scipy.special.inv_boxcox#

scipy.special.inv_boxcox(y, lmbda, out=None) = <ufunc 'inv_boxcox'>#

Compute the inverse of the Box-Cox transformation.

Find \(x\) such that

\[\begin{split}y = \begin{cases} (x^\lambda - 1) / \lambda & \text{if } \lambda \neq 0 \\ \log(x) & \text{if } \lambda = 0 \end{cases}\end{split}\]

Parameters:

yarray_like: Transformed data (input to the inverse transform).
lmbdaarray_like: Power parameter \(\lambda\) of the Box-Cox transform.
outndarray, optional: Optional output array for the function values.

Returns:

xscalar or ndarray: Original data (inverse Box-Cox transform of y).

See also

boxcox: Box-Cox transformation.
inv_boxcox1p: Inverse of the Box-Cox transformation of 1 + x.

Notes

Added in version 0.16.0.

Array API Standard Support

inv_boxcox has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	✅
PyTorch	✅	⛔
JAX	✅	⛔
Dask	✅	n/a

See Support for the array API standard for more information.

Examples

>>> from scipy.special import boxcox, inv_boxcox
>>> y = boxcox([1, 4, 10], 2.5)
>>> inv_boxcox(y, 2.5)
array([1., 4., 10.])