maad.util.running_mean

maad.util.running_mean(x, N, mode='nearest')[source]

Compute fast running mean for a window size N.

Parameters:

x1d ndarray of scalars

Vector

mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional,

The mode parameter determines how the input array is extended when the filter overlaps a border. Default is ‘nearest’. Behavior for each valid value is as follows:

‘reflect’ (d c b a | a b c d | d c b a): The input is extended by reflecting about the edge of the last pixel.
‘constant’ (k k k k | a b c d | k k k k): The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.
‘nearest’ (a a a a | a b c d | d d d d): The input is extended by replicating the last pixel.
‘mirror’ (d c b | a b c d | c b a): The input is extended by reflecting about the center of the last pixel.
‘wrap’ (a b c d | a b c d | a b c d): The input is extended by wrapping around to the opposite edge.

Nint

length of window to compute the mean

Returns:

x_mean1d ndarray of scalars: Vector with the same dimensions than the original variable x

Examples

>>> maad.util.running_mean([2, 8, 0, 4, 1, 9, 9, 0], N=3)
    array([4, 3, 4, 1, 4, 6, 6, 3])