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clip(a, a_min, a_max, out=None, **kwargs)

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 .

Notes

When :None:None:`a_min` is greater than :None:None:`a_max`, clip returns an array in which all values are equal to :None:None:`a_max`, as shown in the second example.

Parameters

a : array_like: Array containing elements to clip.
a_min, a_max : array_like or None: Minimum and maximum value. If None , clipping is not performed on the corresponding edge. Only one of :None:None:`a_min` and :None:None:`a_max` may be None . Both are broadcast against a.
out : ndarray, optional: The results will be placed in this array. It may be the input array for in-place clipping. :None:None:`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 <ufuncs.kwargs> .

versionadded

Returns

clipped_array : ndarray: An array with the elements of a, but where values < :None:None:`a_min` are replaced with :None:None:`a_min`, and those > :None:None:`a_max` with :None:None:`a_max`.

Clip (limit) the values in an array.

Examples

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

>>> np.clip(a, 1, 8)
array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])

>>> np.clip(a, 8, 1)
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

>>> np.clip(a, 3, 6, out=a)
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])

>>> 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])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.clip pandas.core.generic.NDFrame.clip skimage.restoration._denoise.denoise_bilateral skimage.restoration._denoise.denoise_wavelet dask.array.core.Array.clip

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