lenstronomy.Workflow package

Submodules

lenstronomy.Workflow.alignment_matching module

class AlignmentFitting(multi_band_list, kwargs_model, kwargs_params, band_index=0, likelihood_mask_list=None, align_offset=True, align_rotation=False)

Bases: object

Class which executes the different sampling methods.

__init__(multi_band_list, kwargs_model, kwargs_params, band_index=0, likelihood_mask_list=None, align_offset=True, align_rotation=False)

Initialise the classes of the chain and for parameter options.

Parameters:

• align_offset (boolean) – aligns shift in Ra and Dec

• align_rotation (boolean) – aligns coordinate rotation

pso(n_particles=10, n_iterations=10, delta_shift=0.2, delta_rot=0.1, threadCount=1, mpi=False, print_key='default')

Returns the best fit for the lens model on catalogue basis with particle swarm optimizer.

Parameters:

• n_particles –

• n_iterations –

• delta_shift – astrometric shift tolerance

• delta_rot – rotation angle tolerance [in radian]

• threadCount –

• mpi –

• print_key –

Returns:

class AlignmentLikelihood(multi_band_list, kwargs_model, kwargs_params, band_index=0, likelihood_mask_list=None, align_offset=True, align_rotation=False)

Bases: object

__init__(multi_band_list, kwargs_model, kwargs_params, band_index=0, likelihood_mask_list=None, align_offset=True, align_rotation=False)

Initializes all the classes needed for the chain.

Parameters:

• align_offset (boolean) – aligns shift in Ra and Dec

• align_rotation (boolean) – aligns coordinate rotation

likelihood(a)

setup()

update_multi_band(args)

Parameters:

args – list of parameters

Returns:

updated multi_band_list

update_data(args)

Parameters:

args –

Returns:

get_args(kwargs_data)

Parameters:

kwargs_data – keyword arguments for ImageData()

Returns:

arguments being sampled

property num_param

lower_upper_limit(delta_shift, delta_rot)

Parameters:

• delta_shift – astrometric shift tolerance

• delta_rot – rotation angle tolerance [in radian]

Returns:

lower_limit, upper_limit

lenstronomy.Workflow.fitting_sequence module

class FittingSequence(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params, mpi=False, verbose=True)

Bases: object

Class to define a sequence of fitting applied, inherit the Fitting class this is a Workflow manager that allows to update model configurations before executing another step in the modelling The user can take this module as an example of how to create their own workflows or build their own around the FittingSequence.

__init__(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params, mpi=False, verbose=True)

Parameters:

• kwargs_data_joint – keyword argument specifying the data according to LikelihoodModule

• kwargs_model – keyword arguments to describe all model components used in class_creator.create_class_instances()

• kwargs_constraints – keyword arguments of the Param() class to handle parameter constraints during the sampling (except upper and lower limits and sampling input mean and width)

• kwargs_likelihood – keyword arguments of the Likelihood() class to handle parameters and settings of the likelihood

• kwargs_params – setting of the sampling bounds and initial guess mean and spread. The argument is organized as: ‘lens_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘source_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘lens_light_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘point_source_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘extinction_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘special’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘tracer_source_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper]

• mpi – MPI option (bool), if True, will launch an MPI Pool job for the steps in the fitting sequence where possible

• verbose – bool, if True prints temporary results and indicators of the fitting process

property kwargs_fixed

Returns the updated kwargs_fixed from the update manager.

Returns:

list of fixed kwargs, see UpdateManager()

fit_sequence(fitting_list)

Parameters:

fitting_list – list of [[‘string’, {kwargs}], ..] with ‘string being the specific fitting option and kwargs being the arguments passed to this option

Returns:

fitting results

best_fit(bijective=False)

Parameters:

bijective – bool, if True, the mapping of image2source_plane and the mass_scaling parameterisation are inverted. If you do not use those options, there is no effect.

Returns:

best fit model of the current state of the FittingSequence class

update_state(kwargs_update)

Updates current best fit state to the input model keywords specified.

