Source code for SALib.sample.latin

import numpy as np

from . import common_args
from ..util import read_param_file, scale_samples, compute_groups_matrix


[docs]def sample(problem, N, seed=None): """Generate model inputs using Latin hypercube sampling (LHS). Returns a NumPy matrix containing the model inputs generated by Latin hypercube sampling. The resulting matrix contains N rows and D columns, where D is the number of parameters. Parameters ---------- problem : dict The problem definition N : int The number of samples to generate References ---------- .. [1] McKay, M.D., Beckman, R.J., Conover, W.J., 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245. https://doi.org/10.2307/1268522 .. [2] Iman, R.L., Helton, J.C., Campbell, J.E., 1981. An Approach to Sensitivity Analysis of Computer Models: Part I—Introduction, Input Variable Selection and Preliminary Variable Assessment. Journal of Quality Technology 13, 174–183. https://doi.org/10.1080/00224065.1981.11978748 """ num_samples = N if seed: np.random.seed(seed) groups = problem.get('groups') if groups: num_groups = len(set(groups)) G, group_names = compute_groups_matrix(groups) else: num_groups = problem['num_vars'] result = np.empty([num_samples, problem['num_vars']]) temp = np.empty([num_samples]) d = 1.0 / num_samples temp = np.array([np.random.uniform(low=sample * d, high=(sample + 1) * d, size=num_groups) for sample in range(num_samples)]) for group in range(num_groups): np.random.shuffle(temp[:, group]) for sample in range(num_samples): if groups: grouped_variables = np.where(G[:, group] == 1) result[sample, grouped_variables[0]] = temp[sample, group] else: result[sample, group] = temp[sample, group] result = scale_samples(result, problem) return result
# No additional CLI options cli_parse = None
[docs]def cli_action(args): """Run sampling method Parameters ---------- args : argparse namespace """ problem = read_param_file(args.paramfile) param_values = sample(problem, args.samples, seed=args.seed) np.savetxt(args.output, param_values, delimiter=args.delimiter, fmt='%.' + str(args.precision) + 'e')
if __name__ == "__main__": common_args.run_cli(cli_parse, cli_action)