pyrocko.multitrace

Multi-component waveform data model.

Classes

MultiTrace([traces, assemble, data, ...])

Container for multi-component waveforms with common time span and sampling.

class MultiTrace(traces=None, assemble='concatenate', data=None, nsamples=None, codes=None, tmin=None, deltat=None)

Bases: Object

Container for multi-component waveforms with common time span and sampling.

Instances of this class can be used to efficiently represent multi-component waveforms of a single sensor or of a sensor array. The data samples are stored in a single 2D array where the first index runs over components and the second index over time. Metadata contains sampling rate, start-time and CodesNSLCE identifiers for the contained traces.

The data is held as a NumPy numpy.ma.MaskedArray where missing or invalid data is masked.

Parameters:

traces (list of Trace) – If given, construct multi-trace from given single-component waveforms (see get_traces_data_as_array()) and ignore any other arguments.

♦ codes

list of pyrocko.squirrel.model.CodesNSLCE objects, default: []

List of codes identifying the components.

♦ nsamples

int

Number of samples.

♦ data

numpy.ndarray (pyrocko.guts_array.Array), optional

Array containing the data samples indexed as (icomponent, isample).

♦ spectrum

numpy.ndarray (pyrocko.guts_array.Array), optional

Array containing the spectral coefficients indexed as (icomponent, ifrequency).

♦ tmin

pyrocko.util.get_time_float (pyrocko.guts.Timestamp), default: str_to_time('1970-01-01 00:00:00')

Start time.

♦ deltat

float, default: 1.0

Sampling interval [s]

property summary

Textual summary of the waveform’s metadata attributes.

copy(data='copy')

Create a copy

Parameters:

data (str) – 'copy' to deeply copy the data, or 'reference' to create a shallow copy, referencing the original data.

property tmax

End time (time of last sample, read-only).

get_trace(i, span=slice(None, None, None))

Get single component waveform (shared data).

Parameters:

i (int) – Component index.

snuffle(what='valid')

Show in Snuffler.

nyquist_check(frequency, intro='Corner frequency', warn=True, raise_exception=False)

Check if a given frequency is above the Nyquist frequency of the trace.

Parameters:

• intro (str) – String used to introduce the warning/error message.

• warn (bool) – Whether to emit a warning message.

• raise_exception (bool) – Whether to raise AboveNyquist.

lfilter(b, a, demean=True)

Filter waveforms with scipy.signal.lfilter().

Sample data is converted to type float, possibly demeaned and filtered using scipy.signal.lfilter().

Parameters:

• b (float) – Numerator coefficients.

• a (float) – Denominator coefficients.

• demean (bool) – Subtract mean before filttering.

lowpass(order, corner, nyquist_warn=True, nyquist_exception=False, demean=True)

Filter waveforms using a Butterworth lowpass.

Sample data is converted to type float, possibly demeaned and filtered using scipy.signal.lfilter(). Filter coefficients are generated with scipy.signal.butter().

Parameters:

• order (int) – Order of the filter.

• corner (float) – Corner frequency of the filter [Hz].

• demean (bool) – Subtract mean before filtering.

• nyquist_warn (bool) – Warn if corner frequency is greater than Nyquist frequency.

• nyquist_exception (bool) – Raise pyrocko.trace.AboveNyquist if corner frequency is greater than Nyquist frequency.

highpass(order, corner, nyquist_warn=True, nyquist_exception=False, demean=True)

Filter waveforms using a Butterworth highpass.

Sample data is converted to type float, possibly demeaned and filtered using scipy.signal.lfilter(). Filter coefficients are generated with scipy.signal.butter().

Parameters:

• order (int) – Order of the filter.

• corner (float) – Corner frequency of the filter [Hz].

• demean (bool) – Subtract mean before filtering.

• nyquist_warn (bool) – Warn if corner frequency is greater than Nyquist frequency.

• nyquist_exception (bool) – Raise AboveNyquist if corner frequency is greater than Nyquist frequency.