Metadata-Version: 1.1
Name: gwsurrogate
Version: 0.8
Summary: An easy to use interface to gravitational wave surrogate models
Home-page: UNKNOWN
Author: Jonathan Blackman, Scott Field, Chad Galley
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.
        
        gwsurrogate is available at https://pypi.python.org
        
        
        # Installation #
        
        gwsurrogate is a pure-Python module, thus installation is very easy. 
        
        ## 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 into gws_folder run
        
        ```
        >>> python setup.py install --prefix=absolute_path_to_gws_folder
        ```
        
        and edit the PYTHONPATH environment variable as described above.
        
        
        # 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. Also visit
        the NR surrogate [database](https://www.black-holes.org/surrogates/).
        
        
        # Tests #
        
        If you have downloaded the entire project as a tar.gz file, from the 
        top folder, do
        
        ```
        >>> py.test
        ```
        
        # 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
