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        "author": "Stephan Hoyer",
        "author_email": "shoyer@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
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        "description": "h5netcdf\n========\n\n.. image:: https://travis-ci.org/shoyer/h5netcdf.svg?branch=master\n    :target: https://travis-ci.org/shoyer/h5netcdf\n.. image:: https://badge.fury.io/py/h5netcdf.svg\n    :target: https://pypi.python.org/pypi/h5netcdf/\n\nA Python interface for the netCDF4_ file-format that reads and writes local or\nremote HDF5 files directly via h5py_ or h5pyd_, without relying on the Unidata\nnetCDF library.\n\n.. _netCDF4: http://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html#netcdf_4_spec\n.. _h5py: http://www.h5py.org/\n.. _h5pyd: https://github.com/HDFGroup/h5pyd\n\nWhy h5netcdf?\n-------------\n\n- We've seen occasional reports of better performance with h5py than\n  netCDF4-python, though in many cases performance is identical. For\n  `one workflow`_, h5netcdf was reported to be almost **4x faster** than\n  `netCDF4-python`_.\n- It has one less massive binary dependency (netCDF C). If you already have h5py\n  installed, reading netCDF4 with h5netcdf may be much easier than installing\n  netCDF4-Python.\n- Anecdotally, HDF5 users seem to be unexcited about switching to netCDF --\n  hopefully this will convince them that the netCDF4 is actually quite sane!\n- Finally, side-stepping the netCDF C library (and Cython bindings to it)\n  gives us an easier way to identify the source of performance issues and\n  bugs.\n\n.. _one workflow: https://github.com/Unidata/netcdf4-python/issues/390#issuecomment-93864839\n.. _xarray: http://github.com/pydata/xarray/\n\nInstall\n-------\n\nEnsure you have a recent version of h5py installed (I recommend using conda_).\nAt least version 2.1 is required (for dimension scales); versions 2.3 and newer\nhave been verified to work, though some tests only pass on h5py 2.6. Then:\n``pip install h5netcdf``\n\n.. _conda: http://conda.io/\n\nUsage\n-----\n\nh5netcdf has two APIs, a new API and a legacy API. Both interfaces currently\nreproduce most of the features of the netCDF interface, with the notable\nexception of support for operations the rename or delete existing objects.\nWe simply haven't gotten around to implementing this yet. Patches\nwould be very welcome.\n\nNew API\n~~~~~~~\n\nThe new API supports direct hierarchical access of variables and groups. Its\ndesign is an adaptation of h5py to the netCDF data model. For example:\n\n.. code-block:: python\n\n    import h5netcdf\n    import numpy as np\n\n    with h5netcdf.File('mydata.nc', 'w') as f:\n        # set dimensions with a dictionary\n        f.dimensions = {'x': 5}\n        # and update them with a dict-like interface\n        # f.dimensions['x'] = 5\n        # f.dimensions.update({'x': 5})\n\n        v = f.create_variable('hello', ('x',), float)\n        v[:] = np.ones(5)\n\n        # you don't need to create groups first\n        # you also don't need to create dimensions first if you supply data\n        # with the new variable\n        v = f.create_variable('/grouped/data', ('y',), data=np.arange(10))\n\n        # access and modify attributes with a dict-like interface\n        v.attrs['foo'] = 'bar'\n\n        # you can access variables and groups directly using a hierarchical\n        # keys like h5py\n        print(f['/grouped/data'])\n\n        # add an unlimited dimension\n        f.dimensions['z'] = None\n        # explicitly resize a dimension and all variables using it\n        f.resize_dimension('z', 3)\n\nLegacy API\n~~~~~~~~~~\n\nThe legacy API is designed for compatibility with netCDF4-python_. To use it, import\n``h5netcdf.legacyapi``:\n\n.. _netCDF4-python: https://github.com/Unidata/netcdf4-python\n\n.. code-block:: python\n\n    import h5netcdf.legacyapi as netCDF4\n    # everything here would also work with this instead:\n    # import netCDF4\n    import numpy as np\n\n    with netCDF4.Dataset('mydata.nc', 'w') as ds:\n        ds.createDimension('x', 5)\n        v = ds.createVariable('hello', float, ('x',))\n        v[:] = np.ones(5)\n\n        g = ds.createGroup('grouped')\n        g.createDimension('y', 10)\n        g.createVariable('data', 'i8', ('y',))\n        v = g['data']\n        v[:] = np.arange(10)\n        v.foo = 'bar'\n        print(ds.groups['grouped'].variables['data'])\n\nThe legacy API is designed to be easy to try-out for netCDF4-python users, but it is not an\nexact match. Here is an incomplete list of functionality we don't include:\n\n- Utility functions ``chartostring``, ``num2date``, etc., that are not directly necessary\n  for writing netCDF files.\n- We don't support the ``endian`` argument to ``createVariable`` yet (see `GitHub issue`_).