Python 2.7 module for robust estimation of a Fourier Series, and methods for estimating periodic signals in data.
This module contains methods to calculate the periodogram for time series data by estimating the coefficents of a discreat Fourier Series. The g-statistic is also estimated and tested to identify the periodic component that is statistically significant. This uses the robust non-uniform sampling algorithm described in Ahdesmai 2007 (Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data) and the genral pereto distribution estimates from Knijnenburg 2009 (Fewer permutations, more accurate P-values) Also uses the robust regression form scikits statsmodels (http://statsmodels.sourceforge.net/install.html)
The basic method is estSigGStat; however, many other methods are avalible.
Please excuse the spelling and poor commenting :)
creatJobList* and runPeriodEst* are used for multi-processor jobs with GOLEM
Copyright (C) 2003-2012 Institute for Systems Biology Seattle, Washington, USA.
This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
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