homodyne-analysis 1.0.0

High-performance Python package for analyzing homodyne scattering in X-ray Photon Correlation Spectroscopy (XPCS) with frame counting fixes (v1.0.0+) and conditional angle subsampling for preserving angular information

Homodyne Scattering Analysis Package

Overview

homodyne-analysis is a research-grade Python package for analyzing homodyne scattering in X-ray Photon Correlation Spectroscopy (XPCS) under nonequilibrium conditions. This package implements theoretical frameworks from He et al. PNAS 2024 for characterizing transport properties in flowing soft matter systems through time-dependent intensity correlation functions.

The package provides comprehensive analysis of nonequilibrium dynamics by capturing the interplay between Brownian diffusion and advective shear flow in complex fluids, with applications to biological systems, colloids, and active matter under flow conditions.

Key Features

Analysis Capabilities

• Three analysis modes: Static Isotropic (3 parameters), Static Anisotropic (3 parameters), Laminar Flow (7 parameters)
• Multiple optimization methods: Classical (Nelder-Mead, Gurobi), Robust (Wasserstein DRO, Scenario-based, Ellipsoidal)
• Conditional angle subsampling: Automatic preservation of angular information when n_angles < 4 for anisotropic analysis
• Frame counting convention: Consistent 1-based inclusive counting with automatic conversion to 0-based Python slicing

Performance

• Numba JIT compilation: 3-5x speedup for core calculations
• Vectorized operations: Optimized NumPy array processing
• Memory optimization: Ellipsoidal optimization with 90% memory limit for large datasets (8M+ data points)
• Smart caching: Intelligent data caching with automatic dimension validation

Data Format Support

• APS and APS-U formats: Auto-detection and loading of both old and new synchrotron data formats
• Cached data compatibility: Automatic cache dimension adjustment and validation
• Configuration templates: Comprehensive templates with documented subsampling strategies

Validation and Quality

• Statistical validation: Cross-validation and bootstrap analysis for parameter reliability
• Experimental validation: Tested at synchrotron facilities (APS Sector 8-ID-I)
• Regression testing: Comprehensive test suite with performance benchmarks

Quick Start

# Install
pip install homodyne-analysis[all]

# Create configuration
homodyne-config --mode laminar_flow --sample my_sample

# Run analysis
homodyne --config my_config.json --method all

# Run robust optimization for noisy data
homodyne --config my_config.json --method robust

Quick Reference (v1.0.0)

Most Common Workflows

1. First-time Setup:

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install with all dependencies
pip install homodyne-analysis[all]

# Generate configuration template
homodyne-config --mode laminar_flow --sample my_experiment

2. Standard Analysis (Clean Data):

# Edit my_config.json with your experimental parameters
# Then run classical optimization
homodyne --config my_config.json --method classical

# Results saved to: ./homodyne_results/

3. Robust Analysis (Noisy Data):

# For data with outliers or measurement noise
homodyne --config my_config.json --method robust --verbose

# Compare both methods
homodyne --config my_config.json --method all

4. Fast Static Isotropic Analysis:

# Generate isotropic config (3 parameters, fastest)
homodyne-config --mode static_isotropic --output fast_config.json

# Run analysis
homodyne --config fast_config.json --static-isotropic

5. Python API Usage:

import numpy as np
import json
from homodyne.analysis.core import HomodyneAnalysisCore
from homodyne.optimization.classical import ClassicalOptimizer

# Load config and initialize
with open("my_config.json", 'r') as f:
    config = json.load(f)
core = HomodyneAnalysisCore(config)

# Load your experimental data
phi_angles = np.array([0, 36, 72, 108, 144])
c2_data = load_your_data()  # Your data loading function

# Run optimization
optimizer = ClassicalOptimizer(core, config)
params, results = optimizer.run_classical_optimization_optimized(
    phi_angles=phi_angles,
    c2_experimental=c2_data
)

print(f"D₀ = {params[0]:.3e} Ų/s")
print(f"χ² = {results.chi_squared:.6e}")

6. Troubleshooting:

# Enable verbose logging for debugging
homodyne --config my_config.json --verbose

# Run test suite to verify installation
pytest -v -m "not slow"

# Check frame counting (v1.0.0 critical fix)
pytest homodyne/tests/test_time_length_calculation.py -v

Installation

Standard Installation

pip install homodyne-analysis[all]

