[](https://github.com/vijayvarma392/surfinBH)
[](https://pypi.org/project/surfinBH/)
[](https://anaconda.org/conda-forge/surfinbh)
[](https://zenodo.org/badge/latestdoi/145179417)
[](https://github.com/vijayvarma392/surfinBH/blob/master/LICENSE)
[](https://github.com/vijayvarma392/surfinBH/actions/workflows/test.yml)
# Welcome to surfinBH!
<img src="https://raw.githubusercontent.com/vijayvarma392/surfinBH/master/images/interaction.jpeg" alt="BHScattering" width="600px"/>
<br/>
_**surfinBH**_ provides _**sur**rogate **fin**al **B**lack_ _**H**ole_
properties for mergers of binary black holes (BBH).
These fits are described in the following papers: <br/>
[1] V. Varma, D. Gerosa, L. C. Stein, F. HeĢbert and H. Zhang,
[arxiv:1809.09125](https://arxiv.org/abs/1809.09125).
[2] V. Varma, S. E. Field, M. A. Scheel, J. Blackman, D. Gerosa, L. C.
Stein, L. E. Kidder, H. P. Pfeiffer,
[arxiv:1905.09300](https://arxiv.org/abs/1905.09300).
[3] M. Boschini, D. Gerosa, V. Varma, et al.,
[arXiv:2307.03435](https://arxiv.org/abs/2307.03435)
If you find this package useful in your work, please cite reference [1] and,
if available, the relevant paper describing the particular model. Please also
cite this package, see the DOI badge at the top of this page for BibTeX keys.
This package lives on [GitHub](https://github.com/vijayvarma392/surfinBH), is
compatible with `python3`, and is tested every week. You can see the current
build status of the master branch at the top of this page.
## Installation
### PyPI
_**surfinBH**_ is available through [PyPI](https://pypi.org/project/surfinBH/):
```shell
pip install surfinBH
```
### Conda
_**surfinBH**_ is available on [conda-forge](https://anaconda.org/conda-forge/surfinbh):
```shell
conda install -c conda-forge surfinbh
```
### From source
```shell
git clone git@github.com:vijayvarma392/surfinBH.git
cd surfinBH
git submodule init
git submodule update
python setup.py install
```
If you do not have root permissions, replace the last step with
`python setup.py install --user`
## Dependencies
All of these can be installed through pip or conda.
* [numpy](https://docs.scipy.org/doc/numpy/user/install.html)
* [scipy](https://www.scipy.org/install.html)
* [h5py](http://docs.h5py.org/en/latest/build.html)
* [scikit-learn](http://scikit-learn.org/stable/install.html) (at least 0.19.1)
* [lalsuite](https://pypi.org/project/lalsuite) (at least 6.70)
* [gwsurrogate](https://pypi.org/project/gwsurrogate)
* [NRSur7dq2](https://pypi.org/project/NRSur7dq2) (only for surfinBH7dq2)
## Usage
```python
import surfinBH
```
### See list of available fits
```python
print(list(surfinBH.fits_collection.keys()))
>>> ['NRSur3dq8Remnant', 'surfinBH7dq2', 'NRSur7dq4Remnant', 'NRSur7dq4EmriRemnant']
```
Pick your favorite fit and get some basic information about it.
```python
fit_name = 'NRSur7dq4Remnant'
surfinBH.fits_collection[fit_name].desc
>>> 'Fits for remnant mass, spin and kick veclocity for generically precessing BBH systems up to mass ratio 4.'
surfinBH.fits_collection[fit_name].refs
>>> 'arxiv:1905.09300'
```
### Load the fit
This only needs to be done **once** at the start of your script.
If the fit data is not already downloaded, this will also download the data.
```python
fit = surfinBH.LoadFits(fit_name)
>>> Loaded NRSur7dq4Remnant fit.
```
### Evaluation
The evaluation of each fit is different, so be sure to read the documentation.
This also describes the frames in which different quantities are defined.
