joint)
if len(multi_band_list) == 0:
image_likelihood = False
self.param = param_class
self._lower_limit, self._upper_limit = self.param.param_limits()
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
**kwargs_model)
self.PointSource = point_source_class
self._prior_likelihood = PriorLikelihood(
prior_lens,
prior_source,
prior_lens_light,
prior_ps,
prior_special,
prior_extinction,
prior_lens_kde,
prior_source_kde,
prior_lens_light_kde,
prior_ps_kde,
prior_special_kde,
prior_extinction_kde,
prior_lens_lognormal,
prior_source_lognormal,
prior_lens_light_lognormal,
prior_ps_lognormal,
prior_special_lognormal,
prior_extinction_lognormal,
)
self._time_delay_likelihood = time_delay_likelihood
if self._time_delay_likelihood is True:
self.time_delay_likelihood = TimeDelayLikelihood(
time_delays_measured, time_delays_uncertainties,
lens_model_class, point_source_class)
self._image_likelihood = image_likelihood
if self._image_likelihood is True:
self.image_likelihood = ImageLikelihood(
multi_band_list,
image_type,
kwargs_model,
bands_compute=bands_compute,
likelihood_mask_list=image_likelihood_mask_list,
source_marg=source_marg,
linear_prior=linear_prior,
force_minimum_source_surface_brightness=
force_minimum_source_surface_brightness,
flux_min=flux_min)
self._position_likelihood = PositionLikelihood(
point_source_class,
astrometric_likelihood=astrometric_likelihood,
image_position_likelihood=image_position_likelihood,
source_position_likelihood=source_position_likelihood,
ra_image_list=ra_image_list,
dec_image_list=dec_image_list,
image_position_uncertainty=image_position_uncertainty,
check_matched_source_position=check_matched_source_position,
source_position_tolerance=source_position_tolerance,
source_position_sigma=source_position_sigma,
force_no_add_image=force_no_add_image,
restrict_image_number=restrict_image_number,
max_num_images=max_num_images)
self._flux_ratio_likelihood = flux_ratio_likelihood
self._kwargs_flux_compute = kwargs_flux_compute
if self._flux_ratio_likelihood is True:
self.flux_ratio_likelihood = FluxRatioLikelihood(
lens_model_class, flux_ratios, flux_ratio_errors,
**self._kwargs_flux_compute)
self._check_positive_flux = check_positive_flux
self._check_bounds = check_bounds
self._custom_logL_addition = custom_logL_addition
def __init__(self, kwargs_model,
kwargs_fixed_lens=None, kwargs_fixed_source=None, kwargs_fixed_lens_light=None, kwargs_fixed_ps=None,
kwargs_fixed_special=None, kwargs_fixed_extinction=None,
kwargs_lower_lens=None, kwargs_lower_source=None, kwargs_lower_lens_light=None, kwargs_lower_ps=None,
kwargs_lower_special=None, kwargs_lower_extinction=None,
kwargs_upper_lens=None, kwargs_upper_source=None, kwargs_upper_lens_light=None, kwargs_upper_ps=None,
kwargs_upper_special=None, kwargs_upper_extinction=None,
kwargs_lens_init=None, linear_solver=True, joint_lens_with_lens=[], joint_lens_light_with_lens_light=[],
joint_source_with_source=[], joint_lens_with_light=[], joint_source_with_point_source=[],
joint_lens_light_with_point_source=[], joint_extinction_with_lens_light=[],
joint_lens_with_source_light=[], mass_scaling_list=None, point_source_offset=False,
num_point_source_list=None, image_plane_source_list=None, solver_type='NONE', Ddt_sampling=None,
source_size=False, num_tau0=0):
"""
:return:
"""
self._lens_model_list = kwargs_model.get('lens_model_list', [])
self._source_light_model_list = kwargs_model.get('source_light_model_list', [])
self._lens_light_model_list = kwargs_model.get('lens_light_model_list', [])
self._point_source_model_list = kwargs_model.get('point_source_model_list', [])
self._optical_depth_model_list = kwargs_model.get('optical_depth_model_list', [])
lens_model_class, source_model_class, _, _, _ = class_creator.create_class_instances(all_models=True, **kwargs_model)
self._image2SourceMapping = Image2SourceMapping(lensModel=lens_model_class, sourceModel=source_model_class)
if kwargs_fixed_lens is None:
kwargs_fixed_lens = [{} for i in range(len(self._lens_model_list))]
if kwargs_fixed_source is None:
kwargs_fixed_source = [{} for i in range(len(self._source_light_model_list))]
if kwargs_fixed_lens_light is None:
kwargs_fixed_lens_light = [{} for i in range(len(self._lens_light_model_list))]
if kwargs_fixed_ps is None:
kwargs_fixed_ps = [{} for i in range(len(self._point_source_model_list))]
if kwargs_fixed_special is None:
kwargs_fixed_special = {}
self._joint_lens_with_lens = joint_lens_with_lens
self._joint_lens_light_with_lens_light = joint_lens_light_with_lens_light