\n- h5netcdf variables do not support automatic masking or scaling (e.g., of values matching\n  the ``_FillValue`` attribute). We prefer to leave this functionality to client libraries\n  (e.g., xarray_), which can implement their exact desired scaling behavior.\n- No support yet for automatic resizing of unlimited dimensions with array\n  indexing. This would be a welcome pull request. For now, dimensions can be\n  manually resized with ``Group.resize_dimension(dimension, size)``.\n\n.. _GitHub issue: https://github.com/shoyer/h5netcdf/issues/15\n\nInvalid netCDF files\n~~~~~~~~~~~~~~~~~~~~\n\nh5py implements some features that do not (yet) result in valid netCDF files:\n\n- Data types:\n    - Booleans\n    - Complex values\n    - Non-string variable length types\n    - Enum types\n    - Reference types\n- Arbitrary filters:\n    - Scale-offset filters\n\nBy default [*]_, h5netcdf will not allow writing files using any of these features,\nas files with such features are not readable by other netCDF tools.\n\nHowever, these are still valid HDF5 files. If you don't care about netCDF\ncompatibility, you can use these features by setting ``invalid_netcdf=True``\nwhen creating a file:\n\n.. code-block:: python\n\n  # avoid the .nc extension for non-netcdf files\n  f = h5netcdf.File('mydata.h5', invalid_netcdf=True)\n  ...\n\n  # works with the legacy API, too, though compression options are not exposed\n  ds = h5netcdf.legacyapi.Dataset('mydata.h5', invalid_netcdf=True)\n  ...\n\n.. [*] Currently, we only issue a warning, but in a future version of h5netcdf,\n       we will raise ``h5netcdf.CompatibilityError``. Use\n       ``invalid_netcdf=False`` to switch to the new behavior now.\n\nChange Log\n----------\n\nVersion 0.7.4 (June 1, 2019):\n\n- Fixed a flakey test on Python 2.7 and 3.4.\n\nVersion 0.7.3 (May 20, 2019):\n\n- Fixed another bug that could result in reusing dimension IDs, when modifying\n  existing files.\n\nVersion 0.7.1 (Mar 16, 2019):\n\n- Fixed a bug where h5netcdf could write invalid netCDF files with reused\n  dimension IDs when dimensions are written in multiple groups.\n  netCDF-C 4.6.2 will crash when reading these files, but you can still read\n  these files with older versions of the netcdf library (or h5netcdf).\n- Updated to use version 2 of ``_NCProperties`` attribute.\n\nVersion 0.7 (Feb 26, 2019):\n\n- Support for reading and writing file-like objects (requires h5py 2.9 or\n  newer).\n  By `Scott Henderson <https://github.com/scottyhq>`_.\n\nVersion 0.6.2 (Aug 19, 2018):\n\n- Fixed a bug that prevented creating variables with the same name as\n  previously created dimensions in reopened files.\n\nVersion 0.6.1 (Jun 8, 2018):\n\n- Compression with arbitrary filters no longer triggers warnings about invalid\n  netCDF files, because this is now\n  `supported by netCDF <https://github.com/Unidata/netcdf-c/pull/399>`__.\n\nVersion 0.6 (Jun 7, 2018):\n\n- Support for reading and writing data to remote HDF5 files via the HDF5 REST\n  API using the h5pyd_ package. Any file \"path\" starting with either\n  ``http://``, ``https://``, or ``hdf5://`` will automatically trigger the use\n  of this package.\n  By `Aleksandar Jelenak <https://github.com/ajelenak-thg>`_.\n\nVersion 0.5.1 (Apr 11, 2018):\n\n- Bug fix for files with an unlimited dimension with no associated variables.\n  By `Aleksandar Jelenak <https://github.com/ajelenak-thg>`_.\n\nVersion 0.5 (Oct 17, 2017):\n\n- Support for creating unlimited dimensions.\n  By `Lion Krischer <https://github.com/krischer>`_.\n\nVersion 0.4.3 (Oct 10, 2017):\n\n- Fix test suite failure with recent versions of netCDF4-Python.\n\nVersion 0.4.2 (Sep 12, 2017):\n\n- Raise ``AttributeError`` rather than ``KeyError`` when attributes are not\n  found using the legacy API. This fixes an issue that prevented writing to\n  h5netcdf with dask.\n\nVersion 0.4.1 (Sep 6, 2017):\n\n- Include tests in source distribution on pypi.\n\nVersion 0.4 (Aug 30, 2017):\n\n- Add ``invalid_netcdf`` argument. Warnings are now issued by default when\n  writing an invalid NetCDF file. See the \"Invalid netCDF files\" section of the\n  README for full details.\n\nVersion 0.3.1 (Sep 2, 2016):\n\n- Fix garbage collection issue.\n- Add missing ``.flush()`` method for groups.\n- Allow creating dimensions of size 0.\n\nVersion 0.3.0 (Aug 7, 2016):\n\n- Datasets are now loaded lazily. This should increase performance when opening\n  files with a large number of groups and/or variables.\n- Support for writing arrays of variable length unicode strings with\n  ``dtype=str`` via the legacy API.\n- h5netcdf now writes the ``_NCProperties`` attribute for identifying netCDF4\n  files.\n\nLicense\n-------\n\n`3-clause BSD`_\n\n.. _3-clause BSD: https://github.com/shoyer/h5netcdf/blob/master/LICENSE\n\n\n",
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