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        "author": "Tom Eulenfeld",
        "author_email": "tom.eulenfeld@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "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",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "Saves and writes ObsPy streams to hdf5 files.\nStats attributes are preserved if they are numbers, strings,\nUTCDateTime objects or numpy arrays.\nIt can be used as a plugin to obspy's read function to read a whole hdf5 file.\nAlternatively you can iterate over the traces in a hdf5 file with the iterh5\nfunction.\n\nInstallation\n^^^^^^^^^^^^\nInstall h5py and obspy. After that install obspyh5 using pip by::\n\n    pip install obspyh5\n\nAlternatively you can install obspyh5 by downloading the source code and\nrunning::\n\n    python setup.py install\n\nWith conda you can install into a freesh environment with::\n\n    conda config --add channels conda-forge\n    conda create -n obsh5 numpy obspy h5py\n    conda activate obsh5\n    conda install obspyh5\n\nUsage\n^^^^^\nBasic example using the obspy plugin::\n\n    >>> from obspy import read\n    >>> stream = read()  # load example stream\n    >>> print(stream)\n    ..3 Trace(s) in Stream:\n    BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    BW.RJOB..EHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    BW.RJOB..EHE | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    >>> stream.write('test.h5', 'H5')  # declare 'H5' as format\n    >>> print(read('test.h5'))  # Order is not preserved!\n    3 Trace(s) in Stream:\n    BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    BW.RJOB..EHE | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    BW.RJOB..EHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n\nExample iterating over traces in a huge hdf5 file. After each iteration the\ntrace is not kept in memory and therefore it is possible to process a huge hdf5\nfile on a PC without problems. ::\n\n    >>> from obspyh5 import iterh5\n    >>> for trace in iterh5('huge_in.h5')\n            trace.do_something()\n            trace.write('huge_out.h5', 'H5', mode='a')  # append mode to write into file\n\nAlternative indexing\n^^^^^^^^^^^^^^^^^^^^\nobspyh5 supports alternative indexing. ::\n\n    >>> from obspy import read\n    >>> import obspyh5\n    >>> print(obspyh5._INDEX)  # default index\n    waveforms/{network}.{station}/{location}.{channel}/{starttime.datetime:%Y-%m-%dT%H:%M:%S}_{endtime.datetime:%Y-%m-%dT%H:%M:%S}\n\nThe index gets populated by the stats object when writing a trace, e.g. ::\n\n    >>> stats = read()[0].stats\n    >>> print(obspyh5._INDEX.format(**stats))\n    'waveforms/BW.RJOB/.EHZ/2009-08-24T00:20:03_2009-08-24T00:20:32'\n\nTo change the index use set_index. ::\n\n    >>> obspyh5.set_index('xcorr')  # xcorr indexing\n    >>> obspyh5.set_index('waveforms/{network}.{station}/{distance}')  # custom indexing\n\nWhen using the 'xcorr' indexing stats needs the entries 'network1', 'station1',\n'location1', 'channel1', 'network2', 'station2', 'location2' and 'channel2'\nof the first and second station. An example: ::\n\n    >>> from obspy import read\n    >>> import obspyh5\n    >>> obspyh5.set_index('xcorr')  # activate xcorr indexing\n    >>> stream = read()\n    >>> for i, tr in enumerate(stream):  # manipulate stats object\n            station1, station2 = 'ST1', 'ST%d' % i\n            channel1, channel2 = 'HHZ', 'HHN'\n            s = tr.stats\n            # we manipulate seed id so that important information gets\n            # printed by obspy\n            s.network, s.station = s.station1, s.channel1 = station1, channel1\n            s.location, s.channel = s.station2, s.channel2 = station2, channel2\n            s.network1 = s.network2 = 'BW'\n            s.location1 = s.location2 = ''\n    >>> print(stream)\n    ST1.HHZ.ST0.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    ST1.HHZ.ST1.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    ST1.HHZ.ST2.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    >>> stream.write('test_xcorr.h5', 'H5')\n    >>> print(read('test_xcorr.h5'))\n    ST1.HHZ.ST0.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    ST1.HHZ.ST1.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n    ST1.HHZ.ST2.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples\n\nNote\n^^^^\nSee also ASDF_ for a more comprehensive approach.\n\nUse case: Cross-correlation of late Okhotsk coda (notebook_).\n\n.. _ASDF: https://seismic-data.org/\n\n.. _notebook: http://nbviewer.jupyter.org/github/trichter/notebooks/blob/master/cross_correlation_okhotsk_coda.ipynb",
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