self._joint_source_with_source = joint_source_with_source
self._joint_lens_with_light = joint_lens_with_light
self._joint_lens_with_source_light = joint_lens_with_source_light
self._joint_source_with_point_source = copy.deepcopy(joint_source_with_point_source)
for param_list in self._joint_source_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
self._joint_lens_light_with_point_source = copy.deepcopy(joint_lens_light_with_point_source)
for param_list in self._joint_lens_light_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
if mass_scaling_list is None:
mass_scaling_list = [False] * len(self._lens_model_list)
self._mass_scaling_list = mass_scaling_list
if 1 in self._mass_scaling_list:
self._num_scale_factor = np.max(self._mass_scaling_list)
self._mass_scaling = True
else:
self._num_scale_factor = 0
self._mass_scaling = False
self._point_source_offset = point_source_offset
if num_point_source_list is None:
num_point_source_list = [1] * len(self._point_source_model_list)
# Attention: if joint coordinates with other source profiles, only indicate one as bool
if image_plane_source_list is None:
image_plane_source_list = [False] * len(self._source_light_model_list)
self._image_plane_source_list = image_plane_source_list
try:
self._num_images = num_point_source_list[0]
except:
self._num_images = 0
self._solver_type = solver_type
if self._solver_type == 'NONE':
self._solver = False
else:
self._solver = True
self._solver_module = Solver(solver_type=self._solver_type, lensModel=lens_model_class,
num_images=self._num_images)
self._joint_extinction_with_lens_light = joint_extinction_with_lens_light
# fix parameters joint within the same model types
kwargs_fixed_lens_updated = self._add_fixed_lens(kwargs_fixed_lens, kwargs_lens_init)
kwargs_fixed_lens_updated = self._fix_joint_param(kwargs_fixed_lens_updated, self._joint_lens_with_lens)
kwargs_fixed_lens_updated = self._fix_joint_param(kwargs_fixed_lens_updated, self._joint_lens_with_source_light)
kwargs_fixed_lens_light_updated = self._fix_joint_param(kwargs_fixed_lens_light, self._joint_lens_light_with_lens_light)
kwargs_fixed_source_updated = self._fix_joint_param(kwargs_fixed_source, self._joint_source_with_source)
kwargs_fixed_ps_updated = copy.deepcopy(kwargs_fixed_ps)
kwargs_fixed_extinction_updated = self._fix_joint_param(kwargs_fixed_extinction, self._joint_extinction_with_lens_light)
# fix parameters joint with other model types
kwargs_fixed_lens_updated = self._fix_joint_param(kwargs_fixed_lens_updated, self._joint_lens_with_light)
kwargs_fixed_source_updated = self._fix_joint_param(kwargs_fixed_source_updated, self._joint_source_with_point_source)
kwargs_fixed_lens_light_updated = self._fix_joint_param(kwargs_fixed_lens_light_updated,
self._joint_lens_light_with_point_source)
self.lensParams = LensParam(self._lens_model_list, kwargs_fixed_lens_updated, num_images=self._num_images,
solver_type=self._solver_type, kwargs_lower=kwargs_lower_lens,
kwargs_upper=kwargs_upper_lens)
self.lensLightParams = LightParam(self._lens_light_model_list, kwargs_fixed_lens_light_updated, type='lens_light',
linear_solver=linear_solver, kwargs_lower=kwargs_lower_lens_light,
kwargs_upper=kwargs_upper_lens_light)
self.souceParams = LightParam(self._source_light_model_list, kwargs_fixed_source_updated, type='source_light',
linear_solver=linear_solver, kwargs_lower=kwargs_lower_source,
kwargs_upper=kwargs_upper_source)
self.pointSourceParams = PointSourceParam(self._point_source_model_list, kwargs_fixed_ps_updated,
num_point_source_list=num_point_source_list,
linear_solver=linear_solver, kwargs_lower=kwargs_lower_ps,
kwargs_upper=kwargs_upper_ps)
self.extinctionParams = LightParam(self._optical_depth_model_list, kwargs_fixed_extinction_updated,
kwargs_lower=kwargs_lower_extinction, kwargs_upper=kwargs_upper_extinction,
linear_solver=False)
self.specialParams = SpecialParam(Ddt_sampling=Ddt_sampling, mass_scaling=self._mass_scaling,
kwargs_fixed=kwargs_fixed_special, num_scale_factor=self._num_scale_factor,
kwargs_lower=kwargs_lower_special, kwargs_upper=kwargs_upper_special,
point_source_offset=self._point_source_offset, num_images=self._num_images,
source_size=source_size, num_tau0=num_tau0)
for lens_source_joint in self._joint_lens_with_source_light:
i_source = lens_source_joint[0]
if i_source in self._image_plane_source_list:
raise ValueError("linking a source light model with a lens model AND simultaneously parameterizing the"
" source position in the image plane is not valid!")
