dask.array.frexp

dask.array.frexp

dask.array.frexp(x, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])[source]

This docstring was copied from numpy.frexp.

Some inconsistencies with the Dask version may exist.

Decompose the elements of x into mantissa and twos exponent.

Returns (mantissa, exponent), where x = mantissa * 2**exponent. The mantissa lies in the open interval(-1, 1), while the twos exponent is a signed integer.

Parameters
xarray_like

Array of numbers to be decomposed.

out1ndarray, optional

Output array for the mantissa. Must have the same shape as x.

out2ndarray, optional

Output array for the exponent. Must have the same shape as x.

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
mantissandarray

Floating values between -1 and 1. This is a scalar if x is a scalar.

exponentndarray

Integer exponents of 2. This is a scalar if x is a scalar.

See also

ldexp

Compute y = x1 * 2**x2, the inverse of frexp.

Notes

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

Examples

>>> import numpy as np  
>>> x = np.arange(9)  
>>> y1, y2 = np.frexp(x)  
>>> y1  
array([ 0.   ,  0.5  ,  0.5  ,  0.75 ,  0.5  ,  0.625,  0.75 ,  0.875,
        0.5  ])
>>> y2  
array([0, 1, 2, 2, 3, 3, 3, 3, 4])
>>> y1 * 2**y2  
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.])