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https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms

Blazingly fast EMRI waveforms
https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms

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Blazingly fast EMRI waveforms

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README 

        
# few: Fast EMRI Waveforms



This package contains the highly modular framework for fast and accurate extreme mass ratio inspiral (EMRI) waveforms from [arxiv.org/2104.04582](https://arxiv.org/abs/2104.04582) and [arxiv.org/2008.06071](https://arxiv.org/abs/2008.06071). The waveforms in this package combine a variety of separately accessible modules to form EMRI waveforms on both CPUs and GPUs. Generally, the modules fall into four categories: trajectory, amplitudes, summation, and utilities. Please see the [documentation](https://bhptoolkit.org/FastEMRIWaveforms/) for further information on these modules. The code can be found on Github [here](https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms). The data necessary for various modules in this package will automatically download the first time it is needed. If you would like to view the data, it can be found on [Zenodo](https://zenodo.org/record/3981654#.XzS_KRNKjlw). The current and all past code release zip files can also be found on Zenodo [here](https://zenodo.org/record/8190418). Please see the [citation](#citation) section below for information on citing FEW.



This package is a part of the [Black Hole Perturbation Toolkit](https://bhptoolkit.org/).



If you use all or any parts of this code, please cite [arxiv.org/2104.04582](https://arxiv.org/abs/2104.04582) and [arxiv.org/2008.06071](https://arxiv.org/abs/2008.06071). See the [documentation](https://bhptoolkit.org/FastEMRIWaveforms/) to properly cite specific modules.



## Getting Started



Install with pip (CPU only for now):

```

pip install fastemriwaveforms

```



In a python file or notebook:

```

import few

```

See [examples notebook](https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms/blob/master/examples/FastEMRIWaveforms_tutorial.ipynb).



### Prerequisites



To install this software for CPU usage, you need [gsl >2.0](https://www.gnu.org/software/gsl/) , [lapack (3.6.1)](https://www.netlib.org/lapack/lug/node14.html), Python >3.4, wget, and NumPy. If you install lapack with conda, the new version (3.9) seems to not install the correct header files. Therefore, the lapack version must be 3.6.1. To run the examples, you will also need jupyter and matplotlib. We generally recommend installing everything, including gcc and g++ compilers, in the conda environment as is shown in the examples here. This generally helps avoid compilation and linking issues. If you use your own chosen compiler, you will need to make sure all necessary information is passed to the setup command (see below). You also may need to add information to the `setup.py` file.



To install this software for use with NVIDIA GPUs (compute capability >2.0), you need the [CUDA toolkit](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) and [CuPy](https://cupy.chainer.org/). The CUDA toolkit must have cuda version >8.0. Be sure to properly install CuPy within the correct CUDA toolkit version. Make sure the nvcc binary is on `$PATH` or set it as the `CUDA_HOME` environment variable.



There are a set of files required for total use of this package. They will download automatically the first time they are needed. Files are generally under 10MB. However, there is a 100MB file needed for the slow waveform and the bicubic amplitude interpolation. This larger file will only download if you run either of those two modules. The files are hosted on the [Black Hole Perturbation Toolkit Download Server](https://download.bhptoolkit.org/few/data).



### Installing



Install with pip (CPU only for now):

```

pip install fastemriwaveforms

```



To install from source:



0) [Install Anaconda](https://docs.anaconda.com/anaconda/install/) if you do not have it.



1) Clone the repository.



```

git clone https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms.git

cd FastEMRIWaveforms

```



2) Installation is made easy through [install.sh](install.sh). This is a bash script that will create a conda environment, install FEW, run tests, and install any additional packages needed for sampling or development. It will look for an `nvcc` binary, the `CUDA_HOME` variable, or the `CUDAHOME` variable. If it finds that information, it will install for CUDA as well (including installing the proper version of `cupy`). **Note**: If you already have performed installation and you are updating FEW after a `git pull`, then run `pip install .` rather than the following command.



  ```

  bash install.sh

  ```



  Options for installation can be applied by running `bash install.sh key=value`. These can be found with `bash install.sh -h`:

  

  ```

  keyword argument options (given as key=value):

    env_name:  Name of generated conda environment. Default is 'few_env'.

    install_type:  Type of install. 'basic', 'development', or 'sampling'. 

        'development' adds packages needed for development and documentation.

        'sampling' adds packages for sampling like eryn, lisatools, corner, chainconsumer.

        Default is 'basic'. 

    run_tests: Either true or false. Whether to run tests after install. Default is true.

  ```



3) Load the environment (change "few_env" to the correct environment name is specified in previous step):



```

conda activate few_env

```



Please contact the developers if the installation does not work.



### More Customized Installation (legacy)



0) [Install Anaconda](https://docs.anaconda.com/anaconda/install/) if you do not have it.



