smooth(ph,
dt_in,
dt_out,
extra=0.0,
wt=None)
 source code

Smooths a data set, simultaneously resampling to a lower sampling
rate. It uses successive boxcar averages followed by decimations for
the initial smooth, then a convolution with a Gaussian. Even if
dt_out>>dt_in , it only uses
O[log(dt_out/dt_in) operations.
 Parameters:
dt_in (float (in the same units as extra and dt_out).)  input sampling rate.
dt_out (float (in the same units as dt_in and extra).)  output sampling rate.
extra (float (in the same units as dt_in and dt_out).)  extra smoothing time constant to apply. Extra is the standard
deviation of a Gaussian kernel smooth that is applied as the last
step. This last step is not implemented efficiently, so if if
extra>>dt_out it can slow down the algorithm
substantially.
ph (numpy.ndarray .)  Normally a 1dimensional array containing data to be smoothed. If
the data is higherdimensional, the time axis is assumed to run
along axis=0, and the return value will be an array of the same
dimension.
ph (numpy.ndarray .)  None (which indicates a uniform weighting) or a numpy.ndarray that is the same length (axis
0) as ph .
wt (numpy.ndarray )
 Returns:
(rv, t0) where rv is a numpy array and
t0 it a float offset of the first
element, relative to the start of the input data.
