xarray.ufuncs.frexp(*args, **kwargs) = <xarray.ufuncs._UFuncDispatcher object>

xarray specific variant of numpy.frexp. Handles xarray.Dataset, xarray.DataArray, xarray.Variable, numpy.ndarray and dask.array.Array objects with automatic dispatching.

Documentation from numpy:

frexp(x[, out1, out2], / [, out=(None, None)], *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

Decompose the elements of x into mantissa and twos exponent.

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

  • x (array_like) – Array of numbers to be decomposed.

  • out1 (ndarray, optional) – Output array for the mantissa. Must have the same shape as x.

  • out2 (ndarray, optional) – Output array for the exponent. Must have the same shape as x.

  • 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 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.


  • mantissa (ndarray) – Floating values between -1 and 1. This is a scalar if x is a scalar.

  • exponent (ndarray) – Integer exponents of 2. This is a scalar if x is a scalar.

See also


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


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


>>> 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.])