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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 than
numpy.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 in
Laurence 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/>__.
pnormalize <http://pycorrelate.readthedocs.io/en/latest/api.html#pycorrelate.pycorrelate.pnormalize>__).ucorrelate <http://pycorrelate.readthedocs.io/en/latest/api.html#pycorrelate.pycorrelate.ucorrelate>__ argument from maxlags to maxlag.theory page <http://pycorrelate.readthedocs.io/en/latest/theory.html>__ in the documentation, showing the exact formula used for CCF calculations.FAQs
Fast and accurate timestamps correlation in python.
We found that pycorrelate demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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