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        "author": "Aleksi Nummelin",
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            "Development Status :: 2 - Pre-Alpha",
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            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6"
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        "description": "============\nMicroInverse\n============\n\n\n.. image:: https://img.shields.io/pypi/v/MicroInverse.svg\n        :target: https://pypi.python.org/pypi/MicroInverse\n\n.. image:: https://img.shields.io/travis/AleksiNummelin/MicroInverse.svg\n        :target: https://travis-ci.org/AleksiNummelin/MicroInverse\n\n.. image:: https://readthedocs.org/projects/MicroInverse/badge/?version=latest\n        :target: https://MicroInverse.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\n\n\nMicroInverse is a Python package for inversion of a transport operator from tracer data.\n\nIt is based on the simple stochastic climate model approximation\n\n.. image:: http://latex.codecogs.com/gif.latex?%5Cfrac%7Bd%7D%7Bdt%7D%20%5Cmathbf%7Bx%7D%28t%29%20%3D%20%5Cmathbf%7BB%7D%5Cmathbf%7Bx%7D%28t%29%20+%20%5Cmathbf%7Bf%7D%28t%29\n\nWhere **x** is the vector of tracer anomaly timeseries, **B** is the transport operator, and **f** is \nthe forcing of the system. Assuming that the forcing has a shorter decorrelation timescale than\nthe tracer we can solve for the transport operator:\n\n.. image:: http://latex.codecogs.com/gif.latex?%5Cmathbf%7BB%7D%3D%5Cfrac%7B1%7D%7B%5Ctau%7D%5Clog%20%5Cleft%28%5Cleft%5B%20%5Cmathbf%7Bx%7D%28t+%5Ctau%29%5Cmathbf%7Bx%7D%5ET%28t%29%5Cright%20%5D%20%5C%20%5Cleft%5B%5Cmathbf%7Bx%7D%28t%29%5Cmathbf%7Bx%7D%5ET%28t%29%20%5Cright%5D%5E%7B-1%7D%5Cright%29\n\nWhere tau is the chosen decorrelation timescale which should be larger than the forcing decorrelation timescale, \nbut smaller than the decorrelation timescale of the tracer. \n\nIn practice tau is hard to choose a priori which is why we suggest first inverting your data at multiple values \nof tau and combining the results afterwards using MicroInverse.MicroInverse_utils.combine_Taus().\n\nMicroInverse will also relate **B** to velocity, diffusivity, and decay via advection-diffusion-relaxation equation (see `Nummelin et al. (2018)`_ for details)\n\n* Free software: MIT license\n* Documentation: https://MicroInverse.readthedocs.io.\n\n\nFeatures\n--------\n\n* Estimates of velocity, diffusivity, and decay timescale from a timeseries of 2D tracer.\n\nCredits\n-------\n\nThis package is based on work by `Nummelin et al. (2018)`_ and Jeffress and Haine (2014a_, 2014b_)\n\n.. _Nummelin et al. (2018): http://pages.jh.edu/~anummel1/\n.. _2014a: https://doi.org/10.1002/qj.2313\n.. _2014b: https://doi.org/10.1088/1367-2630/16/10/105001 \n\nPackage is created with Cookiecutter_ using the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.3.0 (2018-08-29)\n------------------\n\n* Several fixed bugs\n\n0.2.0 (2018-04-05)\n------------------\n\n* First properly working release\n\n0.1.0 (2018-04-03)\n------------------\n\n* First release on PyPI.\n\n\n",
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