hod-mod 0.0.1

HOD galaxy clustering and weak lensing prediction and fitting

hod_mod

JAX-accelerated HOD galaxy clustering and weak lensing predictions and fitting.

Overview

hod_mod is a Python 3.11+ package for forward-modelling galaxy clustering (w_p) and weak gravitational lensing (ΔΣ) from Halo Occupation Distribution (HOD) and inverse-SHMR (ICSMF) models. All numerical code is JAX-native, enabling automatic differentiation and JIT compilation for efficient MCMC inference.

Install

Create and activate the conda environment, then install the package in editable mode:

mamba env create -f environment.yml
mamba activate hod_mod
pip install -e .

Tests

pytest                      # run all tests
pytest tests/test_cosmology.py          # single module
pytest -x                   # stop on first failure
pytest -v                   # verbose output
pytest --tb=short           # compact tracebacks

The test suite covers cosmology, HOD models, clustering predictions, data I/O, and fitting. Tests that require optional backends (camb, colossus) are skipped automatically if those packages are absent.

Supported HOD models

Class	Reference
HODModel	Zheng et al. 2007
MoreHODModel	More et al. 2015 (BOSS CMASS)
Kravtsov04HODModel	Kravtsov et al. 2004
Guo18ICSMFModel	Guo et al. 2018
Guo19ICSMFModel	Guo et al. 2019 (eBOSS ELGs)
Zacharegkas25HODModel	Zacharegkas et al. 2025
VanUitert16CSMFModel	van Uitert et al. 2016
ZuMandelbaum15HODModel	Zu & Mandelbaum 2015 (iHOD)
ZuMandelbaum16QuenchingModel	Zu & Mandelbaum 2016
Leauthaud12HODModel	Leauthaud et al. 2012

All clustering HOD classes subclass HODBase (ABC) and implement nc_ns() and default_params().

Quick start

import hod_mod
from hod_mod.cosmology.power_spectrum import LinearPowerSpectrum
from hod_mod.cosmology.halo_mass_function import make_hmf
from hod_mod.cosmology.halo_profiles import HaloProfile
from hod_mod.galaxies import MoreHODModel, FullHaloModelPrediction
import jax.numpy as jnp

pk_lin = LinearPowerSpectrum()
theta  = pk_lin.default_cosmology()
hmf    = make_hmf("tinker08", pk_func=pk_lin.pk_linear)

colossus_cosmo = dict(flat=True, H0=67.36, Om0=0.31, Ob0=0.0493, sigma8=0.811, ns=0.965)
hp = HaloProfile(colossus_cosmo, cm_relation="diemer19")

hod    = MoreHODModel(hmf, hmf.bias)
pred   = FullHaloModelPrediction(pk_lin, hod, hp, profile="nfw")

rp     = jnp.logspace(-1, 1.5, 20)
params = MoreHODModel.default_params()
wp     = pred.wp(rp, pi_max=60.0, z=0.5, theta_cosmo=theta, hod_params=params)

HOD fitting

Run from the repository root (paths in configs are resolved relative to it):

from hod_mod.fitting import load_config, WpFitter

cfg     = load_config("configs/hod_fit_more2015_cmass.yml")
fitter  = WpFitter(cfg)
result  = fitter.map_fit()               # Nelder-Mead MAP → dict
sampler = fitter.sample()               # emcee MCMC → EnsembleSampler
chain   = sampler.get_chain(flat=True)  # shape (n_steps * n_walkers, n_free)

The sample data file data/more2015_boss_cmass/wp_cmass_z052.csv is included in the repository (More+2015, arXiv:1407.1856, Figure 2).

Citation

If you use hod_mod in published work please cite the papers for the HOD models you use (see table above) and include a reference to this package.

License

MIT — see LICENSE.