lenstronomy.SimulationAPI package

Subpackages

• lenstronomy.SimulationAPI.ObservationConfig package
  • Submodules
  • lenstronomy.SimulationAPI.ObservationConfig.DES module
    • DES
      • DES.__init__()
      • DES.kwargs_single_band()
  • lenstronomy.SimulationAPI.ObservationConfig.Euclid module
    • Euclid
      • Euclid.__init__()
      • Euclid.kwargs_single_band()
  • lenstronomy.SimulationAPI.ObservationConfig.HST module
    • HST
      • HST.__init__()
      • HST.kwargs_single_band()
  • lenstronomy.SimulationAPI.ObservationConfig.LSST module
    • LSST
      • LSST.__init__()
      • LSST.kwargs_single_band()
  • Module contents

Submodules

lenstronomy.SimulationAPI.data_api module

class DataAPI(numpix, kwargs_pixel_grid=None, **kwargs_single_band)

Bases: SingleBand

This class is a wrapper of the general description of data in SingleBand() to translate those quantities into configurations in the core lenstronomy Data modules to simulate images according to those quantities.

This class is meant to be an example of a wrapper. More possibilities in terms of PSF and data type options are available. Have a look in the specific modules if you are interested in.

__init__(numpix, kwargs_pixel_grid=None, **kwargs_single_band)

Parameters:

• numpix – number of pixels per axis in the simulation to be modelled

• kwargs_pixel_grid – if None, uses default pixel grid option if defined, must contain keyword arguments PixelGrid() class

• kwargs_single_band – keyword arguments used to create instance of SingleBand class

property data_class

Creates a Data() instance of lenstronomy based on knowledge of the observation.

Returns:

instance of Data() class

property kwargs_data

Returns:

keyword arguments for ImageData class instance

lenstronomy.SimulationAPI.model_api module

class ModelAPI(lens_model_list=None, z_lens=None, z_source=None, lens_redshift_list=None, source_light_model_list=None, lens_light_model_list=None, point_source_model_list=None, source_redshift_list=None, cosmo=None, z_source_convention=None, tabulated_deflection_angles=None, observed_convention_index=None)

Bases: object

This class manages the model choices.

The role is to return instances of the lenstronomy LightModel, LensModel, PointSource modules according to the options chosen by the user. Currently, all other model choices are equivalent to the ones provided by LightModel, LensModel, PointSource. The current options of the class instance only describe a subset of possibilities.

__init__(lens_model_list=None, z_lens=None, z_source=None, lens_redshift_list=None, source_light_model_list=None, lens_light_model_list=None, point_source_model_list=None, source_redshift_list=None, cosmo=None, z_source_convention=None, tabulated_deflection_angles=None, observed_convention_index=None)

# TODO: make inputs follow the kwargs_model of the class_creator instances of ‘kwargs_model’, # i.e. multi-plane options, perhaps others

Parameters:

• lens_model_list – list of strings with lens model names

• z_lens – redshift of the deflector (only considered when operating in single plane mode). Is only needed for specific functions that require a cosmology.

• z_source – redshift of the source: Needed in multi_plane option only, not required for the core functionalities in the single plane mode. This will be the redshift of the source plane (if not further specified the ‘source_redshift_list’) and the point source redshift (regardless of ‘source_redshift_list’)

• lens_redshift_list – list of deflector redshift (corresponding to the lens model list), only applicable in multi_plane mode.

• source_light_model_list – list of strings with source light model names (lensed light profiles)

• lens_light_model_list – list of strings with lens light model names (not lensed light profiles)

• point_source_model_list – list of strings with point source model names

• source_redshift_list – list of redshifts of the source profiles (optional)

• cosmo – instance of the astropy cosmology class. If not specified, uses the default cosmology.

• z_source_convention – float, redshift of a source to define the reduced deflection angles of the lens models. If None, ‘z_source’ is used.

• tabulated_deflection_angles – a class that returns deflection angles given a set of (x, y) coordinates. Effectively a fixed lens model. See documentation in Profiles.numerical_alpha

• observed_convention_index – a list of indices that correspond to lens models where the center_x,center_y values correspond to the observed (lensed positions), not the physical positions in space

property lens_model_class

Returns:

instance of lenstronomy LensModel class

property lens_light_model_class

Returns:

instance of lenstronomy LightModel class describing the non-lensed light profiles

property source_model_class

Returns:

instance of lenstronomy LightModel class describing the source light profiles

property point_source_model_class

Returns:

instance of lenstronomy PointSource class describing the point sources (lensed and unlensed)

physical2lensing_conversion(kwargs_mass)

Parameters:

kwargs_mass – list of keyword arguments of all the lens models. Einstein radius ‘theta_E’ are replaced by ‘sigma_v’, velocity dispersion in km/s, ‘alpha_Rs’ and ‘Rs’ of NFW profiles are replaced by ‘M200’ and ‘concentration’

Returns:

kwargs_lens in reduced deflection angles compatible with the LensModel instance of this module

lenstronomy.SimulationAPI.observation_api module

class Instrument(pixel_scale, read_noise=None, ccd_gain=None)

Bases: object

Basic access points to instrument properties.