def __init__(self,
kwargs_data_joint,
kwargs_model,
param_class,
image_likelihood=True,
check_bounds=True,
check_solver=False,
point_source_likelihood=False,
position_uncertainty=0.004,
check_positive_flux=False,
solver_tolerance=0.001,
force_no_add_image=False,
source_marg=False,
restrict_image_number=False,
max_num_images=None,
bands_compute=None,
time_delay_likelihood=False,
force_minimum_source_surface_brightness=False,
flux_min=0,
image_likelihood_mask_list=None,
flux_ratio_likelihood=False,
kwargs_flux_compute={},
prior_lens=[],
prior_source=[],
prior_lens_light=[],
prior_ps=[],
prior_cosmo=[]):
"""
initializing class
:param param_class: instance of a Param() class that can cast the sorted list of parameters that are sampled into the
conventions of the imSim_class
:param image_likelihood: bool, option to compute the imaging likelihood
:param check_bounds: bool, option to punish the hard bounds in parameter space
:param check_solver: bool, option to check whether point source position solver finds a solution to match all
the image positions in the same source plane coordinate
:param point_source_likelihood: bool, additional likelihood term of the predicted vs modelled point source position
:param flaot, position_uncertainty: 1-sigma Gaussian uncertainty on the point source position
(only used if point_source_likelihood=True)
:param check_positive_flux: bool, option to punish models that do not have all positive linear amplitude parameters
:param solver_tolerance: float, punishment of check_solver occures when image positions are predicted further
away than this number
:param image_likelihood_mask_list: list of boolean 2d arrays of size of images marking the pixels to be evaluated in the likelihood
:param force_no_add_image: bool, if True: computes ALL image positions of the point source. If there are more
images predicted than modelled, a punishment occures
:param source_marg: marginalization addition on the imaging likelihood based on the covariance of the infered
linear coefficients
:param restrict_image_number: bool, if True: computes ALL image positions of the point source. If there are more
images predicted than indicated in max_num_images, a punishment occures
:param max_num_images: int, see restrict_image_number
:param bands_compute: list of bools with same length as data objects, indicates which "band" to include in the fitting
:param time_delay_likelihood: bool, if True computes the time-delay likelihood of the FIRST point source
:param force_minimum_source_surface_brightness: bool, if True, evaluates the source surface brightness on a grid
and evaluates if all positions have positive flux
:param kwargs_flux_compute: keyword arguments of how to compute the image position fluxes (see FluxRatioLikeliood)
"""
multi_band_list = kwargs_data_joint.get('multi_band_list', [])
multi_band_type = kwargs_data_joint.get('image_type', 'single-band')
time_delays_measured = kwargs_data_joint.get('time_delays_measured',
None)
time_delays_uncertainties = kwargs_data_joint.get(
'time_delays_uncertainties', None)
flux_ratios = kwargs_data_joint.get('flux_ratios', None)
flux_ratio_errors = kwargs_data_joint.get('flux_ratio_errors', None)
self.param = param_class
self._lower_limit, self._upper_limit = self.param.param_limits()
lens_model_class, source_model_class, lens_light_model_class, point_source_class = class_reator.create_class_instances(
**kwargs_model)
self.PointSource = point_source_class
self._prior_likelihood = PriorLikelihood(prior_lens, prior_source,
prior_lens_light, prior_ps,
prior_cosmo)
self._time_delay_likelihood = time_delay_likelihood
if self._time_delay_likelihood is True:
self.time_delay_likelihood = TimeDelayLikelihood(
time_delays_measured, time_delays_uncertainties,
lens_model_class, point_source_class, param_class)
self._image_likelihood = image_likelihood
if self._image_likelihood is True:
self.image_likelihood = ImageLikelihood(
multi_band_list,
multi_band_type,
kwargs_model,
bands_compute=bands_compute,
likelihood_mask_list=image_likelihood_mask_list,
source_marg=source_marg,
force_minimum_source_surface_brightness=
force_minimum_source_surface_brightness,
flux_min=flux_min)
self._position_likelihood = PositionLikelihood(
point_source_class, param_class, point_source_likelihood,
position_uncertainty, check_solver, solver_tolerance,
force_no_add_image, restrict_image_number, max_num_images)
self._flux_ratio_likelihood = flux_ratio_likelihood
self._kwargs_flux_compute = kwargs_flux_compute
if self._flux_ratio_likelihood is True:
self.flux_ratio_likelihood = FluxRatioLikelihood(
point_source_class, lens_model_class, param_class, flux_ratios,
flux_ratio_errors, **self._kwargs_flux_compute)
self._check_positive_flux = check_positive_flux
self._check_bounds = check_bounds
def __init__(self, kwargs_model):
