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:

y = (x**lmbda - 1) / lmbda  if lmbda != 0
    log(x)                  if lmbda == 0

Parameters:

yarray_like: Data to be transformed.
lmbdaarray_like: Power parameter of the Box-Cox transform.
outndarray, optional: Optional output array for the function values

Returns:

xscalar or ndarray: Transformed data.

Notes

Added in version 0.16.0.

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	⚠️ no JIT	⛔
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.])