obspy.signal.interpolation.weighted_average_slopes

weighted_average_slopes(data, old_start, old_dt, new_start, new_dt, new_npts, *args, **kwargs)[source]

Implements the weighted average slopes interpolation scheme proposed in [Wiggins1976] for evenly sampled data. The scheme guarantees that there will be no additional extrema after the interpolation in contrast to spline interpolation.

The slope $s_{i}$ at each knot is given by a weighted average of the adjacent linear slopes $m_{i}$ and $m_{i + j}$ :

s_{i} = (w_{i} m_{i} + w_{i + 1} m_{i + 1}) / (w_{i} + w_{i + 1})

where

Missing \begin{split} or extra \end{split}

The value at each data point and the slope are then plugged into a piecewise continuous cubic polynomial used to evaluate the interpolated sample points.

Parameters

data (array_like) – Array to interpolate.
old_start (float) – The start of the array as a number.
old_dt – The time delta of the current array.
new_start (float) – The start of the interpolated array. Must be greater or equal to the current start of the array.
new_dt (float) – The desired new time delta.
new_npts (int) – The new number of samples.