Research Environment Setup

# Create isolated research environment
conda create -n homodyne-research python=3.12
conda activate homodyne-research

# Install with all scientific dependencies
pip install homodyne-analysis[all]

# For development and method extension
git clone https://github.com/imewei/homodyne.git
cd homodyne
pip install -e .[dev]

Optional Dependencies

• Performance: pip install homodyne-analysis[performance] (numba, psutil, performance monitoring)
• Robust optimization: pip install homodyne-analysis[robust] (cvxpy, gurobipy, mosek)
• Development: pip install homodyne-analysis[dev] (testing, linting, documentation tools)

High-Performance Configuration

# Optimize for computational performance
export OMP_NUM_THREADS=8
export NUMBA_DISABLE_INTEL_SVML=1
export HOMODYNE_PERFORMANCE_MODE=1

# Enable advanced optimization (requires license)
pip install homodyne-analysis[gurobi]

Commands

Main Analysis Command

homodyne [OPTIONS]

Key Options:

• --method {classical,robust,all} - Analysis method (default: classical)
• --config CONFIG - Configuration file (default: ./homodyne_config.json)
• --output-dir DIR - Output directory (default: ./homodyne_results)
• --verbose - Debug logging
• --quiet - File logging only
• --static-isotropic - Force 3-parameter isotropic mode
• --static-anisotropic - Force 3-parameter anisotropic mode
• --laminar-flow - Force 7-parameter flow mode
• --plot-experimental-data - Generate data validation plots
• --plot-simulated-data - Plot theoretical correlations

Examples:

# Basic analysis
homodyne --method classical
homodyne --method robust --verbose
homodyne --method all

# Force analysis modes
homodyne --static-isotropic --method classical
homodyne --laminar-flow --method all

# Data validation
homodyne --plot-experimental-data
homodyne --plot-simulated-data --contrast 1.5 --offset 0.1

# Custom configuration
homodyne --config experiment.json --output-dir ./results

Configuration Generator

homodyne-config [OPTIONS]

Options:

• --mode {static_isotropic,static_anisotropic,laminar_flow} - Analysis mode
• --output OUTPUT - Output file (default: my_config.json)
• --sample SAMPLE - Sample name
• --author AUTHOR - Author name
• --experiment EXPERIMENT - Experiment description

Examples:

# Default laminar flow config
homodyne-config

# Static isotropic (fastest)
homodyne-config --mode static_isotropic --output fast_config.json

# With metadata
homodyne-config --sample protein --author "Your Name"

Shell Completion

The package supports shell completion for bash, zsh, and fish shells:

# For bash
homodyne --help  # Shows all available options

# Tab completion works for:
homodyne --method <TAB>     # classical, robust, all
homodyne --config <TAB>     # Completes file paths
homodyne --output-dir <TAB> # Completes directory paths

Note: Shell completion is built into the CLI and works automatically in most modern shells. For advanced completion features, you may need to install optional completion dependencies.

Scientific Background

Physical Model

The package analyzes time-dependent intensity correlation functions in the presence of laminar flow:

$$c_2(\vec{q}, t_1, t_2) = 1 + \beta\left[e^{-q^2\int J(t)dt}\right] \times \text{sinc}^2\left[\frac{1}{2\pi} qh \int\dot{\gamma}(t)\cos(\phi(t))dt\right]$$

where:

• $\vec{q}$: scattering wavevector [Å⁻¹]
• $h$: gap between stator and rotor [Å]
• $\phi(t)$: angle between shear/flow direction and $\vec{q}$ [degrees]
• $\dot{\gamma}(t)$: time-dependent shear rate [s⁻¹]
• $D(t)$: time-dependent diffusion coefficient [Ų/s]
• $\beta$: instrumental contrast parameter

Analysis Modes

| Mode | Parameters | Physical Description | Computational Complexity | Speed | |------|------------|---------------------|--------------------------|-------| | Static Isotropic | 3 | Brownian motion only, isotropic systems | O(N) | ⭐⭐⭐ | | Static Anisotropic | 3 | Static systems with angular dependence | O(N log N) | ⭐⭐ | | Laminar Flow | 7 | Full nonequilibrium with flow and shear | O(N²) | ⭐ |

Model Parameters

Static Parameters (All Modes):

• $D_0$: baseline diffusion coefficient [Ų/s]
• $\alpha$: diffusion scaling exponent
• $D_{\text{offset}}$: additive diffusion offset [Ų/s]

Flow Parameters (Laminar Flow Mode):

• $\dot{\gamma}_0$: baseline shear rate [s⁻¹]
• $\beta$: shear rate scaling exponent
• $\dot{\gamma}_{\text{offset}}$: additive shear rate offset [s⁻¹]
• $\phi_0$: flow direction angle [degrees]

Frame Counting Convention

Overview

The package uses 1-based inclusive frame counting in configuration files, which is then converted to 0-based Python array indices for processing.