```python
help(fit)
```
We also provide ipython examples for usage of different fits:
##### Current fits
* [NRSur7dq4Remnant](https://github.com/vijayvarma392/surfinBH/blob/master/examples/example_7dq4.ipynb) (Ref. [2])
* [NRSur7dq4EmriRemnant](https://github.com/vijayvarma392/surfinBH/blob/master/examples/example_7dq4Emri.ipynb) (Ref. [3])
* [NRSur3dq8Remnant](https://github.com/vijayvarma392/surfinBH/blob/master/examples/example_3dq8.ipynb) (called surfinBH3dq8 in Ref. [1])
##### Older fits
* [surfinBH7dq2](https://github.com/vijayvarma392/surfinBH/blob/master/examples/example_7dq2.ipynb) (Ref. [1]. Superseded by NRSur7dq4Remnant)
## Animations
We also provide a tool to visualize the binary black hole scattering process,
see
[binary black hole explorer](https://vijayvarma392.github.io/binaryBHexp/).
Here's an example:
<img src="https://raw.githubusercontent.com/vijayvarma392/binaryBHexp/master/animations/video.gif" width="500"/>
## Making contributions
See this
[README](https://github.com/vijayvarma392/surfinBH/blob/master/README_developers.md)
for instructions on how to make contributions to this package.
## Credits
The code is maintained by [Vijay Varma](https://vijayvarma.com).
You can find the list of contributors
[here](https://github.com/vijayvarma392/surfinBH/graphs/contributors).
Please report bugs by raising an
issue on our [GitHub](https://github.com/vijayvarma392/surfinBH) repository.
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You can see the current\nbuild status of the master branch at the top of this page.\n\n## Installation\n\n### PyPI\n_**surfinBH**_ is available through [PyPI](https://pypi.org/project/surfinBH/):\n\n```shell\npip install surfinBH\n```\n\n### Conda\n_**surfinBH**_ is available on [conda-forge](https://anaconda.org/conda-forge/surfinbh):\n\n```shell\nconda install -c conda-forge surfinbh\n```\n\n\n### From source\n\n```shell\ngit clone git@github.com:vijayvarma392/surfinBH.git\ncd surfinBH\ngit submodule init\ngit submodule update\npython setup.py install\n```\n\nIf you do not have root permissions, replace the last step with\n`python setup.py install --user`\n\n\n## Dependencies\nAll of these can be installed through pip or conda.\n* [numpy](https://docs.scipy.org/doc/numpy/user/install.html)\n* [scipy](https://www.scipy.org/install.html)\n* [h5py](http://docs.h5py.org/en/latest/build.html)\n* [scikit-learn](http://scikit-learn.org/stable/install.html) (at least 0.19.1)\n* [lalsuite](https://pypi.org/project/lalsuite) (at least 6.70)\n* [gwsurrogate](https://pypi.org/project/gwsurrogate)\n* [NRSur7dq2](https://pypi.org/project/NRSur7dq2) (only for surfinBH7dq2)\n\n## Usage\n\n```python\nimport surfinBH\n```\n\n### See list of available fits\n```python\nprint(list(surfinBH.fits_collection.keys()))\n>>> ['NRSur3dq8Remnant', 'surfinBH7dq2', 'NRSur7dq4Remnant', 'NRSur7dq4EmriRemnant']\n```\n\nPick your favorite fit and get some basic information about it.\n```python\nfit_name = 'NRSur7dq4Remnant'\n\nsurfinBH.fits_collection[fit_name].desc\n>>> 'Fits for remnant mass, spin and kick veclocity for generically precessing BBH systems up to mass ratio 4.'\n\nsurfinBH.fits_collection[fit_name].refs\n>>> 'arxiv:1905.09300'\n```\n\n### Load the fit\nThis only needs to be done **once** at the start of your script.\nIf the fit data is not already downloaded, this will also download the data.\n```python\nfit = surfinBH.LoadFits(fit_name)\n>>> Loaded NRSur7dq4Remnant fit.\n```\n### Evaluation\nThe evaluation of each fit is different, so be sure to read the documentation.\nThis also describes the frames in which different quantities are defined.\n```python\nhelp(fit)\n```\n\nWe also provide ipython examples for usage of different fits:\n\n##### Current fits\n\n* [NRSur7dq4Remnant](https://github.com/vijayvarma392/surfinBH/blob/master/examples/example_7dq4.ipynb) (Ref. 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