obspyh5 0.4.1

安装

安装h5py和obspy。之后，使用pip by安装obspyh5：

pip install obspyh5

或者，您也可以通过下载源代码和 跑步：

python setup.py install

使用conda，您可以使用以下命令安装到自由环境中：

conda config --add channels conda-forge
conda create -n obsh5 numpy obspy h5py
conda activate obsh5
conda install obspyh5

用法

使用obspy插件的基本示例：

>>> 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

在一个巨大的hdf5文件中遍历跟踪的示例。每次迭代后 trace不保存在内存中，因此可以处理一个巨大的hdf5 在电脑上存档没有问题。

>>> 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

OBSPYH5支持交替索引。

>>> 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}

在写入跟踪时，该索引由stats对象填充，例如

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

要更改索引，请使用set_index。

>>> obspyh5.set_index('xcorr')  # xcorr indexing
>>> obspyh5.set_index('waveforms/{network}.{station}/{distance}')  # custom indexing

当使用“XCORR”标引统计信息时需要条目“NETWork1”、“Stand 1”， “位置1”、“通道1”、“网络2”、“站点2”、“位置2”和“通道2” 第一站和第二站。例如：

>>> 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

注

有关更全面的方法，请参见ASDF。

用例：鄂霍次克邦晚期密码的互相关（notebook）。