healpy 1.20.0b1 beta — harmonic_ud_grade for artifact-safe map downgrading

python
astrophysics
healpy
healpix
cmb
Author

Andrea Zonca

Published

May 29, 2026

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pip install --pre healpy==1.20.0b1

harmonic_ud_grade changes map NSIDE through spherical-harmonic transforms with pixel-window and beam transfer corrections following Eq. 1 of Planck 2015 X (arXiv:1502.01588).

The two main modes are:

import numpy as np
import healpy as hp
import matplotlib.pyplot as plt

hp.disable_warnings()
print(f"healpy {hp.__version__}")
healpy 1.19.1.dev34+gb6c6692e5.d20260529
/tmp/ipykernel_2454585/3900260292.py:5: HealpyDeprecationWarning: The disable_warnings function is deprecated and may be removed in a future version.
  hp.disable_warnings()
nside_in = 512
nside_out = 64

np.random.seed(42)
m = hp.synfast(cls=np.arange(1, 1001)**(-2.5), nside=nside_in, lmax=3*nside_in-1, verbose=False)
/tmp/ipykernel_2454585/2046387934.py:5: HealpyDeprecationWarning: "verbose" was deprecated in version 1.15.0 and will be removed in a future version. 
  m = hp.synfast(cls=np.arange(1, 1001)**(-2.5), nside=nside_in, lmax=3*nside_in-1, verbose=False)

fwhm_out=0 — bandlimit truncation only

m_beam0 = hp.harmonic_ud_grade(m, nside_out, fwhm_out=0)
hp.mollview(m_beam0, title="fwhm_out=0 (bandlimit only)", cmap="RdYlBu_r")

fwhm_out=None — effective resolution beam

Applies a Gaussian beam with FWHM = \(3 \times \theta_{\rm pix}\) at the output NSIDE, following the Planck convention.

m_beam_auto = hp.harmonic_ud_grade(m, nside_out, fwhm_out=None)
hp.mollview(m_beam_auto, title="fwhm_out=None (effective beam)", cmap="RdYlBu_r")

Power spectra

lmax_out = 3 * nside_out - 1
cl_beam0 = hp.anafast(m_beam0, lmax=lmax_out)
cl_beam_auto = hp.anafast(m_beam_auto, lmax=lmax_out)
ell = np.arange(lmax_out + 1)

fig, ax = plt.subplots(figsize=(8, 5))
ax.loglog(ell[1:], cl_beam0[1:], label="fwhm_out=0")
ax.loglog(ell[1:], cl_beam_auto[1:], label="fwhm_out=None")
ax.axvline(2 * nside_out, color="gray", ls=":", alpha=0.7)
ax.set_xlabel(r"$\ell$")
ax.set_ylabel(r"$C_\ell$")
ax.set_title(f"Power spectra: NSIDE {nside_in} to {nside_out}")
ax.legend()
ax.set_xlim(1, lmax_out)
fig.tight_layout()

Try it