import pyDOE2 as doe

from sklearn.preprocessing import MinMaxScaler

import pandas as pd

import random

boundary = pd.DataFrame({‘paramA’:[2,9],’paramB’:[0.4,3.2],’paramC’:[0.6,3.6],’paramD’:[0.5,6.5]})

lhs = doe.lhs(4, samples = 5, criterion = ‘center’, random_state = 1)

scaler = MinMaxScaler()

min_max = scaler.fit(boundary)

value = pd.DataFrame(min_max.inverse_transform(lhs), columns=boundary.columns)

print(value)