gwnr

A collection of tools for academic research in gravitational-wave astronomy, astrophysics, and numerical relativity.

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gwnr (imported as the Python package gwnr) is a research toolkit built on top of PyCBC, LALSuite, Bilby and the scientific Python stack. It collects the utilities, analysis pipelines and plotting machinery developed over years of research on gravitational-wave (GW) source modeling, template-bank construction, matched-filter searches, Bayesian parameter estimation, and numerical relativity (NR).

What’s inside

Area	Package	Highlights
Waveform tools	`gwnr.waveform`	Waveform generation and conditioning, alignment, hybridization of inspiral and merger–ringdown modes, eccentricity measurement, tidal corrections, parameter conversions
Data analysis	`gwnr.analysis`	Faithfulness (match) and fitting-factor calculations, PSD handling, GW transient catalog access, stochastic template-bank construction
Numerical relativity	`gwnr.nr`	SXS/SpEC waveform handling, strain-mode containers, SpEC and SpECTRE simulation output parsing, NR data in matched-filtering analyses
Statistics & inference	`gwnr.stats`	Bayesian inference configuration writers (PyCBC Inference, Bilby, LALInference), Fisher-matrix computations, distribution utilities, MCMC sampler helpers
Visualization	`gwnr.graph`	Corner plots, contour/scatter plotting for effectualness and bias studies, ParaView helpers, movie embedding
Cosmology	`gwnr.cosmo`	Redshift–distance conversions, source/detector-frame mass conversions, merger-rate-weighted redshift sampling
Workflow automation	`gwnr.workflow`	HTCondor DAG generation for banksims, faithsims, and batch parameter-estimation campaigns
Bundled data	`gwnr.data`	Detector noise curves (PSDs/ASDs) shipped with the package
General utilities	`gwnr.utils`	LAL/PyCBC type conversions, array helpers, memory profiling, function timeouts

In addition, ~30 command-line tools are installed for building template banks, running banksims/faithsims on HTCondor clusters, and orchestrating parameter-estimation campaigns on GW events and injections.

Quick example

Compute the faithfulness (noise-weighted match) between two waveform models:

from gwnr.analysis import calculate_faithfulness

match = calculate_faithfulness(
    m1=36.0, m2=29.0,            # component masses (solar masses)
    s1z=0.3, s2z=-0.2,           # aligned spin components
    signal_approx="SEOBNRv4",    # "signal" model
    tmplt_approx="IMRPhenomD",   # "template" model
    f_lower=20.0,
    sample_rate=4096,
    signal_duration=32,
    psd_string="aLIGOZeroDetHighPower",
)

Documentation

Citation & license

gwnr is developed by Prayush Kumar and collaborators, and is distributed under the GNU General Public License. If you use it in published work, please cite the repository: https://github.com/gwnr/gwnr.