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        "author": "U\u011fur \u00c7ayo\u011flu",
        "author_email": "urcyglu@gmail.com",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "Natural Language :: English",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: GIS",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "cdochain\n========\n\nThis module helps create chains of cdo commands for easy manipulation of\nclimate data.\n\nFeatures\n^^^^^^^^\n\n-  Method execution is lazy and gets processed only on function call\n   ``Chain.execute()``.\n-  Input supports Unix style pathname pattern search.\n-  The Input will be first run with\n   `glob <https://docs.python.org/3/library/glob.html>`__ and checked if\n   several files match.\n   :exclamation: *If that is the case a temporary file will be created*.\n-  Output can be a file on disc, an\n   `netCDF4.Dataset <http://unidata.github.io/netcdf4-python/#netCDF4.Dataset>`__\n   or (not) masked\n   `numpy.ndarray <http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html>`__.\n\nInstallation\n------------\n\n.. code:: bash\n\n    python3.5 -m pip install cdochain --pre\n\n--- or ---\n\n.. code:: bash\n\n    git clone https://github.com/OnionNinja/cdochain.git\n    cd cdochain\n    python3.5 setup.py install\n\nTL;DR\n-----\n\n.. code:: python\n\n    from cdochain import chaining as cch\n\n    input = './tests/testdata/sresa1b_ncar_ccsm3-example.nc'\n    output = './enso34-mm.nc'\n    data = cch.Chain(ifile=input, ofile=output)\n    enso34 = data.sellonlatbox(190,240,-5,5).monmean()\n    out = enso34.execute()\n\nUsage\n-----\n\nThis module implements `method\nchaining <https://en.wikipedia.org/wiki/Method_chaining>`__ for the\n`Climate Data Operators <https://code.zmaw.de/projects/cdo>`__ (CDO)\ntool from the Max Planck Institute for Meteorology. Let us start:\n\n.. code:: python\n\n    from cdochain import chaining as cch\n\nFor initialisation one has to define **input**, **output**, and may\ndefine several **options**.\n\nInput\n~~~~~\n\nNow we have to define the files we want to work on:\n\n-  To use one file\n\n.. code:: python\n\n    input = './tests/testdata/sresa1b_ncar_ccsm3-example.nc'\n\n-  To use several files you can give a Unix style pattern\n\n.. code:: python\n\n    input = './tests/testdata/*.nc'\n\n*This creates a temporary file* :exclamation:\n\nOutput\n~~~~~~\n\nFor defining the output we have several options.\n\n-  To output a **file on disc**:\n\n.. code:: python\n\n    data = cch.Chain(ifile=input, ofile='/path/to/output.nc')\n\n-  To output an **netcdf4.Dataset** object:\n\n.. code:: python\n\n    data = cch.Chain(ifile=input, ofile='netCDF4')\n\n-  To output an **numpy.ndarray** object:\n\n.. code:: python\n\n    data = cch.Chain(ifile=input, ofile='array:<var>')  # numpy.ndarray\n    # or\n    data = cch.Chain(ifile=input, ofile='maarray:<var>')  # masked numpy.ndarray\n\n``<var>`` defines the variable to be extracted and saved to\nnumpy.ndarray.\n\nOptions\n~~~~~~~\n\nAs for options one can use the same as described on the `CDO\nwebsite <https://code.zmaw.de/projects/cdo/embedded/index.html#x1-70001.2.1>`__.\nThe default is ``options='-O -f nc'``.\n\n**Operations**\n~~~~~~~~~~~~~~\n\nThe operations defined in\n`CDO <https://code.zmaw.de/projects/cdo/embedded/index.html>`__ can now\nbe used on the data element.\n\n.. code:: python\n\n    analysis = data.sellonlatbox(190,240,-5,5).sellevidx(1).mermean()\n    fn = analysis.execute()\n\nHave fun :neckbeard:",
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