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Description:

pycorrelate 0.3

Pycorrelate

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) or
dynamic light scattering (DLS).
Two types of correlations are implemented:

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.
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).
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.

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

History

0.2.1 (2017-11-15)

Added normalization for FCS curves (see pnormalize).
Added example notebook showing how to fit a simple FCS curve
Renamed ucorrelate argument from maxlags to maxlag.
Added theory page in the documentation, showing the exact formula used for CCF calculations.

0.1.0 (2017-07-23)

First release on PyPI.