smpy.mapping_methods.kaiser_squires.kaiser_squires.KaiserSquiresMapper

class smpy.mapping_methods.kaiser_squires.kaiser_squires.KaiserSquiresMapper(config)

Implementation of Kaiser-Squires mass mapping.

This class implements the Kaiser-Squires direct inversion method for reconstructing convergence (mass) maps from weak lensing shear data. The algorithm uses Fourier transforms to perform the inversion and supports optional smoothing for noise reduction.

Notes

The Kaiser-Squires method directly inverts the shear-convergence relation: kappa = D^(-1) * gamma, where D is the differential operator relating convergence to shear in Fourier space.

__init__(config)

Initialize mass mapper with configuration.

Parameters:

config (dict) – Configuration dictionary with nested structure containing ‘general’, ‘methods’, and ‘plotting’ sections.

Methods

__init__(config)	Initialize mass mapper with configuration.
create_maps(g1_grid, g2_grid)	Create convergence maps using Kaiser-Squires inversion.
run(g1_grid, g2_grid, scaled_boundaries, ...)	Run complete mass mapping pipeline.

Attributes

name	Name identifier for the Kaiser-Squires method.

property name

Name identifier for the Kaiser-Squires method.

Returns:

method_name – String identifier ‘kaiser_squires’.

Return type:

str

create_maps(g1_grid, g2_grid)

Create convergence maps using Kaiser-Squires inversion.

Perform direct inversion of shear components to reconstruct both E-mode and B-mode convergence maps using Fourier transforms. Applies optional smoothing if configured.

Parameters:

• g1_grid (numpy.ndarray) – First shear component grid.

• g2_grid (numpy.ndarray) – Second shear component grid.

Returns:

• kappa_e (numpy.ndarray) – E-mode convergence map.

• kappa_b (numpy.ndarray) – B-mode convergence map.

Notes

The inversion is performed in Fourier space using the relations: kappa_E = ((k1^2 - k2^2) * g1 + 2 * k1 * k2 * g2) / k^2 kappa_B = ((k1^2 - k2^2) * g2 - 2 * k1 * k2 * g1) / k^2