Python/numba code for manipulating time-dependent functions of spin-weighted spherical harmonics on future null infinity
If you use this code for academic work (I can't actually imagine any other use for it), please cite the latest version that you used in your publication. The DOI is:
Also please cite the papers for/by which it was produced:
- "Angular velocity of gravitational radiation from precessing binaries and the corotating frame", Boyle, Phys. Rev. D, 87, 104006 (2013).
- "Gravitational-wave modes from precessing black-hole binaries", Boyle et al., http://arxiv.org/abs/1409.4431 (2014).
- "Transformations of asymptotic gravitational-wave data", Boyle, Phys. Rev. D, 93, 084031 (2015).
Bibtex entries for these articles can be found here. It might also be nice of you to provide a link directly to this source code.
Note that installation is not possible on Windows due to missing FFTW support.
Installation is as simple as
conda install -c conda-forge scrior
python -m pip install scriIf the latter command complains about permissions, you're probably using your system's version of python, which you should avoid at all costs; use conda/mamba instead.
Then, in python, you can check to make sure installation worked with
import scri
w = scri.WaveformModes()Here, w is an object to contain time and waveform data, as well as various
related pieces of information -- though it is trivial in this case, because we
haven't given it any data. For more information, see the docstrings of scri,
scri.WaveformModes, etc.
Tutorials and automatically generated API documentation are available on Read the Docs: scri.
This code is, of course, hosted on github; because it is an open-source project, the hosting is free, and all the wonderful features of github are available, including free wiki space and web page hosting, pull requests, a nice interface to the git logs, etc.
Every change in this code is auomatically tested on Travis-CI. This is a free service (for open-source projects like this one), which integrates beautifully with github, detecting each commit and automatically re-running the tests. The code is downloaded and installed fresh each time, and then tested, on both versions of python (2 and 3). This ensures that no change I make to the code breaks either installation or any of the features that I have written tests for.
Every change to this code is also recompiled automatically, bundled into a
conda package, and made available for download from
anaconda.org. Again, because this is an
open-source project all those nice features are free.
The work of creating this code was supported in part by the Sherman Fairchild Foundation and by NSF Grants No. PHY-1306125 and AST-1333129.