wale.LoadSimulations module

wale.LoadSimulations.get_simulation_l1(cosmo_index_to_run, tomobin, edges, centers, snr, R_pixels=30, filter_type='tophat', plot=False)

Load simulation data for a specific cosmology and compute L1 norms and PDFs. Parameters: - cosmo_index_to_run: Index of the cosmology to run (0-9 for 10 different cosmologies). - R_pixels: Physical scale in pixels for smoothing. - filter_type: Type of filter to use for smoothing (‘tophat’ or ‘gaussian’). Returns: - sim_l1_runs: Array of L1 norms for each simulation realization. - sim_pdf_runs: Array of PDF counts for each simulation realization. - avg_sim_l1: Average L1 norm across all realizations. - std_sim_l1: Standard deviation of L1 norms across realizations. - avg_sim_pdf: Average PDF counts across all realizations. - std_sim_pdf: Standard deviation of PDF counts across realizations.

wale.LoadSimulations.get_smoothed_app_pdf(mass_map, window_radius, binedges, filter_type, **kwargs)

Applies top-hat smoothing in Fourier space at two scales and returns the PDF of the difference map.

The map is filtered with a top-hat window of radius R and 2R, then the difference is computed.

Parameters:

• mass_map – 2D numpy array.

• window_radius – The smoothing scale (R) in physical units.

• binedges – Bin edges for the histogram.

• L – Physical size of the map (default 505 MPC/h).

Returns:

(bin_edges, pdf_counts, difference_map)

Return type:

tuple