lenstronomy.Plots package

Submodules

lenstronomy.Plots.chain_plot module

plot_chain_list(chain_list, index=0, num_average=100)[source]

Plots the output of a chain of samples (MCMC or PSO) with the some diagnostics of convergence. This routine is an example and more tests might be appropriate to analyse a specific chain.

Parameters:

  • chain_list – list of chains with arguments [type string, samples etc…]

  • index – index of chain to be plotted

  • num_average – in chains, number of steps to average over in plotting diagnostics

Returns:

plotting instance figure, axes (potentially multiple)

plot_chain(chain, param_list)[source]
plot_mcmc_behaviour(ax, samples_mcmc, param_mcmc, dist_mcmc=None, num_average=100)[source]

Plots the MCMC behaviour and looks for convergence of the chain.

Parameters:

  • ax – matplotlib.axis instance

  • samples_mcmc – parameters sampled 2d numpy array

  • param_mcmc – list of parameters

  • dist_mcmc – log likelihood of the chain

  • num_average – number of samples to average (should coincide with the number of samples in the emcee process)

Returns:

psf_iteration_compare(kwargs_psf, **kwargs)[source]
Parameters:

  • kwargs_psf – keyword arguments that initiate a PSF() class

  • kwargs – kwargs to send to matplotlib.pyplot.matshow()

Returns:

lenstronomy.Plots.lens_plot module

lens_model_plot(ax, lensModel, kwargs_lens, numPix=500, deltaPix=0.01, sourcePos_x=0, sourcePos_y=0, point_source=False, with_caustics=False, with_convergence=True, coord_center_ra=0, coord_center_dec=0, coord_inverse=False, fast_caustic=True, **kwargs)[source]

Plots a lens model (convergence) and the critical curves and caustics.

Parameters:

  • ax – matplotlib axis instance

  • lensModel – LensModel() class instance

  • kwargs_lens – lens model keyword argument list

  • numPix – total number of pixels (for convergence map)

  • deltaPix – width of pixel (total frame size is deltaPix x numPix)

  • sourcePos_x – float, x-position of point source (image positions computed by the lens equation)

  • sourcePos_y – float, y-position of point source (image positions computed by the lens equation)

  • point_source – bool, if True, illustrates and computes the image positions of the point source

  • with_caustics – bool, if True, illustrates the critical curve and caustics of the system

  • with_convergence – bool, if True, illustrates the convergence map

  • coord_center_ra – float, x-coordinate of the center of the frame

  • coord_center_dec – float, y-coordinate of the center of the frame

  • coord_inverse – bool, if True, inverts the x-coordinates to go from right-to- left (effectively the RA definition)

  • fast_caustic – boolean, if True, uses faster but less precise caustic calculation (might have troubles for the outer caustic (inner critical curve)

  • with_convergence – boolean, if True, plots the convergence of the deflector

Returns:

matplotlib axis instance with plot

arrival_time_surface(ax, lensModel, kwargs_lens, numPix=500, deltaPix=0.01, sourcePos_x=0, sourcePos_y=0, with_caustics=False, point_source=False, n_levels=10, kwargs_contours=None, image_color_list=None, letter_font_size=20, name_list=None)[source]
Parameters:

  • ax – matplotlib axis instance

  • lensModel – LensModel() class instance

  • kwargs_lens – lens model keyword argument list

  • numPix –

  • deltaPix –

  • sourcePos_x –

  • sourcePos_y –

  • with_caustics –

  • point_source –

  • name_list (list of strings, longer or equal the number of point sources) – list of names of images

Returns:

curved_arc_illustration(ax, lensModel, kwargs_lens, with_centroid=True, stretch_scale=0.1, color='k')[source]
Parameters:

  • ax – matplotlib axis instance

  • lensModel – LensModel() instance

  • kwargs_lens – list of lens model keyword arguments (only those of CURVED_ARC considered

  • with_centroid – plots the center of the curvature radius

  • stretch_scale – float, relative scale of banana to the tangential and radial stretches (effectively intrinsic source size)

  • color – string, matplotlib color for plot

Returns:

matplotlib axis instance

plot_arc(ax, tangential_stretch, radial_stretch, curvature, direction, center_x, center_y, stretch_scale=0.1, with_centroid=True, linewidth=1, color='k', dtan_dtan=0)[source]
Parameters:

