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Nearly Orthogonal Latin Hypercube Generator

Project description

This library allows to generate Nearly Orthogonal Latin Hypercubes (NOLH) according to Cioppa (2007) and De Rainville et al. (2012) and reference therein.


Clone the repository

$ git clone

and from the cloned directory type

$ python install

PyNOLH requires Numpy.


The library contains a single generator and a function to retrieve the necessary parameters from a desired dimensionality. To generate a 6 dimension NOLH from the indentity permutation:

import pynolh

dim = 6
m, q, r = pynolh.params(dim)
conf = range(q)
remove = range(dim - r, dim)
nolh = pynolh.nolh(conf, remove)

The NOLH returned is a numpy array with one row being one sample.

You can also produce a NOLH from a random permutation configuration vector and remove random columns:

import pynolh
import random

dim = 6
m, q, r = pynolh.params(dim)
conf = random.sample(range(q), q)
remove = random.sample(range(q), r)
nolh = pynolh.nolh(conf, remove)

The nolh() function accepts configurations with either numbers in [0 q-1] or [1 q].

import pynolh

dim = 6
m, q, r = pynolh.params(dim)
conf = range(1, q + 1)
remove = range(dim - r + 1, dim + 1)
nolh = pynolh.nolh(conf, remove)

Some prebuilt configurations are given within the library. The CONF module attribute is a dictionary with the dimension as key and a configuration, columns to remove pair as value.

import pynolh

conf, remove = pynolh.CONF[6]
nolh = pynolh.nolh(conf, remove)

The configurations for dimensions 2 to 7 are from Cioppa (2007) and 8 to 29 are from De Rainville et al. 2012.

Configuration Repository

See the Quasi Random Sequences Repository for more configurations.


Cioppa, T. M., & Lucas, T. W. (2007). Efficient nearly orthogonal and space-filling Latin hypercubes. Technometrics, 49(1).

De Rainville, F.-M., Gagné, C., Teytaud, O., & Laurendeau, D. (2012). Evolutionary optimization of low-discrepancy sequences. ACM Transactions on Modeling and Computer Simulation (TOMACS), 22(2), 9.

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