dpnp.compress

dpnp.compress(condition, a, axis=None, out=None)[source]

Return selected slices of an array along given axis.

A slice of a is returned for each index along axis where condition is True.

For full documentation refer to numpy.choose.

Parameters:

condition ({array_like, dpnp.ndarray, usm_ndarray}) -- Array that selects which entries to extract. If the length of condition is less than the size of a along axis, then the output is truncated to the length of condition.
a ({dpnp.ndarray, usm_ndarray}) -- Array to extract from.
axis ({None, int}, optional) --
Axis along which to extract slices. If None, works over the flattened array.

Default: None.
out ({None, dpnp.ndarray, usm_ndarray}, optional) --
If provided, the result will be placed in this array. It should be of the appropriate shape and dtype.

Default: None.

Returns:

out -- A copy of the slices of a where condition is True.

Return type:

dpnp.ndarray

See also

dpnp.take: Take elements from an array along an axis.
dpnp.choose: Construct an array from an index array and a set of arrays to choose from.
dpnp.diag: Extract a diagonal or construct a diagonal array.
dpnp.diagonal: Return specified diagonals.
dpnp.select: Return an array drawn from elements in choicelist, depending on conditions.
dpnp.ndarray.compress: Equivalent method.
dpnp.extract: Equivalent function when working on 1-D arrays.

Examples

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4], [5, 6]])
>>> a
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.compress([0, 1], a, axis=0)
array([[3, 4]])
>>> np.compress([False, True, True], a, axis=0)
array([[3, 4],
       [5, 6]])
>>> np.compress([False, True], a, axis=1)
array([[2],
       [4],
       [6]])

Working on the flattened array does not return slices along an axis but selects elements.

>>> np.compress([False, True], a)
array([2])