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.

GWexpy is an independent package built on top of GWpy. It is not an official component of the GWpy project.

Install

python -m pip install gwexpy

GWexpy is published on PyPI. A conda-forge feedstock is also available, though the conda-forge package may lag the latest PyPI release.

python -m pip install gwexpy
# or, once the desired version is available on conda-forge:
conda install -c conda-forge gwexpy

The experimental GUI app is not part of the supported package surface.

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

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

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