# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Damien Guenot
# OTHER AUTHORS - MACROSCOPIC CHANGES
# :author: Francois Gallard
"""
Run a DOE from a file containing samples values
***********************************************
"""
from __future__ import absolute_import, division, print_function, unicode_literals
from future import standard_library
from numpy import atleast_2d, loadtxt
from gemseo.algos.doe.doe_lib import DOELibrary
standard_library.install_aliases()
from gemseo import LOGGER
[docs]class CustomDOE(DOELibrary):
"""Class used for creation of DOE samples provided by user
This samples are provided as file in text or csv format.
"""
ALGO_LIST = ["CustomDOE"]
DELIMITER_KEYWORD = "delimiter"
SKIPROWS_KEYWORD = "skiprows"
DOE_FILE = "doe_file"
def __init__(self):
"""
Constructor, initializes the DOE samples
For this class of DOE library, samples are provided as file
in text or csv format. csv file format is assume to have a header
whereas text file (extension .txt) has not.
"""
super(CustomDOE, self).__init__()
self.file_dv_names_list = None
desc = {}
desc["CustomDOE"] = (
"The **CustomDOE** class is used for creation"
" of DOE samples provided by user. This samples"
" are provided as file in text or csv format."
)
for algo in self.ALGO_LIST:
self.lib_dict[algo] = {
DOELibrary.LIB: self.__class__.__name__,
DOELibrary.INTERNAL_NAME: algo,
DOELibrary.DESCRIPTION: desc[algo],
}
def _get_options(
self,
doe_file,
delimiter=",", # pylint: disable=W0221
comments="#",
skiprows=0,
eval_jac=False,
n_processes=1,
wait_time_between_samples=0.0,
**kwargs
):
"""Sets the options
:param doe_file: path and name of file
:type doe_file: str
:param delimiter: The string used to separate values.
:type delimiter: str
:param comments: the characters or list of characters used to
indicate the start of a comment
:type comments: str
:param skiprows: skip the first `skiprows` lines
:type skiprows: int
:param eval_jac: evaluate jacobian
:type eval_jac: bool
:param n_processes: number of processes
:type n_processes: int
:param wait_time_between_samples: waiting time between two samples
:type wait_time_between_samples: float
:param kwargs: additional arguments
"""
wtbs = wait_time_between_samples
return self._process_options(
doe_file=doe_file,
delimiter=delimiter,
comments=comments,
skiprows=skiprows,
eval_jac=eval_jac,
n_processes=n_processes,
wait_time_between_samples=wtbs,
**kwargs
)
[docs] def read_file(self, doe_file, delimiter=",", comments="#", skiprows=0):
"""Read a file containing a DOE
:param doe_file: path and name of file
:type doe_file: str
:param delimiter: The string used to separate values.
:type delimiter: str
:param comments: the characters or list of characters used to
indicate the start of a comment
:type comments: str
:param skiprows: skip the first `skiprows` lines
:type skiprows: int
:returns: sample (an array of samples)
:rtype: numpy array
"""
try:
samples = loadtxt(
doe_file,
comments=comments,
delimiter=delimiter,
skiprows=skiprows,
unpack=False,
)
samples = atleast_2d(samples)
if (
samples.shape[1] != self.problem.dimension
and self.problem.dimension == 1
):
samples = samples.T
except ValueError:
LOGGER.error("Failed to load DOE input file : %s", str(doe_file))
raise
self.__check_input_dv_lenght(samples)
# Normalize samples
normalize_vect = self.problem.design_space.normalize_vect
for i in range(samples.shape[0]):
samples[i, :] = normalize_vect(samples[i, :])
return samples
def __check_input_dv_lenght(self, samples):
"""
Check that file contains all variables given as design variable
at initialization
"""
dim = self.problem.dimension
if samples.shape[1] != dim:
raise ValueError(
"Mismatch between problem design variables "
+ str(dim)
+ " and doe input file dimension : "
+ str(samples.shape[1])
)
def _generate_samples(self, **options):
"""
Generates the list of x samples
:param options: the options dict for the algorithm,
see associated JSON file
"""
samples = self.read_file(
options[self.DOE_FILE],
delimiter=options[self.DELIMITER_KEYWORD],
skiprows=options[self.SKIPROWS_KEYWORD],
)
return samples