Parameters:

kwargs_update – format of kwargs_result

Returns:

None

property best_fit_likelihood

Returns the log likelihood of the best fit model of the current state of this class.

Returns:

log likelihood, float

property bic

Bayesian information criterion (BIC) of the model.

Returns:

bic value, float

property param_class

Returns:

Param() class instance reflecting the current state of FittingSequence

property likelihoodModule

Returns:

Likelihood() class instance reflecting the current state of FittingSequence

simplex(n_iterations, method='Nelder-Mead')

Downhill simplex optimization using the Nelder-Mead algorithm.

Parameters:

• n_iterations – maximum number of iterations to perform

• method – the optimization method used, see documentation in scipy.optimize.minimize

Returns:

result of the best fit

mcmc(n_burn, n_run, walkerRatio=None, n_walkers=None, sigma_scale=1, threadCount=1, init_samples=None, re_use_samples=True, sampler_type='EMCEE', progress=True, backend_filename=None, start_from_backend=False, **kwargs_zeus)

MCMC routine.

Parameters:

• n_burn – number of burn in iterations (will not be saved)

• n_run – number of MCMC iterations that are saved

• walkerRatio – ratio of walkers/number of free parameters

• n_walkers – integer, number of walkers of emcee (optional, if set, overwrites the walkerRatio input

• sigma_scale – scaling of the initial parameter spread relative to the width in the initial settings

• threadCount – number of CPU threads. If MPI option is set, threadCount=1

• init_samples – initial sample from where to start the MCMC process

• re_use_samples – bool, if True, re-uses the samples described in init_samples.nOtherwise starts from scratch.

• sampler_type – string, which MCMC sampler to be used. Options are ‘emcee’ and ‘zeus’

• progress – boolean, if True shows progress bar in EMCEE

• backend_filename (string) – name of the HDF5 file where sampling state is saved (through emcee backend engine)

• start_from_backend (bool) – if True, start from the state saved in backup_filename. O therwise, create a new backup file with name backup_filename (any already existing file is overwritten!).

• kwargs_zeus – zeus-specific kwargs

Returns:

list of output arguments, e.g. MCMC samples, parameter names, logL distances of all samples specified by the specific sampler used

pso(n_particles, n_iterations, sigma_scale=1, print_key='PSO', threadCount=1)

Particle Swarm Optimization.

Parameters:

• n_particles – number of particles in the Particle Swarm Optimization

• n_iterations – number of iterations in the optimization process

• sigma_scale – scaling of the initial parameter spread relative to the width in the initial settings

• print_key – string, printed text when executing this routine

• threadCount – number of CPU threads. If MPI option is set, threadCount=1

Returns:

result of the best fit, the PSO chain of the best fit parameter after each iteration [lnlikelihood, parameters, velocities], list of parameters in same order as in chain

nested_sampling(sampler_type='dynesty', kwargs_run={}, prior_type='uniform', width_scale=1, sigma_scale=1, output_basename='chain', remove_output_dir=True, dypolychord_dynamic_goal=0.8, polychord_settings={}, dypolychord_seed_increment=200, output_dir='nested_sampling_chains', dynesty_bound='multi', dynesty_sample='auto')

Run (Dynamic) Nested Sampling algorithms, depending on the type of algorithm.

Parameters:

• sampler_type – ‘MULTINEST’, ‘DYPOLYCHORD’, ‘DYNESTY’

• kwargs_run – keywords passed to the core sampling method

• prior_type – ‘uniform’ of ‘gaussian’, for converting the unit hypercube to param cube :param width_scale: scale the width (lower/upper limits) of the parameters space by this factor

• sigma_scale – if prior_type is ‘gaussian’, scale the gaussian sigma by this factor

• output_basename – name of the folder in which the core MultiNest/PolyChord code will save output files

• remove_output_dir – if True, the above folder is removed after completion

• dypolychord_dynamic_goal – dynamic goal for DyPolyChord (trade-off between evidence (0) and posterior (1) computation) :param polychord_settings: settings dictionary to send to pypolychord. Check dypolychord documentation for details.

