jax.numpy.clip¶

jax.numpy.clip(a, a_min=None, a_max=None, out=None)[source]¶

Clip (limit) the values in an array.

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

Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.

Equivalent to but faster than np.minimum(a_max, np.maximum(a, a_min)).

No check is performed to ensure a_min < a_max.

Parameters

a (array_like) – Array containing elements to clip.
a_min (scalar or array_like or None) – Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.
a_max (scalar or array_like or None) – Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None. If a_min or a_max are array_like, then the three arrays will be broadcasted to match their shapes.
out (ndarray, optional) – The results will be placed in this array. It may be the input array for in-place clipping. out must be of the right shape to hold the output. Its type is preserved.

Returns

clipped_array – An array with the elements of a, but where values < a_min are replaced with a_min, and those > a_max with a_max.

Return type

ndarray

See also

ufuncs-output-type()

Examples

>>> a = np.arange(10)
>>> np.clip(a, 1, 8)
array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, 3, 6, out=a)
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])