Frame Counting Formula

time_length = end_frame - start_frame + 1  # Inclusive counting

Examples:

• start_frame=1, end_frame=100 → time_length=100 (not 99!)
• start_frame=401, end_frame=1000 → time_length=600 (not 599!)
• start_frame=1, end_frame=1 → time_length=1 (single frame)

Convention Details

Config Convention (1-based, inclusive):

• start_frame=1 means "start at first frame"
• end_frame=100 means "include frame 100"
• Both boundaries are inclusive: [start_frame, end_frame]

Python Slice Convention (0-based, exclusive end):

• Internally converted using: python_start = start_frame - 1
• python_end = end_frame (kept as-is for exclusive slice)
• Array slice [python_start:python_end] gives exactly time_length elements

Example Conversion:

# Config values
start_frame = 401  # 1-based
end_frame = 1000   # 1-based

# Convert to Python indices
python_start = 400  # 0-based (401 - 1)
python_end = 1000   # 0-based, exclusive

# Slice gives correct number of frames
data_slice = full_data[:, 400:1000, 400:1000]  # 600 frames
time_length = 1000 - 401 + 1  # = 600 ✓

Cached Data Compatibility

Cache Filename Convention:

• Cache files use config values: cached_c2_isotropic_frames_{start_frame}_{end_frame}.npz
• Example: cached_c2_isotropic_frames_401_1000.npz contains 600 frames

Cache Dimension Validation: The analysis core automatically detects and adjusts for dimension mismatches:

# Automatic adjustment if cache dimensions differ
if c2_experimental.shape[1] != self.time_length:
    logger.info(f"Auto-adjusting time_length to match cached data")
    self.time_length = c2_experimental.shape[1]

Migration for Existing Users: If you have cached files created before v0.6.5, they may have incorrect dimensions:

1. Delete old cache files
2. Regenerate from raw data using the fixed conversion script
3. Or accept auto-adjustment (dimensions will match data, not config)

Utility Functions

Two centralized utility functions ensure consistency:

from homodyne.core.io_utils import calculate_time_length, config_frames_to_python_slice

# Calculate time_length from config frames
time_length = calculate_time_length(start_frame=401, end_frame=1000)
# Returns: 600

# Convert for Python array slicing
python_start, python_end = config_frames_to_python_slice(401, 1000)
# Returns: (400, 1000) for use in data[400:1000]

Conditional Angle Subsampling

Strategy

The package automatically preserves angular information when the number of available angles is small:

# Automatic angle preservation
if n_angles < 4:
    # Use all available angles to preserve angular information
    angle_subsample_size = n_angles
else:
    # Subsample for performance (default: 4 angles)
    angle_subsample_size = config.get("n_angles", 4)

Impact

• Before fix: 2 angles → 1 angle (50% angular information loss)
• After fix: 2 angles → 2 angles (100% preservation)
• Time subsampling still applied: ~16x performance improvement with <10% χ² degradation

Configuration

All configuration templates include subsampling documentation:

{
  "subsampling": {
    "n_angles": 4,
    "n_time_points": 16,
    "comment": "Conditional: n_angles preserved when < 4 for angular info retention"
  }
}

Optimization Methods

Classical Methods

1. Nelder-Mead Simplex: Derivative-free optimization for robust convergence
2. Gurobi Quadratic Programming: High-performance commercial solver with trust region methods

Robust Optimization Framework

Advanced uncertainty-aware optimization for noisy experimental data:

1. Distributionally Robust Optimization (DRO):

   • Wasserstein uncertainty sets for data distribution robustness
   • Optimal transport-based uncertainty quantification

2. Scenario-Based Optimization:

   • Bootstrap resampling for statistical robustness
   • Monte Carlo uncertainty propagation

3. Ellipsoidal Uncertainty Sets:

   • Bounded uncertainty with confidence ellipsoids
   • Analytical uncertainty bounds
   • Memory optimization: 90% limit for large datasets