• dypolychord_seed_increment – seed increment for dypolychord with MPI. Check dypolychord documentation for details.

• dynesty_bound – see https://dynesty.readthedocs.io

• sampler_type – ‘MULTINEST’, ‘DYPOLYCHORD’, ‘DYNESTY’

• kwargs_run – keywords passed to the core sampling method

• prior_type – ‘uniform’ of ‘gaussian’, for converting the unit hypercube to param cube

• width_scale – scale the width (lower/upper limits) of the parameters space by this factor

• sigma_scale – if prior_type is ‘gaussian’, scale the gaussian sigma by this factor

• output_basename – name of the folder in which the core MultiNest/PolyChord code will save output files

• remove_output_dir – if True, the above folder is removed after completion

• dypolychord_dynamic_goal – dynamic goal for DyPolyChord (trade-off between evidence (0) and posterior (1) computation)

• polychord_settings – settings dictionary to send to pypolychord. Check dypolychord documentation for details.

• dypolychord_seed_increment – seed increment for dypolychord with MPI. Check dypolychord documentation for details.

• dynesty_bound – see https://dynesty.readthedocs.io for details

• dynesty_sample – see https://dynesty.readthedocs.io for details

Returns:

list of output arguments : samples, mean inferred values, log- likelihood, log-evidence, error on log-evidence for each sample

psf_iteration(compute_bands=None, **kwargs_psf_iter)

Iterative PSF reconstruction.

Parameters:

• compute_bands – bool list, if multiple bands, this process can be limited to a subset of bands

• kwargs_psf_iter – keyword arguments as used or available in PSFIteration.update_iterative() definition

Returns:

0, updated PSF is stored in self.multi_band_list

align_images(n_particles=10, n_iterations=10, align_offset=True, align_rotation=False, threadCount=1, compute_bands=None, delta_shift=0.2, delta_rot=0.1)

Aligns the coordinate systems of different exposures within a fixed model parameterisation by executing a PSO with relative coordinate shifts as free parameters.

Parameters:

• n_particles – number of particles in the Particle Swarm Optimization

• n_iterations – number of iterations in the optimization process

• align_offset (boolean) – aligns shift in Ra and Dec

• align_rotation (boolean) – aligns coordinate rotation

• delta_shift – astrometric shift tolerance

• delta_rot – rotation angle tolerance [in radian]

• compute_bands – bool list, if multiple bands, this process can be limited to a subset of bands for which the coordinate system is being fit for best alignment to the model parameters

Returns:

0, updated coordinate system for the band(s)

flux_calibration(n_particles=10, n_iterations=10, threadCount=1, calibrate_bands=None, scaling_lower_limit=0, scaling_upper_limit=1000)

Calibrates flux_scaling between multiple images. This routine only works in ‘join-linear’ model when fluxes are meant to be identical for different bands.

Parameters:

• n_particles – number of particles in the Particle Swarm Optimization

• n_iterations – number of iterations in the optimization process

• calibrate_bands (list of booleans of length of the imaging bands) – state which bands the flux calibration is applied to

• threadCount (integer) – number of CPU threads. If MPI option is set, threadCount=1

• scaling_lower_limit – lower limit of flux_scaling

• scaling_upper_limit – upper limit of flux scaling

Returns:

0, updated coordinate system for the band(s)

update_settings(kwargs_model=None, kwargs_constraints=None, kwargs_likelihood=None, lens_add_fixed=None, source_add_fixed=None, lens_light_add_fixed=None, ps_add_fixed=None, special_add_fixed=None, tracer_source_add_fixed=None, lens_remove_fixed=None, source_remove_fixed=None, lens_light_remove_fixed=None, ps_remove_fixed=None, special_remove_fixed=None, tracer_source_remove_fixed=None, change_source_lower_limit=None, change_source_upper_limit=None, change_lens_lower_limit=None, change_lens_upper_limit=None, change_sigma_lens=None, change_sigma_source=None, change_sigma_lens_light=None)

Updates lenstronomy settings “on the fly”.

