# 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
seed : int
Seed to generate a random number

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

cli_parse = None

[docs]
def cli_action(args):
"""Run sampling method

Parameters
----------
args : argparse namespace
"""