Revision a37ef9c769b8b48a7afa83da534f33223061d769 authored by Jacopo on 02 March 2022, 21:51:40 UTC, committed by GitHub on 02 March 2022, 21:51:40 UTC
* Added tests for subsample optimization * New subsample_optimization option to match * testing phase retrieval * Corrected phase retrieval testing * Fixed usage of baseline cyclic time shift * Ran black on the match and optimization_match functions * Complying with the 79 character per line limit * Moved optimized match functionality to own function Also renamed the function to optimized_match, and fixed docs accordingly * Matched tests to the new optimized_match API * Ran black on the new function * Ran black on the old, modified match funcion * Ignoring pylint warning about unbalanced tuple unpacking; it is a false positive * Now using norms given by the user, if available * A bodge to solve the pylint false positive * applying the mask to the frequencies as well * New tests for optimized match Now testing: - optimized match with a given PSD (not None) - optimized match with high and low frequency constraints * Conforming optimized_match to pycbc standards - computing the cutoff indices with get_cutoff_indices - computing the normalization with sigmasq - computing the integral with the rectangle rule * Reformatted optimized_match with * Fixed line too long
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README.md
[PyCBC](http://pycbc.org) is a software package used to explore astrophysical sources of gravitational waves.
It contains algorithms to analyze gravitational-wave data,
detect coalescing compact binaries, and make bayesian inferences from gravitational-wave data.
PyCBC was used in the [first direct detection of gravitational waves](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102) and
is used in flagship analyses of LIGO and Virgo data.
PyCBC is collaboratively developed by the community and is lead by a team of GW astronomers with the
aim to build accessible tools for gravitational-wave data analysis.
The PyCBC home page is located on github at
* https://pycbc.org/
Documentation is automatically built from the latest master version
* https://pycbc.org/pycbc/latest/html/
For the detailed installation instructions of PyCBC
* https://pycbc.org/pycbc/latest/html/install.html
Want to get going using PyCBC?
* [Try out our tutorials](https://github.com/gwastro/PyCBC-Tutorials). No software installation required and these can run entirely from the browser.
Quick Installation
```
pip install pycbc
```
To test the code on your machine
```
pip install pytest tox
tox
```
If you use any code from PyCBC in a scientific publication, then please see our [citation guidelines](http://pycbc.org/pycbc/latest/html/credit.html) for more details on how to cite pycbc algorithms and
programs.
For the citation of the ``pycbc library``, please use a bibtex entry and DOI for the
appropriate release of the PyCBC software (or the latest available release).
A bibtex key and DOI for each release is avaliable from [Zenodo](http://zenodo.org/).
[](https://zenodo.org/badge/latestdoi/31596861) [](https://travis-ci.org/gwastro/pycbc)
[](https://badge.fury.io/py/PyCBC)  [](https://anaconda.org/conda-forge/pycbc) [](https://anaconda.org/conda-forge/pycbc)
[](http://www.astropy.org/)
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