Parameters:

• kwargs_model – kwargs, specified keyword arguments overwrite the existing ones

• kwargs_constraints – kwargs, specified keyword arguments overwrite the existing ones

• kwargs_likelihood – kwargs, specified keyword arguments overwrite the existing ones

• lens_add_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• source_add_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• lens_light_add_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• ps_add_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• special_add_fixed – [‘param1’, ‘param2’,…]

• special_add_fixed – [‘param1’, ‘param2’,…]

• tracer_source_add_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• lens_remove_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• source_remove_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• lens_light_remove_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• ps_remove_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• special_remove_fixed – [‘param1’, ‘param2’,…]

• special_remove_fixed – [‘param1’, ‘param2’,…]

• tracer_source_remove_fixed – [[i_model, [‘param1’, ‘param2’,…], […]]

• change_lens_lower_limit – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_lens_upper_limit – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_source_lower_limit – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_source_upper_limit – [[i_model, [‘’param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_sigma_lens – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_sigma_source – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_sigma_lens_light – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

Returns:

0, the settings are overwritten for the next fitting step to come

set_param_value(**kwargs)

Set a parameter to a specific value. kwargs are below.

Parameters:

• lens – [[i_model, [‘param1’, ‘param2’,…], […]]

• source – [[i_model, [‘param1’, ‘param2’,…], […]]

• lens_light – [[i_model, [‘param1’, ‘param2’,…], […]]

• ps – [[i_model, [‘param1’, ‘param2’,…], […]]

Returns:

0, the value of the param is overwritten

Return type:

fix_not_computed(free_bands)

Fixes lens model parameters of imaging bands/frames that are not computed and frees the parameters of the other lens models to the initial kwargs_fixed options.

Parameters:

free_bands – bool list of length of imaging bands in order of imaging bands, if False: set fixed lens model

Returns:

None

best_fit_from_samples(samples, logl)

Return best fit (max likelihood) value of samples in lenstronomy conventions.

Parameters:

• samples – samples of multi-dimensional parameter space

• logl – likelihood values for each sample

Returns:

kwargs_result in lenstronomy convention

lenstronomy.Workflow.psf_fitting module

class PsfFitting(image_model_class)

Bases: object

Class to find subsequently a better psf The method make use of a model and subtracts all the non-point source components of the model from the data. If the model is sufficient, then the data will be a (better) representation of the actual PSF. The method cuts out those point sources and combines them to update the estimate of the PSF. This is done in an iterative procedure as the model components of the extended features is PSF-dependent (hopefully not too much).

Various options can be chosen. There is no guarantee that the method works for specific data and models.

‘stacking_method’: ‘median’, ‘mean’; the different estimates of the PSF are stacked and combined together. The choices are:

• ‘mean’: mean of pixel values as the estimator (not robust to outliers)

• ‘median’: median of pixel values as the estimator (outlier rejection robust but needs >2 point sources in the data

‘block_center_neighbour’: angle, radius of neighbouring point sources around their centers the estimates is ignored.

Default is zero, meaning a not optimal subtraction of the neighbouring point sources might contaminate the estimate.

‘keep_error_map’: bool, if True, does not replace the error term associated with the PSF estimate.

If false, re-estimates the variance between the PSF estimates.

‘psf_symmetry’: number of rotational invariant symmetries in the estimated PSF.

=1 mean no additional symmetries. =4 means 90 deg symmetry. This is enforced by a rotatioanl stack according to the symmetry specified. These additional imposed symmetries can help stabelize the PSF estimate when there are limited constraints/number of point sources in the image.

The procedure only requires and changes the ‘point_source_kernel’ in the PSF() class and the ‘psf_error_map’. Any previously set subgrid kernels or pixel_kernels are removed and constructed from the ‘point_source_kernel’.

