Module power

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 1-dimensional array containing data to be smoothed. If the data is higher-dimensional, 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.

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.array.) - a 1???-dimensionan array containing data to be smoothed. (Query: will this work for higher-dimensional data?)

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.

THIS IS WRONG! IT PROBABLY SHOULD BE something like sqrt(d**2 + hilbert_transform(d)**2). The hilbert transform just supplies the imaginary part of an analytic function. Current code leaves out the read part!