Tools for generating and analyzing Gaussian random fields (GRFs) and their power spectra on regular grids, in 1, 2, or 3 dimensions. Built for astrophysical/cosmological applications where fields are specified by a power spectrum in Fourier space and realized on a real-space grid.
Features:
- Generate scalar or vector Gaussian random field realizations from an
arbitrary power spectrum (
GaussianRandomField), including divergence-free vector fields. - Built-in power spectrum models (
PowerLaw,PowerLawBetaModel), or supply your own callable. - FFT-based analysis (
FourierAnalysis): binned power spectra, divergence and curl of vector fields, windowing to reduce FFT boundary effects.
Install
pip install kspace
For development, clone the repo and use uv:
uv sync
To also install the heavier dependencies used by the example notebooks
(matplotlib, pandas, yt, h5py, pooch):
uv sync --group docs
Quick example
import numpy as np
from kspace import GaussianRandomField, FourierAnalysis, PowerLawBetaModel
# A power-law power spectrum with large- and small-scale cutoffs
power_spec = PowerLawBetaModel(l_min=10.0, l_max=200.0, alpha=-11.0 / 3.0)
power_spec.renormalize(f_rms=10.0)
le = np.array([0.0, 0.0, 0.0])
re = np.array([750.0, 750.0, 750.0])
ddims = [256, 256, 256]
grf = GaussianRandomField(le, re, ddims, power_spec, seed=10)
field = grf.generate_scalar_field_realization()
fa = FourierAnalysis(re - le, ddims)
kbins, pk = fa.make_binned_powerspec(field, nbins=60)
See docs/examples/ for more complete, runnable notebooks (GRF generation,
vector field decomposition, power spectrum estimation, and analysis of
simulation data).
1 maintainer
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