  • ax – matplotlib.axes instance

  • tangential_stretch – float, stretch of intrinsic source in tangential direction

  • radial_stretch – float, stretch of intrinsic source in radial direction

  • curvature – 1/curvature radius

  • direction – float, angle in radian

  • center_x – center of source in image plane

  • center_y – center of source in image plane

  • with_centroid – plots the center of the curvature radius

  • stretch_scale – float, relative scale of banana to the tangential and radial stretches (effectively intrinsic source size)

  • linewidth – linewidth

  • color (string in matplotlib color convention) – color

  • dtan_dtan – tangential eigenvector differential in tangential direction (not implemented yet as illustration)

Returns:

distortions(lensModel, kwargs_lens, num_pix=100, delta_pix=0.05, center_ra=0, center_dec=0, differential_scale=0.0001, smoothing_scale=None, **kwargs)[source]
Parameters:

  • lensModel – LensModel instance

  • kwargs_lens – lens model keyword argument list

  • num_pix – number of pixels per axis

  • delta_pix – pixel scale per axis

  • center_ra – center of the grid

  • center_dec – center of the grid

  • differential_scale – scale of the finite derivative length in units of angles

  • smoothing_scale – float or None, Gaussian FWHM of a smoothing kernel applied before plotting

Returns:

matplotlib instance with different panels

lenstronomy.Plots.model_band_plot module

class ModelBandPlot(multi_band_list, kwargs_model, model, error_map, cov_param, param, kwargs_params, likelihood_mask_list=None, band_index=0, arrow_size=0.02, cmap_string='gist_heat', fast_caustic=True)[source]

Bases: ModelBand

Class to plot a single band given the modeling results.

__init__(multi_band_list, kwargs_model, model, error_map, cov_param, param, kwargs_params, likelihood_mask_list=None, band_index=0, arrow_size=0.02, cmap_string='gist_heat', fast_caustic=True)[source]
Parameters:

  • multi_band_list – list of imaging data configuration [[kwargs_data, kwargs_psf, kwargs_numerics], […]]

  • kwargs_model – model keyword argument list for the full multi-band modeling

  • model – 2d numpy array of modeled image for the specified band

  • error_map – 2d numpy array of size of the image, additional error in the pixels coming from PSF uncertainties

  • cov_param – covariance matrix of the linear inversion

  • param – 1d numpy array of the linear coefficients of this imaging band

  • kwargs_params – keyword argument of keyword argument lists of the different model components selected for the imaging band, NOT including linear amplitudes (not required as being overwritten by the param list)

  • likelihood_mask_list – list of 2d numpy arrays of likelihood masks (for all bands)

  • band_index – integer of the band to be considered in this class

  • arrow_size – size of the scale and orientation arrow

  • cmap_string – string of color map (or cmap matplotlib object)

  • fast_caustic – boolean; if True, uses fast (but less accurate) caustic calculation method

data_plot(ax, v_min=None, v_max=None, text='Observed', font_size=15, colorbar_label='log$_{10}$ flux', **kwargs)[source]
Parameters:

ax –

Returns:

model_plot(ax, v_min=None, v_max=None, image_names=False, colorbar_label='log$_{10}$ flux', font_size=15, text='Reconstructed', **kwargs)[source]
Parameters:

  • ax – matplotib axis instance

  • v_min –

  • v_max –

Returns:

convergence_plot(ax, text='Convergence', v_min=None, v_max=None, font_size=15, colorbar_label='$\\log_{10}\\ \\kappa$', **kwargs)[source]
Parameters:

ax – matplotib axis instance

Returns:

convergence plot in ax instance

substructure_plot(ax, index_macromodel, text='Substructure convergence', subtract_mean=True, v_min=-0.05, v_max=0.05, font_size=15, colorbar_label='$\\kappa - \\kappa_{\\rm{macro}}$', cmap='bwr', with_critical_curves=False, crit_curve_color='k', image_name_list=None, **kwargs)[source]

Plots the convergence of a full lens model minus the convergence from a few specified lens models to more clearly show the presence of substructure.

