Metadata-Version: 1.1
Name: obspyh5
Version: 0.6.0
Summary: HDF5 write/read support for obspy
Home-page: https://github.com/trichter/obspyh5
Author: Tom Eulenfeld
Author-email: tom.eulenfeld@gmail.com
License: MIT
Description:    :target: https://doi.org/10.5281/zenodo.3953668
        
        
        Saves and writes ObsPy streams to hdf5 files.
        Stats attributes are preserved if they are numbers, strings,
        UTCDateTime objects or numpy arrays.
        It can be used as a plugin to obspy's read function to read a whole hdf5 file.
        Alternatively you can iterate over the traces in a hdf5 file with the iterh5
        function.
        
        Installation
        ^^^^^^^^^^^^
        Install h5py and obspy. After that install obspyh5 using pip by::
        
            pip install obspyh5
        
        With conda the package can be installed into a fresh environment with::
        
            conda config --add channels conda-forge
            conda create -n obsh5 numpy obspy h5py
            conda activate obsh5
            pip install obspyh5
        
        Usage
        ^^^^^
        Basic example using the obspy plugin::
        
            >>> from obspy import read
            >>> stream = read()  # load example stream
            >>> print(stream)
            ..3 Trace(s) in Stream:
            BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            BW.RJOB..EHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            BW.RJOB..EHE | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            >>> stream.write('test.h5', 'H5')  # declare 'H5' as format
            >>> print(read('test.h5'))  # order is preserved only for default index
            3 Trace(s) in Stream:
            BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            BW.RJOB..EHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            BW.RJOB..EHE | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
        
        Example iterating over traces in a huge hdf5 file. After each iteration the
        trace is not kept in memory and therefore it is possible to process a huge hdf5
        file on a PC without problems. ::
        
            >>> from obspyh5 import iterh5
            >>> for trace in iterh5('huge_in.h5')
                    trace.do_something()
                    trace.write('huge_out.h5', 'H5', mode='a')  # append mode to write into file
        
        Alternative indexing
        ^^^^^^^^^^^^^^^^^^^^
        obspyh5 supports alternative indexing. ::
        
            >>> from obspy import read
            >>> import obspyh5
            >>> print(obspyh5._INDEX)  # default index
            waveforms/{trc_num:03d}_{id}_{starttime.datetime:%Y-%m-%dT%H:%M:%S}_{duration:.1f}s
        
        The index gets populated by the stats object and the trace number when writing a trace, e.g. ::
        
            'waveforms/000_BW.RJOB..EHZ/2009-08-24T00:20:03_30.0s'
        
        To change the index use set_index. ::
        
            >>> obspyh5.set_index('flat')  # flat index wihtout trace number, writing a trace with the same metadata twice will overwrite
            >>> obspyh5.set_index('nested')  # nested index
            >>> obspyh5.set_index('xcorr')  # xcorr indexing
            >>> obspyh5.set_index('waveforms/{network}.{station}/{distance}')  # custom indexing
            >>> obspyh5.set_index('waveforms/{trc_num:03d}_{station}')  # use of the trace number
            >>> obspyh5.set_index()  # default index
        
        When using the 'xcorr' indexing stats needs the entries 'network1', 'station1',
        'location1', 'channel1', 'network2', 'station2', 'location2' and 'channel2'
        of the first and second station. An example: ::
        
            >>> from obspy import read
            >>> import obspyh5
            >>> obspyh5.set_index('xcorr')  # activate xcorr indexing
            >>> stream = read()
            >>> for i, tr in enumerate(stream):  # manipulate stats object
                    station1, station2 = 'ST1', 'ST%d' % i
                    channel1, channel2 = 'HHZ', 'HHN'
                    s = tr.stats
                    # we manipulate seed id so that important information gets
                    # printed by obspy
                    s.network, s.station = s.station1, s.channel1 = station1, channel1
                    s.location, s.channel = s.station2, s.channel2 = station2, channel2
                    s.network1 = s.network2 = 'BW'
                    s.location1 = s.location2 = ''
            >>> print(stream)
            ST1.HHZ.ST0.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            ST1.HHZ.ST1.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            ST1.HHZ.ST2.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            >>> stream.write('test_xcorr.h5', 'H5')
            >>> print(read('test_xcorr.h5'))
            ST1.HHZ.ST0.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            ST1.HHZ.ST1.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
            ST1.HHZ.ST2.HHN | 2009-08-24T00:20:03.000000Z - 2009-08-24T00:20:32.990000Z | 100.0 Hz, 3000 samples
        
        
        Note
        ^^^^
        See also ASDF_ for a more comprehensive approach.
        
        Use case: Cross-correlation of late Okhotsk coda (notebook_).
        
        .. _ASDF: https://seismic-data.org/
        
        .. _notebook: http://nbviewer.jupyter.org/github/trichter/notebooks/blob/master/cross_correlation_okhotsk_coda.ipynb
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Physics
