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.