Usage Guidelines:

• Use --method robust for noisy data with outliers
• Use --method classical for clean, low-noise data
• Use --method all to run both and compare results

Python API

Basic Usage

import numpy as np
import json
from homodyne.analysis.core import HomodyneAnalysisCore
from homodyne.optimization.classical import ClassicalOptimizer
from homodyne.data.xpcs_loader import load_xpcs_data

# Load configuration file
config_file = "config_static_isotropic.json"
with open(config_file, 'r') as f:
    config = json.load(f)

# Initialize analysis core
core = HomodyneAnalysisCore(config)

# Load experimental XPCS data
phi_angles = np.array([0, 36, 72, 108, 144])  # Example angles in degrees
c2_data = load_xpcs_data(
    data_path=config['experimental_data']['data_folder_path'],
    phi_angles=phi_angles,
    n_angles=len(phi_angles)
)

# Run classical optimization
classical = ClassicalOptimizer(core, config)
optimal_params, results = classical.run_classical_optimization_optimized(
    phi_angles=phi_angles,
    c2_experimental=c2_data
)

print(f"Optimal parameters: {optimal_params}")
print(f"Chi-squared: {results.chi_squared:.6e}")
print(f"Method: {results.best_method}")

Research Workflow

import numpy as np
import json
from homodyne.analysis.core import HomodyneAnalysisCore
from homodyne.optimization.classical import ClassicalOptimizer
from homodyne.optimization.robust import RobustHomodyneOptimizer
from homodyne.data.xpcs_loader import load_xpcs_data

# Load experimental configuration
config_file = "config_laminar_flow.json"
with open(config_file, 'r') as f:
    config = json.load(f)

# Initialize analysis core
core = HomodyneAnalysisCore(config)

# Load XPCS correlation data
phi_angles = np.array([0, 36, 72, 108, 144])  # Scattering angles in degrees
c2_data = load_xpcs_data(
    data_path=config['experimental_data']['data_folder_path'],
    phi_angles=phi_angles,
    n_angles=len(phi_angles)
)

# Classical analysis for clean data
classical = ClassicalOptimizer(core, config)
classical_params, classical_results = classical.run_classical_optimization_optimized(
    phi_angles=phi_angles,
    c2_experimental=c2_data
)

# Robust analysis for noisy data
robust = RobustHomodyneOptimizer(core, config)
robust_result_dict = robust.optimize(
    phi_angles=phi_angles,
    c2_experimental=c2_data,
    method="wasserstein",  # Options: "wasserstein", "scenario", "ellipsoidal"
    epsilon=0.1  # Uncertainty radius for DRO
)

# Extract results
robust_params = robust_result_dict['optimal_params']
robust_chi2 = robust_result_dict['chi_squared']

print(f"Classical D₀: {classical_params[0]:.3e} Ų/s")
print(f"Classical χ²: {classical_results.chi_squared:.6e}")
print(f"\nRobust D₀: {robust_params[0]:.3e} Ų/s")
print(f"Robust χ²: {robust_chi2:.6e}")

Performance Benchmarking

import time
import numpy as np
import json
from homodyne.analysis.core import HomodyneAnalysisCore
from homodyne.optimization.classical import ClassicalOptimizer
from homodyne.data.xpcs_loader import load_xpcs_data

# Load configuration
config_file = "config_laminar_flow.json"
with open(config_file, 'r') as f:
    config = json.load(f)

# Initialize
core = HomodyneAnalysisCore(config)
phi_angles = np.array([0, 36, 72, 108, 144])
c2_data = load_xpcs_data(
    data_path=config['experimental_data']['data_folder_path'],
    phi_angles=phi_angles,
    n_angles=len(phi_angles)
)

# Benchmark classical optimization
classical = ClassicalOptimizer(core, config)
start_time = time.perf_counter()
params, results = classical.run_classical_optimization_optimized(
    phi_angles=phi_angles,
    c2_experimental=c2_data
)
elapsed_time = time.perf_counter() - start_time

print(f"Classical optimization completed in {elapsed_time:.2f} seconds")
print(f"Chi-squared: {results.chi_squared:.6e}")
print(f"Best method: {results.best_method}")

Configuration

Creating Configurations

# Generate templates
homodyne-config --mode static_isotropic --sample protein_01
homodyne-config --mode laminar_flow --sample microgel

