API reference

Focal statistics

focal_sum(a, *, window[, fraction_accepted, ...])	Focal sum
focal_min(a, *, window[, fraction_accepted, ...])	Focal minimum.
focal_max(a, *, window[, fraction_accepted, ...])	Focal maximum.
focal_mean(a, *, window[, ...])	Focal mean
focal_std(a, *, window[, fraction_accepted, ...])	Focal standard deviation
focal_majority(a, *, window[, ...])	Focal majority.
focal_correlation(a1, a2, *, window[, ...])	Focal correlation.
focal_linear_regression(x, y, *, window[, ...])	Focal linear regression.

Grouped statistics

grouped_count(ind, v[, filtered, chunks, ...])	Compute the count of each index.
grouped_min(ind, v[, filtered, chunks, verbose])	Compute the minimum at each index.
grouped_max(ind, v[, filtered, chunks, verbose])	Compute the maximum at each index.
grouped_mean(ind, v[, filtered, chunks, ...])	Compute the mean of each index.
grouped_std(ind, v[, filtered, chunks, ...])	Compute the standard deviation at each index.
grouped_correlation(ind, v1, v2[, filtered, ...])	Compute the standard deviation at each index.
grouped_linear_regression(ind, x, y[, ...])	Compute the linear regression at each index.

Zonal statistics

zonal_count(ind, v[, verbose])	Calculate the count of each index.
zonal_min(ind, v[, verbose])	Calculate the minimum value at each index.
zonal_max(ind, v[, verbose])	Calculate the maximum value at each index.
zonal_mean(ind, v[, verbose])	Calculate the mean value in each index.
zonal_std(ind, v[, verbose])	Calculate the standard deviation at each index.
zonal_correlation(ind, v1, v2[, verbose])	Calculate the correlation coefficient between two variables in each index.
zonal_linear_regression(ind, x, y[, verbose])	Perform a linear regression in each index.

Rolling functions

rolling_window(a, *, window[, flatten, reduce])	Takes an array and returns a windowed version, similar to :stat_func:`numpy.lib.stride_tricks.as_strided`. If flatten is True, or a masked window is provided, the windowed view will be flattened, resulting in an array that has only one dimension more than the input array. This will require a copy of the data, increasing the memory usage. This can be problematic for large arrays and large window sizes.
rolling_sum(a, *, window[, reduce])	Takes an array and returns the rolling sum.
rolling_mean(a, *, window[, reduce])	Takes an array and returns the rolling mean.

Windows

Window()	Abstract base class for windows
RectangularWindow(window_size)
MaskedWindow(mask)