Usage 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. Turn Csv File Into Xml Format. 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]. Mirc Download more. 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.

This software has been developed to generate Latin hypercube multivariate samples. This version runs on Linux or UNIX platforms. This manual covers the use of the LHS. A User's Guide to Sandia's Latin Hypercube Sampling Software: LHS UNIX Library Standalone Version,' Sandia (2004). Download as a PDF. Fault tree analysis software for constructing fault trees to calculate failure probabilities. Includes Binary Decision Diagrams (BDD) for exact results.