Spherical and wide-field analysis

When to use spherical cubes

The Cartesian visibility representation uses a flat-sky geometry. For a large field of view, ps_eor.sphcube provides the corresponding HEALPix and spherical-harmonic representations:

SphImageCube

Frequency-dependent HEALPix maps shaped (n_frequencies, 12 * nside**2).

SphDataCube

Complex spherical-harmonic coefficients shaped (n_frequencies, n_modes) with matching (ell, m) coordinates. The transverse coordinate used by the power-spectrum code is u = ell / (2 pi).

Converting Cartesian data

A Cartesian cube can be reprojected and transformed to spherical harmonics:

from ps_eor import sphcube

alm_cube = sphcube.SphDataCube.from_cartcube(
    cart_cube,
    nside=128,
    lmax=600,
)
alm_cube.filter_lm(lmin=20, lmax=500)
alm_cube.save("spherical-visibilities.h5")

The image transform is useful for inspection:

sky_cube = alm_cube.image(
    mask_corrected=True,
    mask_correction_threshold=0.1,
)
sky_cube.plot(fmhz="med")

Mask correction divides by the recorded sky window only where its response exceeds the selected threshold. It can amplify noise near the mask boundary, so it should be used for inspection with an explicit threshold rather than treated as an unrestricted inverse.

Combining and estimating power

SphDataCubeCombiner forms a weighted mean of observations on a common harmonic grid and their shared frequency channels. As with Cartesian data, most filter_* methods modify a spherical cube in place.

The same PowerSpectraBuilder used for Cartesian data detects a spherical cube and returns the spherical-geometry estimator:

from ps_eor import pspec

ps_gen = pspec.PowerSpectraBuilder(config).get(alm_cube)
products = ps_gen.get_all(kbins, alm_cube)

This keeps the public power-spectrum workflow consistent while using the appropriate angular normalization internally.