Data conventions¶

Array layout¶

Raw .npy files saved by the preprocessing pipeline are channels-last: shape (N, H, W, C). PyTorch requires channels-first (N, C, H, W), so train.py transposes on load.

C = 1 for single-channel files (separate CIB and tSZ files).
C = 2 for joint files (CIB channel 0, tSZ channel 1).
DDPM samples output by sample.py are channels-first (N, 2, H, W).

Patch geometry¶

All patches are 6° × 6° flat-sky projections of HEALPix maps, pixelised onto a 256 × 256 grid.

Parameter	Value
`nx`, `ny`	256
`dx`, `dy` (arcmin/pixel)	1.40625 (= 6° × 60 / 256)
`flatskymapparams`	`[256, 256, 1.40625, 1.40625]`

Pass flatskymapparams to all flatmaps functions.

Normalisation¶

Two normalisation schemes are used, one per channel:

Channel	Scheme	File suffix	Notes
CIB 150 GHz	Min–max → [0, 1]	`_zero`	CIB pixels are non-negative by construction
tSZ Compton-y	Z-score (μ=0, σ=1)	`_norm`	tSZ spans negative and positive values

Normalisation parameters (cib_mean, cib_std, tsz_mean, tsz_std) are saved alongside the patches as norm_params_{ptsrc}mJy.npy. Use denormalize_dm_maps() to invert.

Masking and filtering¶

Point-source mask: sources brighter than 2 mJy at 150 GHz are masked; masked pixels are set to zero (not NaN).
Low-pass filter: modes above ℓ = 7000 are removed; negative pixels arising from filtering artefacts are zeroed.
Patch rejection: patches where all pixels are zero (footprint entirely within the Galactic-plane mask) are discarded before training.

Multipole conventions¶

map2cl() returns the unbinned power spectrum \(C_\ell\) in map-unit² sr. To convert to \(D_\ell = \ell(\ell+1)C_\ell / 2\pi\):

el, cl, _ = mean_cls(maps, flatskymapparams, lmin=300, lmax=4000, binsize=60)
dl = el * (el + 1) / (2 * np.pi) * cl