1) Create a virtual environment.



```

conda create -n few_env -c conda-forge gcc_linux-64 gxx_linux-64 wget gsl lapack=3.6.1 hdf5 numpy Cython scipy tqdm jupyter ipython h5py requests rich matplotlib python=3.7

conda activate few_env

```



    If on MACOSX, substitute `gcc_linux-64` and `gxx_linus-64` with `clang_osx-64` and `clangxx_osx-64`.



    If you want a faster install, you can install the python packages (numpy, Cython, scipy, tqdm, jupyter, ipython, h5py, requests, rich, matplotlib) with pip.



2) Clone the repository.



```

git clone https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms.git

cd FastEMRIWaveforms

```



3) If using GPUs, use pip to [install cupy](https://docs-cupy.chainer.org/en/stable/install.html). If you have cuda version 12.1, for example:



```

pip install cupy-cuda12x

```



4) Run install.



```

python scripts/prebuild.py

python setup.py install

```



When installing lapack and gsl, the setup file will default to assuming lib and include for both are in installed within the conda environment. To provide other lib and include directories you can provide command line options when installing. You can also remove usage of OpenMP.



```

python setup.py --help

usage: setup.py [-h] [--lapack_lib LAPACK_LIB]

                [--lapack_include LAPACK_INCLUDE] [--lapack LAPACK]

                [--gsl_lib GSL_LIB] [--gsl_include GSL_INCLUDE] [--gsl GSL]

                [--ccbin CCBIN]



optional arguments:

  -h, --help            show this help message and exit

  --lapack_lib LAPACK_LIB

                        Directory of the lapack lib. If you add lapack lib,

                        must also add lapack include.

  --lapack_include LAPACK_INCLUDE

                        Directory of the lapack include. If you add lapack

                        includ, must also add lapack lib.

  --lapack LAPACK       Directory of both lapack lib and include. '/include'

                        and '/lib' will be added to the end of this string.

  --gsl_lib GSL_LIB     Directory of the gsl lib. If you add gsl lib, must

                        also add gsl include.

  --gsl_include GSL_INCLUDE

                        Directory of the gsl include. If you add gsl include,

                        must also add gsl lib.

  --gsl GSL             Directory of both gsl lib and include. '/include' and

                        '/lib' will be added to the end of this string.

  --ccbin CCBIN         path/to/compiler to link with nvcc when installing

                        with CUDA.

```



When installing the package with `python setup.py install`, the setup file uses the C compiler present in your `PATH`. However, it might happen that the setup file incorrectly uses another compiler present on your path. To solve this issue you can directly specify the C compiler using the flag `--ccbin` as in the following example:



```

python setup.py install --ccbin /path/to/anaconda3/envs/few_env/bin/x86_64-conda-linux-gnu-gcc

```



or if on MACOSX:



```

python setup.py install --ccbin /path/to/anaconda3/envs/few_env/bin/x86_64-apple-darwin13.4.0-clang

```



## Running the Tests



In the main directory of the package run in the terminal (if you run [install.sh](install.sh) with defaults, the tests will be performed):

```

python -m unittest discover

```



## Contributing



Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.



If you want to develop FEW and produce documentation, install `few` with

```

bash install.sh install_type=development

```

This will install necessary packages for building the documentation (`sphinx`, `pypandoc`, `sphinx_rtd_theme`, `nbsphinx`). The documentation source files are in `docs/source`. To compile the documentation, change to the `docs` directory and run `make html`. 



## Versioning



We use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms/tags).



Current Version: 1.5.5



## Authors/Developers



* **Michael Katz**

* Lorenzo Speri

* Christian Chapman-Bird

* Alvin J. K. Chua

* Niels Warburton

* Scott Hughes



### Contibutors



* Philip Lynch

* Soichiro Isoyama

* Ryuichi Fujita

* Monica Rizzo



## License



This project is licensed under the GNU License - see the [LICENSE.md](LICENSE.md) file for details.