Mode Selection

Configuration files specify analysis mode:

{
  "analysis_settings": {
    "static_mode": true/false,
    "static_submode": "isotropic" | "anisotropic" | null
  }
}

Rules:

• static_mode: false → Laminar Flow Mode (7 params)
• static_mode: true, static_submode: "isotropic" → Static Isotropic (3 params)
• static_mode: true, static_submode: "anisotropic" → Static Anisotropic (3 params)

Subsampling Configuration

{
  "subsampling": {
    "n_angles": 4,
    "n_time_points": 16,
    "strategy": "conditional",
    "preserve_angular_info": true
  }
}

Performance Impact:

• Time subsampling: ~16x speedup
• Angle subsampling: Conditional based on available angles
• Combined impact: 20-50x speedup with <10% χ² degradation

Data Formats and Standards

XPCS Correlation Data Format:

• Time correlation functions: c2(q, φ, t1, t2) as HDF5 or NumPy arrays
• Scattering angles: φ values in degrees [0°, 360°)
• Time delays: τ = t2 - t1 in seconds
• Wavevector magnitude: q in Å⁻¹

Configuration Schema:

{
  "analysis_settings": {
    "static_mode": false,
    "static_submode": null,
    "angle_filtering": true,
    "optimization_method": "all"
  },
  "experimental_parameters": {
    "q_magnitude": 0.0045,
    "gap_height": 50000.0,
    "temperature": 293.15,
    "viscosity": 1.0e-3
  },
  "frame_settings": {
    "start_frame": 401,
    "end_frame": 1000,
    "time_length_comment": "Calculated as end_frame - start_frame + 1 = 600"
  },
  "optimization_bounds": {
    "D0": [1e-15, 1e-10],
    "alpha": [0.1, 2.0],
    "D_offset": [-1e-12, 1e-12]
  }
}

Output Structure

When running homodyne --method all, the complete analysis produces a comprehensive results directory with all optimization methods:

homodyne_results/
├── homodyne_analysis_results.json    # Summary with all methods
├── run.log                           # Detailed execution log
│
├── classical/                        # Classical optimization results
│   ├── nelder_mead/                  # Nelder-Mead simplex method
│   │   ├── parameters.json           # Optimal parameters with metadata
│   │   ├── fitted_data.npz          # Fitted correlation functions + experimental metadata
│   │   ├── analysis_results_nelder_mead.json  # Complete results + chi-squared
│   │   ├── convergence_metrics.json  # Iterations, function evaluations, diagnostics
│   │   └── c2_heatmaps_phi_*.png    # Experimental vs fitted comparison plots
│   └── gurobi/                       # Gurobi quadratic programming (if available)
│       ├── parameters.json
│       ├── fitted_data.npz
│       ├── analysis_results_gurobi.json
│       ├── convergence_metrics.json
│       └── c2_heatmaps_phi_*.png
│
├── robust/                           # Robust optimization results
│   ├── wasserstein/                  # Distributionally Robust Optimization (DRO)
│   │   ├── parameters.json           # Robust optimal parameters
│   │   ├── fitted_data.npz          # Fitted correlations with uncertainty bounds
│   │   ├── analysis_results_wasserstein.json  # DRO results + uncertainty radius
│   │   ├── convergence_metrics.json  # Optimization convergence info
│   │   └── c2_heatmaps_phi_*.png    # Robust fit comparison plots
│   ├── scenario/                     # Scenario-based bootstrap optimization
│   │   ├── parameters.json
│   │   ├── fitted_data.npz
│   │   ├── analysis_results_scenario.json
│   │   ├── convergence_metrics.json
│   │   └── c2_heatmaps_phi_*.png
│   └── ellipsoidal/                  # Ellipsoidal uncertainty sets
│       ├── parameters.json
│       ├── fitted_data.npz
│       ├── analysis_results_ellipsoidal.json
│       ├── convergence_metrics.json
│       └── c2_heatmaps_phi_*.png
│
└── comparison_plots/                 # Method comparison visualizations
    ├── method_comparison_phi_*.png   # Classical vs Robust comparison
    └── parameter_comparison.png      # Parameter values across methods

Key Output Files

homodyne_analysis_results.json: Main summary containing:

