===========
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 textbook linear cross-correlation between two signals defined at **uniformly-spaced** intervals
  (both signals having the same interval size).
  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
  (i.e. a series of timestamps). Input arrays are 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/>`__.

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