API reference

Focal statistics

Focal statistics functions operating on array-like input data. They share functionality of window_size (determining the size of the sliding window), mask (superseding window_size and allowing for non-rectangular windows), fraction_accepted (nan-behaviour based on the fraction of available data in the input window) and reduce (switch between returning same shape as input data and patching the sliding window without overlapping, leading to a much smaller output array). focal_correlation() calculates the correlation between two arrays in contrast to the other functions that operate on a single array.

`focal_min`	Focal minimum
`focal_max`	Focal maximum
`focal_mean`	Focal mean
`focal_std`	Focal standard deviation
`focal_sum`	Focal summation
`focal_majority`	Focal majority
`focal_correlation`	Focal correlation

Rolling functions

This module additionally implements standalone rolling functions, accepting ND arrays and accepting window_size, mask and reduce similarly to the focal statistics functionality. It does however not guard against NaN occurrences specifically, staying with the raw numpy behaviour. The functions are intended to be used to define custom focal statistics functionality, potentially in higher than 2D dimensionality.

`rolling_window`(a, *[, window_size, mask, ...])	Takes an array and returns a windowed version
`rolling_sum`(a, *[, window_size, mask, reduce])	Takes an array and returns the rolling sum.
`rolling_mean`(a, *[, window_size, mask, reduce])	Takes an array and returns the rolling mean.