ldexp(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Some inconsistencies with the Dask version may exist.

Returns x1 * 2**x2, element-wise.

The mantissas :None:None:`x1` and twos exponents :None:None:`x2` are used to construct floating point numbers x1 * 2**x2 .

Notes

Complex dtypes are not supported, they will raise a TypeError.

ldexp is useful as the inverse of :None:None:`frexp`, if used by itself it is more clear to simply use the expression x1 * 2**x2 .

Parameters

x1 : array_like

Array of multipliers.

x2 : array_like, int

Array of twos exponents. If x1.shape != x2.shape , they must be broadcastable to a common shape (which becomes the shape of the output).

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

This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs> .

Returns

y : ndarray or scalar

The result of x1 * 2**x2 . This is a scalar if both :None:None:`x1` and :None:None:`x2` are scalars.

This docstring was copied from numpy.ldexp.

See Also

frexp

Return (y1, y2) from x = y1 * 2**y2 , inverse to :None:None:`ldexp`.

Examples

This example is valid syntax, but we were not able to check execution

>>> np.ldexp(5, np.arange(4))  # doctest: +SKIP
array([ 5., 10., 20., 40.], dtype=float16)

This example is valid syntax, but we were not able to check execution

>>> x = np.arange(6)  # doctest: +SKIP
... np.ldexp(*np.frexp(x))  # doctest: +SKIP
array([ 0.,  1.,  2.,  3.,  4.,  5.])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.ufunc.ldexp

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