dask.array.nanmax¶
- dask.array.nanmax(a, axis=None, keepdims=False, split_every=None, out=None)[source]¶
Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a
RuntimeWarning
is raised and NaN is returned for that slice.This docstring was copied from numpy.nanmax.
Some inconsistencies with the Dask version may exist.
- Parameters
- aarray_like
Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted.
- axis{int, tuple of int, None}, optional
Axis or axes along which the maximum is computed. The default is to compute the maximum of the flattened array.
- outndarray, optional
Alternate output array in which to place the result. The default is
None
; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See Output type determination for more details.New in version 1.8.0.
- keepdimsbool, 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 original a.
If the value is anything but the default, then keepdims will be passed through to the max method of sub-classes of ndarray. If the sub-classes methods does not implement keepdims any exceptions will be raised.
New in version 1.8.0.
- Returns
- nanmaxndarray
An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned.
See also
nanmin
The minimum value of an array along a given axis, ignoring any NaNs.
amax
The maximum value of an array along a given axis, propagating any NaNs.
fmax
Element-wise maximum of two arrays, ignoring any NaNs.
maximum
Element-wise maximum of two arrays, propagating any NaNs.
isnan
Shows which elements are Not a Number (NaN).
isfinite
Shows which elements are neither NaN nor infinity.
amin
,fmin
,minimum
Notes
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.
If the input has a integer type the function is equivalent to np.max.
Examples
>>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nanmax(a) 3.0 >>> np.nanmax(a, axis=0) array([3., 2.]) >>> np.nanmax(a, axis=1) array([2., 3.])
When positive infinity and negative infinity are present:
>>> np.nanmax([1, 2, np.nan, np.NINF]) 2.0 >>> np.nanmax([1, 2, np.nan, np.inf]) inf