Metadata-Version: 1.1
Name: gwsurrogate
Version: 0.9.2
Summary: An easy to use interface to gravitational wave surrogate models
Home-page: UNKNOWN
Author: Jonathan Blackman, Scott Field, Chad Galley, Vijay Varma
Author-email: sfield@umassd.edu
License: MIT
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.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.
        
        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.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Other Audience
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