self.LensModel, self.SourceModel, self.LensLightModel, self.PointSource, extinction_class = class_creator.create_class_instances(
all_models=True, **kwargs_model)
#self.LensLightModel = LightModel(kwargs_model.get('lens_light_model_list', []))
#self.LensModel = LensModel(lens_model_list=kwargs_model.get('lens_model_list', []),
# z_source=kwargs_model.get('z_source', None),
# lens_redshift_list=kwargs_model.get('lens_redshift_list', None),
# multi_plane=kwargs_model.get('multi_plane', False))
#self.SourceModel = LightModel(kwargs_model.get('source_light_model_list', []))
self._lensModelExtensions = LensModelExtensions(self.LensModel)
#self.PointSource = PointSource(point_source_type_list=kwargs_model.get('point_source_model_list', []), lensModel=self.LensModel)
self.kwargs_model = kwargs_model
self.NumLensModel = NumericLens(
lens_model_list=kwargs_model.get('lens_model_list', []))
def test_create_class_instances(self):
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
**self.kwargs_model)
assert lens_model_class.lens_model_list[0] == 'SIS'
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
**self.kwargs_model_2)
assert lens_model_class.lens_model_list[0] == 'SIS'
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
**self.kwargs_model_3)
assert lens_model_class.lens_model_list[0] == 'SIS'
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
**self.kwargs_model_4)
assert lens_model_class.lens_model_list[0] == 'SIS'
assert lens_model_class.lens_model._observed_convention_index[0] == 0
def __init__(self,
kwargs_model,
kwargs_fixed_lens=None,
kwargs_fixed_source=None,
kwargs_fixed_lens_light=None,
kwargs_fixed_ps=None,
kwargs_fixed_special=None,
kwargs_fixed_extinction=None,
kwargs_lower_lens=None,
kwargs_lower_source=None,
kwargs_lower_lens_light=None,
kwargs_lower_ps=None,
kwargs_lower_special=None,
kwargs_lower_extinction=None,
kwargs_upper_lens=None,
kwargs_upper_source=None,
kwargs_upper_lens_light=None,
kwargs_upper_ps=None,
kwargs_upper_special=None,
kwargs_upper_extinction=None,
kwargs_lens_init=None,
linear_solver=True,
joint_lens_with_lens=[],
joint_lens_light_with_lens_light=[],
joint_source_with_source=[],
joint_lens_with_light=[],
joint_source_with_point_source=[],
joint_lens_light_with_point_source=[],
joint_extinction_with_lens_light=[],
joint_lens_with_source_light=[],
mass_scaling_list=None,
point_source_offset=False,
num_point_source_list=None,
image_plane_source_list=None,
solver_type='NONE',
Ddt_sampling=None,
source_size=False,
num_tau0=0,
lens_redshift_sampling_indexes=None,
source_redshift_sampling_indexes=None,
source_grid_offset=False,
num_shapelet_lens=0):
"""
:param kwargs_model:
:param kwargs_fixed_lens:
:param kwargs_fixed_source:
:param kwargs_fixed_lens_light:
:param kwargs_fixed_ps:
:param kwargs_fixed_special:
:param kwargs_fixed_extinction:
:param kwargs_lower_lens:
:param kwargs_lower_source:
:param kwargs_lower_lens_light:
:param kwargs_lower_ps:
:param kwargs_lower_special:
:param kwargs_lower_extinction:
:param kwargs_upper_lens:
:param kwargs_upper_source:
:param kwargs_upper_lens_light:
:param kwargs_upper_ps:
:param kwargs_upper_special:
:param kwargs_upper_extinction:
:param kwargs_lens_init:
:param linear_solver:
:param joint_lens_with_lens:
:param joint_lens_light_with_lens_light:
:param joint_source_with_source:
:param joint_lens_with_light:
:param joint_source_with_point_source:
:param joint_lens_light_with_point_source:
:param joint_extinction_with_lens_light:
:param joint_lens_with_source_light:
:param mass_scaling_list:
:param point_source_offset:
:param num_point_source_list:
:param image_plane_source_list: optional, list of booleans for the source_light components.
If a component is set =True it will parameterized the positions in the image plane and ray-trace the
parameters back to the source position on the fly during the fitting.
:param solver_type:
:param Ddt_sampling:
:param source_size:
:param num_tau0:
:param lens_redshift_sampling_indexes: list of integers corresponding to the lens model components whose redshifts
are a free parameter (only has an effect in multi-plane lensing) with same indexes indicating joint redshift,
in ascending numbering e.g. [-1, 0, 0, 1, 0, 2], -1 indicating not sampled fixed indexes
:param source_redshift_sampling_indexes: list of integers corresponding to the source model components whose redshifts
are a free parameter (only has an effect in multi-plane lensing) with same indexes indicating joint redshift,
in ascending numbering e.g. [-1, 0, 0, 1, 0, 2], -1 indicating not sampled fixed indexes. These indexes are
the sample as for the lens
:param source_grid_offset: optional, if True when using a pixel-based modelling (e.g. with STARLETS-like profiles),
adds two additional sampled parameters describing RA/Dec offsets between data coordinate grid and pixelated source plane coordinate grid.