__init__(image_model_class)

Parameters:

image_model_class – ImageModel class instance

static calc_cornermask(kernelsize, psf_symmetry)

Calculate the completeness numerically when rotational symmetry is imposed. This is the simplest ‘mask’ which throws away anywhere the rotations are not fully complete ->e.g. in the corners. This ONLY accounts for information loss in corners, not due e.g. to losses at the edges of the images.

Parameters:

• kernelsize – int, size of kernel array

• psf_symmetry – int, the symmetry being imposed on the data

Returns:

mask showing where the psf with symmetry n is incomplete due to rotation.

update_iterative(kwargs_psf, kwargs_params, num_iter=10, keep_psf_error_map=True, no_break=True, verbose=True, **kwargs_psf_update)

Parameters:

• kwargs_psf – keyword arguments to construct the PSF() class

• kwargs_params – keyword arguments of the parameters of the model components (e.g. ‘kwargs_lens’ etc)

• num_iter – number of iterations in the PSF fitting and image fitting process

• keep_psf_error_map – boolean, if True keeps previous psf_error_map

• no_break – boolean, if True, runs until the end regardless of the next step getting worse, and then reads out the overall best fit

• verbose – print statements informing about progress of iterative procedure

• kwargs_psf_update – keyword arguments providing the settings for a single iteration of the PSF, as being passed to update_psf() method

Returns:

keyword argument of PSF constructor for PSF() class with updated PSF

update_psf(kwargs_psf, kwargs_params, corner_mask=None, stacking_method='median', psf_symmetry=1, psf_iter_factor=0.2, block_center_neighbour=0, error_map_radius=None, block_center_neighbour_error_map=None, new_procedure=True, corner_symmetry=None)

Parameters:

• kwargs_psf – keyword arguments to construct the PSF() class

• kwargs_params – keyword arguments of the parameters of the model components (e.g. ‘kwargs_lens’ etc)

• stacking_method – ‘median’, ‘mean’; the different estimates of the PSF are stacked and combined together. The choices are: ‘mean’: mean of pixel values as the estimator (not robust to outliers) ‘median’: median of pixel values as the estimator (outlier rejection robust but needs >2 point sources in the data

• psf_symmetry – number of rotational invariant symmetries in the estimated PSF. =1 mean no additional symmetries. =4 means 90 deg symmetry. This is enforced by a rotatioanl stack according to the symmetry specified. These additional imposed symmetries can help stabelize the PSF estimate when there are limited constraints/number of point sources in the image.

• psf_iter_factor – factor in (0, 1] of ratio of old vs new PSF in the update in the iteration.

• block_center_neighbour – angle, radius of neighbouring point sources around their centers the estimates is ignored. Default is zero, meaning a not optimal subtraction of the neighbouring point sources might contaminate the estimate.

• block_center_neighbour_error_map – angle, radius of neighbouring point sources around their centers the estimates of the ERROR MAP is ignored. If None, then the value of block_center_neighbour is used (recommended)

• error_map_radius – float, radius (in arc seconds) of the outermost error in the PSF estimate (e.g. to avoid double counting of overlapping PSF errors), if None, all of the pixels are considered (unless blocked through other means)

• new_procedure – boolean, uses post lenstronomy 1.9.2 procedure which is more optimal for super-sampled PSF’s

• corner_mask – a mask which tracks completeness due to non 90 degree rotation for PSF symmetry. computed before this function to save time.

