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 not preserved!
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..EHE | 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

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/{network}.{station}/{location}.{channel}/{starttime.datetime:%Y-%m-%dT%H:%M:%S}_{endtime.datetime:%Y-%m-%dT%H:%M:%S}

The index gets populated by the stats object and the trace number when writing a trace, e.g.

>>> stats = read()[0].stats
>>> print(obspyh5._INDEX.format(trc_num=0, **stats))
'waveforms/BW.RJOB/.EHZ/2009-08-24T00:20:03_2009-08-24T00:20:32'

To change the index use set_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

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).