kspace 0.1.0

Documentation

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).