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        "author": "Jonathan Blackman, Scott Field, Chad Galley, Vijay Varma, Kevin Barkett",
        "author_email": "sfield@umassd.edu",
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            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
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            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics",
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        "description": "# Welcome to GWSurrogate! #\n\nGWSurrogate is an easy to use interface to gravitational wave surrogate models.\n\nSurrogates provide a fast and accurate evaluation mechanism for gravitational\nwaveforms which would otherwise be found through solving differential \nequations. These equations must be solved in the ``building\" phase, which \nwas performed using other codes. For details see\n\n[1] Scott Field, Chad Galley, Jan Hesthaven, Jason Kaye, and Manuel Tiglio. \n`\"Fast prediction and evaluation of gravitational waveforms using surrogate \nmodels\". Phys. Rev. X 4, 031006 (2014). arXiv: gr-qc:1308.3565\n\nIf you find this package useful in your work, please cite reference [1] and, \nif available, the relevant paper describing the specific surrogate used. \n\nAll available models can be found in gwsurrogate.catalog.list()\n\ngwsurrogate is available at https://pypi.python.org\n\n# Installation #\n\n## Dependency ##\n\ngwsurrogate requires:\n\n1)  gwtools. If you are installing gwsurrogate with pip you\nwill automatically get gwtools. If you are installing gwsurrogate from \nsource, please see https://bitbucket.org/chadgalley/gwtools/\n\n2) gsl. For speed, the long (hybrid) surrogates use gsl's spline function. \nTo build gwsurrogate you must have gsl installed. Fortunately, this is a\ncommon library and can be easily installed with a package manager. \n\nNote that at runtime (ie when you do import gwsurrogate) you may need to let\ngsl know where your BLAS library is installed. This can be done by setting\nyour LD_PRELOAD or LD_LIBRARY_PATH environment variables. \n\n## From pip ##\n\nThe python package pip supports installing from PyPI (the Python Package \nIndex). gwsurrogate can be installed to the standard location \n(e.g. /usr/local/lib/pythonX.X/dist-packages) with\n\n```\n>>> pip install gwsurrogate\n```\n\n## From source ##\n\nFirst, please make sure you have the necessary dependencies\ninstalled (see above). Next, Download and unpack gwsurrogate-X.X.tar.gz\nto any folder gws_folder of your choosing. The gwsurrogate module can\nbe used immediately by adding\n\n```\nimport sys\nsys.path.append('absolute_path_to_gws_folder')\n```\n\nat the beginning of any script/notebook which uses gwsurrogate. \n\nAlternatively, if you are a bash or sh user, edit your .profile \n(or .bash_profile) file and add the line\n\n```\nexport PYTHONPATH=~absolute_path_to_gws_folder:$PYTHONPATH\n```\n\nFor a \"proper\" installation\n\n```\n>>> python setup.py install    # option 1\n>>> pip install -e gwsurrogate # option 2\n```\n\nwhere the \"-e\" installs an editable (development) project with pip. This allows\nyour local code edits to be automatically seen by the system-wide installation.\n\n\n# Getting Started #\n\nPlease read the gwsurrogate docstring found in the __init__.py file\nor from ipython with\n\n```\n>>> import gwsurrogate as gws\n>>> gws?\n```\n\nAdditional examples can be found in the accompanying Jupyter notebooks\nlocated in the 'tutorial' folder. To open a notebook, for example\nbasics.ipynb, do\n\n```\n  >>> jupyter notebook basics.ipynb\n```\nfrom the directory 'notebooks'\n\n\n# Where to find surrogates? #\n\nSurrogates can be downloaded directly from gwsurrogate. For download \ninstructions, see the basics.ipynb Jupyter notebook.\n\n\n# Tests #\n\nIf you have downloaded the entire project as a tar.gz file, its a good idea\nto run some regression tests. Note that if you are running the model regression\ntests, regression data must be generated locally on your machine.\n\n\n```\n>>> cd test                          # move into the folder test\n>>> python test_model_regression.py  # create model regression data\n>>> cd ..                            # move back to the top-level folder\n>>> pytest                           # run all tests\n>>> pytest -v -s                     # run all tests with high verbosity\n```\n\n# NSF Support #\n\nThis package is based upon work supported by the National Science Foundation \nunder PHY-1316424 and PHY-1208861.\n\nAny opinions, findings, and conclusions or recommendations expressed in \ngwsurrogate are those of the authors and do not necessarily reflect the \nviews of the National Science Foundation.",
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