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        "author": "S. Brewster Malevich",
        "author_email": "malevich@email.arizona.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering"
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        "description": "baysplinepy\n===========\n\n.. image:: https://travis-ci.org/brews/baysplinepy.svg?branch=master\n    :target: https://travis-ci.org/brews/baysplinepy\n\n\nAn open source Python package for `alkenone UK'37 <https://en.wikipedia.org/wiki/Alkenone>`_ calibration.\n\n**baysplinepy** is based on the original BAYSPLINE software for MATLAB (https://github.com/jesstierney/BAYSPLINE). BAYSPLINE is a Bayesian calibration for the alkenone paleothermometer, as published in `Tierney & Tingley (2018) <http://doi.org/10.1002/2017PA003201>`_. \n\nNOTE that this package is under active development. Code and documentation may not be complete and may change in the near future.\n\n\nExample\n-------\n\nFirst, load packages and an example dataset::\n\n    import numpy as np\n    import bayspline as bsl\n\n    example_file = bsl.get_example_data('tierney2016-p178-15p.csv')\n    d = np.genfromtxt(example_file, delimiter=',', names=True)\n\nThis dataset (from `Tierney et al. 2015 <https://doi.org/10.1038/ngeo2603>`_)\nhas three columns giving core depth (cm), sediment age (calendar years BP), and UK'37.\n\nWe can predict sea-surface temperatures (SST) from UK'37 with ``bsl.predict_sst()``::\n\n    prediction = bsl.predict_sst(d['uk37'], prior_std=10)\n\nTo see actual numbers from the prediction, directly parse ``prediction.ensemble`` or use ``prediction.percentile()`` to get the 5%, 50% and 95% percentiles.\n\nYou can also plot your prediction with ``bsl.predictplot()`` or ``bsl.densityplot()``.\n\nAlternatively, we can make inferences about UK'37 from SST with ``bsl.predict_uk()``::\n\n    sst = np.arange(1, 25)\n    prediction = bsl.predict_uk(sst)\n\n\nInstallation\n------------\n\nInstall **baysplinepy** in ``conda`` with::\n\n    $ conda install baysplinepy -c sbmalev\n\nTo install with ``pip``, run::\n\n    $ pip install baysplinepy\n\nUnfortunately, **baysplinepy** is not compatible with Python 2.\n\n\nSupport and development\n-----------------------\n\n- Please feel free to report bugs and issues or view the source code on GitHub (https://github.com/brews/baysplinepy).\n\n\nLicense\n-------\n\n**baysplinepy** is available under the Open Source GPLv3 (https://www.gnu.org/licenses).\n\n\n",
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        "keywords": "marine paleoclimate uk37 calibration",
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