Reading and writing time-domain data¶

Reading¶

To read a TimeSeries from ASCII:

>>> t = TimeSeries.read('data.txt')

See numpy.loadtxt() for keyword argument options.

Writing¶

To write a TimeSeries to ASCII:

>>> t.write('data.txt')

See numpy.savetxt() for keyword argument options.

Reading¶

To read data from a GWF file, pass the input file path (or paths) and the name of the data channel to read:

>>> data = TimeSeries.read('HLV-HW100916-968654552-1.gwf', 'L1:LDAS-STRAIN')

Reading a StateVector uses the same syntax:

>>> data = StateVector.read('my-state-data.gwf', 'L1:GWO-STATE_VECTOR')

Multiple files can be read by passing a list of files:

>>> data = TimeSeries.read([file1, file2], 'L1:LDAS-STRAIN')

When reading multiple files, the nproc keyword argument can be used to distribute the reading over multiple CPUs, which should make it faster:

>>> data = TimeSeries.read([file1, file2, file3, file4], 'L1:LDAS-STRAIN', nproc=2)

The above command will separate the input list of 4 file paths into two sets of 2 files, combining the results into a single TimeSeries before returning.

The start and end keyword arguments can be used to downselect data to a specific [start, end) time segment when reading:

>>> data = TimeSeries.read('HLV-HW100916-968654552-1.gwf', 'L1:LDAS-STRAIN', start=968654552.5, end=968654553)

Additionally, the following keyword arguments can be passed to manipulate the data on-the-fly when reading:

For example:

>>> data = TimeSeries.read('HLV-HW100916-968654552-1.gwf', 'L1:LDAS-STRAIN',
...                        resample=2048)

Reading multiple channels¶

To read multiple channels from one or more GWF files (rather than opening and closing the files multiple times), use the TimeSeriesDict or StateVectorDict classes, and pass a list of data channel names:

>>> data = TimeSeriesDict.read('HLV-HW100916-968654552-1.gwf', ['H1:LDAS-STRAIN', 'L1:LDAS-STRAIN'])

In this case the resample and dtype keywords can be given as a single value used for all data channels, or a dict mapping an argument for each data channel name:

>>> data = TimeSeriesDict.read('HLV-HW100916-968654552-1.gwf', ['H1:LDAS-STRAIN', 'L1:LDAS-STRAIN'],
...                            resample={'H1:LDAS-STRAIN': 2048})

The above example will resample only the 'H1:LDAS-STRAIN' TimeSeries and will not modify that for 'L1:LDAS-STRAIN'.

Writing¶

To write data held in any of the gwpy.timeseries classes to a GWF file, simply use:

>>> data.write('output.gwf')

If the output file already exists it will be overwritten.

Reading¶

To read TimeSeries or StateVector data held in HDF5 files pass the filename (or filenames) or the source, and the path of the data inside the HDF5 file:

>>> data = TimeSeries.read('HLV-HW100916-968654552-1.hdf', 'L1:LDAS-STRAIN')

As with GWF, the start and end keyword arguments can be used to downselect data to a specific [start, end) time segment when reading:

>>> data = TimeSeries.read('HLV-HW100916-968654552-1.hdf', 'L1:LDAS-STRAIN', start=968654552.5, end=968654553)

Analogously to GWF, you can read multiple TimeSeries from an HDF5 file via TimeSeriesDict.read():

>>> data = TimeSeriesDict.read('HLV-HW100916-968654552-1.hdf')

By default, all matching datasets in the file will be read, to restrict the output, specify the names of the datasets you want:

>>> data = TimeSeriesDict.read('HLV-HW100916-968654552-1.hdf', ['H1:LDAS-STRAIN', 'L1:LDAS-STRAIN'])

Writing¶

Data held in a TimeSeries, TimeSeriesDict, `StateVector, or StateVectorDict can be written to an HDF5 file via:

>>> data.write('output.hdf')

The output argument ('output.hdf') can be a file path, an open h5py.File object, or a h5py.Group object, to append data to an existing file.

If the target file already exists, an IOError will be raised, use overwrite=True to force a new file to be written.

To write a TimeSeries to an existing file, use append=True:

>>> data.write('output.hdf', append=True)

To replace an existing dataset in an existing file, while preserving other data, use both append=True and overwrite=True:

>>> data.write('output.hdf', append=True, overwrite=True)

Reading¶

To read a TimeSeries from WAV:

>>> t = TimeSeries.read('data.wav')

See scipy.io.wavfile.read() for any keyword argument options.

Writing¶

To write a TimeSeries to WAV:

>>> t.write('data.wav')

See scipy.io.wavfile.write() for keyword argument options.