coarsen(reduction, x, axes, trim_excess=False, **kwargs)

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

Coarsen array by applying reduction to fixed size neighborhoods

Examples

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

>>> coarsen(np.max, x, {0: 3})
array([3, 6])

Provide dictionary of scale per dimension

This example is valid syntax, but we were not able to check execution

>>> 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]])

This example is valid syntax, but we were not able to check execution

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

You must avoid excess elements explicitly

This example is valid syntax, but we were not able to check execution

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.chunk.coarsen

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