:param num_shapelet_lens: number of shapelet coefficients in the 'SHAPELETS_CART' or 'SHAPELETS_POLAR' mass profile.
"""
self._lens_model_list = kwargs_model.get('lens_model_list', [])
self._lens_redshift_list = kwargs_model.get('lens_redshift_list', None)
self._source_light_model_list = kwargs_model.get(
'source_light_model_list', [])
self._source_redshift_list = kwargs_model.get('source_redshift_list',
None)
self._lens_light_model_list = kwargs_model.get('lens_light_model_list',
[])
self._point_source_model_list = kwargs_model.get(
'point_source_model_list', [])
self._optical_depth_model_list = kwargs_model.get(
'optical_depth_model_list', [])
self._kwargs_model = kwargs_model
# check how many redshifts need to be sampled
num_z_sampling = 0
if lens_redshift_sampling_indexes is not None:
num_z_sampling = int(np.max(lens_redshift_sampling_indexes) + 1)
if source_redshift_sampling_indexes is not None:
num_z_source = int(np.max(source_redshift_sampling_indexes) + 1)
num_z_sampling = max(num_z_sampling, num_z_source)
self._num_z_sampling, self._lens_redshift_sampling_indexes, self._source_redshift_sampling_indexes = num_z_sampling, lens_redshift_sampling_indexes, source_redshift_sampling_indexes
self._lens_model_class, self._source_model_class, _, _, _ = class_creator.create_class_instances(
all_models=True, **kwargs_model)
self._image2SourceMapping = Image2SourceMapping(
lensModel=self._lens_model_class,
sourceModel=self._source_model_class)
if kwargs_fixed_lens is None:
kwargs_fixed_lens = [{} for i in range(len(self._lens_model_list))]
if kwargs_fixed_source is None:
kwargs_fixed_source = [
{} for i in range(len(self._source_light_model_list))
]
if kwargs_fixed_lens_light is None:
kwargs_fixed_lens_light = [
{} for i in range(len(self._lens_light_model_list))
]
if kwargs_fixed_ps is None:
kwargs_fixed_ps = [
{} for i in range(len(self._point_source_model_list))
]
if kwargs_fixed_special is None:
kwargs_fixed_special = {}
self._joint_lens_with_lens = joint_lens_with_lens
self._joint_lens_light_with_lens_light = joint_lens_light_with_lens_light
self._joint_source_with_source = joint_source_with_source
self._joint_lens_with_light = joint_lens_with_light
self._joint_lens_with_source_light = joint_lens_with_source_light
self._joint_source_with_point_source = copy.deepcopy(
joint_source_with_point_source)
for param_list in self._joint_source_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
self._joint_lens_light_with_point_source = copy.deepcopy(
joint_lens_light_with_point_source)
for param_list in self._joint_lens_light_with_point_source:
if len(param_list) == 2:
param_list.append(['center_x', 'center_y'])
if mass_scaling_list is None:
mass_scaling_list = [False] * len(self._lens_model_list)
self._mass_scaling_list = mass_scaling_list
if 1 in self._mass_scaling_list:
self._num_scale_factor = np.max(self._mass_scaling_list)
self._mass_scaling = True
else:
self._num_scale_factor = 0
self._mass_scaling = False
self._point_source_offset = point_source_offset
if num_point_source_list is None:
num_point_source_list = [1] * len(self._point_source_model_list)
# Attention: if joint coordinates with other source profiles, only indicate one as bool
if image_plane_source_list is None:
image_plane_source_list = [False] * len(
self._source_light_model_list)
self._image_plane_source_list = image_plane_source_list
try:
self._num_images = num_point_source_list[0]
except:
self._num_images = 0
self._solver_type = solver_type
if self._solver_type == 'NONE':
self._solver = False
else:
self._solver = True
self._solver_module = Solver(solver_type=self._solver_type,
lensModel=self._lens_model_class,
num_images=self._num_images)
source_model_list = self._source_light_model_list
if (len(source_model_list) != 1 or source_model_list[0]
not in ['SLIT_STARLETS', 'SLIT_STARLETS_GEN2']):
# source_grid_offset only defined for source profiles compatible with pixel-based solver
source_grid_offset = False
self._joint_extinction_with_lens_light = joint_extinction_with_lens_light
# fix parameters joint within the same model types
kwargs_fixed_lens_updated = self._add_fixed_lens(
kwargs_fixed_lens, kwargs_lens_init)
kwargs_fixed_lens_updated = self._fix_joint_param(
kwargs_fixed_lens_updated, self._joint_lens_with_lens)
kwargs_fixed_lens_updated = self._fix_joint_param(
kwargs_fixed_lens_updated, self._joint_lens_with_source_light)
