`scipy.stats._qmc:scale`

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_qmc.py:84

Signature

def scale(sample: npt.ArrayLike, l_bounds: npt.ArrayLike, u_bounds: npt.ArrayLike, *, reverse: bool = False) → np.ndarray

Summary

Sample scaling from unit hypercube to different bounds.

Extended Summary

To convert a sample from $[0, 1)$ to $[a, b), b > a$ , with $a$ the lower bounds and $b$ the upper bounds. The following transformation is used:

(b - a) \cdot sample + a

Parameters

sample : array_like (n, d): Sample to scale.
l_bounds, u_bounds : array_like (d,): Lower and upper bounds (resp. $a$ , $b$ ) of transformed data. If reverse is True, range of the original data to transform to the unit hypercube.
reverse : bool, optional: Reverse the transformation from different bounds to the unit hypercube. Default is False.

Returns

sample : array_like (n, d): Scaled sample.

Examples

Transform 3 samples in the unit hypercube to bounds:

from scipy.stats import qmc
l_bounds = [-2, 0]
u_bounds = [6, 5]
sample = [[0.5 , 0.75],
          [0.5 , 0.5],
          [0.75, 0.25]]
sample_scaled = qmc.scale(sample, l_bounds, u_bounds)
sample_scaled

✓

array([[2.  , 3.75],
       [2.  , 2.5 ],
       [4.  , 1.25]])

And convert back to the unit hypercube:

sample_ = qmc.scale(sample_scaled, l_bounds, u_bounds, reverse=True)
sample_

✓

array([[0.5 , 0.75],
       [0.5 , 0.5 ],
       [0.75, 0.25]])