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    "info": {
        "author": "Mike Neish",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Atmospheric Science"
        ],
        "description": "Overview\n========\nThis module provides a mechanism for converting between FSTD and netCDF file formats, either through Python or the command-line.\n\nBasic Usage\n===========\n\nFrom the command-line\n---------------------\n```\npython -m fstd2nc [options] <infile> <outfile>\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --version             show program's version number and exit\n  --no-progress         Disable the progress bar.\n  --minimal-metadata    Don't include RPN record attributes and other internal\n                        information in the output metadata. This is the\n                        default behaviour.\n  --rpnstd-metadata     Include all RPN record attributes in the output\n                        metadata.\n  --rpnstd-metadata-list nomvar,...\n                        Specify a minimal set of RPN record attributes to\n                        include in the output file.\n  --ignore-typvar       Tells the converter to ignore the typvar when deciding\n                        if two records are part of the same field. Default is\n                        to split the variable on different typvars.\n  --ignore-etiket       Tells the converter to ignore the etiket when deciding\n                        if two records are part of the same field. Default is\n                        to split the variable on different etikets.\n  --vars VAR1,VAR2,...  Comma-separated list of variables to convert. By\n                        default, all variables are converted.\n  --fill-value FILL_VALUE\n                        The fill value to use for masked (missing) data. Gets\n                        stored as '_FillValue' attribute in the metadata.\n                        Default is '1e+30'.\n  --datev, --squash-forecasts\n                        Use the date of validity for the \"time\" axis. This is\n                        the default.\n  --dateo, --forecast-axis\n                        Use the date of original analysis for the time axis,\n                        and put the forecast times into a separate \"forecast\"\n                        axis.\n  --ensembles           Collect different etikets for the same variable\n                        together into an \"ensemble\" axis.\n  --profile-momentum-vars VAR1,VAR2,...\n                        Comma-separated list of variables that use momentum\n                        levels.\n  --profile-thermodynamic-vars VAR1,VAR2,...\n                        Comma-separated list of variables that use\n                        thermodynamic levels.\n  --missing-bottom-profile-level\n                        Assume the bottom level of the profile data is\n                        missing.\n  --strict-vcoord-match\n                        Require the IP1/IP2/IP3 parameters of the vertical\n                        coordinate to match the IG1/IG2/IG3 paramters of the\n                        field in order to be used. The default behaviour is to\n                        use the vertical record anyway if it's the only one in\n                        the file.\n  --diag-as-model-level\n                        Treat diagnostic (near-surface) data as model level\n                        '1.0'. Normally, this data goes in a separate variable\n                        because it has incompatible units for the vertical\n                        coordinate. Use this option if your variables are\n                        getting split with suffixes '_vgrid4' and '_vgrid5',\n                        and you'd rather keep both sets of levels together in\n                        one variable.\n  --ignore-diag-level   Ignore data on diagnostic (near-surface) height.\n  --subgrid-axis        For data on supergrids, split the subgrids along a\n                        \"subgrid\" axis. The default is to leave the subgrids\n                        stacked together as they are in the RPN file.\n  --filter CONDITION    Subset RPN file records using the given criteria. For\n                        example, to convert only 24-hour forecasts you could\n                        use --filter ip2==24\n  --exclude NAME,NAME,...\n                        Exclude some axes or derived variables from the\n                        output. Note that axes will only be excluded if they\n                        have a length of 1.\n  --metadata-file METADATA_FILE\n                        Use metadata from the specified file. You can repeat\n                        this option multiple times to build metadata from\n                        different sources.\n  --rename OLDNAME=NEWNAME,...\n                        Apply the specified name changes to the variables.\n  --time-units {seconds,minutes,hours,days}\n                        The units for the output time axis. Default is hours.\n  --reference-date YYYY-MM-DD\n                        The reference date for the output time axis. The\n                        default is the starting date in the RPN file.\n  --msglvl {0,DEBUG,2,INFORM,4,WARNIN,6,ERRORS,8,FATALE,10,SYSTEM,CATAST}\n                        How much information to print to stdout during the\n                        conversion. Default is WARNIN.