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 ifx == -1
andlmbda < 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 variableSCIPY_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])