obspy.signal.cross_correlation

Signal processing routines based on cross correlation techniques.

copyright

The ObsPy Development Team (devs@obspy.org)

license

GNU Lesser General Public License, Version 3 (https://www.gnu.org/copyleft/lesser.html)

Public Functions

correlate

Cross-correlation of two signals up to a specified maximal shift.

correlate_stream_template

Calculate cross-correlation of traces in stream with traces in template.

correlate_template

Normalized cross-correlation of two signals with specified mode.

correlation_detector

Detector based on the cross-correlation of waveforms.

templates_max_similarity

Compares all event templates in the streams_templates list of streams against the given stream around the time of the suspected event.

xcorr_3c

Calculates the cross correlation on each of the specified components separately, stacks them together and estimates the maximum and shift of maximum on the stack.

xcorr_max

Return shift and value of the maximum of the cross-correlation function.

xcorr_pick_correction

Calculate the correction for the differential pick time determined by cross correlation of the waveforms in narrow windows around the pick times.

Private Functions

Warning

Private functions are mainly for internal/developer use and their API might change without notice.

_calc_mean

Return trace with mean of traces in stream.

_correlate_prepared_stream_template

Calculate cross-correlation of traces in stream with traces in template.

_find_peaks

Peak finding function used for Scipy versions smaller than 1.1.

_get_item

_insert_amplitude_ratio

Insert amplitude ratio and magnitude into detections.

_pad_zeros

Pad num zeros at both sides of array a

_plot_detections

Plot detections together with similarity traces and data stream.

_prep_streams_correlate

Prepare stream and template for cross-correlation.

_similarity_detector

Detector based on the similarity of waveforms.

_window_sum

Rolling sum of data.

_xcorr_padzeros

Cross-correlation using SciPy with mode='valid' and precedent zero padding.

_xcorr_slice

Cross-correlation using SciPy with mode='full' and subsequent slicing.