__init__(pixel_scale, read_noise=None, ccd_gain=None)

Parameters:

• read_noise – std of noise generated by read-out (in units of electrons)

• pixel_scale – scale (in arcseconds) of pixels

• ccd_gain – electrons/ADU (analog-to-digital unit). A gain of 8 means that the camera digitizes the CCD signal so that each ADU corresponds to 8 photoelectrons.

class Observation(exposure_time, sky_brightness=None, seeing=None, num_exposures=1, psf_type='GAUSSIAN', kernel_point_source=None, truncation=5, point_source_supersampling_factor=1)

Bases: object

Basic access point to observation properties.

__init__(exposure_time, sky_brightness=None, seeing=None, num_exposures=1, psf_type='GAUSSIAN', kernel_point_source=None, truncation=5, point_source_supersampling_factor=1)

Parameters:

• exposure_time – exposure time per image (in seconds)

• sky_brightness – sky brightness (in magnitude per square arcseconds)

• seeing – full width at half maximum of the PSF (if not specific psf_model is specified)

• num_exposures – number of exposures that are combined

• psf_type – string, type of PSF (‘GAUSSIAN’ and ‘PIXEL’ supported)

• kernel_point_source – 2d numpy array, model of PSF centered with odd number of pixels per axis (optional when psf_type=’PIXEL’ is chosen)

• point_source_supersampling_factor – int, supersampling factor of kernel_point_source (optional when psf_type=’PIXEL’ is chosen)

update_observation(exposure_time=None, sky_brightness=None, seeing=None, num_exposures=None, psf_type=None, kernel_point_source=None)

Updates class instance with new properties if specific argument is not None.

Parameters:

• exposure_time – exposure time per image (in seconds)

• sky_brightness – sky brightness (in magnitude per square arcseconds)

• seeing – full width at half maximum of the PSF (if not specific psf_model is specified)

• num_exposures – number of exposures that are combined

• psf_type – string, type of PSF (‘GAUSSIAN’ and ‘PIXEL’ supported)

• kernel_point_source – 2d numpy array, model of PSF centered with odd number of pixels per axis (optional when psf_type=’PIXEL’ is chosen)

Returns:

None, updated class instance

property exposure_time

Total exposure time.

Returns:

summed exposure time

property kwargs_psf

Keyword arguments to initiate a PSF() class.

Returns:

kwargs_psf

property psf_class

Creates instance of PSF() class based on knowledge of the observations For the full possibility of how to create such an instance, see the PSF() class documentation.

Returns:

instance of PSF() class

class SingleBand(pixel_scale, exposure_time, magnitude_zero_point, read_noise=None, ccd_gain=None, sky_brightness=None, seeing=None, num_exposures=1, psf_type='GAUSSIAN', kernel_point_source=None, truncation=5, point_source_supersampling_factor=1, data_count_unit='e-', background_noise=None)

Bases: Instrument, Observation

Class that combines Instrument and Observation.

__init__(pixel_scale, exposure_time, magnitude_zero_point, read_noise=None, ccd_gain=None, sky_brightness=None, seeing=None, num_exposures=1, psf_type='GAUSSIAN', kernel_point_source=None, truncation=5, point_source_supersampling_factor=1, data_count_unit='e-', background_noise=None)

Parameters:

• read_noise – std of noise generated by read-out (in units of electrons)

• pixel_scale – scale (in arcseconds) of pixels

• ccd_gain – electrons/ADU (analog-to-digital unit). A gain of 8 means that the camera digitizes the CCD signal so that each ADU corresponds to 8 photoelectrons.

• exposure_time – exposure time per image (in seconds)

• sky_brightness – sky brightness (in magnitude per square arcseconds in units of electrons)

• seeing – Full-Width-at-Half-Maximum (FWHM) of PSF

• magnitude_zero_point – magnitude corresponding to 1 electron per second

• num_exposures – number of exposures that are combined

• point_source_supersampling_factor – int, supersampling factor of kernel_point_source (optional when psf_type=’PIXEL’ is chosen)

• data_count_unit – string, unit of the data (not noise properties - see other definitions), ‘e-’: (electrons assumed to be IID), ‘ADU’: (analog-to-digital unit)

• background_noise – sqrt(variance of background) as a total contribution from readnoise, sky brightness etc. in units of the data_count_units (e- or ADU) If you set this parameter, it will use this value regardless of the values of read_noise, sky_brightness

property sky_brightness

Returns:

sky brightness (counts per square arcseconds in unit of data (e- or ADU’s) per unit time)

property background_noise

Gaussian sigma of noise level per pixel in counts (e- or ADU) per second.

Returns:

sqrt(variance) of background noise level in data units

flux_noise(flux)

Parameters:

flux – float or array, units of count_unit/seconds, needs to be positive semi-definite in the flux value

Returns:

Gaussian approximation of Poisson statistics in IIDs sqrt(variance)

flux_iid(flux_per_second)

IID counts. This can be used by lenstronomy to estimate the Poisson errors keeping the assumption that the counts are IIDs (even if they are not).

