- nlcpy.maximum = <ufunc 'nlcpy_maximum'>
Computes the element-wise maximum of the inputs.
Compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated.
- x1, x2array_like
Input arrays or scalars, containing the elements to be compared. If
x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
- outndarray or None, optional
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
- wherearray_like, optional
This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default
out=None, locations within it where the condition is False will remain uninitialized.
For other keyword-only arguments, see the section Optional Keyword Arguments.
The maximum of x1 and x2, element-wise. If x1 and x2 are both scalars, this function returns the result as a 0-dimension ndarray.
Computes the element-wise minimum of the inputs. Computes the element-wise minimum of the inputs.
Computes the element-wise maximum of the inputs
Returns the maximum of an array or maximum along an axis.
Returns the maximum of an array or maximum along an axis, ignoring any NaNs.
The maximum is equivalent to
nlcpy.where(x1 >= x2, x1, x2)when neither x1 nor x2 are nans, but it is faster and does proper broadcasting.
>>> import nlcpy as vp >>> vp.maximum([2, 3, 4], [1, 5, 2]) array([2, 5, 4]) >>> vp.maximum(vp.eye(2), [0.5, 2]) # broadcasting array([[1. , 2. ], [0.5, 2. ]]) >>> vp.maximum([vp.nan, 0, vp.nan], [0, vp.nan, vp.nan]) array([nan, nan, nan]) >>> vp.maximum(vp.Inf, 1) array(inf)