## Citation



Please make sure to cite FEW papers and the FEW software on [Zenodo](https://zenodo.org/record/8190418). There are other papers that require citation based on the classes used. For most classes this applies to, you can find these by checking the `citation` attribute for that class. Below is a list of citable papers that have lead to the development of FEW. 



```

@article{Chua:2020stf,

    author = "Chua, Alvin J. K. and Katz, Michael L. and Warburton, Niels and Hughes, Scott A.",

    title = "{Rapid generation of fully relativistic extreme-mass-ratio-inspiral waveform templates for LISA data analysis}",

    eprint = "2008.06071",

    archivePrefix = "arXiv",

    primaryClass = "gr-qc",

    doi = "10.1103/PhysRevLett.126.051102",

    journal = "Phys. Rev. Lett.",

    volume = "126",

    number = "5",

    pages = "051102",

    year = "2021"

}



@article{Katz:2021yft,

    author = "Katz, Michael L. and Chua, Alvin J. K. and Speri, Lorenzo and Warburton, Niels and Hughes, Scott A.",

    title = "{FastEMRIWaveforms: New tools for millihertz gravitational-wave data analysis}",

    eprint = "2104.04582",

    archivePrefix = "arXiv",

    primaryClass = "gr-qc",

    month = "4",

    year = "2021"

}



@software{michael_l_katz_2023_8190418,

  author       = {Michael L. Katz and

                  Lorenzo Speri and

                  Alvin J. K. Chua and

                  Christian E. A. Chapman-Bird and

                  Niels Warburton and

                  Scott A. Hughes},

  title        = {{BlackHolePerturbationToolkit/FastEMRIWaveforms: 

                   Frequency Domain Waveform Added!}},

  month        = jul,

  year         = 2023,

  publisher    = {Zenodo},

  version      = {v1.5.1},

  doi          = {10.5281/zenodo.8190418},

  url          = {https://doi.org/10.5281/zenodo.8190418}

}



@article{Chua:2018woh,

    author = "Chua, Alvin J.K. and Galley, Chad R. and Vallisneri, Michele",

    title = "{Reduced-order modeling with artificial neurons for gravitational-wave inference}",

    eprint = "1811.05491",

    archivePrefix = "arXiv",

    primaryClass = "astro-ph.IM",

    doi = "10.1103/PhysRevLett.122.211101",

    journal = "Phys. Rev. Lett.",

    volume = "122",

    number = "21",

    pages = "211101",

    year = "2019"

}



@article{Fujita:2020zxe,

    author = "Fujita, Ryuichi and Shibata, Masaru",

    title = "{Extreme mass ratio inspirals on the equatorial plane in the adiabatic order}",

    eprint = "2008.13554",

    archivePrefix = "arXiv",

    primaryClass = "gr-qc",

    doi = "10.1103/PhysRevD.102.064005",

    journal = "Phys. Rev. D",

    volume = "102",

    number = "6",

    pages = "064005",

    year = "2020"

}



@article{Stein:2019buj,

    author = "Stein, Leo C. and Warburton, Niels",

    title = "{Location of the last stable orbit in Kerr spacetime}",

    eprint = "1912.07609",

    archivePrefix = "arXiv",

    primaryClass = "gr-qc",

    doi = "10.1103/PhysRevD.101.064007",

    journal = "Phys. Rev. D",

    volume = "101",

    number = "6",

    pages = "064007",

    year = "2020"

}



@article{Chua:2015mua,

    author = "Chua, Alvin J.K. and Gair, Jonathan R.",

    title = "{Improved analytic extreme-mass-ratio inspiral model for scoping out eLISA data analysis}",

    eprint = "1510.06245",

    archivePrefix = "arXiv",

    primaryClass = "gr-qc",

    doi = "10.1088/0264-9381/32/23/232002",

    journal = "Class. Quant. Grav.",

    volume = "32",

    pages = "232002",

    year = "2015"

}



@article{Chua:2017ujo,

    author = "Chua, Alvin J.K. and Moore, Christopher J. and Gair, Jonathan R.",

    title = "{Augmented kludge waveforms for detecting extreme-mass-ratio inspirals}",

    eprint = "1705.04259",

    archivePrefix = "arXiv",

    primaryClass = "gr-qc",

    doi = "10.1103/PhysRevD.96.044005",

    journal = "Phys. Rev. D",

    volume = "96",

    number = "4",

    pages = "044005",

    year = "2017"

}



@article{Barack:2003fp,

    author = "Barack, Leor and Cutler, Curt",

    title = "{LISA capture sources: Approximate waveforms, signal-to-noise ratios, and parameter estimation accuracy}",

    eprint = "gr-qc/0310125",

    archivePrefix = "arXiv",

    doi = "10.1103/PhysRevD.69.082005",

    journal = "Phys. Rev. D",

    volume = "69",

    pages = "082005",

    year = "2004"

}



@article{Speri:2023jte,

    author = "Speri, Lorenzo and Katz, Michael L. and Chua, Alvin J. K. and Hughes, Scott A. and Warburton, Niels and Thompson, Jonathan E. and Chapman-Bird, Christian E. A. and Gair, Jonathan R.",

    title = "{Fast and Fourier: Extreme Mass Ratio Inspiral Waveforms in the Frequency Domain}",

    eprint = "2307.12585",

    archivePrefix = "arXiv",

    primaryClass = "gr-qc",

    month = "7",

    year = "2023"

}

```



## Acknowledgments



* This research resulting in this code was supported by National Science Foundation under grant DGE-0948017 and the Chateaubriand Fellowship from the Office for Science \& Technology of the Embassy of France in the United States.

* It was also supported in part through the computational resources and staff contributions provided for the Quest/Grail high performance computing facility at Northwestern University.