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
Alternatively you can install obspyh5 by downloading the source code and running:
python setup.py install
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
{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 when writing a trace, e.g.
>>> stats = read()[0].stats >>> print obspyh5._INDEX.format(**stats) '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('{newtork}.{station}/{distance}') # custom indexing
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).
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Release files for obspyh5 0.2.2
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