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    "info": {
        "author": "Kyle Wilcox",
        "author_email": "kyle@axiomdatascience.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering"
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        "description": "# pyncml  [![Build Status](https://travis-ci.org/axiom-data-science/pyncml.svg?branch=master)](https://travis-ci.org/axiom-data-science/pyncml)\n\n#### A simple python library to apply NcML logic to NetCDF files\n\n\n## Installation\n\n##### Stable\n\n```bash\n$ pip install pyncml\n# or\n$ conda install -c conda-forge pyncml\n```\n\n\n##### Development\n\n    pip install git+https://github.com/axiom-data-science/pyncml.git\n\n\n## Supported\n\n*  Adding things\n    *  Attributes: `<attribute name=\"some_new_attribute\" type=\"string\" value=\"some_standard_name\" />`\n\n*  Renaming things\n    *  Variables: `<variable name=\"new_var\" orgName=\"old_var\" />`\n    *  Attributes: `<attribute name=\"new_attr\" orgName=\"old_attr\" />`\n    *  Dimensions: `<dimension name=\"new_dim\" orgName=\"old_dim\" />`\n\n*  Removing things\n    *  Variables: `<remove name=\"some_variable\" type=\"variable\" />`\n    *  Attributes: `<remove name=\"some_variable\" type=\"variable\" />`\n\n*  Aggregating things\n    *  Scans: `<scan location=\"some_directory/foo/bar/\" suffix=\".nc\" subdirs=\"true\" />`\n\n## Not supported\n\n*  Adding variables (could be implemented in the future)\n*  Groups (could be implemented in the future)\n*  Setting actual data values on variables (could be implemented in the future)\n*  Creating a file from scratch (could be implemented in the future)\n*  Removing Dimensions (not implemented in the C library)\n*  Aggregation scans that utilize the `dateFormatMark` attribute (most likely will never be implemented)\n\n## Usage\n\n### Apply\n\nThe `apply` function takes in a path to the `input_file` NetCDF file, an `ncml` object (string, file path, or python etree `Element` object), and an optional `output_file`.  **If an output_file is not specified, the `input_file` will be edited in place**.  The object returned from the `apply` function is a netcdf4-python object, ready to be used.\n\nAny `location` attributes in the NcML are **ignored** and the NcML is applied against the file specified as the `input_file`.\n\n##### Editing a file in place\n```python\nnetcdf = '/some/file/path/in.nc'\nncml   = '/some/file/path/foo.ncml'\nimport pyncml\nnc = pyncml.apply(input_file=netcdf, ncml=ncml)\n```\n\n##### Using an NcML file\n```python\nnetcdf = '/some/file/path/in.nc'\nout    = '/some/file/path/out.nc'\nncml   = '/some/file/path/foo.ncml'\nimport pyncml\nnc = pyncml.apply(input_file=netcdf, ncml=ncml, output_file=out)\n```\n\n##### Using an NcML string\n```python\nnetcdf = '/some/file/path/in.nc'\nout    = '/some/file/path/out.nc'\nncml   = \"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n         <netcdf xmlns=\"http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2\">\n             <attribute name=\"new_attribute\" value=\"works\" />\n             <attribute name=\"new_history\" orgName=\"history\" />\n             <attribute name=\"new_file_format\" orgName=\"file_format\" value=\"New Format\" />\n             <remove name=\"source\" type=\"attribute\" />\n         </netcdf>\n         \"\"\"\nimport pyncml\nnc = pyncml.apply(input_file=netcdf, ncml=ncml, output_file=out)\n```\n\n##### Using an `etree` object\n```python\nimport pyncml\nnetcdf = '/some/file/path/in.nc'\nout    = '/some/file/path/out.nc'\nncml   = pyncml.etree.fromstring(\"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n         <netcdf xmlns=\"http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2\">\n             <attribute name=\"new_attribute\" value=\"works\" />\n         </netcdf>\n         \"\"\")\nnc = pyncml.apply(input_file=netcdf, ncml=ncml, output_file=out)\n```\n\n### Scan\n\nThe `scan` function takes in a path to an `ncml` object (string, file path, or python etree `Element` object).