An easy to use interface to gravitational wave surrogate models
Project description
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-hypernode.com
Installation
Dependency
gwsurrogate requires:
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.
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
From source
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
export PYTHONPATH=~absolute_path_to_gws_folder:$PYTHONPATH
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.
Getting Started
Please read the gwsurrogate docstring found in the init.py file or from ipython with
>>> import gwsurrogate as gws >>> gws?
Additional examples can be found in the accompanying Jupyter notebooks located in the ‘tutorial’ folder. To open a notebook, for example basics.ipynb, do
>>> jupyter notebook basics.ipynb
from the directory ‘notebooks’
Where to find surrogates?
Surrogates can be downloaded directly from gwsurrogate. For download instructions, see the basics.ipynb Jupyter notebook.
Tests
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
NSF Support
This package is based upon work supported by the National Science Foundation under PHY-1316424 and PHY-1208861.
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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file gwsurrogate-0.9.0.tar.gz.
File metadata
- Download URL: gwsurrogate-0.9.0.tar.gz
- Upload date:
- Size: 4.5 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/40.0.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 777c8cfd98c99c0a9912465c03e780383adabaca33cf9a0415bbb279fb73304d |
|
| MD5 | be767156c7ffc390ba9f3c417d320ad5 |
|
| BLAKE2b-256 | 1b39faf6b91b3b89ef3ec434a30c9554e4c11157ab5133c9ce3560a7fd4ba5a4 |
