scipy.special.boxcox1p#

scipy.special.boxcox1p(x, lmbda, out=None) = <ufunc 'boxcox1p'>#

Compute the Box-Cox transformation of 1 + x.

The Box-Cox transformation computed by boxcox1p is:

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

Returns nan if x < -1. Returns -inf if x == -1 and lmbda < 0.

Parameters:
xarray_like

Data to be transformed.

lmbdaarray_like

Power parameter of the Box-Cox transform.

outndarray, optional

Optional output array for the function values

Returns:
yscalar or ndarray

Transformed data.

Notes

Added in version 0.14.0.

boxcox1p 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 boxcox1p
>>> boxcox1p(1e-4, [0, 0.5, 1])
array([  9.99950003e-05,   9.99975001e-05,   1.00000000e-04])
>>> boxcox1p([0.01, 0.1], 0.25)
array([ 0.00996272,  0.09645476])