5.5. correlate

Correlation functions for multi-channel cross-correlation of seismic data.

Various routines used mostly for testing, including links to a compiled routine using FFTW, a Numpy fft routine which uses bottleneck for normalisation and a compiled time-domain routine. These have varying levels of efficiency, both in terms of overall speed, and in memory usage. The time-domain is the most memory efficient but slowest routine (although fastest for small cases of less than a few hundred correlations), the Numpy routine is fast, but memory inefficient due to a need to store large double-precision arrays for normalisation. The fftw compiled routine is fastest and more memory efficient than the Numpy routine.

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

5.5.1. Classes & Functions

fftw_multi_normxcorr	Use a C loop rather than a Python loop - in some cases this will be fast.
fftw_normxcorr	Normalised cross-correlation using the fftw library.
multichannel_normxcorr	Cross-correlate multiple channels either in parallel or not
numpy_normxcorr	Compute the normalized cross-correlation of multiple templates with data.
time_multi_normxcorr	Compute cross-correlations in the time-domain using C routine.