Welcome to GWSurrogate!

GWSurrogate is an easy to use interface to gravitational wave surrogate models.

Surrogates provide a fast and accurate evaluation mechanism for gravitational waveforms which would otherwise be found through solving differential equations. These equations must be solved in the ``building" phase, which was performed using other codes. For details see

[1] Scott Field, Chad Galley, Jan Hesthaven, Jason Kaye, and Manuel Tiglio. `"Fast prediction and evaluation of gravitational waveforms using surrogate models". Phys. Rev. X 4, 031006 (2014). arXiv: gr-qc:1308.3565

If you find this package useful in your work, please cite reference [1] and, if available, the relevant paper describing the specific surrogate used.

All available models can be found in gwsurrogate.catalog.list()

gwsurrogate is available at https://pypi.python.org

Installation

Dependency

gwsurrogate requires:

1. gwtools. If you are installing gwsurrogate with pip you will automatically get gwtools. If you are installing gwsurrogate from source, please see https://bitbucket.org/chadgalley/gwtools/

2. gsl. For speed, the long (hybrid) surrogates use gsl's spline function. To build gwsurrogate you must have gsl installed. Fortunately, this is a common library and can be easily installed with a package manager.

Note that at runtime (ie when you do import gwsurrogate) you may need to let gsl know where your BLAS library is installed. This can be done by setting your LD_PRELOAD or LD_LIBRARY_PATH environment variables. A relevant example:

>>> export LD_PRELOAD=~/anaconda3/envs/python27/lib/libgslcblas.so

From pip

The python package pip supports installing from PyPI (the Python Package Index). gwsurrogate can be installed to the standard location (e.g. /usr/local/lib/pythonX.X/dist-packages) with

>>> pip install gwsurrogate

If there is no binary/wheel package already available for your operating system, the installer will try to build the package from the sources. For that, you would need to have gsl installed already. The installer will look for GSL inside /opt/local/. You may provide additional paths with the CPPFLAGS and LDFLAGS environment variables.

In the case of an homebrew installation, you may install the package like this:

>>> export HOMEBREW_HOME=`brew --prefix`
>>> 
>>> export CPPFLAGS="-I$HOMEBREW_HOME/include/"
>>> export LDFLAGS="-L$HOMEBREW_HOME/lib/"
>>> pip install gwsurrogate

From conda

gwsurrogate is on conda-forge, and can be installed with

>>> conda install -c conda-forge gwsurrogate

From source (pip)

First, please ensure you have the necessary dependencies installed (see above). Next, git clone this project, to any folder of your choosing. Then run

git submodule init
git submodule update

For a "proper" installation, run the following commands from the top-level gwsurrogate folder containing setup.py

>>> python -m pip install .            # option 1
>>> python -m pip install --editable . # option 2

where the "--editable" installs an editable (development) project with pip. This allows your local code edits to be automatically seen by the system-wide installation.

From source (tar.gz)

Please note this is not the recommended installation strategy, and certain functionality may not work.

You can download and unpack gwsurrogate-X.X.tar.gz to any folder gws_folder of your choosing. The gwsurrogate module can be used by adding

import sys
sys.path.append('absolute_path_to_gws_folder')

at the beginning of any script/notebook which uses gwsurrogate.

Alternatively, if you are a bash or sh user, edit your .profile (or .bash_profile) file and add the line

export PYTHONPATH=~absolute_path_to_gws_folder:$PYTHONPATH

Usage

Available models

To get a list of all available surrogate models, do:

>>> import gwsurrogate
>>> gwsurrogate.catalog.list()
>>> gwsurrogate.catalog.list(verbose=True)      # Use this for more details

Current NR models

The most up-to-date models trained on numerical relativity data are listed below, along with links to example notebooks.

• NRSur7dq4: For generically precessing BBHs, trained on mass ratios q≤4. Paper: arxiv:1905.09300.
• NRHybSur3dq8: For nonprecessing BBHs, trained on mass ratios q≤8. Paper: arxiv:1812.07865.
• NRHybSur2dq15: For nonprecessing BBHs, trained on q≤15, chi1≤0.5, chi2=0. Paper: arxiv:2203.10109.
• NRHybSur3dq8_CCE: For nonprecessing BBHs, trained on CCE (Cauchy-characteristic evolution) waveforms of mass ratios q≤8. Unlike all of the other models, NRHybSur3dq8_CCE includes memory effects. Paper: arxiv:2306.03148.

Current point-particle blackhole perturbation theory models

The most up-to-date models trained on point-particle blackhole perturbation data and calibrated to numerical relativity (NR) in the comparable mass regime.

• BHPTNRSur1dq1e4: Nonspinning BBHs, trained on mass ratios q≤10000 and harmonics up to ell=10. Paper: arxiv:2204.01972.

Download surrogate data and load it

Pick a model, let's say NRSur7dq4 and download the data. Note this only needs to be done once.

gwsurrogate.catalog.pull('NRSur7dq4')       # This can take a few minutes

Load the surrogate, this only needs to be done once at the start of a script

sur = gwsurrogate.LoadSurrogate('NRSur7dq4')

Evaluate the surrogate

q = 4           # mass ratio, mA/mB >= 1.
chiA = [-0.2, 0.4, 0.1]         # Dimensionless spin of heavier BH
chiB = [-0.5, 0.2, -0.4]        # Dimensionless of lighter BH
dt = 0.1                        # timestep size, Units of total mass M
f_low = 0                # initial frequency, f_low=0 returns the full surrogate

# h is dictionary of spin-weighted spherical harmonic modes
# t is the corresponding time array in units of M
# dyn stands for dynamics, do dyn.keys() to see contents
t, h, dyn = sur(q, chiA, chiB, dt=dt, f_low=f_low)

There are many more options, such as using MKS units, returning the polarizations instead of the modes, etc. Read the documentation for more details.

help(sur)

Jupyter notebooks located in tutorial/website give a more comprehensive overview of individual models.

Tests

If you have downloaded the entire project as a tar.gz file, its a good idea to run some regression tests.

>>> cd test                              # move into the folder test
>>> python download_regression_models.py # download all surrogate models to test
>>> python test_model_regression.py      # (optional - if developing a new test) generate regression data locally on your machine
>>> cd ..                                # move back to the top-level folder
>>> pytest                               # run all tests
>>> pytest -v -s                         # run all tests with high verbosity

NSF Support

This package is based upon work supported by the National Science Foundation under PHY-1316424, PHY-1208861, and PHY-1806665.

Any opinions, findings, and conclusions or recommendations expressed in gwsurrogate are those of the authors and do not necessarily reflect the views of the National Science Foundation.