# -*- coding: utf-8 -*-
from setuptools import setup

packages = \
['sxs',
 'sxs.catalog',
 'sxs.horizons',
 'sxs.metadata',
 'sxs.utilities',
 'sxs.waveforms',
 'sxs.zenodo',
 'sxs.zenodo.api']

package_data = \
{'': ['*']}

install_requires = \
['h5py>=2.10.0,<3.0.0',
 'inflection>=0.5.1,<0.6.0',
 'numpy>=1.15,<2.0',
 'pandas>=1.1.2,<2.0.0',
 'pytest-forked>=1.3.0,<2.0.0',
 'pytz>=2020.1,<2021.0',
 'quaternionic>=0.1.16',
 'requests>=2.24.0,<3.0.0',
 'scipy>=1.0,<2.0',
 'spherical>=0.1,<0.2',
 'tqdm>=4.48.2,<5.0.0',
 'urllib3>=1.25.10,<2.0.0']

extras_require = \
{':implementation_name == "cpython"': ['numba>=0.50'],
 ':python_version < "3.8"': ['importlib-metadata>=1.0,<2.0'],
 'ecosystem': ['ipywidgets>=7.5.1,<8.0.0',
               'ipykernel>=5.3.4,<6.0.0',
               'jupyter_contrib_nbextensions>=0.5.1,<0.6.0',
               'jupyterlab>=2.2.8,<3.0.0',
               'line_profiler>=3.0.2,<4.0.0',
               'memory_profiler>=0.57.0,<0.58.0',
               'matplotlib>=2.1.1',
               'sympy>=1.6.2,<2.0.0',
               'corner>=2.1.0,<3.0.0',
               'qgrid>=1.3.1,<2.0.0',
               'rise>=5.6.1,<6.0.0',
               'quaternion>=0.3.1,<0.4.0',
               'spinsfast>=104.2020.8,<105.0.0',
               'spherical_functions>=2020.8.18,<2021.0.0',
               'scri>=2020.8.18,<2021.0.0'],
 'mkapi:python_version >= "3.7"': ['mkapi>=1.0.13,<2.0.0'],
 'mkdocs:implementation_name == "cpython"': ['mkdocs>=1.1.2'],
 'pymdown-extensions:implementation_name == "cpython"': ['pymdown-extensions>=8,<9']}

setup_kwargs = {
    'name': 'sxs',
    'version': '2020.9.12',
    'description': 'Interface to data produced by the Simulating eXtreme Spacetimes collaboration',
    'long_description': '[![Test Status](https://github.com/sxs-collaboration/sxs/workflows/tests/badge.svg)](https://github.com/sxs-collaboration/sxs/actions)\n[![Documentation Status](https://readthedocs.org/projects/sxs/badge/?version=main)](https://sxs.readthedocs.io/en/main/?badge=main)\n[![PyPI Version](https://img.shields.io/pypi/v/sxs?color=)](https://pypi.org/project/sxs/)\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/sxs.svg?color=)](https://anaconda.org/conda-forge/sxs)\n[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/sxs-collaboration/sxs/blob/main/LICENSE)\n\n\n# Simulating eXtreme Spacetimes python package\n\nThe `sxs` python package provides a high-level interface for using data\nproduced by the SXS collaboration.  In particular, the function `sxs.load` can\nautomatically find, download, and load data, returning objects that provide\ncommon interfaces to the various types of data, without forcing the user to\nworry about details like data formats or where to find the data.  It can also\nautomatically select the newest or highest-resolution dataset for a given\nsimulation, or return a range of versions or resolutions.  Currently, the\nhigh-level objects encapsulate\n\n  * Catalog — a listing of all data produced by the SXS collaboration\n  * Metadata — data describing the simulation parameters\n  * Horizons — time-series data describing the apparent horizons\n  * Waveforms — time-series data describing the extrapolated gravitational-wave\n    modes\n\n\n## Installation\n\nBecause this package is pure python code, installation is very simple.  In\nparticular, with a reasonably modern installation, you can just run a command\nlike\n\n```bash\nconda install -c conda-forge sxs\n```\n\nor\n\n```bash\npython -m pip install sxs\n```\n\nThese commands will download and install just the `sxs` package.  If you want\nto install all the goodies that enable things like interactive tables in\njupyter notebooks, you could run\n\n```bash\nconda install -c conda-forge sxs-ecosystem\n```\n\nor\n\n```bash\npython -m pip install sxs[ecosystem]\n```\n\nEither way, you will probably also want to set some sensible defaults to\nautomatically download and cache data:\n\n```bash\npython -c "import sxs; sxs.write_config(download=True, cache=True)"\n```\n\nThis will write the configuration file in the directory returned by\n`sxs.sxs_directory("config")`, and data will be cached in the directory\nreturned by `sxs.sxs_directory("cache")`.\n\n\n## Usage\n\nThere are four important objects to understand in this package:\n\n```python\nimport sxs\n\ncatalog = sxs.load("catalog")\nmetadata = sxs.load("SXS:BBH:0123/Lev/metadata.json")\nhorizons = sxs.load("SXS:BBH:0123/Lev/Horizons.h5")\nwaveform = sxs.load("SXS:BBH:0123/Lev/rhOverM", extrapolation_order=2)\n```\n\n[The `catalog` object](api/sxs.catalog.catalog/#sxs.catalog.catalog.Catalog)\ncontains information about every simulation in the catalog, including all\navailable data files, and information about how to get them.  You probably\ndon\'t need to actually know about details like where to get the data, but\n`catalog` can help you find the simulations you care about.  Most importantly,\n`catalog.simulations` is a `dict` object, where the keys are names of\nsimulations (like "SXS:BBH:0123") and the values are the same types as [the\n`metadata` object](api/sxs.metadata.metadata/#sxs.metadata.metadata.Metadata),\nwhich contains metadata about that simulation — things like mass ratio, spins,\netc.  This `metadata` reflects the actual output of the simulations, which\nleads to some inconsistencies in their formats.  A more consistent interface\n(though it is biased toward returning NaNs where a human might glean more\ninformation) is provided by `catalog.simulations_dataframe`, which returns a\n[`pandas`](https://pandas.pydata.org/docs/) `DataFrame` with specific data\ntypes for each column.\n\nThe actual data itself is primarily contained in the next two objects.  [The\n`horizons` object](api/sxs.horizons/#sxs.horizons.Horizons) has three\nattributes — `horizons.A`, `horizons.B`, and `horizons.C` — typically\nrepresenting the original two horizons of the black-hole binary and the common\nhorizon that forms at merger.  In matter simulations, one or more of these may\nbe `None`.  Otherwise, each of these three is a\n[`HorizonQuantities`](api/sxs.horizons/#sxs.horizons.HorizonQuantities) object,\ncontaining several timeseries relating to mass, spin, and position.\n\nFinally, the\n[`waveform`](api/sxs.waveforms.waveform_modes/#sxs.waveforms.waveform_modes.WaveformModes)\nencapsulates the modes of the waveform and the corresponding time information,\nalong with relevant metadata like data type, spin weight, etc., and useful\nfeatures like numpy-array-style slicing.\n',
    'author': 'Michael Boyle',
    'author_email': 'michael.oliver.boyle@gmail.com',
    'maintainer': None,
    'maintainer_email': None,
    'url': 'https://github.com/sxs-collaboration/sxs',
    'packages': packages,
    'package_data': package_data,
    'install_requires': install_requires,
    'extras_require': extras_require,
    'python_requires': '>=3.6.1,<4.0.0',
}


setup(**setup_kwargs)