• corner_symmetry – int, if the imposed symmetry is an odd number, the edges of the reconstructed PSF in its default form will be clipped at the corners. corner_symmetry 1) tracks where the residuals are being clipped by the imposed symmetry and then 2) creates a psf with no symmetry 3) adds the corner_symmetry psf (which has information at the corners) to the odd symmetry PSF, in the regions where the odd-symmetry PSF does not have information

Returns:

kwargs_psf_new, logL_after, error_map

image_single_point_source(image_model_class, kwargs_params)

Return model without including the point source contributions as a list (for each point source individually)

Parameters:

• image_model_class – ImageModel class instance

• kwargs_params – keyword arguments of model component keyword argument lists

Returns:

list of images with point source isolated

cutout_psf(ra_image, dec_image, x, y, image_list, kernel_size, kernel_init, block_center_neighbour=0)

Parameters:

• ra_image – coordinate array of images in angles

• dec_image – coordinate array of images in angles

• x – image position array in x-pixel

• y – image position array in y-pixel

• image_list – list of images (i.e. data - all models subtracted, except a single point source)

• kernel_size – width in pixel of the kernel

• kernel_init – initial guess of kernel (pixels that are masked are replaced by those values)

• block_center_neighbour – angle, radius of neighbouring point sources around their centers the estimates is ignored. Default is zero, meaning a not optimal subtraction of the neighbouring point sources might contaminate the estimate.

Returns:

list of de-shifted kernel estimates

psf_estimate_individual(ra_image, dec_image, point_amp, residuals, cutout_size, kernel_guess, supersampling_factor, block_center_neighbour)

Parameters:

• ra_image – list; position in angular units of the image

• dec_image – list; position in angular units of the image

• point_amp – list of model amplitudes of point sources

• residuals – data - model

• cutout_size – pixel size of cutout around single star/quasar to be considered for the psf reconstruction

• kernel_guess – initial guess of super-sampled PSF

• supersampling_factor – int, super-sampling factor

• block_center_neighbour –

Returns:

list of best-guess PSF’s for each star based on the residual patterns

static point_like_source_cutouts(x_pos, y_pos, image_list, cutout_size)

Cutouts of point-like objects.

Parameters:

• x_pos – list of image positions in pixel units

• y_pos – list of image position in pixel units

• image_list – list of 2d numpy arrays with cleaned images, with all contaminating sources removed except the point-like object to be cut out.

• cutout_size – odd integer, size of cutout.

Returns:

list of cutouts

static cutout_psf_single(x, y, image, mask, kernel_size, kernel_init)

Parameters:

• x – x-coordinate of point source

• y – y-coordinate of point source

• image – image (i.e. data - all models subtracted, except a single point source)

• mask – mask of pixels in the image not to be considered in the PSF estimate (being replaced by kernel_init)

• kernel_size – width in pixel of the kernel

• kernel_init – initial guess of kernel (pixels that are masked are replaced by those values)

Returns:

estimate of the PSF based on the image and position of the point source

static combine_psf(kernel_list_new, kernel_old, factor=1.0, stacking_option='median', symmetry=1, corner_symmetry=None, corner_mask=None)

Updates psf estimate based on old kernel and several new estimates.

Parameters:

• kernel_list_new – list of new PSF kernels estimated from the point sources in the image (un-normalized)

• kernel_old – old PSF kernel

• factor – weight of updated estimate based on new and old estimate, factor=1 means new estimate, factor=0 means old estimate

• stacking_option – option of stacking, mean or median

• symmetry – imposed symmetry of PSF estimate

• corner_symmetry – int, if the imposed symmetry is an odd number, the edges of the reconstructed PSF in its default form will be clipped at the corners. corner_symmetry 1) tracks where the residuals are being clipped by the imposed symmetry and then 2) creates a psf with symmetry=corner symmetry which is either 1 or 360/symm = n*90. (e.g for a symmetry 6 psf you could use symmetry 2 in the corners). 3) adds the corner_symmetry psf (which has information at the corners) to the odd symmetry PSF, in the regions where the odd-symmetry PSF does not have complete information.

Returns:

updated PSF estimate

error_map_estimate_new(psf_kernel, psf_kernel_list, ra_image, dec_image, point_amp, supersampling_factor, error_map_radius=None)

Relative uncertainty in the psf model (in quadrature) per pixel based on residuals achieved in the image.

