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foregrounds-diffusion

foregrounds-diffusion

Getting started

Installation
Quickstart
Data conventions

API reference

API Reference

Tutorials

01 — Halo Catalogue
02 — Masking
03 — Patch Extraction and Normalisation
04 — Model Architecture and Training
05 — Sampling and Post-Processing
06 — Power Spectra Comparison
07 — Higher-Order Statistics (Bispectrum and Trispectrum)
08 — Pixel Histograms and Minkowski Functionals
09 — tSZ Cluster Stacking
10 — Peak and Minima Counts
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Imports and physical constants.
pixel_res_arcmin is used by the Gaussian smoothing kernel inside
compute_peak_minima_counts to convert FWHM in arcmin to pixels.
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Load normalised CIB (_zero suffix = min-max to [0,1]) and tSZ (_norm suffix
= z-score). norm_params stores (cib_mean, cib_std, tsz_mean, tsz_std) in
physical µK, computed over the full training set during patch extraction.
DDPM samples are stored channels-first: shape (N, 2, H, W).
Gaussian baseline maps (channels-first, same shape) were generated by
drawing realisations from the Agora-measured power spectrum.
Denormalise both back to µK before computing statistics.
compute_peak_minima_counts:
maps_nhw : (N, H, W) array in physical units (µK)
thresholds_peaks : 1D array of ν = T/σ values for peak binning
thresholds_minima : 1D array of ν values for minima binning (typically < 0)
smoothing_scales_arcmin: list of FWHM values for the pre-smoothing Gaussian
Returns dict keyed by scale → {‘peaks’: (N, T), ‘minima’: (N, T)}
11 — Scattering Transforms
Check for the Cheng et al. scattering backend and add it to sys.path.
This must be done before importing scattering_stats, which tries to import
the backend at module level and falls back to kymatio if not found.
Load normalised arrays (same split and denormalisation as notebooks 06-10).
Use float32 throughout to halve memory and avoid kymatio’s implicit f64 cast.
compute_scattering_coefficients(maps, J, L):
maps : (N, H, W) float32 array in µK
J : number of dyadic scales (wavelet support ≈ 2^j pixels per scale)
L : number of orientations (angular resolution = 180° / L)
Returns dict with keys ‘S0’, ‘S1’ (N, J, L), ‘S2’ (N, J, J, L)
12 — Minkowski Tensors
Load modules and set configuration.
THRESHOLDS is in the normalised [0, 1] range (after apply_maxmin_normalization);
avoid ν = 0 and ν = 1 where the excursion set is trivially full or empty.
Load normalised patches. The Minkowski tensor code re-normalises internally
via norm_fn, so the absolute pixel values here are less critical than for
power spectra. Using the standard z-score / min-max loading ensures
consistency with the train/test split.
DDPM samples shape: (N, 2, H, W) channels-first.
Index [:, 0] = CIB channel, [:, 1] = tSZ channel.
Denormalise: CIB = raw * (cib_max - cib_min) + cib_min;
tSZ = raw * tsz_std + tsz_mean.
compute_minkowski_tensors(maps_nhw, norm_fn, thresholds, tensor_types, centred)
norm_fn : normalisation applied per-map before thresholding
(use apply_maxmin_normalization so ν ∈ [0,1] is meaningful)
centred : if True, subtract excursion-set centroid from r⊗r tensors
(affects W200, W201 only; default True)
n_jobs : pass n_jobs=-1 to use all CPU cores via joblib
13 — Profiling and Benchmarking Baseline
Paper figures
Fig 1
Minkowski Functionals

API Reference¶

All public modules in the foregrounds_diffusion package.

flatmaps — Flat-sky Fourier utilities
preprocessing — Normalisation and patch extraction
moments — Power spectra and higher-order statistics
morphology — Minkowski functionals and tensors
masking — Peak masks and AGORA cluster masks
peak_counts — Peak and minima counting
statistics — 2D Gaussian fitting
stacking — tSZ cluster stacking
scattering_stats — Scattering transform statistics
plot_style — Publication-quality Matplotlib style

flatmaps — Flat-sky Fourier utilities

Data conventions

Copyright © 2025, Alex Blake Martin

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