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Added function synopses to README.rst
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felixpatzelt committed Sep 28, 2017
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Expand Up @@ -4,8 +4,8 @@ scorr
Fast two- and three-point correlation analysis for time series
using spectral methods.

The methods are FFT-based for optimal performance and offer many options for
normalisation, mean removal, averaging, and zero-padding. In particular,
The calculations are FFT-based for optimal performance and offer many options
for normalisation, mean removal, averaging, and zero-padding. In particular,
averaging over pandas groups of different sizes (e.g. different days) is
supported.

Expand All @@ -16,6 +16,30 @@ The algorithms to calculate three-point correlations described in:
Journal of Statistical Mechanics (2017)
Preprint at arXiv:1706.04163


====================== ======================================================
Command Synopsis
====================== ======================================================
acorr Calculate autocorrelation or autocovariance
acorr_grouped_df Calculate acorr for pandas groups and average
corr_mat Convert correlation vector to matrix
fft2x Calculate cross-bispectrum
fftcrop Return cropped fft or correlation
get_nfft Find a good FFT segment size for pandas groups of
different sizes
padded_x3corr_norm Normalise and debias three-point cross-correlations
padded_xcorr_norm Normalise and debias two-point cross-correlations
x3corr Calculate three-point cross-correlation matrix
x3corr_grouped_df Calculate x3corr for pandas groups and average
xcorr Calculate two-point cross-correlation or covariance
xcorr_grouped_df Calculate xcorr for pandas groups and average
xcorrshift Convert xcorr output so lag zero is centered
====================== ======================================================


Please find further explanations in the docstrings and in the examples
directory.


Installation
------------
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