• Analysis timestamp and methods run
• Experimental parameters (q, dt, gap size, frames)
• Optimization results for all methods:
  • classical_nelder_mead, classical_gurobi, classical_best
  • robust_wasserstein, robust_scenario, robust_ellipsoidal, robust_best

fitted_data.npz: NumPy compressed archive with:

• Experimental metadata: wavevector_q, dt, stator_rotor_gap, start_frame, end_frame
• Correlation data: c2_experimental, c2_theoretical_raw, c2_theoretical_scaled
• Scaling parameters: contrast_params, offset_params
• Quality metrics: residuals

analysis_results_{method}.json: Method-specific detailed results:

• Optimized parameters with names
• Chi-squared and reduced chi-squared values
• Experimental metadata
• Scaling parameters for each angle
• Success status and timestamp

convergence_metrics.json: Optimization diagnostics:

• Number of iterations
• Function evaluations
• Convergence message
• Final chi-squared value

Performance

Environment Optimization

export OMP_NUM_THREADS=8
export NUMBA_DISABLE_INTEL_SVML=1
export HOMODYNE_PERFORMANCE_MODE=1

Optimizations

• Numba JIT: 3-5x speedup for core calculations
• Vectorized operations: Optimized array processing
• Memory efficiency: Smart caching and allocation
• Batch processing: Vectorized chi-squared calculation
• Conditional subsampling: 20-50x speedup with minimal accuracy loss

Benchmarking Results

Performance Comparison (Intel Xeon, 8 cores):

| Data Size | Pure Python | Numba JIT | Speedup | |-----------|-------------|-----------|---------| | 100 points | 2.3 s | 0.7 s | 3.3× | | 500 points | 12.1 s | 3.2 s | 3.8× | | 1000 points | 45.2 s | 8.9 s | 5.1× | | 5000 points | 892 s | 178 s | 5.0× |

Memory Optimization:

| Dataset Size | Before | After | Improvement | |--------------|--------|-------|-------------| | 8M data points | Memory error | 90% limit success | Enabled | | 4M data points | 85% usage | 75% usage | 12% reduction |

Testing

Quick Test Suite (Development)

# Fast test suite excluding slow tests (recommended for development)
pytest -v -m "not slow"

# Run frame counting regression tests (v1.0.0 formula)
pytest homodyne/tests/test_time_length_calculation.py -v

# Run with coverage
pytest -v --cov=homodyne --cov-report=html -m "not slow"

Comprehensive Test Suite (CI/CD)

# Full test suite including slow performance tests
pytest homodyne/tests/ -v

# Performance benchmarks only
pytest homodyne/tests/ -v -m performance

# Run with parallel execution for speed
pytest -v -n auto

Testing Guide

For comprehensive testing documentation including:

• Frame counting convention (v1.0.0 changes)
• Test markers and categorization
• Temporary file management best practices
• Writing new tests for v1.0.0

See TESTING.md for detailed testing guidelines.

Citation

If you use this software in your research, please cite the original paper:

@article{he2024transport,
  title={Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter},
  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},
  journal={Proceedings of the National Academy of Sciences},
  volume={121},
  number={31},
  pages={e2401162121},
  year={2024},
  publisher={National Academy of Sciences},
  doi={10.1073/pnas.2401162121}
}

For the software package:

@software{homodyne_analysis,
  title={homodyne-analysis: High-performance XPCS analysis with robust optimization},
  author={Chen, Wei and He, Hongrui},
  year={2024-2025},
  url={https://github.com/imewei/homodyne_analysis},
  version={1.0.0},
  institution={Argonne National Laboratory}
}

Development

Development setup:

git clone https://github.com/imewei/homodyne_analysis.git
cd homodyne
pip install -e .[dev]

# Run tests
pytest homodyne/tests/ -v

# Code quality
ruff check homodyne/
ruff format homodyne/
black homodyne/
isort homodyne/
mypy homodyne/

See DEVELOPMENT.md for detailed development guidelines.

License

This research software is distributed under the MIT License, enabling open collaboration while maintaining attribution requirements for academic use.

Research Use: Freely available for academic research with proper citation Commercial Use: Permitted under MIT License terms Modification: Encouraged with contribution back to the community

---

Contact Information:

• Primary Investigator: Wei Chen ([e-mail])
• Technical Support: GitHub Issues
• Research Collaboration: Argonne National Laboratory, X-ray Science Division

Authors: Wei Chen, Hongrui He (Argonne National Laboratory) License: MIT