homodyne 2.23.3

CPU-optimized JAX-based homodyne scattering analysis for XPCS under nonequilibrium conditions

Homodyne

[!IMPORTANT] Deprecation Notice: The homodyne and heterodyne packages have been merged into a single consolidated ecosystem: xpcsjax. This repository is now deprecated and maintained primarily for archival and reproducibility of the original PNAS 2024 and 2025 papers. All active development, performance optimizations, and future updates have moved to xpcsjax. We strongly recommend migrating your workflows to the new package.

CPU-optimized JAX package for X-ray Photon Correlation Spectroscopy (XPCS) analysis, implementing the theoretical framework from He et al. PNAS 2024 and He et al. PNAS 2025 for characterizing transport properties in flowing soft matter systems.

Homodyne Model

The package implements a single-component scattering model where correlation decay encodes diffusion and shear dynamics. The scattered intensity auto-correlates to produce a two-time correlation function whose shape reveals transport coefficients.

Two-Time Correlation

$$c_2(\vec{q}, t_1, t_2) = 1 + \beta \times [c_1(\vec{q}, t_1, t_2)]^2$$

where $\beta$ the optical contrast and the intermediate scattering function combines diffusion and shear:

$$c_1(\vec{q}, t_1, t_2) = \exp\left(-q^2 \int_{t_1}^{t_2} J(t')\ dt'\right) \times \mathrm{sinc}\left(\frac{qL\cos(\phi)}{2} \int_{t_1}^{t_2} \dot{\gamma}(t')\ dt'\right)$$

where $\phi$ is the angle between the scattering vector and the flow direction.

Rate Functions

Transport coefficients follow power-law forms:

$$J(t) = D_0 \cdot t^{\alpha} + D_{\text{offset}} \qquad \dot{\gamma}(t) = \dot{\gamma}0 \cdot t^{\beta} + \dot{\gamma}{\text{offset}}$$

All time integrals are evaluated numerically via cumulative trapezoid on the discrete time grid -- no analytical antiderivatives are ever used, ensuring correctness for the general power-law form.

Parameters

The model has up to 7 physics parameters organized into three groups, plus 2 per-angle scaling parameters:

Diffusion transport -- $J(t) = D_0 t^\alpha + D_{\text{offset}}$

Parameter	Description	Default	Units
D0	Diffusion prefactor	1e4	Ų/s
alpha	Transport exponent (0 = Wiener, 1 = ballistic)	0.0	--
D_offset	Transport rate offset	0.0	Ų/s

Shear rate (laminar_flow only) -- $\dot\gamma(t) = \dot\gamma_0 t^\beta + \dot\gamma_{\text{offset}}$

Parameter	Description	Default	Units
gamma_dot_0	Shear rate prefactor	1e-3	s⁻¹
beta	Shear rate exponent (0 = constant shear)	0.0	--
gamma_dot_offset	Shear rate offset	0.0	s⁻¹

Flow angle

Parameter	Description	Default	Units
phi0	Flow angle offset relative to q-vector	0.0	degrees

Per-angle scaling (2 parameters per detector angle)

Parameter	Description	Default
contrast	Optical contrast (speckle contrast)	0.5
offset	Baseline offset	1.0

Mode	Parameters	Count
static	D0, alpha, D_offset	3
laminar_flow	D0, alpha, D_offset, gamma_dot_0, beta, gamma_dot_offset, phi0	7

Per-angle contrast and offset are added automatically based on the number of azimuthal angles.

Installation

pip install homodyne

For development:

git clone https://github.com/imewei/homodyne.git
cd homodyne
make dev    # or: uv sync

Requirements: Python 3.12+, CPU-only (no GPU). Runs on Linux, macOS, and Windows.

Quick Start

CLI

# Generate a config template
homodyne-config --mode static --output config.yaml

# Run NLSQ optimization
homodyne --method nlsq --config config.yaml

# Run Consensus Monte Carlo for uncertainty quantification
homodyne --method cmc --config config.yaml

Python API

from homodyne.data import load_xpcs_data
from homodyne.config import ConfigManager
from homodyne.optimization import fit_nlsq_jax
from homodyne.optimization.cmc import fit_mcmc_jax

# Load data and config
data = load_xpcs_data("config.yaml")
config = ConfigManager("config.yaml")

# NLSQ trust-region optimization
nlsq_result = fit_nlsq_jax(data, config)

# CMC with NLSQ warm-start for Bayesian uncertainty
cmc_result = fit_mcmc_jax(data, config, nlsq_result=nlsq_result)

Data Flow

YAML config --> XPCSDataLoader(HDF5) --> HomodyneModel --> NLSQ or CMC --> Results (JSON + NPZ)

Optimization Methods

NLSQ (primary) -- JAX-native trust-region Levenberg-Marquardt with automatic anti-degeneracy defense, CMA-ES global search for multi-scale problems, and memory-aware routing for large datasets.

CMC (secondary) -- Consensus Monte Carlo using NumPyro NUTS sampling with automatic sharding, NLSQ warm-start priors, and multiprocessing across CPU cores. Produces publication-quality posterior distributions with ArviZ diagnostics.

Configuration

Homodyne uses YAML configuration files. Generate a template:

homodyne-config --mode laminar_flow --output config.yaml

Key sections:

analysis_mode: "laminar_flow"
experimental_data:
  file_path: "data.h5"
optimization:
  method: "nlsq"
  nlsq:
    anti_degeneracy:
      per_angle_mode: "auto"   # auto, constant, individual, fourier
  cmc:
    sharding:
      max_points_per_shard: "auto"

See the User Guide for full configuration reference.

CLI Commands

Command	Purpose
homodyne	Run XPCS analysis (NLSQ/CMC)
homodyne-config	Generate and validate config files
homodyne-config-xla	Configure XLA device settings
homodyne-post-install	Install shell completion (bash/zsh/fish)
homodyne-cleanup	Remove shell completion files

Shell completion and aliases are available after running homodyne-post-install --interactive.

Development

make test       # Unit tests
make test-all   # Full suite + coverage
make quality    # Format + lint + type-check

Documentation

• User Guide
• API Reference
• Changelog

Citation

If you use Homodyne in your research, please cite:

@article{He2024,
  author  = {He, Hongrui and Liang, Hao and Chu, Miaoqi and Jiang, Zhang and
             de Pablo, Juan J and Tirrell, Matthew V and Narayanan, Suresh
             and Chen, Wei},
  title   = {Transport coefficient approach for characterizing nonequilibrium
             dynamics in soft matter},
  journal = {Proceedings of the National Academy of Sciences},
  volume  = {121},
  number  = {31},
  year    = {2024},
  doi     = {10.1073/pnas.2401162121}
}

@article{He2025,
  author  = {He, Hongrui and Liang, Heyi and Chu, Miaoqi and Jiang, Zhang and
             de Pablo, Juan J and Tirrell, Matthew V and Narayanan, Suresh
             and Chen, Wei},
  title   = {Bridging microscopic dynamics and rheology in the yielding
             of charged colloidal suspensions},
  journal = {Proceedings of the National Academy of Sciences},
  volume  = {122},
  number  = {42},
  year    = {2025},
  doi     = {10.1073/pnas.2514216122}
}

License

MIT License -- see LICENSE for details.

Authors

• Wei Chen ([e-mail]) -- Argonne National Laboratory
• Hongrui He ([e-mail]) -- Argonne National Laboratory