kwargs_fixed_lens_light_updated = self._fix_joint_param(
kwargs_fixed_lens_light, self._joint_lens_light_with_lens_light)
kwargs_fixed_source_updated = self._fix_joint_param(
kwargs_fixed_source, self._joint_source_with_source)
kwargs_fixed_ps_updated = copy.deepcopy(kwargs_fixed_ps)
kwargs_fixed_extinction_updated = self._fix_joint_param(
kwargs_fixed_extinction, self._joint_extinction_with_lens_light)
# fix parameters joint with other model types
kwargs_fixed_lens_updated = self._fix_joint_param(
kwargs_fixed_lens_updated, self._joint_lens_with_light)
kwargs_fixed_source_updated = self._fix_joint_param(
kwargs_fixed_source_updated, self._joint_source_with_point_source)
kwargs_fixed_lens_light_updated = self._fix_joint_param(
kwargs_fixed_lens_light_updated,
self._joint_lens_light_with_point_source)
self.lensParams = LensParam(self._lens_model_list,
kwargs_fixed_lens_updated,
num_images=self._num_images,
solver_type=self._solver_type,
kwargs_lower=kwargs_lower_lens,
kwargs_upper=kwargs_upper_lens,
num_shapelet_lens=num_shapelet_lens)
self.lensLightParams = LightParam(self._lens_light_model_list,
kwargs_fixed_lens_light_updated,
type='lens_light',
linear_solver=linear_solver,
kwargs_lower=kwargs_lower_lens_light,
kwargs_upper=kwargs_upper_lens_light)
self.souceParams = LightParam(self._source_light_model_list,
kwargs_fixed_source_updated,
type='source_light',
linear_solver=linear_solver,
kwargs_lower=kwargs_lower_source,
kwargs_upper=kwargs_upper_source)
self.pointSourceParams = PointSourceParam(
self._point_source_model_list,
kwargs_fixed_ps_updated,
num_point_source_list=num_point_source_list,
linear_solver=linear_solver,
kwargs_lower=kwargs_lower_ps,
kwargs_upper=kwargs_upper_ps)
self.extinctionParams = LightParam(
self._optical_depth_model_list,
kwargs_fixed_extinction_updated,
kwargs_lower=kwargs_lower_extinction,
kwargs_upper=kwargs_upper_extinction,
linear_solver=False)
self.specialParams = SpecialParam(
Ddt_sampling=Ddt_sampling,
mass_scaling=self._mass_scaling,
kwargs_fixed=kwargs_fixed_special,
num_scale_factor=self._num_scale_factor,
kwargs_lower=kwargs_lower_special,
kwargs_upper=kwargs_upper_special,
point_source_offset=self._point_source_offset,
num_images=self._num_images,
source_size=source_size,
num_tau0=num_tau0,
num_z_sampling=num_z_sampling,
source_grid_offset=source_grid_offset)
for lens_source_joint in self._joint_lens_with_source_light:
i_source = lens_source_joint[0]
if i_source in self._image_plane_source_list:
raise ValueError(
"linking a source light model with a lens model AND simultaneously parameterizing the"
" source position in the image plane is not valid!")
def __init__(self, multi_band_list, kwargs_model, likelihood_mask_list=None, band_index=0):
self.type = 'single-band-multi-model'
if likelihood_mask_list is None:
likelihood_mask_list = [None for i in range(len(multi_band_list))]
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(band_index=band_index, **kwargs_model)
kwargs_data = multi_band_list[band_index][0]
kwargs_psf = multi_band_list[band_index][1]
kwargs_numerics = multi_band_list[band_index][2]
data_i = ImageData(**kwargs_data)
psf_i = PSF(**kwargs_psf)
index_lens_model_list = kwargs_model.get('index_lens_model_list', [None for i in range(len(multi_band_list))])
self._index_lens_model = index_lens_model_list[band_index]
index_source_list = kwargs_model.get('index_source_light_model_list', [None for i in range(len(multi_band_list))])
self._index_source = index_source_list[band_index]
index_lens_light_list = kwargs_model.get('index_lens_light_model_list', [None for i in range(len(multi_band_list))])
self._index_lens_light = index_lens_light_list[band_index]
index_point_source_list = kwargs_model.get('index_point_source_model_list', [None for i in range(len(multi_band_list))])
self._index_point_source = index_point_source_list[band_index]
index_optical_depth = kwargs_model.get('index_optical_depth_model_list',
[None for i in range(len(multi_band_list))])
self._index_optical_depth = index_optical_depth[band_index]
super(SingleBandMultiModel, self).__init__(data_i, psf_i, lens_model_class, source_model_class,
lens_light_model_class, point_source_class, extinction_class,
kwargs_numerics=kwargs_numerics, likelihood_mask=likelihood_mask_list[band_index])
def __init__(self,
z_lens,
z_source,
kwargs_model,
cosmo=None,
lens_model_kinematics_bool=None,
light_model_kinematics_bool=None):
