Source code for SALib.sample.finite_diff

from typing import Dict

import numpy as np

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


[docs]def sample( problem: Dict, N: int, delta: float = 0.01, seed: int = None, skip_values: int = 1024, ) -> np.ndarray: """ Generate matrix of samples for derivative-based global sensitivity measure (dgsm). Start from a QMC (Sobol') sequence and finite difference with delta % steps Parameters ---------- problem : dict SALib problem specification N : int Number of samples delta : float Finite difference step size (percent) seed : int or None Random seed value skip_values : int How many values of the Sobol sequence to skip Returns ---------- np.array : DGSM sequence References ---------- .. [1] Sobol', I.M., Kucherenko, S., 2009. Derivative based global sensitivity measures and their link with global sensitivity indices. Mathematics and Computers in Simulation 79, 3009–3017. https://doi.org/10.1016/j.matcom.2009.01.023 .. [2] Sobol', I.M., Kucherenko, S., 2010. Derivative based global sensitivity measures. Procedia - Social and Behavioral Sciences 2, 7745–7746. https://doi.org/10.1016/j.sbspro.2010.05.208 """ if seed: np.random.seed(seed) D = problem["num_vars"] bounds = problem["bounds"] # Create base sequence - could be any type of sampling base_sequence = sobol_sequence.sample(N + skip_values, D) # scale before finite differencing base_sequence = scale_samples(base_sequence, problem) dgsm_sequence = np.empty([N * (D + 1), D]) index = 0 for i in range(skip_values, N + skip_values): # Copy the initial point dgsm_sequence[index, :] = base_sequence[i, :] index += 1 for j in range(D): temp = np.zeros(D) bnd_j = bounds[j] temp[j] = base_sequence[i, j] * delta dgsm_sequence[index, :] = base_sequence[i, :] + temp dgsm_sequence[index, j] = min(dgsm_sequence[index, j], bnd_j[1]) dgsm_sequence[index, j] = max(dgsm_sequence[index, j], bnd_j[0]) index += 1 return dgsm_sequence
[docs]def cli_parse(parser): """Add method specific options to CLI parser. Parameters ---------- parser : argparse object Returns ---------- Updated argparse object """ parser.add_argument( "-d", "--delta", type=float, required=False, default=0.01, help="Finite difference step size (percent)", ) return parser
[docs]def cli_action(args): """Run sampling method Parameters ---------- args : argparse namespace """ problem = read_param_file(args.paramfile) param_values = sample(problem, args.samples, args.delta, 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)