Parameters:

  • ax – matplotib axis instance

  • index_macromodel – a list of indexes corresponding to the lens models with convergence to be subtracted

  • text – text appearing in frame

  • subtract_mean – bool; displays the substructure convergence relative to the mean convergence in the frame

  • v_min – minimum color scale

  • v_max – max color scale

  • font_size – font size for text appearing in image

  • colorbar_label – label for the color bar

  • cmap – colormap for use in the visualization

  • with_critical_curves – bool; plots the critical curves in the frame

  • crit_curve_color – color of the critical curves

  • image_name_list – labels the images, default is A, B, C, …

  • kwargs – any additional keyword arguments

Returns:

matplotib axis

normalized_residual_plot(ax, v_min=-6, v_max=6, font_size=15, text='Normalized Residuals', colorbar_label='(f${}_{\\rm model}$ - f${}_{\\rm data}$)/$\\sigma$', no_arrow=False, color_bar=True, **kwargs)[source]
Parameters:

  • ax –

  • v_min –

  • v_max –

  • kwargs – kwargs to send to matplotlib.pyplot.matshow()

  • color_bar – Option to display the color bar

Returns:

absolute_residual_plot(ax, v_min=-1, v_max=1, font_size=15, text='Residuals', colorbar_label='(f$_{model}$-f$_{data}$)')[source]
Parameters:

ax –

Returns:

source(numPix, deltaPix, center=None, image_orientation=True)[source]
Parameters:

  • numPix – number of pixels per axes

  • deltaPix – pixel size

  • image_orientation – bool, if True, uses frame in orientation of the image, otherwise in RA-DEC coordinates

Returns:

2d surface brightness grid of the reconstructed source and Coordinates() instance of source grid

source_plot(ax, numPix, deltaPix_source, center=None, v_min=None, v_max=None, with_caustics=False, caustic_color='yellow', font_size=15, plot_scale='log', scale_size=0.1, text='Reconstructed source', colorbar_label='log$_{10}$ flux', point_source_position=True, **kwargs)[source]
Parameters:

  • ax –

  • numPix –

  • deltaPix_source –

  • center – [center_x, center_y], if specified, uses this as the center

  • v_min –

  • v_max –

  • caustic_color –

  • font_size –

  • plot_scale – string, log or linear, scale of surface brightness plot

  • kwargs –

Returns:

error_map_source_plot(ax, numPix, deltaPix_source, v_min=None, v_max=None, with_caustics=False, font_size=15, point_source_position=True)[source]

Plots the uncertainty in the surface brightness in the source from the linear inversion by taking the diagonal elements of the covariance matrix of the inversion of the basis set to be propagated to the source plane. #TODO illustration of the uncertainties in real space with the full covariance matrix is subtle. # The best way is probably to draw realizations from the covariance matrix.

Parameters:

  • ax – matplotlib axis instance

  • numPix – number of pixels in plot per axis

  • deltaPix_source – pixel spacing in the source resolution illustrated in plot

  • v_min – minimum plotting scale of the map

  • v_max – maximum plotting scale of the map

  • with_caustics – plot the caustics on top of the source reconstruction (may take some time)

  • font_size – font size of labels

  • point_source_position – boolean, if True, plots a point at the position of the point source

Returns:

plot of source surface brightness errors in the reconstruction on the axis instance

magnification_plot(ax, v_min=-10, v_max=10, image_name_list=None, font_size=15, no_arrow=False, text='Magnification model', colorbar_label='$\\det\\ (\\mathsf{A}^{-1})$', **kwargs)[source]
Parameters:

  • ax – matplotib axis instance

  • v_min – minimum range of plotting

  • v_max – maximum range of plotting

  • kwargs – kwargs to send to matplotlib.pyplot.matshow()

Returns:

deflection_plot(ax, v_min=None, v_max=None, axis=0, with_caustics=False, image_name_list=None, text='Deflection model', font_size=15, colorbar_label='arcsec')[source]
Returns:

decomposition_plot(ax, text='Reconstructed', v_min=None, v_max=None, unconvolved=False, point_source_add=False, font_size=15, source_add=False, lens_light_add=False, **kwargs)[source]
Parameters:

  • ax –

  • text –

  • v_min –

  • v_max –

  • unconvolved –

  • point_source_add –

  • source_add –

  • lens_light_add –

  • kwargs – kwargs to send matplotlib.pyplot.matshow()

Returns:

subtract_from_data_plot(ax, text='Subtracted', v_min=None, v_max=None, point_source_add=False, source_add=False, lens_light_add=False, font_size=15)[source]
plot_main(with_caustics=False)[source]

Print the main plots together in a joint frame.

Returns:

plot_separate()[source]

Plot the different model components separately.

