lenstronomy.ImSim.MultiBand package¶
Submodules¶
lenstronomy.ImSim.MultiBand.joint_linear module¶
- class JointLinear(multi_band_list, kwargs_model, compute_bool=None, likelihood_mask_list=None)[source]¶
Bases:
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
- __init__(multi_band_list, kwargs_model, compute_bool=None, likelihood_mask_list=None)[source]¶
- Parameters:
multi_band_list – list of imaging band configurations [[kwargs_data, kwargs_psf, kwargs_numerics],[…], …]
kwargs_model – model option keyword arguments
likelihood_mask_list – list of likelihood masks (booleans with size of the individual images)
compute_bool – (optional), bool list to indicate which band to be included in the modeling
linear_solver – bool, if True (default) fixes the linear amplitude parameters ‘amp’ (avoid sampling) such that they get overwritten by the linear solver solution.
- 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
- 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:
- property 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)
- 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.multi_data_base module¶
- class MultiDataBase(image_model_list, compute_bool=None)[source]¶
Bases:
object
Base class with definitions that are shared among all variations of modelling multiple data sets.
- __init__(image_model_list, compute_bool=None)[source]¶
- Parameters:
image_model_list – list of ImageModel instances (supporting linear inversions)
compute_bool – list of booleans for each imaging band indicating whether to model it or not.
- property num_bands¶
- property num_response_list¶
List of number of data elements that are used in the minimization.
- Returns:
list of integers
- reset_point_source_cache(cache=True)[source]¶
Deletes all the cache in the point source class and saves it from then on.
- Returns:
- property num_data_evaluate¶
lenstronomy.ImSim.MultiBand.multi_linear module¶
- class MultiLinear(multi_band_list, kwargs_model, likelihood_mask_list=None, compute_bool=None, kwargs_pixelbased=None, linear_solver=True)[source]¶
Bases:
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.
- __init__(multi_band_list, kwargs_model, likelihood_mask_list=None, compute_bool=None, kwargs_pixelbased=None, linear_solver=True)[source]¶
- Parameters:
multi_band_list – list of imaging band configurations [[kwargs_data, kwargs_psf, kwargs_numerics],[…], …]
kwargs_model – model option keyword arguments
likelihood_mask_list – list of likelihood masks (booleans with size of the individual images)
compute_bool – (optional), bool list to indicate which band to be included in the modeling
linear_solver – bool, if True (default) fixes the linear amplitude parameters ‘amp’ (avoid sampling) such that they get overwritten by the linear solver solution.
- 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)
- update_linear_kwargs(param, model_band, kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps)[source]¶
Links linear parameters to kwargs arguments.
- Parameters:
param (list of array) – linear parameter vector corresponding to the response matrix
model_band – for which band the model parameters need to be retrieved
kwargs_lens –
kwargs_source –
kwargs_lens_light –
kwargs_ps –
- Returns:
updated list of kwargs with linear parameter values for specific band
lenstronomy.ImSim.MultiBand.single_band_multi_model module¶
- class SingleBandMultiModel(multi_band_list, kwargs_model, likelihood_mask_list=None, band_index=0, kwargs_pixelbased=None, linear_solver=True)[source]¶
Bases:
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.
- __init__(multi_band_list, kwargs_model, likelihood_mask_list=None, band_index=0, kwargs_pixelbased=None, linear_solver=True)[source]¶
- Parameters:
multi_band_list – list of imaging band configurations [[kwargs_data, kwargs_psf, kwargs_numerics],[…], …]
kwargs_model – model option keyword arguments
likelihood_mask_list – list of likelihood masks (booleans with size of the individual images
band_index – integer, index of the imaging band to model
kwargs_pixelbased – keyword arguments with various settings related to the pixel-based solver (see SLITronomy documentation)
linear_solver – bool, if True (default) fixes the linear amplitude parameters ‘amp’ (avoid sampling) such that they get overwritten by the linear solver solution.
- image(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_extinction=None, kwargs_special=None, unconvolved=False, source_add=True, lens_light_add=True, point_source_add=True)[source]¶
Make an image with a realisation of linear parameter values “param”.
- 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
unconvolved – if True: returns the unconvolved light distribution (prefect seeing)
source_add – if True, compute source, otherwise without
lens_light_add – if True, compute lens light, otherwise without
point_source_add – if True, add point sources, otherwise without
- Returns:
2d array of surface brightness pixels of the simulation
- source_surface_brightness(kwargs_source, kwargs_lens=None, kwargs_extinction=None, kwargs_special=None, unconvolved=False, de_lensed=False, k=None, update_pixelbased_mapping=True)[source]¶
Computes the source surface brightness distribution.
- Parameters:
kwargs_source – list of keyword arguments corresponding to the superposition of different source light profiles
kwargs_lens – list of keyword arguments corresponding to the superposition of different lens profiles
kwargs_extinction – list of keyword arguments of extinction model
unconvolved – if True: returns the unconvolved light distribution (prefect seeing)
de_lensed – if True: returns the un-lensed source surface brightness profile, otherwise the lensed.
k – integer, if set, will only return the model of the specific index
- Returns:
2d array of surface brightness pixels
- lens_surface_brightness(kwargs_lens_light, unconvolved=False, k=None)[source]¶
Computes the lens surface brightness distribution.
- Parameters:
kwargs_lens_light – list of keyword arguments corresponding to different lens light surface brightness profiles
unconvolved – if True, returns unconvolved surface brightness (perfect seeing), otherwise convolved with PSF kernel
- Returns:
2d array of surface brightness pixels
- point_source(kwargs_ps, kwargs_lens=None, kwargs_special=None, unconvolved=False, k=None)[source]¶
Computes the point source positions and paints PSF convolutions on them.
- Parameters:
kwargs_ps –
kwargs_lens –
kwargs_special –
unconvolved –
k –
- Returns:
- 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, linear_solver=True)[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)
- num_param_linear(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None)[source]¶
- Returns:
number of linear coefficients to be solved for in the linear inversion
- 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:
- 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)
- error_response(kwargs_lens, kwargs_ps, kwargs_special)[source]¶
Returns the 1d array of the error estimate corresponding to the data response.
- Returns:
1d numpy array of response, 2d array of additional errors (e.g. point source uncertainties)
- update_linear_kwargs(param, kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps, model_band=None)[source]¶
links linear parameters to kwargs arguments ATTENTION: this function requires input dictionary lists to be already contracted to the ones applied to the specific band
- Parameters:
param – linear parameter vector corresponding to the response matrix
- Returns:
updated list of kwargs with linear parameter values
- extinction_map(kwargs_extinction=None, kwargs_special=None)[source]¶
Differential extinction per pixel.
- Parameters:
kwargs_extinction – list of keyword arguments corresponding to the optical depth models tau, such that extinction is exp(-tau)
kwargs_special – keyword arguments, additional parameter to the extinction
- Returns:
2d array of size of the image