sqrt= <xarray.ufuncs._UFuncDispatcher object>¶
xarray specific variant of numpy.sqrt. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.
Documentation from numpy:
sqrt(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])
Return the positive square-root of an array, element-wise.
x : array_like
The values whose square-roots are required.
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 freshly-allocated 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.
For other keyword-only arguments, see the ufunc docs.
y : ndarray
An array of the same shape as x, containing the positive square-root of each element in x. If any element in x is complex, a complex array is returned (and the square-roots of negative reals are calculated). If all of the elements in x are real, so is y, with negative elements returning
nan. If out was provided, y is a reference to it.
- A version which returns complex numbers when given negative reals.
sqrt has–consistent with common convention–as its branch cut the real “interval” [-inf, 0), and is continuous from above on it. A branch cut is a curve in the complex plane across which a given complex function fails to be continuous.
>>> np.sqrt([1,4,9]) array([ 1., 2., 3.])
>>> np.sqrt([4, -1, -3+4J]) array([ 2.+0.j, 0.+1.j, 1.+2.j])
>>> np.sqrt([4, -1, numpy.inf]) array([ 2., NaN, Inf])