mindspore.numpy.clip

mindspore.numpy.clip(x, xmin, xmax, dtype=None)[source]

Clips (limits) the values in an array.

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.

Parameters:

x (Tensor) – Tensor containing elements to clip.
xmin (Tensor, scalar, None) – Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of xmin and xmax may be None.
xmax (Tensor, scalar, None) – Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of xmin and xmax may be None. If xmin or xmax is a tensor, then the tensors will be broadcasted to match their shapes.
dtype (mindspore.dtype, optional) – Default: None . Overrides the dtype of the output Tensor.

Returns:

Tensor, a tensor with the elements of x, but where values < xmin are replaced with xmin, and those > xmax with xmax.

Raises:

TypeError – If inputs have types not specified above.
ValueError – If the shapes of x, xmin and xmax cannot broadcast, or both xmin and xmax are None.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, 0, 3, 2, 0])
>>> output = np.clip(x, 0, 2)
>>> print(output)
[1 2 2 0 0 2 2 0]