\n  --nc-format {NETCDF4,NETCDF4_CLASSIC,NETCDF3_CLASSIC,NETCDF3_64BIT_OFFSET,NETCDF3_64BIT_DATA}\n                        Which variant of netCDF to write. Default is NETCDF4.\n  --zlib                Turn on compression for the netCDF file. Only works\n                        for NETCDF4 and NETCDF4_CLASSIC formats.\n  --compression COMPRESSION\n                        Compression level for the netCDF file. Only used if\n                        --zlib is set. Default: 4.\n  -f, --force           Overwrite the output file if it already exists.\n  --no-history          Don't put the command-line invocation in the netCDF\n                        metadata.\n```\n\nUsing in a Python script\n========================\n\nSimple conversion\n--------------------------------------\n```python\nimport fstd2nc\ndata = fstd2nc.Buffer(\"myfile.fst\")\ndata.to_netcdf(\"myfile.nc\")\n```\n\nYou can control `fstd2nc.Buffer` using parameters similar to the command-line arguments.  The usual convention is *--arg-name* from the command-line would be passed as *arg_name* from Python.\n\nFor example:\n```python\nimport fstd2nc\n# Select only TT,HU variables.\ndata = fstd2nc.Buffer(\"myfile.fst\", vars=['TT','HU'])\n# Set the reference date to Jan 1, 2000 in the netCDF file.\ndata.to_netcdf(\"myfile.nc\", reference_date='2000-01-01')\n```\n\nInterfacing with xarray\n---------------------------------------------------------------------------------\n\nFor more complicated conversions, you can manipulate the data as an [xarray.Dataset](http://xarray.pydata.org/en/stable/data-structures.html#dataset) object by using the `to_xarray()` method:\n```python\nimport fstd2nc\n\n# Open the FSTD file.\ndata = fstd2nc.Buffer(\"myfile.fst\")\n\n# Access the data as an xarray.Dataset object.\ndataset = data.to_xarray()\nprint (dataset)\n\n# Convert surface pressure to Pa.\ndataset['P0'] *= 100\ndataset['P0'].attrs['units'] = 'Pa'\n\n# (Can further manipulate the dataset here)\n# ...\n\n# Write the final result to netCDF using xarray:\ndataset.to_netcdf(\"myfile.nc\")\n```\n\nInterfacing with iris\n---------------------------------------------------------------------------------\n\nYou can interface with [iris](https://scitools.org.uk/iris/docs/latest/index.html) by using the `.to_iris()` method (requires iris version 2.0 or greater).\nThis will give you an [iris.cube.CubeList](https://scitools.org.uk/iris/docs/latest/iris/iris/cube.html#iris.cube.CubeList) object:\n```python\nimport fstd2nc\nimport iris.quickplot as qp\nfrom matplotlib import pyplot as pl\n\n# Open the FSTD file.\ndata = fstd2nc.Buffer(\"myfile.fst\")\n\n# Access the data as an iris.cube.CubeList object.\ncubes = data.to_iris()\nprint (cubes)\n\n# Plot all the data (assuming we have 2D fields)\nfor cube in cubes:\n  qp.contourf(cube)\n  pl.gca().coastlines()\n\npl.show()\n```\n\nInterfacing with pygeode\n---------------------------------------------------------------------------------\n\nYou can create a [pygeode.Dataset](http://pygeode.github.io/dataset.html) object using the `.to_pygeode()` method (requires pygeode version 1.2.2 or greater):\n```python\nimport fstd2nc\n\n# Open the FSTD file.\ndata = fstd2nc.Buffer(\"myfile.fst\")\n\n# Access the data as a pygeode.Dataset object.\ndataset = data.to_pygeode()\nprint (dataset)\n```\n\n\nInstalling\n==========\n\nThe easiest way to install is using [pip](https://pip.pypa.io/en/stable):\n```\npip install fstd2nc\n```\n\nIf you're processing many input files into a single netCDF file, you could get some useful features (progress bar, quick file scans) by running:\n```\npip install fstd2nc[manyfiles]\n```\n\nAlternatively, you can install it in a [conda](https://conda.io/docs/index.html) environment:\n```\nconda install -c neishm fstd2nc\n```\n\n\nUsing in a Pydap server\n=======================\n\nThis package includes a handler for [Pydap](https://github.com/pydap/pydap), which enables you to serve your FSTD files via the OPeNDAP protocol.\n\nTo install all the pre-requisites:\n```\npip install pydap fstd2nc[dap]\n```\n\nYou can then test it by running\n```\npydap -d [your data directory]\n```\n\n\nRequirements\n============\n\nBasic requirements\n--------------------\n\nThis package requires [Python-RPN](https://github.com/meteokid/python-rpn) for reading/writing FSTD files, and [netcdf4-python](https://github.com/Unidata/netcdf4-python) for reading/writing netCDF files.\n\nOptional requirements\n---------------------\n\nFor reading large numbers of input files (>100), this utility can leverage [pandas](https://github.com/pandas-dev/pandas) to quickly process the FSTD record headers.\n\nThe [progress](https://github.com/verigak/progress) module is required in order to use the `--progress` option.\n\nThe `.to_xarray()` Python method requires the [xarray](https://github.com/pydata/xarray) and [dask](https://github.com/dask/dask) packages.\n\nThe `.to_iris()` Python method requires the [iris](https://scitools.org.uk/iris/docs/latest/index.html) package, along with the `.to_xarray()` dependencies.\n\nThe `.to_pygeode()` Python method requires the [pygeode](https://github.com/pygeode/pygeode) package, along ith the `.to_xarray()` dependencies.",
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