Metadata-Version: 1.1
Name: gwent
Version: 0.4.0
Summary: Gravitational Wave Detector Design Toolkit.
Home-page: https://github.com/ark0015/gwent
Author: Andrew Kaiser
Author-email: andrewkaiser70@gmail.com
License: MIT license
Description: =====
        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/gwent-dev/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{2020arXiv201002135K,
                   author = {{Kaiser}, Andrew R. and {McWilliams}, Sean T.},
                    title = "{Sensitivity of present and future black-hole binary observations across the gravitational wave spectrum}",
                  journal = {arXiv e-prints},
                 keywords = {General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics},
                     year = 2020,
                    month = oct,
                      eid = {arXiv:2010.02135},
                    pages = {arXiv:2010.02135},
            archivePrefix = {arXiv},
                   eprint = {2010.02135},
             primaryClass = {gr-qc},
                   adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv201002135K},
                  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
            }
        
        Credits
        -------
        We utilize and include within the package a specific commit of ``pygwinc`` found at https://git.ligo.org/gwinc/pygwinc to create many of the ground-based gravitational wave detector sensitivity curves. At the time of creation, there is no ``pygwinc`` availability on PyPI, so we explicitly include the necessary portions of the code within.
        
        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
        
        
        =======
        History
        =======
        0.4.0 (2021-1-8)
        -------------------
        * Adding functionality to use lalsuite waveforms
        * Changing locations of multiple functions from BBHFrequencyDomain functions to general binary functions
        * Moved several functions from binary to waveform
        * Fixed several bugs with SNR calculations
        * Clarified certain constants
        * Added JOSS paper draft
        * Expanded testing and switched from TravisCI to Github Actions
        
        0.3.0 (2020-10-5)
        -------------------
        * Turning pygwinc install into vendor inside gwent
        
        0.2.1 (2020-10-5)
        -------------------
        * Update to ground-based SNR calculation
        * Minor changes to snrplot functionality
        * Changed fitting for WD background
        * Misc bug fixing
        
        0.2.0 (2020-4-29)
        -------------------
        * Major Changes to PTA detector setup
        * Major Changes to snrplot
        * Overhaul of tutorials
        * Minor Changes to binary and snr functions
        * Other Minor Changes sprinkled throughout 
        
        0.1.16 (2020-1-19)
        -------------------
        * Removing install of pygwinc in setup.py
        * pygwinc must now be manually installed 
        
        0.1.15 (2020-1-18)
        -------------------
        * Including easy install of pygwinc
        
        0.1.14 (2020-1-7)
        -------------------
        * Major addition of pygwinc
        * Minor fixes to snr sampling and plotting
        
        0.1.13 (2019-10-28)
        -------------------
        * Removing SNR Files in LoadFiles
        * Fixing Error in PTA Initialization that Ignored User Input
        
        0.1.12 (2019-10-8)
        -------------------
        * Adding New Files for NANOGrav
        * Updating Loading from files for Detectors
        
        0.1.11 (2019-09-19)
        -------------------
        * Fixing bugs and removing empty functionality
        
        0.1.10 (2019-09-14)
        -------------------
        * Removed Python 2.7 support
        
        0.1.0 (2019-09-04)
        ------------------
        
        * First release on PyPI.
        
Keywords: gwent
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
