lenstronomy.ImSim.MultiBand package

Submodules

lenstronomy.ImSim.MultiBand.joint_linear module

class lenstronomy.ImSim.MultiBand.joint_linear.JointLinear(multi_band_list, kwargs_model, compute_bool=None, likelihood_mask_list=None)[source]

Bases: lenstronomy.ImSim.MultiBand.multi_linear.MultiLinear

class to model multiple exposures in the same band and makes a constraint fit to all bands simultaneously with joint constraints on the surface brightness of the model. This model setting require the same surface brightness models to be called in all available images/bands

data_response

returns the 1d array of the data element that is fitted for (including masking)

Returns:1d numpy array
error_response(kwargs_lens, kwargs_ps, kwargs_special=None)[source]

returns the 1d array of the error estimate corresponding to the data response

Returns:1d numpy array of response, 2d array of additonal errors (e.g. point source uncertainties)
image_linear_solve(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None, inv_bool=False)[source]

computes the image (lens and source surface brightness with a given lens model). The linear parameters are computed with a weighted linear least square optimization (i.e. flux normalization of the brightness profiles)

Parameters:
  • kwargs_lens – list of keyword arguments corresponding to the superposition of different lens profiles
  • kwargs_source – list of keyword arguments corresponding to the superposition of different source light profiles
  • kwargs_lens_light – list of keyword arguments corresponding to different lens light surface brightness profiles
  • kwargs_ps – keyword arguments corresponding to “other” parameters, such as external shear and point source image positions
  • inv_bool – if True, invert the full linear solver Matrix Ax = y for the purpose of the covariance matrix.
Returns:

1d array of surface brightness pixels of the optimal solution of the linear parameters to match the data

likelihood_data_given_model(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None, source_marg=False, linear_prior=None, check_positive_flux=False)[source]

computes the likelihood of the data given a model This is specified with the non-linear parameters and a linear inversion and prior marginalisation.

Parameters:
  • kwargs_lens
  • kwargs_source
  • kwargs_lens_light
  • kwargs_ps
  • check_positive_flux – bool, if True, checks whether the linear inversion resulted in non-negative flux components and applies a punishment in the likelihood if so.
Returns:

log likelihood (natural logarithm) (sum of the log likelihoods of the individual images)

linear_response_matrix(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None)[source]

computes the linear response matrix (m x n), with n being the data size and m being the coefficients

Parameters:
  • kwargs_lens
  • kwargs_source
  • kwargs_lens_light
  • kwargs_ps
Returns:

lenstronomy.ImSim.MultiBand.multi_data_base module

class lenstronomy.ImSim.MultiBand.multi_data_base.MultiDataBase(imageModel_list, compute_bool=None)[source]

Bases: object

Base class with definitions that are shared among all variations of modelling multiple data sets

num_bands
num_data_evaluate
num_param_linear(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps)[source]
Parameters:compute_bool
Returns:number of linear coefficients to be solved for in the linear inversion
num_response_list

list of number of data elements that are used in the minimization

Returns:list of integers
reduced_residuals(model_list, error_map_list=None)[source]
Parameters:
  • model_list – list of models
  • error_map_list – list of error maps
Returns:

reset_point_source_cache(bool=True)[source]

deletes all the cache in the point source class and saves it from then on

Returns:

lenstronomy.ImSim.MultiBand.multi_linear module

class lenstronomy.ImSim.MultiBand.multi_linear.MultiLinear(multi_band_list, kwargs_model, likelihood_mask_list=None, compute_bool=None, kwargs_pixelbased=None)[source]

Bases: lenstronomy.ImSim.MultiBand.multi_data_base.MultiDataBase

class to simulate/reconstruct images in multi-band option. This class calls functions of image_model.py with different bands with joint non-linear parameters and decoupled linear parameters.

the class supports keyword arguments ‘index_lens_model_list’, ‘index_source_light_model_list’, ‘index_lens_light_model_list’, ‘index_point_source_model_list’, ‘index_optical_depth_model_list’ in kwargs_model These arguments should be lists of length the number of imaging bands available and each entry in the list is a list of integers specifying the model components being evaluated for the specific band.

E.g. there are two bands and you want to different light profiles being modeled. - you define two different light profiles lens_light_model_list = [‘SERSIC’, ‘SERSIC’] - set index_lens_light_model_list = [[0], [1]] - (optional) for now all the parameters between the two light profiles are independent in the model. You have the possibility to join a subset of model parameters (e.g. joint centroid). See the Param() class for documentation.

image_linear_solve(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None, inv_bool=False)[source]

computes the image (lens and source surface brightness with a given lens model). The linear parameters are computed with a weighted linear least square optimization (i.e. flux normalization of the brightness profiles)

Parameters:
  • kwargs_lens – list of keyword arguments corresponding to the superposition of different lens profiles
  • kwargs_source – list of keyword arguments corresponding to the superposition of different source light profiles
  • kwargs_lens_light – list of keyword arguments corresponding to different lens light surface brightness profiles
  • kwargs_ps – keyword arguments corresponding to “other” parameters, such as external shear and point source image positions
  • inv_bool – if True, invert the full linear solver Matrix Ax = y for the purpose of the covariance matrix.
Returns:

1d array of surface brightness pixels of the optimal solution of the linear parameters to match the data

likelihood_data_given_model(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None, source_marg=False, linear_prior=None, check_positive_flux=False)[source]

computes the likelihood of the data given a model This is specified with the non-linear parameters and a linear inversion and prior marginalisation.

