dask.array.clip

dask.array.clip¶

dask.array.clip(*args, **kwargs)¶

Clip (limit) the values in an array.

This docstring was copied from numpy.clip.

Some inconsistencies with the Dask version may exist.

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

aarray_like (Not supported in Dask): Array containing elements to clip.
a_min, a_maxarray_like or None: Minimum and maximum value. If None, clipping is not performed on the corresponding edge. If both a_min and a_max are None, the elements of the returned array stay the same. Both are broadcasted against a.
outndarray, optional (Not supported in Dask): 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.
min, maxarray_like or None: Array API compatible alternatives for a_min and a_max arguments. Either a_min and a_max or min and max can be passed at the same time. Default: None.

New in version 2.1.0.
**kwargs: For other keyword-only arguments, see the ufunc docs.

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.

See also

Output type determination

Notes

When a_min is greater than a_max, clip returns an array in which all values are equal to a_max, as shown in the second example.

Examples

>>> import numpy as np  
>>> 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])

dask.array.choose

dask.array.compress