nums.numpy.max

nums.numpy.max(a, axis=None, out=None, keepdims=False, initial=None, where=None)[source]

Return the maximum of an array or maximum along an axis.

This docstring was copied from numpy.max.

Some inconsistencies with the NumS version may exist.

Parameters

  • a (BlockArray) – Input data.

  • axis (None or int or tuple of ints, optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before.

  • out (BlockArray, optional) – Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See ufuncs-output-type for more details.

  • keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the amax method of sub-classes of BlockArray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

  • initial (scalar, optional) – The minimum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.

  • where (BlockArray of bool, optional) – Elements to compare for the maximum. See ~numpy.ufunc.reduce for details.

Returns

amax – Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.

Return type

BlockArray or scalar

See also

amin

The minimum value of an array along a given axis, propagating any NaNs.

nanmax

The maximum value of an array along a given axis, ignoring any NaNs.

maximum

Element-wise maximum of two arrays, propagating any NaNs.

fmax

Element-wise maximum of two arrays, ignoring any NaNs.

argmax

Return the indices of the maximum values.

nanmin, minimum, fmin

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax.

Don’t use amax for element-wise comparison of 2 arrays; when a.shape[0] is 2, maximum(a[0], a[1]) is faster than amax(a, axis=0).

‘initial’ is currently not supported.

‘where’ is currently not supported.

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.arange(4).reshape((2,2))  
>>> a.get()  
array([[0, 1],
       [2, 3]])
>>> nps.amax(a).get()           # Maximum of the flattened array  
array(3)
>>> nps.amax(a, axis=0).get()   # Maxima along the first axis  
array([2, 3])
>>> nps.amax(a, axis=1).get()   # Maxima along the second axis  
>>> b = nps.arange(5, dtype=float)  
>>> b[2] = nps.NaN  
>>> nps.amax(b).get()  
array(nan)
>>> nps.nanmax(b).get()  
array(4.)