Returns:

plot_subtract_from_data_all()[source]

Subtract model components from data.

Returns:

plot_extinction_map(ax, v_min=None, v_max=None, **kwargs)[source]
Parameters:

  • ax –

  • v_min –

  • v_max –

Returns:

lenstronomy.Plots.model_plot module

class ModelPlot(multi_band_list, kwargs_model, kwargs_params, image_likelihood_mask_list=None, bands_compute=None, multi_band_type='multi-linear', source_marg=False, linear_prior=None, arrow_size=0.02, cmap_string='gist_heat', fast_caustic=True, linear_solver=True)[source]

Bases: object

Class that manages the summary plots of a lens model The class uses the same conventions as being used in the FittingSequence and interfaces with the ImSim module.

The linear inversion is re-done given the likelihood settings in the init of this class (make sure this is the same as you perform the FittingSequence) to make sure the linear amplitude parameters are computed as they are not part of the output of the FittingSequence results.

__init__(multi_band_list, kwargs_model, kwargs_params, image_likelihood_mask_list=None, bands_compute=None, multi_band_type='multi-linear', source_marg=False, linear_prior=None, arrow_size=0.02, cmap_string='gist_heat', fast_caustic=True, linear_solver=True)[source]
Parameters:

  • multi_band_list – list of [[kwargs_data, kwargs_psf, kwargs_numerics], [], ..]

  • multi_band_type – string, option when having multiple imaging data sets modelled simultaneously. Options are: - ‘multi-linear’: linear amplitudes are inferred on single data set - ‘linear-joint’: linear amplitudes ae jointly inferred - ‘single-band’: single band

  • kwargs_model – model keyword arguments

  • bands_compute – (optional), bool list to indicate which band to be included in the modeling

  • image_likelihood_mask_list – list of image likelihood mask (same size as image_data with 1 indicating being evaluated and 0 being left out)

  • kwargs_params – keyword arguments of ‘kwargs_lens’, ‘kwargs_source’ etc. as coming as kwargs_result from FittingSequence class

  • source_marg –

  • linear_prior –

  • arrow_size –

  • cmap_string –

  • fast_caustic – boolean; if True, uses fast (but less accurate) caustic calculation method

  • linear_solver – bool, if True (default) fixes the linear amplitude parameters ‘amp’ (avoid sampling) such that they get overwritten by the linear solver solution.

reconstruction_all_bands(**kwargs)[source]
Parameters:

kwargs – arguments of plotting

Returns:

3 x n_data plot with data, model, reduced residual plots of all the images/bands that are being modeled

data_plot(band_index=0, **kwargs)[source]

Illustrates data.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

model_plot(band_index=0, **kwargs)[source]

Illustrates model.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

convergence_plot(band_index=0, **kwargs)[source]

Illustrates lensing convergence in data frame.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

substructure_plot(band_index=0, **kwargs)[source]

Illustrates substructure in the lens system.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

normalized_residual_plot(band_index=0, **kwargs)[source]

Illustrates normalized residuals between data and model fit.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

absolute_residual_plot(band_index=0, **kwargs)[source]

Illustrates absolute residuals between data and model fit.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

source_plot(band_index=0, **kwargs)[source]

Illustrates reconstructed source (de-lensed de-convolved)

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

error_map_source_plot(band_index=0, **kwargs)[source]

Illustrates surface brightness variance in the reconstruction in the source plane.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

magnification_plot(band_index=0, **kwargs)[source]

Illustrates lensing magnification in the field of view of the data frame.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

deflection_plot(band_index=0, **kwargs)[source]

Illustrates lensing deflections on the field of view of the data frame.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

decomposition_plot(band_index=0, **kwargs)[source]

Illustrates decomposition of model components.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

subtract_from_data_plot(band_index=0, **kwargs)[source]

Subtracts individual model components from the data.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

plot_main(band_index=0, **kwargs)[source]

Plot a set of ‘main’ modelling diagnostics.

Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance

plot_separate(band_index=0)[source]

Plot a set of ‘main’ modelling diagnostics.

Parameters:

band_index – index of band

Returns:

plot instance

plot_subtract_from_data_all(band_index=0)[source]

Plot a set of ‘main’ modelling diagnostics.

