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multipletau
===========
|PyPI Version| |Build Status| |Coverage Status|
Multipe-tau correlation is computed on a logarithmic scale (less
data points are computed) and is thus much faster than conventional
correlation on a linear scale such as `numpy.correlate <http://docs.scipy.org/doc/numpy/reference/generated/numpy.correlate.html>`__.
Installation
------------
``multipletau`` supports Python 2.6+ and Python 3.3+ with a common codebase.
The only requirement for ``multipletau`` is `NumPy <http://www.numpy.org/>`__ (for fast
operations on arrays). Install ``multipletau`` from the Python package index:
::
pip install multipletau
Documentation
-------------
A full code reference including examples is available `here <http://FCS-analysis.github.io/multipletau/>`__.
Usage
-----
.. code:: python
import numpy as np
import multipletau
a = np.linspace(2,5,42)
v = np.linspace(1,6,42)
multipletau.correlate(a, v, m=2)
array([[ 0. , 569.56097561],
[ 1. , 549.87804878],
[ 2. , 530.37477692],
[ 4. , 491.85812017],
[ 8. , 386.39500297]])
Citing
------
The multipletau package should be cited like this
(replace "x.x.x" with the actual version of multipletau that you used
and "DD Month YYYY" with a matching date).
Paul Müller (2012) *Python multiple-tau algorithm* (Version x.x.x) [Computer program]. Available at `<https://pypi.python.org/pypi/multipletau/>`__ (Accessed DD Month YYYY)
You can find out what version you are using by typing (in a Python console):
.. code:: python
>>> import multipletau
>>> multipletau.__version__
'0.1.4'
.. |PyPI Version| image:: http://img.shields.io/pypi/v/multipletau.svg
:target: https://pypi.python.org/pypi/multipletau
.. |Build Status| image:: http://img.shields.io/travis/FCS-analysis/multipletau.svg
:target: https://travis-ci.org/FCS-analysis/multipletau
.. |Coverage Status| image:: https://img.shields.io/coveralls/FCS-analysis/multipletau.svg
:target: https://coveralls.io/r/FCS-analysis/multipletau
|