"""
:param z_lens: redshift of lens
:param z_source: redshift of source
:param kwargs_model: model keyword arguments
:param cosmo: astropy.cosmology instance, if None then will be set to the default cosmology
:param lens_model_kinematics_bool: bool list of length of the lens model. Only takes a subset of all the models
as part of the kinematics computation (can be used to ignore substructure, shear etc that do not describe the
main deflector potential
:param light_model_kinematics_bool: bool list of length of the light model. Only takes a subset of all the models
as part of the kinematics computation (can be used to ignore light components that do not describe the main
deflector
"""
self.z_d = z_lens
self.z_s = z_source
self.lensCosmo = LensCosmo(z_lens, z_source, cosmo=cosmo)
self.LensModel, self.SourceModel, self.LensLightModel, self.PointSource, extinction_class = class_creator.create_class_instances(
all_models=True, **kwargs_model)
self._lensLightProfile = LightProfileAnalysis(
light_model=self.LensLightModel)
self._lensMassProfile = LensProfileAnalysis(lens_model=self.LensModel)
self.kwargs_model = kwargs_model
self._kwargs_cosmo = {
'D_d': self.lensCosmo.D_d,
'D_s': self.lensCosmo.D_s,
'D_ds': self.lensCosmo.D_ds
}
self._lens_model_kinematics_bool = lens_model_kinematics_bool
self._light_model_kinematics_bool = light_model_kinematics_bool
def setup(self):
np.random.seed(42)
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
kwargs_model = {'lens_model_list': ['SPEP'],
'lens_light_model_list': ['SERSIC'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['SOURCE_POSITION'],
'fixed_magnification_list': [True]}
# PSF specification
kwargs_band = sim_util.data_configure_simple(numPix, deltaPix, exp_time, sigma_bkg)
data_class = ImageData(**kwargs_band)
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': fwhm, 'pixel_size': deltaPix}
psf_class = PSF(**kwargs_psf)
print(np.shape(psf_class.kernel_point_source), 'test kernel shape -')
kwargs_spep = {'theta_E': 1., 'gamma': 1.95, 'center_x': 0, 'center_y': 0, 'e1': 0.1, 'e2': 0.1}
self.kwargs_lens = [kwargs_spep]
kwargs_sersic = {'amp': 1/0.05**2., 'R_sersic': 0.1, 'n_sersic': 2, 'center_x': 0, 'center_y': 0}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {'amp': 1., 'R_sersic': .6, 'n_sersic': 3, 'center_x': 0, 'center_y': 0}
self.kwargs_lens_light = [kwargs_sersic]
self.kwargs_source = [kwargs_sersic_ellipse]
self.kwargs_ps = [{'ra_source': 0.55, 'dec_source': 0.02,
'source_amp': 1.}] # quasar point source position in the source plane and intrinsic brightness
self.kwargs_cosmo = {'D_dt': 1000}
kwargs_numerics = {'supersampling_factor': 1, 'supersampling_convolution': False}
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(**kwargs_model)
imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class, point_source_class, extinction_class, kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens, self.kwargs_source,
self.kwargs_lens_light, self.kwargs_ps)
ra_pos, dec_pos = imageModel.PointSource.image_position(kwargs_ps=self.kwargs_ps, kwargs_lens=self.kwargs_lens)
data_class.update_data(image_sim)
kwargs_band['image_data'] = image_sim
self.data_class = data_class
self.psf_class = psf_class
self.kwargs_model = kwargs_model
self.kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False}
kwargs_constraints = {
'num_point_source_list': [4],
'solver_type': 'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
'Ddt_sampling': True
}
def condition_definition(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps=None, kwargs_special=None, kwargs_extinction=None):
logL = 0
if kwargs_lens_light[0]['R_sersic'] > kwargs_source[0]['R_sersic']:
logL -= 10**15
return logL
kwargs_likelihood = {'force_no_add_image': True,
'source_marg': True,
'astrometric_likelihood': True,
'image_position_uncertainty': 0.004,
'check_matched_source_position': False,
'source_position_tolerance': 0.001,
'source_position_sigma': 0.001,
'check_positive_flux': True,
'flux_ratio_likelihood': True,
'prior_lens': [[0, 'theta_E', 1, 0.1]],
'custom_logL_addition': condition_definition,
'image_position_likelihood': True
}
self.kwargs_data = {'multi_band_list': [[kwargs_band, kwargs_psf, kwargs_numerics]], 'multi_band_type': 'single-band',
'time_delays_measured': np.ones(4),
'time_delays_uncertainties': np.ones(4),
'flux_ratios': np.ones(4),
'flux_ratio_errors': np.ones(4),
