This package contains the highly modular framework for fast and accurate extreme mass ratio inspiral (EMRI) waveforms from arxiv.org/2104.04582 and arxiv.org/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 for further information on these modules. The code can be found on Github here. 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. The current and all past code release zip files can also be found on Zenodo here. Please see the citation section below for information on citing FEW.
This package is a part of the Black Hole Perturbation Toolkit.
If you use all or any parts of this code, please cite arxiv.org/2104.04582 and arxiv.org/2008.06071. See the documentation to properly cite specific modules.
Install with pip (CPU only for now):
pip install fastemriwaveforms
In a python file or notebook:
import few
See examples notebook.
To install this software for CPU usage, you need gsl >2.0 , lapack (3.6.1), 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 and CuPy. 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.
Install with pip (CPU only for now):
pip install fastemriwaveforms
To install from source:
-
Install Anaconda if you do not have it.
-
Clone the repository.
git clone https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms.git
cd FastEMRIWaveforms
- Installation is made easy through 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
nvccbinary, theCUDA_HOMEvariable, or theCUDAHOMEvariable. If it finds that information, it will install for CUDA as well (including installing the proper version ofcupy). Note: If you already have performed installation and you are updating FEW after agit pull, then runpip 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.
- 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.
-
Install Anaconda if you do not have it.
-
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.
- Clone the repository.
git clone https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms.git
cd FastEMRIWaveforms
- If using GPUs, use pip to install cupy. If you have cuda version 12.1, for example:
pip install cupy-cuda12x
- 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
In the main directory of the package run in the terminal (if you run install.sh with defaults, the tests will be performed):
python -m unittest discover
Please read 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.
We use SemVer for versioning. For the versions available, see the tags on this repository.
Current Version: 1.5.5
- Michael Katz
- Lorenzo Speri
- Christian Chapman-Bird
- Alvin J. K. Chua
- Niels Warburton
- Scott Hughes
- Philip Lynch
- Soichiro Isoyama
- Ryuichi Fujita
- Monica Rizzo
This project is licensed under the GNU License - see the LICENSE.md file for details.
Please make sure to cite FEW papers and the FEW software on Zenodo. 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"
}
- 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.