smpy.mapping_methods.ks_plus.ks_plus.KSPlusMapper

class smpy.mapping_methods.ks_plus.ks_plus.KSPlusMapper(config)[source]

Implementation of Kaiser-Squires Plus mass mapping.

The KS+ method extends the standard Kaiser-Squires approach with several enhancements for improved mass reconstruction accuracy:

  1. Correcting for missing data using DCT-domain sparsity

  2. Reducing field border effects through field extension

  3. Iteratively correcting for reduced shear approximation

  4. Preserving proper statistical properties using wavelet constraints

Notes

The KS+ algorithm implements the iterative inpainting scheme described in the literature, combining sparsity priors in the DCT domain with wavelet-based power spectrum preservation for robust mass map reconstruction in the presence of missing data and systematic effects.

The binary mask encodes data availability only. Pixels are marked as data (1) if measurements exist and are valid; gaps (0) are used solely where data are missing or invalid. Zero shear values are valid data and must not be masked.

__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 Plus inversion.

run(g1_grid, g2_grid, scaled_boundaries, ...)

Run complete mass mapping pipeline.

Attributes

name

Name identifier for the KS+ method.
property name

Name identifier for the KS+ method.

Returns:

method_name – String identifier ‘ks_plus’.

Return type:

str

create_maps(g1_grid, g2_grid)[source]

Create convergence maps using Kaiser-Squires Plus inversion.

Perform enhanced mass mapping reconstruction with iterative inpainting, reduced shear corrections, and wavelet-based power spectrum preservation.

Parameters:

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

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

Returns:

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

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

Notes

The algorithm performs the following steps: 1. Field extension to reduce border effects 2. Iterative reduced shear correction 3. DCT-domain inpainting with sparsity constraints 4. Wavelet-based power spectrum preservation 5. Optional Gaussian smoothing