# 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.
"""A factory to post-process a :class:`.CalibrationScenario`."""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any
from gemseo.algos.opt_problem import OptimizationProblem
from gemseo.datasets.dataset import Dataset
from gemseo.post.post_factory import PostFactory
from gemseo_calibration.post_processor import CalibrationPostProcessor
LOGGER = logging.getLogger(__name__)
[docs]class CalibrationPostFactory(PostFactory):
"""A factory for calibration post-processing."""
_CLASS = CalibrationPostProcessor
_MODULE_NAMES = ("gemseo_calibration.post",)
[docs] def execute(
self,
opt_problem: str | OptimizationProblem,
reference_data: Dataset,
prior_model_data: Dataset,
posterior_model_data: Dataset,
post_name: str,
save: bool = True,
show: bool = False,
file_path: str | Path = "",
directory_path: str | Path = "",
file_name: str = "",
file_extension: str = "",
**options: Any,
) -> CalibrationPostProcessor:
"""Compute the post-processing.
Args:
opt_problem: The optimization problem containing the data to post-process.
reference_data: The reference data used during the calibration stage.
prior_model_data: The model data before the calibration stage.
posterior_model_data: The model data after the calibration stage.
post_name: The name of the post-processing method.
save: Whether to save the figure.
show: Whether to display the figure.
file_path: The path of the file to save the figures.
If the extension is missing, use ``file_extension``.
If empty,
create a file path
from ``directory_path``, ``file_name`` and ``file_extension``.
directory_path: The path of the directory to save the figures.
If empty, use the current working directory.
file_name: The name of the file to save the figures.
If empty, use a default one generated by the post-processing.
file_extension: A file extension, e.g. 'png', 'pdf', 'svg', ...
If empty, use a default file extension.
**options: The options of the post-processor.
Returns:
The executed post-processing of the optimization problem.
"""
if isinstance(opt_problem, str):
opt_problem = OptimizationProblem.from_hdf(opt_problem)
post = self.create(
post_name,
opt_problem,
reference_data,
prior_model_data,
posterior_model_data,
)
post.execute(
save=save,
show=show,
file_path=file_path or None,
directory_path=directory_path or None,
file_name=file_name or None,
file_extension=file_extension or None,
**options,
)
self.executed_post.append(post)
return post
[docs] def create(
self,
post_name: str,
opt_problem: OptimizationProblem,
reference_data: Dataset,
prior_model_data: Dataset,
posterior_model_data: Dataset,
) -> CalibrationPostProcessor:
"""Create the post-processing.
Args:
opt_problem: The optimization problem containing the data to post-process.
reference_data: The reference data used during the calibration stage.
prior_model_data: The model data before the calibration stage.
posterior_model_data: The model data after the calibration stage.
post_name: The name of the post-processing method.
Returns:
The post-processing of the optimization problem.
"""
return super().create(
post_name,
reference_data=reference_data,
prior_model_data=prior_model_data,
posterior_model_data=posterior_model_data,
opt_problem=opt_problem,
)