Parameters:

band_index – index of band

Returns:

plot instance

plot_extinction_map(band_index=0, **kwargs)[source]
Parameters:

  • band_index – index of band

  • kwargs – arguments of plotting

Returns:

plot instance of differential extinction map

source(band_index=0, **kwargs)[source]
Parameters:

  • band_index – index of band

  • kwargs – keyword arguments accessible in model_band_plot.source()

Returns:

2d array of source surface brightness

lenstronomy.Plots.plot_util module

sqrt(inputArray, scale_min=None, scale_max=None)[source]

Performs sqrt scaling of the input numpy array.

Parameters:

  • inputArray (numpy array) – image data array

  • scale_min (float) – minimum data value

  • scale_max (float) – maximum data value

Return type:

numpy array

Returns:

image data array

text_description(ax, d, text, color='w', backgroundcolor='k', flipped=False, font_size=15)[source]
scale_bar(ax, d, dist=1.0, text='1"', color='w', font_size=15, flipped=False)[source]
Parameters:

  • ax – matplotlib.axes instance

  • d – diameter of frame

  • dist – distance scale printed

  • text – string printed on scale bar

  • color – color of scale bar

  • font_size – font size

  • flipped – boolean

Returns:

None, updated ax instance

coordinate_arrows(ax, d, coords, color='w', font_size=15, arrow_size=0.05)[source]
Parameters:

  • ax – matplotlib axes instance

  • d – diameter of frame in ax

  • coords – lenstronomy.Data.coord_transforms Coordinates() instance

  • color – color string

  • font_size – font size of length scale

  • arrow_size – size of arrow

Returns:

updated ax instance

plot_line_set(ax, coords, line_set_list_x, line_set_list_y, origin=None, flipped_x=False, points_only=False, *args, **kwargs)[source]

Plotting a line set on a matplotlib instance where the coordinates are defined in pixel units with the lower left corner (defined as origin) is by default (0, 0). The coordinates are moved by 0.5 pixels to be placed in the center of the pixel in accordance with the matplotlib.matshow() routine.

Parameters:

  • ax – matplotlib.axis instance

  • coords – Coordinates() class instance

  • origin – [x0, y0], lower left pixel coordinate in the frame of the pixels

  • line_set_list_x – numpy arrays corresponding of different disconnected regions of the line (e.g. caustic or critical curve)

  • line_set_list_y – numpy arrays corresponding of different disconnected regions of the line (e.g. caustic or critical curve)

  • color – string with matplotlib color

  • flipped_x – bool, if True, flips x-axis

  • points_only – bool, if True, sets plotting keywords to plot single points without connecting lines

Returns:

plot with line sets on matplotlib axis in pixel coordinates

image_position_plot(ax, coords, ra_image, dec_image, color='w', image_name_list=None, origin=None, flipped_x=False, plot_out_of_image=True)[source]
Parameters:

  • ax – matplotlib axis instance

  • coords – Coordinates() class instance or inherited class (such as PixelGrid(), or Data())

  • ra_image – Ra/x-coordinates of image positions (list of arrays in angular units)

  • dec_image – Dec/y-coordinates of image positions (list of arrays in angular units)

  • color – color of ticks and text

  • image_name_list – list of strings for names of the images in the same order as the positions

  • origin – [x0, y0], lower left pixel coordinate in the frame of the pixels

  • flipped_x – bool, if True, flips x-axis

  • plot_out_of_image (bool) – if True, plots images even appearing out of the Coordinate frame

Returns:

matplotlib axis instance with images plotted on

source_position_plot(ax, coords, ra_source, dec_source, marker='*', markersize=10, **kwargs)[source]
Parameters:

  • ax – matplotlib axis instance

  • coords – Coordinates() class instance or inherited class (such as PixelGrid(), or Data())

  • ra_source – list of source position in angular units

  • dec_source – list of source position in angular units

  • marker – marker style for matplotlib

  • markersize – marker size for matplotlib

Returns:

matplotlib axis instance with images plotted on

result_string(x, weights=None, title_fmt='.2f', label=None)[source]
Parameters:

  • x – marginalized 1-d posterior

  • weights – weights of posteriors (optional)

  • title_fmt – format to what digit the results are presented

  • label – string of parameter label (optional)

Returns:

string with mean \(\pm\) quartile

cmap_conf(cmap_string)[source]

Configures matplotlib color map.

Parameters:

cmap_string – string of cmap name, or cmap instance

Returns:

cmap instance with setting for bad pixels and values below the threshold

Module contents