pycorrelate
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Fast and accurate cross-correlation over arbitrary time lags. Moved to:
=========== Pycorrelate
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Pycorrelate computes fast and accurate cross-correlation over
arbitrary time lags.
Cross-correlations can be calculated on "uniformly-sampled" signals
or on "point-processes", such as photon timestamps.
Pycorrelate allows computing cross-correlation at log-spaced lags covering
several orders of magnitude. This type of cross-correlation is
commonly used in physics or biophysics for techniques such as
fluorescence correlation spectroscopy (FCS <https://en.wikipedia.org/wiki/Fluorescence_correlation_spectroscopy>
) or
dynamic light scattering (DLS <https://en.wikipedia.org/wiki/Dynamic_light_scattering>
).
Two types of correlations are implemented:
-
ucorrelate <https://pycorrelate.readthedocs.io/en/latest/api.html#pycorrelate.pycorrelate.ucorrelate>
: the classical text-book linear cross-correlation between two signals defined at uniformly 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 <https://docs.scipy.org/doc/numpy/reference/generated/numpy.correlate.html#numpy.correlate>
. -
pcorrelate <https://pycorrelate.readthedocs.io/en/latest/api.html#pycorrelate.pycorrelate.pcorrelate>
: cross-correlation of discrete events in a point-process. In this case input arrays can be timestamps or positions of "events", for example photon arrival times. This function implements the algorithm inLaurence et al. Optics Letters (2006) <https://doi.org/10.1364/OL.31.000829>
. This is a generalization of the multi-tau algorithm which retains high execution speed while allowing arbitrary time-lag bins.
Pycorrelate is implemented in Python 3 and operates on standard numpy arrays.
Execution speed is optimized using numba <https://numba.pydata.org/>
__.
- Free software: GNU General Public License v3
- Documentation: https://pycorrelate.readthedocs.io.