import numpy as np
from SALib.util import read_param_file
from typing import Dict, Optional
from .. import common_args
from .radial_funcs import combine_samples
__all__ = ["sample"]
[docs]
def sample(problem: Dict, N: int, seed: Optional[int] = None):
"""Generates `N` monte carlo samples for a Radial OAT approach using a uniform distribution.
Notes
-----
Compatible with:
- :func:`SALib.analyze.radial_ee.analyze`
- :func:`SALib.analyze.sobol_jansen.analyze`
Parameters
----------
problem : dict
SALib problem specification
N : int
The number of sample sets to generate
seed : int
Seed value to use for np.random.seed
Returns
---------
numpy.ndarray : An array of samples
Usage Example
-------------
```python
>>> X = sample(problem, N, seed)
```
`X` will now hold:
[
[x_{1,1}, x_{1,2}, ..., x_{1,p}]
[b_{1,1}, x_{1,2}, ..., x_{1,p}]
[x_{1,1}, b_{1,2}, ..., x_{1,p}]
[x_{1,1}, x_{1,2}, ..., b_{1,p}]
...
[x_{N,1}, x_{N,2}, ..., x_{N,p}]
[b_{N,1}, x_{N,2}, ..., x_{N,p}]
[x_{N,1}, b_{N,2}, ..., x_{N,p}]
[x_{N,1}, x_{N,2}, ..., b_{N,p}]
]
where `p` denotes the number of parameters as specified in `problem` and
`b` represents perturbed values.
The first parameter set in each sample set acts as the baseline.
We can now run the model using the values in `X`.
The total number of model evaluations will be `N(p+1)`.
References
----------
.. [1] Campolongo, F., Saltelli, A., Cariboni, J., 2011.
From screening to quantitative sensitivity analysis: A unified approach.
Computer Physics Communications 182, 978–988.
https://www.sciencedirect.com/science/article/pii/S0010465510005321
DOI: 10.1016/j.cpc.2010.12.039
.. [2] Campolongo, F., Cariboni, J., Saltelli, A., 2007.
An effective screening design for sensitivity analysis of large models.
Environmental Modelling & Software 22, 1509–1518.
https://doi.org/10.1016/j.envsoft.2006.10.004
"""
if seed:
np.random.seed(seed)
num_vars = problem["num_vars"]
bounds = problem["bounds"]
# Generate the 'nominal' parameter positions
# Total number of parameter sets = N*(p+1)
min_bnds = [lb[0] for lb in bounds]
max_bnds = [lb[1] for lb in bounds]
baselines = np.random.uniform(min_bnds, max_bnds, size=(N * 2, num_vars))
perturb = baselines[N:]
baseline = baselines[:N]
sample_set = combine_samples(baseline, perturb)
return sample_set
# No additional CLI options
cli_parse = None
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)
