amax(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)
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)
.
Input data.
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.
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.
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 :None:None:`keepdims`
will not be passed through to the amax
method of sub-classes of ndarray
, however any non-default value will be. If the sub-class' method does not implement :None:None:`keepdims`
any exceptions will be raised.
The minimum value of an output element. Must be present to allow computation on empty slice. See :None:None:`~numpy.ufunc.reduce`
for details.
Elements to compare for the maximum. See :None:None:`~numpy.ufunc.reduce`
for details.
Maximum of a
. If :None:None:`axis`
is None, the result is a scalar value. If :None:None:`axis`
is given, the result is an array of dimension a.ndim - 1
.
Return the maximum of an array or maximum along an axis.
amin
The minimum value of an array along a given axis, propagating any NaNs.
argmax
Return the indices of the maximum values.
fmax
Element-wise maximum of two arrays, ignoring any NaNs.
maximum
Element-wise maximum of two arrays, propagating any NaNs.
nanmax
The maximum value of an array along a given axis, ignoring any NaNs.
>>> a = np.arange(4).reshape((2,2))
... a array([[0, 1], [2, 3]])
>>> np.amax(a) # Maximum of the flattened array 3
>>> np.amax(a, axis=0) # Maxima along the first axis array([2, 3])
>>> np.amax(a, axis=1) # Maxima along the second axis array([1, 3])
>>> np.amax(a, where=[False, True], initial=-1, axis=0) array([-1, 3])
>>> b = np.arange(5, dtype=float)
... b[2] = np.NaN
... np.amax(b) nan
>>> np.amax(b, where=~np.isnan(b), initial=-1) 4.0
>>> np.nanmax(b) 4.0
You can use an initial value to compute the maximum of an empty slice, or to initialize it to a different value:
>>> np.amax([[-50], [10]], axis=-1, initial=0) array([ 0, 10])
Notice that the initial value is used as one of the elements for which the maximum is determined, unlike for the default argument Python's max function, which is only used for empty iterables.
>>> np.amax([5], initial=6) 6
>>> max([5], default=6) 5See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.amin
scipy.sparse._arrays.coo_array
numpy.argmax
skimage.measure.block.block_reduce
dask.array.chunk.coarsen
numpy.nanmax
scipy.sparse._coo.coo_matrix
dask.array.routines.bincount
scipy.signal._signaltools.filtfilt
numpy.nanmin
numpy.matrixlib.defmatrix.matrix.max
dask.array.reductions.max
matplotlib.axes._axes.Axes.hexbin
numpy.ma.core.minimum
scipy.signal._signaltools.correlate
dask.array.core.Array.max
matplotlib.pyplot.hexbin
numpy.linalg.linalg._multi_svd_norm
numpy.ma.core.maximum
skimage.measure.profile.profile_line
numpy.amax
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