'ra_image_list': ra_pos,
'dec_image_list': dec_pos
}
self.param_class = Param(self.kwargs_model, **kwargs_constraints)
self.imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class,
point_source_class, kwargs_numerics=kwargs_numerics)
self.Likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data, kwargs_model=kwargs_model, param_class=self.param_class, **kwargs_likelihood)
self.kwargs_band = kwargs_band
self.kwargs_psf = kwargs_psf
self.numPix = numPix
def __init__(self,
z_lens,
z_source,
kwargs_model,
cosmo_fiducial=None,
lens_model_kinematics_bool=None,
light_model_kinematics_bool=None,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model=None):
if cosmo_fiducial is None:
cosmo_fiducial = default_cosmology.get()
self._z_lens = z_lens
self._z_source = z_source
self._cosmo_fiducial = cosmo_fiducial
self._lens_cosmo = LensCosmo(z_lens=z_lens,
z_source=z_source,
cosmo=self._cosmo_fiducial)
self.LensModel, self.SourceModel, self.LensLightModel, self.PointSource, extinction_class = class_creator.create_class_instances(
all_models=True, **kwargs_model)
super(TDCosmography, self).__init__(
z_lens=z_lens,
z_source=z_source,
kwargs_model=kwargs_model,
cosmo=cosmo_fiducial,
lens_model_kinematics_bool=lens_model_kinematics_bool,
light_model_kinematics_bool=light_model_kinematics_bool,
kwargs_seeing=kwargs_seeing,
kwargs_aperture=kwargs_aperture,
anisotropy_model=anisotropy_model)
def __init__(self,
z_lens,
z_source,
kwargs_model,
cosmo_fiducial=None,
lens_model_kinematics_bool=None,
light_model_kinematics_bool=None,
kwargs_seeing=None,
kwargs_aperture=None,
anisotropy_model=None,
**kwargs_kin_api):
"""
:param z_lens: redshift of deflector
:param z_source: redshift of source
:param kwargs_model: model configurations (according to FittingSequence)
:param cosmo_fiducial: fiducial cosmology used to compute angular diameter distances where required
:param lens_model_kinematics_bool: (optional) bool list, corresponding to lens models being included into the
kinematics modeling
:param light_model_kinematics_bool: (optional) bool list, corresponding to lens light models being included
into the kinematics modeling
:param kwargs_seeing: seeing conditions (see observation class in Galkin)
:param kwargs_aperture: aperture keyword arguments (see aperture class in Galkin)
:param anisotropy_model: string, anisotropy model type
:param kwargs_kin_api: additional keyword arguments for KinematicsAPI class instance
"""
if cosmo_fiducial is None:
cosmo_fiducial = default_cosmology.get()
if kwargs_seeing is None:
kwargs_seeing = {}
if kwargs_aperture is None:
kwargs_aperture = {}
self._z_lens = z_lens
self._z_source = z_source
self._cosmo_fiducial = cosmo_fiducial
self._lens_cosmo = LensCosmo(z_lens=z_lens,
z_source=z_source,
cosmo=self._cosmo_fiducial)
self.LensModel, self.SourceModel, self.LensLightModel, self.PointSource, extinction_class = class_creator.create_class_instances(
all_models=True, **kwargs_model)
super(TDCosmography, self).__init__(
z_lens=z_lens,
z_source=z_source,
kwargs_model=kwargs_model,
cosmo=cosmo_fiducial,
lens_model_kinematics_bool=lens_model_kinematics_bool,
light_model_kinematics_bool=light_model_kinematics_bool,
kwargs_seeing=kwargs_seeing,
kwargs_aperture=kwargs_aperture,
anisotropy_model=anisotropy_model,
**kwargs_kin_api)
def _class_instances(self, kwargs_model, kwargs_imaging, kwargs_position,
kwargs_flux, kwargs_time_delay):
"""
:param kwargs_model: lenstronomy model keyword arguments
:param kwargs_imaging: keyword arguments for imaging likelihood
:param kwargs_position: keyword arguments for positional likelihood
:param kwargs_flux: keyword arguments for flux ratio likelihood
:param kwargs_time_delay: keyword arguments for time delay likelihood
:return: updated model instances of this class
"""
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(
**kwargs_model)
self.PointSource = point_source_class
if self._time_delay_likelihood is True:
self.time_delay_likelihood = TimeDelayLikelihood(
lens_model_class=lens_model_class,
point_source_class=point_source_class,
**kwargs_time_delay)
if self._image_likelihood is True:
self.image_likelihood = ImageLikelihood(kwargs_model=kwargs_model,
**kwargs_imaging)
self._position_likelihood = PositionLikelihood(point_source_class,
**kwargs_position)
if self._flux_ratio_likelihood is True:
self.flux_ratio_likelihood = FluxRatioLikelihood(
lens_model_class, **kwargs_flux)
