# jax.numpy.compress¶

jax.numpy.compress(condition, a, axis=None, out=None)[source]

Return selected slices of an array along given axis.

LAX-backend implementation of compress(). Original docstring below.

When working along a given axis, a slice along that axis is returned in output for each index where condition evaluates to True. When working on a 1-D array, compress is equivalent to extract.

Parameters
• condition (1-D array of bools) – Array that selects which entries to return. If len(condition) is less than the size of a along the given axis, then output is truncated to the length of the condition array.

• a (array_like) – Array from which to extract a part.

• axis (int, optional) – Axis along which to take slices. If None (default), work on the flattened array.

• out (ndarray, optional) – Output array. Its type is preserved and it must be of the right shape to hold the output.

Returns

compressed_array – A copy of a without the slices along axis for which condition is false.

Return type

ndarray

ndarray.compress()

Equivalent method in ndarray

np.extract()

Equivalent method when working on 1-D arrays

ufuncs-output-type()

Examples

>>> 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])