\n\n##### Results\n\nThe object returned from the `scan` function is a metadata object describing the scan aggregation **it is not a netcdf4-python object of the aggregation**.  You can create a `netcdf4-python` object from the scan aggregation (example below).\n\n```python\nncml   = '/some/file/path/foo.ncml'\nimport pyncml\nagg = pyncml.scan(ncml=ncml)\n\nprint(agg.starting)\n2014-06-20 00:00:00+00:00\n\nprint(agg.ending)\n2014-07-19 23:00:00+00:00\n\nprint(agg.timevar_name)\nu'time'\n\nprint(agg.standard_names)\n[\n  u'time',\n  u'projection_y_coordinate',\n  u'projection_x_coordinate',\n  u'eastward_wind_velocity'\n]\n\nprint(agg.members)  # These are already sorted by the 'starting' date\n[\n  {\n    'title':          'hello'  # Pulled from the global attraibutes 'name' or 'title'\n    'starting':       datetime.datetime(2014, 6, 20, 0, 0, tzinfo=<UTC>),\n    'ending':         datetime.datetime(2014, 6, 20, 0, 0, tzinfo=<UTC>),\n    'path':           '/path/to/aggregation/defined/in/ncml/first_member.nc'\n    'standard_names': [u'time',\n                       u'projection_y_coordinate',\n                       u'projection_x_coordinate',\n                       u'eastward_wind_velocity'],\n  },\n  {\n    'title':          'hello'  # Pulled from the global attraibutes 'name' or 'title'\n    'starting':       datetime.datetime(2014, 6, 20, 1, 0, tzinfo=<UTC>),\n    'ending':         datetime.datetime(2014, 6, 20, 1, 0, tzinfo=<UTC>),\n    'path':           '/path/to/aggregation/defined/in/ncml/second_member.nc'\n    'standard_names': [u'time',\n                       u'projection_y_coordinate',\n                       u'projection_x_coordinate',\n                       u'eastward_wind_velocity'],\n  },\n  ...\n]\n```\n\n##### Applying metadata\n\nBy default only the `scan` object in the `ncml` is used... meaning any attribute changes specified in the `ncml` **will not** be applied to individual members of the aggregation before computing the scan aggregation. If you would like each individual file to have the `ncml` applied to it (using the `apply` method documented above), set the `apply_to_members=True`. This will take longer because it is actually saving a new file with any applied `ncml` and then computing the metadata.\n\n```python\nncml   = '/some/file/path/foo.ncml'\nimport pyncml\nagg = pyncml.scan(ncml=ncml, apply_to_members=True)\n```\n\n##### CPUs\n\n`scan` will utilize all cores minus 1 (`multiprocessing.cpu_count() - 1`). If you want to configure this setting use the `cpu_count=x` parameter to `scan`.\n\n```python\nncml   = '/some/file/path/foo.ncml'\nimport pyncml\nagg = pyncml.scan(ncml=ncml, cpu_count=2)\n```\n\n\n\n##### Creating `netcdf4-python` Aggregation object\n\n<sup>**Note: This will not work with aggregations whose members overlap in time!**</sup>\n\n```python\nncml   = '/some/file/path/foo.ncml'\nimport pyncml\nagg = pyncml.scan(ncml=ncml)\nfiles = [ f.path for f in agg.members ]\nagg = netCDF4.MFDataset(files)\ntime = agg.variables.get(agg.timevar_name)\n\nprint(time)\n<class 'netCDF4._Variable'>\nfloat64 time('time',)\n    long_name: date time\n    units: hours since 1970-01-01 00:00:00\n    _CoordinateAxisType: Time\nunlimited dimensions = ('time',)\ncurrent size = (14,)\n\nprint(time[:])\n[ 389784.  389785.  389786.  389787.  389788.  389789.  389790.  389791.\n  389792.  389793.  390500.  390501.  390502.  390503.]\n\nprint(netCDF4.num2date(time[:], units=time.units))\n[datetime.datetime(2014, 6, 20, 0, 0)\n datetime.datetime(2014, 6, 20, 1, 0)\n datetime.datetime(2014, 6, 20, 2, 0)\n datetime.datetime(2014, 6, 20, 3, 0)\n datetime.datetime(2014, 6, 20, 4, 0)\n datetime.datetime(2014, 6, 20, 5, 0)\n datetime.datetime(2014, 6, 20, 6, 0)\n datetime.datetime(2014, 6, 20, 7, 0)\n datetime.datetime(2014, 6, 20, 8, 0)\n datetime.datetime(2014, 6, 20, 9, 0)\n datetime.datetime(2014, 7, 19, 20, 0)\n datetime.datetime(2014, 7, 19, 21, 0)\n datetime.datetime(2014, 7, 19, 22, 0)\n datetime.datetime(2014, 7, 19, 23, 0)]\n```\n",
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