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gwexpy: GWpy Expansions for Experiments

CI Status Docs Pages License: MIT Python 3.11+

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 preserving the default finite numerical results, sample selection, and axis behavior of corresponding GWpy APIs. Intentional divergence from these guarantees requires explicit user opt-in, except for a named, human-approved safety exception that satisfies every compatibility-policy gate.

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

Read the project-wide GWpy behavioral compatibility policy or its Japanese translation.

Install

GWexpy v0.2.0 is available from both PyPI and conda-forge.

python -m pip install gwexpy
# or
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.

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.

Where gwexpy Fits

gwexpy occupies the layer between GWpy and the workflow packages built on top of it. GWpy provides the standard gravitational-wave data objects; gwexpy adds matrix-aware containers, typed analysis results, broad I/O, and external-tool conversion; detector-characterization, search, and inference pipelines consume those products.

It is complementary to, not a replacement for, packages such as spicypy (signal processing and control systems) and GWDama (HDF5-first data preparation). Site-specific operational pipelines, operator-facing reports, job orchestration, and trigger generation are deliberately out of scope.

For the full comparison, the ecosystem map, and the third-party code policy, see:

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)

This example explicitly registers the full supported surface. Supported public I/O entry points can register their required handlers on demand.

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

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