gwmock 0.5.0

A Python package for simulating gravitational wave detector data for mock data challenges.

gwmock

A Python package for generating Mock Data Challenge (MDC) datasets for the gravitational-wave (GW) community. It simulates strain data for detectors like Einstein Telescope, providing a unified interface for reproducible GW data generation.

Features

• Modular Design: Uses mixins for flexible simulator composition
• Detector Support: Built-in support for various GW detectors with custom configuration options
• Waveform Generation: Integrates with PyCBC and LALSuite for accurate signal simulation
• Noise Models: Supports colored and correlated noise generation (In-Progress)
• Population Models: Handles injection populations for signals and glitches
• Data Formats: Outputs in standard GW formats (GWF frames)
• CLI: Command-line tools for easy simulation workflows

Installation

We recommend using uv to manage virtual environments for installing gwmock.

If you don't have uv installed, you can install it with pip. See the project pages for more details:

• Install via pip: pip install --upgrade pip && pip install uv
• Project pages: uv on PyPI | uv on GitHub
• Full documentation and usage guide: uv docs

Note: The package is built and tested against Python 3.12-3.13. When creating a virtual environment with uv, specify the Python version to ensure compatibility: uv venv --python 3.12 (replace 3.12 with your preferred version in the 3.12-3.13 range). This avoids potential issues with unsupported Python versions.

From PyPI

# Create a virtual environment (recommended with uv)
uv venv --python 3.13
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install gwmock

From Source

git clone [e-mail]:Leuven-Gravity-Institute/gwmock.git
ce gwmock
# Create a virtual environment (recommended with uv)
uv venv --python 3.13
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv sync

Quick Start

Command Line

# Generate simulated data
gwmock simulate config.yaml

Configuration

gwmock uses YAML configuration files for reproducible simulations. New runs use the adapter-backed orchestration surface, which keeps backend selection explicit and lets third-party packages plug in through public protocols.

Key configuration sections:

Section	Purpose
globals	Shared orchestration parameters such as sampling rate, segment duration, start time, and output roots
orchestration.population	Public population backend, source type, sample count, and backend arguments
orchestration.signal	Public signal backend, detector network, waveform model, and output settings
orchestration.noise	Public noise backend, generation arguments, and output settings

Example:

globals:
    working-directory: .
    output-directory: output
    metadata-directory: metadata
    simulator-arguments:
        sampling-frequency: 4096
        duration: 1024
        start-time: 1577491218
        total-duration: 5 hours

orchestration:
    population:
        backend: FilePopulationLoader
        source-type: bbh
        n-samples: 1
        arguments:
            path: population.h5
    signal:
        detectors:
            - H1
        waveform-model: IMRPhenomXPHM
        minimum-frequency: 20
        output:
            output_directory: signal
            file_name: signal-{{ counter }}.gwf
            arguments:
                channel: H1:STRAIN
    noise:
        output:
            output_directory: noise
            file_name: noise-{{ counter }}.gwf

Third-party backends can be exposed through an entry point or referenced directly as module:Class, as long as they satisfy the public protocol for the relevant section. See docs/user-guide/protocols.md, docs/user-guide/orchestration.md, and docs/user-guide/extensibility.md for the protocol model and integration details.

Documentation

Full documentation to be available at https://leuven-gravity-institute.github.io/gwmock.

Contributing

Contributions are welcome!

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a merge request

Release Schedule

Releases follow a fixed schedule: every Tuesday at 00:00 UTC, unless an emergent bugfix is required. This ensures predictable updates while allowing flexibility for critical issues. Users can view upcoming changes in the draft release on the GitHub Releases page.

Testing

Run the test suite:

uv run pytest

License

This project is licensed under GPL-3.0-or-later. See the LICENSE file for the full license text.

Support

For questions or issues, please open an issue on GitHub or contact the maintainers.