numpy.clip()

numpy.clip(a, a_min, a_max, out=None) [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.

Parameters:

Parameters:	a : array_like Array containing elements to clip. a_min : scalar or array_like Minimum value. a_max : scalar or array_like Maximum value. If `a_min` or `a_max` are array_like, then they will be broadcasted to the shape of `a`. 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 : ndarray An array with the elements of `a`, but where values < `a_min` are replaced with `a_min`, and those > `a_max` with `a_max`.

a : array_like

Array containing elements to clip.

a_min : scalar or array_like

Minimum value.

a_max : scalar or array_like

Maximum value. If a_min or a_max are array_like, then they will be broadcasted to the shape of a.

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 : ndarray

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

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])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.clip.html

doc_NumPy

2017-01-10 18:13:04

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