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 Cross correlation of tr1 and tr2 in the time domain using window_len.
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


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.
_call_scipy_correlate Call the correct correlate function depending on Scipy version and method.
_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.
_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.