xarray.ufuncs.minimum¶

xarray.ufuncs.
minimum
= <xarray.ufuncs._UFuncDispatcher object>¶ xarray specific variant of numpy.minimum. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.
Documentation from numpy:
minimum(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])
Elementwise minimum of array elements.
Compare two arrays and returns a new array containing the elementwise minima. 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 minimum of x1 and x2, elementwise. Returns scalar if both x1 and x2 are scalars.
See also
maximum
 Elementwise maximum of two arrays, propagates NaNs.
fmin
 Elementwise minimum of two arrays, ignores NaNs.
amin
 The minimum value of an array along a given axis, propagates NaNs.
nanmin
 The minimum value of an array along a given axis, ignores NaNs.
fmax
,amax
,nanmax
Notes
The minimum 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.minimum([2, 3, 4], [1, 5, 2]) array([1, 3, 2])
>>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting array([[ 0.5, 0. ], [ 0. , 1. ]])
>>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan]) array([ NaN, NaN, NaN]) >>> np.minimum(np.Inf, 1) inf