Parameters:

• psf_kernel – PSF kernel (super-sampled)

• psf_kernel_list – list of individual best PSF kernel estimates

• ra_image – image positions in angles

• dec_image – image positions in angles

• point_amp – image amplitude

• supersampling_factor – super-sampling factor

• error_map_radius – radius (in angle) to cut the error map

Returns:

psf error map such that square of the uncertainty gets boosted by error_map * (psf * amp)**2

error_map_estimate(kernel, star_cutout_list, amp, x_pos, y_pos, error_map_radius=None, block_center_neighbour=0)

Provides a psf_error_map based on the goodness of fit of the given PSF kernel on the point source cutouts, their estimated amplitudes and positions.

Parameters:

• kernel – PSF kernel

• star_cutout_list – list of 2d arrays of cutouts of the point sources with all other model components subtracted

• amp – list of amplitudes of the estimated PSF kernel

• x_pos – pixel position (in original data unit, not in cutout) of the point sources (same order as amp and star cutouts)

• y_pos – pixel position (in original data unit, not in cutout) of the point sources (same order as amp and star cutouts)

• error_map_radius – float, radius (in arc seconds) of the outermost error in the PSF estimate (e.g. to avoid double counting of overlapping PSF erros)

• block_center_neighbour – angle, radius of neighbouring point sources around their centers the estimates is ignored. Default is zero, meaning a not optimal subtraction of the neighbouring point sources might contaminate the estimate.

Returns:

relative uncertainty in the psf model (in quadrature) per pixel based on residuals achieved in the image

static mask_point_source(x_pos, y_pos, x_grid, y_grid, radius, i=0)

Parameters:

• x_pos – x-position of list of point sources

• y_pos – y-position of list of point sources

• x_grid – x-coordinates of grid

• y_grid – y-coordinates of grid

• i – index of point source not to mask out

• radius – radius to mask out other point sources

Returns:

a mask of the size of the image with cutouts around the position

lenstronomy.Workflow.update_manager module

class UpdateManager(kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params)

Bases: object

This class manages the parameter constraints as they may evolve through the steps of the modeling.

This includes: keeping certain parameters fixed during one modelling step

__init__(kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params)

Parameters:

• kwargs_model – keyword arguments to describe all model components used in class_creator.create_class_instances()

• kwargs_constraints – keyword arguments of the Param() class to handle parameter constraints during the sampling (except upper and lower limits and sampling input mean and width)

• kwargs_likelihood – keyword arguments of the Likelihood() class to handle parameters and settings of the likelihood

• kwargs_params – setting of the sampling bounds and initial guess mean and spread. The argument is organized as: ‘lens_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘source_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘lens_light_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘point_source_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘extinction_model’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper] ‘special’: [kwargs_init, kwargs_sigma, kwargs_fixed, kwargs_lower, kwargs_upper]

property init_kwargs

Returns:

keyword arguments for all model components of the initial mean model proposition in the sampling

property sigma_kwargs

Returns:

keyword arguments for all model components of the initial 1-sigma width proposition in the sampling

property fixed_kwargs

set_init_state()

Set the current state of the parameters to the initial one.

Returns:

property parameter_state

Returns:

parameter state saved in this class

best_fit(bijective=False)

Best fit (max likelihood) position for all the model parameters.

Parameters:

bijective – boolean, if True, returns the parameters in the argument of the sampling that might deviate from the convention of the ImSim module. For example, if parameterized in the image position, the parameters remain in the image plane rather than being mapped to the source plane.

Returns:

kwargs_result with all the keyword arguments of the best fit for the model components

update_param_state(kwargs_lens=None, kwargs_source=None, kwargs_lens_light=None, kwargs_ps=None, kwargs_special=None, kwargs_extinction=None, kwargs_tracer_source=None)

Updates the temporary state of the parameters being saved. ATTENTION: Any previous knowledge gets lost if you call this function.

