dask.array.sqrt
dask.array.sqrt¶
- dask.array.sqrt(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'sqrt'>¶
This docstring was copied from numpy.sqrt.
Some inconsistencies with the Dask version may exist.
Return the non-negative square-root of an array, element-wise.
- Parameters
- xarray_like
The values whose square-roots are required.
- outndarray, 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.
- wherearray_like, optional
This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default
out=None
, locations within it where the condition is False will remain uninitialized.- **kwargs
For other keyword-only arguments, see the ufunc docs.
- Returns
- yndarray
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. This is a scalar if x is a scalar.
See also
emath.sqrt
A version which returns complex numbers when given negative reals. Note that 0.0 and -0.0 are handled differently for complex inputs.
Notes
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.
Examples
>>> import numpy as np >>> 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, np.inf]) array([ 2., nan, inf])