Parameters:
  • kwargs_lens
  • kwargs_source
  • kwargs_lens_light
  • kwargs_ps
  • check_positive_flux – bool, if True, checks whether the linear inversion resulted in non-negative flux components and applies a punishment in the likelihood if so.
Returns:

log likelihood (natural logarithm) (sum of the log likelihoods of the individual images)

lenstronomy.ImSim.MultiBand.single_band_multi_model module

class lenstronomy.ImSim.MultiBand.single_band_multi_model.SingleBandMultiModel(multi_band_list, kwargs_model, likelihood_mask_list=None, band_index=0, kwargs_pixelbased=None)[source]

Bases: lenstronomy.ImSim.image_linear_solve.ImageLinearFit

class to simulate/reconstruct images in multi-band option. This class calls functions of image_model.py with different bands with decoupled linear parameters and the option to pass/select different light models for the different bands

the class supports keyword arguments ‘index_lens_model_list’, ‘index_source_light_model_list’, ‘index_lens_light_model_list’, ‘index_point_source_model_list’, ‘index_optical_depth_model_list’ in kwargs_model These arguments should be lists of length the number of imaging bands available and each entry in the list is a list of integers specifying the model components being evaluated for the specific band.

E.g. there are two bands and you want to different light profiles being modeled. - you define two different light profiles lens_light_model_list = [‘SERSIC’, ‘SERSIC’] - set index_lens_light_model_list = [[0], [1]] - (optional) for now all the parameters between the two light profiles are independent in the model. You have the possibility to join a subset of model parameters (e.g. joint centroid). See the Param() class for documentation.

error_map_source(kwargs_source, x_grid, y_grid, cov_param, model_index_select=True)[source]

variance of the linear source reconstruction in the source plane coordinates, computed by the diagonal elements of the covariance matrix of the source reconstruction as a sum of the errors of the basis set.

Parameters:
  • kwargs_source – keyword arguments of source model
  • x_grid – x-axis of positions to compute error map
  • y_grid – y-axis of positions to compute error map
  • cov_param – covariance matrix of liner inversion parameters
  • model_index_select – boolean, if True, selects the model components of this band (default). If False, assumes input kwargs_source is already selected list.
Returns:

diagonal covariance errors at the positions (x_grid, y_grid)

image_linear_solve(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None, inv_bool=False)[source]

computes the image (lens and source surface brightness with a given lens model). The linear parameters are computed with a weighted linear least square optimization (i.e. flux normalization of the brightness profiles) :param kwargs_lens: list of keyword arguments corresponding to the superposition of different lens profiles :param kwargs_source: list of keyword arguments corresponding to the superposition of different source light profiles :param kwargs_lens_light: list of keyword arguments corresponding to different lens light surface brightness profiles :param kwargs_ps: keyword arguments corresponding to “other” parameters, such as external shear and point source image positions :param inv_bool: if True, invert the full linear solver Matrix Ax = y for the purpose of the covariance matrix. :return: 1d array of surface brightness pixels of the optimal solution of the linear parameters to match the data

likelihood_data_given_model(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None, source_marg=False, linear_prior=None, check_positive_flux=False)[source]

computes the likelihood of the data given a model This is specified with the non-linear parameters and a linear inversion and prior marginalisation.

Parameters:
  • kwargs_lens
  • kwargs_source
  • kwargs_lens_light
  • kwargs_ps
  • check_positive_flux – bool, if True, checks whether the linear inversion resulted in non-negative flux components and applies a punishment in the likelihood if so.
Returns:

log likelihood (natural logarithm) (sum of the log likelihoods of the individual images)

linear_response_matrix(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None)[source]

computes the linear response matrix (m x n), with n beeing the data size and m being the coefficients

Parameters:
  • kwargs_lens
  • kwargs_source
  • kwargs_lens_light
  • kwargs_ps
Returns:

num_param_linear(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None)[source]
Parameters:compute_bool
Returns:number of linear coefficients to be solved for in the linear inversion
select_kwargs(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None)[source]

select subset of kwargs lists referenced to this imaging band

Parameters:
  • kwargs_lens
  • kwargs_source
  • kwargs_lens_light
  • kwargs_ps
Returns:

Module contents