Parameters:

• kwargs_lens –

• kwargs_source –

• kwargs_lens_light –

• kwargs_ps –

• kwargs_special –

• kwargs_extinction –

• kwargs_tracer_source –

Returns:

update_param_value(lens=None, source=None, lens_light=None, ps=None)

Set a model parameter to a specific value.

Parameters:

• lens – [[i_model, [‘param1’, ‘param2’,…], […]]

• source – [[i_model, [‘param1’, ‘param2’,…], […]]

• lens_light – [[i_model, [‘param1’, ‘param2’,…], […]]

• ps – [[i_model, [‘param1’, ‘param2’,…], […]]

Returns:

0, the value of the param is overwritten

property param_class

Creating instance of lenstronomy Param() class. It uses the keyword arguments in self.kwargs_constraints as __init__() arguments, as well as self.kwargs_model, and the set of kwargs_fixed___, kwargs_lower___, kwargs_upper___ arguments for lens, lens_light, source, point source, extinction and special parameters.

Returns:

instance of the Param class with the recent options and bounds

update_kwargs_model(kwargs_special)

Update the kwargs_model with the new kwargs_special.

update_options(kwargs_model=None, kwargs_constraints=None, kwargs_likelihood=None)

updates the options by overwriting the kwargs with the new ones being added/changed WARNING: some updates may not be valid depending on the model options. Use carefully!

Parameters:

• kwargs_model – keyword arguments to describe all model components used in class_creator.create_class_instances() that are updated from previous arguments

• kwargs_constraints –

• kwargs_likelihood –

Returns:

kwargs_model, kwargs_constraints, kwargs_likelihood

update_limits(change_source_lower_limit=None, change_source_upper_limit=None, change_lens_lower_limit=None, change_lens_upper_limit=None)

Updates the limits (lower and upper) of the update manager instance.

Parameters:

• change_source_lower_limit – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_lens_lower_limit – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_source_upper_limit – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_lens_upper_limit – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

Returns:

updates internal state of lower and upper limits accessible from outside

update_sigmas(change_sigma_lens=None, change_sigma_source=None, change_sigma_lens_light=None)

Updates individual estimated uncertainty levels for the initialization of search and sampling algorithms.

Parameters:

• change_sigma_lens – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_sigma_source – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

• change_sigma_lens_light – [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, …]]]

Returns:

updated internal state of the spread to initialize samplers

update_fixed(lens_add_fixed=None, source_add_fixed=None, lens_light_add_fixed=None, ps_add_fixed=None, special_add_fixed=None, tracer_source_add_fixed=None, lens_remove_fixed=None, source_remove_fixed=None, lens_light_remove_fixed=None, ps_remove_fixed=None, special_remove_fixed=None, tracer_source_remove_fixed=None)

Adds or removes the values of the keyword arguments that are stated in the _add_fixed to the existing fixed arguments. convention for input arguments are: [[i_model, [‘param_name1’, ‘param_name2’, …], [value1, value2, … (optional)], [], …]

Parameters:

• lens_add_fixed – added fixed parameter in lens model

• source_add_fixed – added fixed parameter in source model

• lens_light_add_fixed – added fixed parameter in lens light model

• ps_add_fixed – added fixed parameter in point source model

• special_add_fixed – added fixed parameter in special model

• tracer_source_add_fixed – added fixed parameter in tracer source model

• lens_remove_fixed – remove fixed parameter in lens model

• source_remove_fixed – remove fixed parameter in source model

• lens_light_remove_fixed – remove fixed parameter in lens light model

• ps_remove_fixed – remove fixed parameter in point source model

• special_remove_fixed – remove fixed parameter in special model

• tracer_source_remove_fixed – remove fixed parameter in tracer source model

Returns:

updated kwargs fixed

fix_image_parameters(image_index=0)

Fixes all parameters that are only assigned to a specific image. This allows to sample only parameters that constraint by the fitting of a sub-set of the images.

Parameters:

image_index – index

Returns:

None

Module contents