dask.array.coarsen

dask.array.coarsen¶

dask.array.coarsen(reduction, x, axes, trim_excess=False, **kwargs)[source]¶

Coarsen array by applying reduction to fixed size neighborhoods

Parameters

reduction: function: Function like np.sum, np.mean, etc…
x: np.ndarray: Array to be coarsened
axes: dict: Mapping of axis to coarsening factor

Examples

>>> x = np.array([1, 2, 3, 4, 5, 6])
>>> coarsen(np.sum, x, {0: 2})
array([ 3,  7, 11])
>>> coarsen(np.max, x, {0: 3})
array([3, 6])

Provide dictionary of scale per dimension

>>> x = np.arange(24).reshape((4, 6))
>>> x
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23]])

>>> coarsen(np.min, x, {0: 2, 1: 3})
array([[ 0,  3],
       [12, 15]])

You must avoid excess elements explicitly

>>> x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
>>> coarsen(np.min, x, {0: 3}, trim_excess=True)
array([1, 4])

dask.array.cbrt

dask.array.ceil