numpy.convolve

numpy.sqrt

# numpy.clip¶

`numpy.``clip`(a, a_min, a_max, out=None, **kwargs)[source]

Clip (limit) 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.

Equivalent to but faster than `np.maximum(a_min, np.minimum(a, a_max))`. No check is performed to ensure `a_min < a_max`.

Parameters
aarray_like

Array containing elements to clip.

a_minscalar 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_maxscalar 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.

outndarray, 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.

**kwargs

For other keyword-only arguments, see the ufunc docs.

New in version 1.17.0.

Returns
clipped_arrayndarray

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

`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])
```