Metadata-Version: 2.1
Name: py-moc
Version: 0.0.1rc4
Summary: A simple model suite for the MOC
Home-page: https://pymoc.github.io
License: UNKNOWN
Description: [![CircleCI](https://circleci.com/gh/pymoc/pymoc/tree/master.svg?style=shield)](https://circleci.com/gh/pymoc/PyMOC/tree/master)
        [![Test Coverage](https://api.codeclimate.com/v1/badges/b03ff00b5c86d7afc364/test_coverage)](https://codeclimate.com/github/pymoc/PyMOC/test_coverage)
        [![Maintainability](https://api.codeclimate.com/v1/badges/b03ff00b5c86d7afc364/maintainability)](https://codeclimate.com/github/pymoc/PyMOC/maintainability)
        [![Documentation](https://img.shields.io/badge/docs-PyMOC-informational)](https://pymoc.github.io/pymoc)
        [![License](https://img.shields.io/badge/license-MIT-informational)](LICENSE)
        
        PyMOC is a simple, modular suite of python ocean column models for
        use in studying the Meridional Overturning Circulation (MOC). The 
        MOC plays a critical role in the uptake and redistribution of heat
        and carbon by the ocean, and as such both mediates and is governed
        by shifts in the climate regime. As such, understanding of the MOC
        is crucial to understanding of climate change.
        
        The model suite consists of several independent modules representing
        various ocean regions and dynamics. Specifically, there are modules
        for calculating the advective-diffusive buoyancy balance in zonally
        constrained ocean basins (such as the Atlantic Ocean) in both
        transient and equilibrium states, in re-entrant surface channel flow
        (such as in the shallow Southern Ocean), for calculating a thermal
        wind balance between basins, and for calculating residual wind and
        eddy driven circulation in a deep channel. These modules may be 
        coupled to study an arbitrary circulatory structure (such as the AMOC).
        
        The intended audiences for this model are educators and students
        of the geophysical sciences. While the goal is to provide an accessible
        model appropriate for newcomers to geophysical modeling, the physics
        captured in PyMOC are robust enough to support basic research as well.
        
        Configuration and execution of the PyMOC suite requires little
        administrative knowledge on the technical end. All modules are written
        in pure Python, and the only core dependencies are the NumPy and SciPy
        libraries. If configuration of your base system environment is undesirable,
        a preconfigured Docker container has been made available with all required
        software libraries pre-installed. Furthermore, a goal of the development
        team is to keep PyMOC well tested, stable, and maintainable to reduce
        pain to the end user. Further details on installation, configuration,
        contribution, and issue reporting is available in the documentation.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
