xarray.ufuncs.maximum¶

xarray.ufuncs.
maximum
= <xarray.ufuncs._UFuncDispatcher object>¶ xarray specific variant of numpy.maximum. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.
Documentation from numpy:
maximum(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])
Elementwise maximum of array elements.
Compare two arrays and returns a new array containing the elementwise 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.
Parameters: x1, x2 : array_like
The arrays holding the elements to be compared. They must have the same shape, or shapes that can be broadcast to a single shape.
out : ndarray, None, or tuple of ndarray and 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 freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
**kwargs
For other keywordonly arguments, see the ufunc docs.
Returns: y : ndarray or scalar
The maximum of x1 and x2, elementwise. Returns scalar if both x1 and x2 are scalars.
See also
minimum
 Elementwise minimum of two arrays, propagates NaNs.
fmax
 Elementwise maximum of two arrays, ignores NaNs.
amax
 The maximum value of an array along a given axis, propagates NaNs.
nanmax
 The maximum value of an array along a given axis, ignores NaNs.
fmin
,amin
,nanmin
Notes
The maximum is equivalent to
np.where(x1 >= x2, x1, x2)
when neither x1 nor x2 are nans, but it is faster and does proper broadcasting.Examples
>>> np.maximum([2, 3, 4], [1, 5, 2]) array([2, 5, 4])
>>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting array([[ 1. , 2. ], [ 0.5, 2. ]])
>>> np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan]) array([ NaN, NaN, NaN]) >>> np.maximum(np.Inf, 1) inf