jax.numpy.clipΒΆ
-
jax.numpy.
clip
(a, a_min=None, a_max=None, out=None)[source]ΒΆ 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) β Array containing elements to clip.
a_min (scalar or array_like or None) β 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) β 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) β 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 β 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
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])