Repository files navigation

Pycorrelate computes fast and accurate cross-correlation overarbitrary time lags.Cross-correlations can be calculated on "uniformly-sampled" signalsor on "point-processes", such as photon timestamps.Pycorrelate allows computing cross-correlation at log-spaced lags coveringseveral orders of magnitude. This type of cross-correlation iscommonly used in physics or biophysics for techniques such asfluorescence correlation spectroscopy (FCS) ordynamic light scattering (DLS).

Two types of correlations are implemented:

• ucorrelate:the classical text-book linear cross-correlation between two signalsdefined atuniformly spaced intervals.Only positive lags are computed and a max lag can be specified.Thanks to the limit in the computed lags, this function can be much faster thannumpy.correlate.
• pcorrelate:cross-correlation of discrete eventsin a point-process. In this case input arrays can be timestamps orpositions of "events", for examplephoton arrival times.This function implements the algorithm inLaurence et al. Optics Letters (2006).This is a generalization of the multi-tau algorithm which retainshigh execution speed while allowing arbitrary time-lag bins.

Pycorrelate is implemented in Python 3 and operates on standard numpy arrays.Execution speed is optimized usingnumba.

• Free software: GNU General Public License v3
• Documentation:https://pycorrelate.readthedocs.io.