# numpy.frexp

numpy.frexp(x, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'frexp'>

  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:
    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.

  Returns:
    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

  `ldexp`

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

  Notes

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

  Examples

      >>> 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.])