===== gwent

.. image:: https://img.shields.io/pypi/v/gwent.svg :target: https://pypi.python.org/pypi/gwent

.. image:: https://github.com/ark0015/gwent/workflows/CI-Tests/badge.svg :target: https://github.com/ark0015/gwent/actions

.. image:: https://readthedocs.org/projects/gwent/badge/?version=latest :target: https://gwent.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

.. image:: https://codecov.io/gh/ark0015/gwent/branch/master/graph/badge.svg?token=897QOE4EBQ :target: https://codecov.io/gh/ark0015/gwent

Gravitational Wave dEtector desigN Toolkit.

Generates strain sensitivity curves and Waterfall plots for various gravitational wave detector designs.

.. image:: https://raw.githubusercontent.com/ark0015/gwent/master/data/full_waterfall_plots_lb.png :align: center :alt: gwent Waterfall Plots

Free software: MIT license
Documentation: https://gwent.readthedocs.io.

Features

Calculates the sensitivity curves for various designs of pulsar timing arrays, space-based detectors, and ground-based detectors. This includes:

NANOGrav
SKA
LISA
aLIGO
Voyager
and more!

Calculates the strain from coalescing black hole binaries. It contains functionality for different source descriptions:

Slowly-evolving sources, ie. BHBs early in their inspiral where they appear to not change in frequency.
Rapidly-evolving sources, ie. BHBs in the final stages of coalescence.
- Uses a fully Pythonic implementation of the phenomenological model IMRPhenomD to accurately represent the inspiral, merger, and ringdown of the BHB.

Calculates the matched-filtered signal-to-noise ratio (SNR) to help assess the detectability of any BHB source configuration by any represented gravitational wave detector.

Includes robust plotting methods to represent these SNRs.

Getting Started

gwent is available on the Python Package Inventory, so the preferred method to install gwent is to install it with pip, as it will always install the most recent stable release.

.. code-block:: console

$ pip install gwent

README Figure and Data

If you are looking for quick data, we conveniently place the figure above in the data <https://github.com/ark0015/gwent/tree/master/data>_ folder on the Github repo. There you can also find the raw data used for this figure in .npz format. To load this data, simply use np.load(filename), and the data can be accessed by the kwargs 'mass', 'redshift', and 'snr'. E.g.,

.. code-block:: python

import numpy as np
import gwent
from gwent.snrplot import Plot_SNR
loaded_file = np.load(filename)
Plot_SNR('M',load_file['mass'],'z',load_file['redshift'],load_file['snr'])

Publication

This work and methodology is available on arXiv_. If you use gwent, please cite this work using the following:

.. _arXiv: https://arxiv.org/abs/2010.02135

.. code-block:: tex

@ARTICLE{Kaiser:2021,
  doi = {10.1088/1361-6382/abd4f6},
  url = {https://doi.org/10.1088/1361-6382/abd4f6},
  year = 2021,
  month = {jan},
  publisher = {{IOP} Publishing},
  volume = {38},
  number = {5},
  pages = {055009},
  author = {A R Kaiser and S T McWilliams},
  title = {Sensitivity of present and future detectors across the black-hole binary gravitational wave spectrum},
  journal = {Classical and Quantum Gravity},
  eprint = {2010.02135},
  adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201002135K},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Credits

This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter .. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage

Gwent

Install / Use

README