An easy to use interface to gravitational wave surrogate models
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
 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  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
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/
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
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
gwsurrogate is on conda-forge, and can be installed with
>>> conda install -c conda-forge gwsurrogate
First, please make sure you have the necessary dependencies installed (see above). Next, Download and unpack gwsurrogate-X.X.tar.gz to any folder gws_folder of your choosing. The gwsurrogate module can be used immediately 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
For a "proper" installation
>>> python setup.py install # option 1 >>> pip install -e gwsurrogate # option 2
where the "-e" installs an editable (development) project with pip. This allows your local code edits to be automatically seen by the system-wide installation.
If you have git cloned this project, you must do
git submodule init git submodule update
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
The most up-to-date models are listed below.
- 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.
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.
Jupyter notebooks located in tutorial/website give a more comprehensive overview of individual models.
If you have downloaded the entire project as a tar.gz file, its a good idea to run some regression tests. Note that if you are running the model regression tests, regression data must be generated locally on your machine.
>>> cd test # move into the folder test >>> python test_model_regression.py # create model regression data >>> cd .. # move back to the top-level folder >>> pytest # run all tests >>> pytest -v -s # run all tests with high verbosity
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.
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