Parameters:

flux_per_second – flux count per second in the units set in this class (ADU or e-)

Returns:

IID count number

noise_for_model(model, background_noise=True, poisson_noise=True, seed=None)

Parameters:

• model – 2d numpy array of modelled image (with pixels in units of data specified in class)

• background_noise – bool, if True, adds background noise

• poisson_noise – bool, if True, adds Poisson noise of modelled flux

• seed – int, seed number to be used to render the noise properties. If None, then uses the current numpy.random seed to render the noise properties.

Returns:

noise realization corresponding to the model

estimate_noise(image)

Parameters:

image – noisy data, background subtracted

Returns:

estimated noise map sqrt(variance) for each pixel as estimated from the instrument and observation

magnitude2cps(magnitude)

Converts an apparent magnitude to counts per second (in units of the data)

The zero point of an instrument, by definition, is the magnitude of an object that produces one count (or data number, DN) per second. The magnitude of an arbitrary object producing DN counts in an observation of length EXPTIME is therefore: m = -2.5 x log10(DN / EXPTIME) + ZEROPOINT

Parameters:

magnitude – magnitude of object

Returns:

counts per second of object

lenstronomy.SimulationAPI.observation_constructor module

observation_constructor(instrument_name, observation_name)

Parameters:

• instrument_name – string, name of instrument referenced in this file

• observation_name – string, name of observation referenced in this file

Returns:

instance of the SimulationAPI.data_type instance

lenstronomy.SimulationAPI.point_source_variability module

class PointSourceVariability(source_x, source_y, variability_func, numpix, kwargs_single_band, kwargs_model, kwargs_numerics, kwargs_lens, kwargs_source_mag=None, kwargs_lens_light_mag=None, kwargs_ps_mag=None)

Bases: object

This class enables to plug in a variable point source in the source plane to be added on top of a fixed lens and extended surface brightness model. The class inherits SimAPI and additionally requires the lens and light model parameters as well as a position in the source plane.

The intrinsic source variability can be defined by the user and additional uncorrelated variability in the image plane can be plugged in as well (e.g. due to micro-lensing)

__init__(source_x, source_y, variability_func, numpix, kwargs_single_band, kwargs_model, kwargs_numerics, kwargs_lens, kwargs_source_mag=None, kwargs_lens_light_mag=None, kwargs_ps_mag=None)

Parameters:

• source_x – RA of source position

• source_y – DEC of source position

• variability_func – function that returns a brightness (in magnitude) as a function of time t

• numpix – number of pixels per axis

• kwargs_single_band –

• kwargs_model –

• kwargs_numerics –

• kwargs_lens –

• kwargs_source_mag –

• kwargs_lens_light_mag –

• kwargs_ps_mag –

property delays

Returns:

time delays

property image_bkg

Returns:

2d numpy array, image of the extended light components without the variable source

image_time(time=0)

Parameters:

time – time relative to the definition of t=0 for the first appearing image

Returns:

image with time variable source at given time

point_source_time(t)

Parameters:

t – time (in units of days)

Returns:

image plane parameters of the point source observed at t

lenstronomy.SimulationAPI.sim_api module

class SimAPI(numpix, kwargs_single_band, kwargs_model)

Bases: DataAPI, ModelAPI

This class manages the model parameters in regard of the data specified in SingleBand.

In particular, this API translates models specified in units of astronomical magnitudes into the amplitude parameters used in the LightModel module of lenstronomy. Optionally, this class can also handle inputs with cosmology dependent lensing quantities and translates them to the optical quantities being used in the lenstronomy LensModel module. All other model choices are equivalent to the ones provided by LightModel, LensModel, PointSource modules

__init__(numpix, kwargs_single_band, kwargs_model)

Parameters:

• numpix – number of pixels per axis

• kwargs_single_band – keyword arguments specifying the class instance of DataAPI

• kwargs_model – keyword arguments specifying the class instance of ModelAPI

image_model_class(kwargs_numerics=None)

Parameters:

kwargs_numerics – keyword arguments list of Numerics module

Returns:

instance of the ImageModel class with all the specified configurations

magnitude2amplitude(kwargs_lens_light_mag=None, kwargs_source_mag=None, kwargs_ps_mag=None)

‘magnitude’ definition are in APPARENT magnitudes as observed on the sky, not intrinsic!

Parameters:

• kwargs_lens_light_mag – keyword argument list as for LightModel module except that ‘amp’ parameters are ‘magnitude’ parameters.

• kwargs_source_mag – keyword argument list as for LightModel module except that ‘amp’ parameters are ‘magnitude’ parameters.

• kwargs_ps_mag – keyword argument list as for PointSource module except that ‘amp’ parameters are ‘magnitude’ parameters.

Returns:

value of the lenstronomy ‘amp’ parameter such that the total flux of the profile type results in this magnitude for all the light models. These keyword arguments conform with the lenstronomy LightModel syntax.

Module contents