gwexpy 0.1.4

A comprehensive Python package for Gravitational Wave experimental data analysis, extending GWpy with strict unit handling and advanced spectral methods.

gwexpy: GWpy Expansions for Experiments

gwexpy is an extension library for GWpy for experimental physics and gravitational-wave data analysis. It adds matrix-aware containers, field operations, fitting workflows, expanded I/O, and interoperability layers while staying close to GWpy-style analysis.

Install

python -m pip install gwexpy

GWexpy is published on PyPI. The experimental GUI app is not part of the supported package surface. You can install the PyPI package inside a Conda-managed environment. A native conda-forge package is under review at https://github.com/conda-forge/staged-recipes/pull/33169 and is not yet available on the conda-forge channel.

For optional extras, external dependencies, and environment-specific setup, use the official installation guides:

• English: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/user_guide/installation.html
• 日本語: https://tatsuki-washimi.github.io/gwexpy/docs/web/ja/user_guide/installation.html

Documentation

The full documentation is maintained in the docs site and is the source of truth for usage details.

• Documentation hub: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/
• ドキュメントハブ: https://tatsuki-washimi.github.io/gwexpy/docs/web/ja/
• Quick Start: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/user_guide/quickstart.html
• Tutorials and case studies: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/user_guide/tutorials/
• File formats and I/O: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/user_guide/io_formats.html
• API reference: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/reference/
• Examples gallery: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/examples/

Why gwexpy?

• Matrix-native analysis: TimeSeriesMatrix, FrequencySeriesMatrix, and SpectrogramMatrix support batch processing, transfer functions, and multichannel workflows.
• Physics-oriented containers: ScalarField, VectorField, and TensorField extend analysis beyond simple series into structured field data.
• Practical workflows: fitting, noise hunting, time-frequency analysis, and interoperability are exposed as user-facing workflows rather than isolated utilities.
• Broad interoperability and I/O: gwexpy bridges scientific Python tools and extends format coverage beyond core GWpy workflows.

Quick Start

import numpy as np
import gwexpy
from gwexpy.timeseries import TimeSeries, TimeSeriesList

gwexpy.register_all()

ts1 = TimeSeries(np.arange(8.0), dt=1.0, name="A")
ts2 = TimeSeries(np.arange(8.0) * 2.0, dt=1.0, name="B")
matrix = TimeSeriesList([ts1, ts2]).to_matrix()
asd = matrix.asd(fftlength=2.0)
print(matrix.shape)

For fitting, I/O, interoperability, and notebook-based workflows, start from the docs hub or the tutorial index above.

More Resources

• Migration notes for GWpy users: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/user_guide/gwexpy_for_gwpy_users_en.html
• Citation: https://tatsuki-washimi.github.io/gwexpy/docs/web/en/user_guide/citation.html
• Reproducibility notes: docs/repro/README.md
• Supported I/O matrix: SUPPORTED_IO_MATRIX.md

Support

• Lightweight bug reports and feature requests: https://forms.gle/Ewx5K69KqDvzrJp57
• Security reports: see SECURITY.md; do not use the form or public issues for vulnerability details.
• Issues: https://github.com/tatsuki-washimi/gwexpy/issues
• Discussions: https://github.com/tatsuki-washimi/gwexpy/discussions
• Contributions: pull requests are welcome on GitHub