# Source code for gemseo.core.mdofunctions.function_generator

# 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
#
# 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: Francois Gallard, Charlie Vanaret
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""A class to creat MDOFunctions from MDODisciplines."""
from __future__ import annotations

import logging
from numbers import Number
from typing import Callable
from typing import Mapping
from typing import Sequence
from typing import TYPE_CHECKING
from typing import Union

from numpy import ndarray

from gemseo.core.discipline import MDODiscipline
from gemseo.core.mdofunctions.make_function import MakeFunction

if TYPE_CHECKING:
from gemseo.core.mdofunctions.mdo_function import MDOFunction

LOGGER = logging.getLogger(__name__)

OperandType = Union[ndarray, Number]
OperatorType = Callable[[OperandType, OperandType], OperandType]

[docs]class MDOFunctionGenerator:
"""Generator of :class:.MDOFunction objects from a :class:.MDODiscipline.

It creates a :class:.MDOFunction
evaluating some of the outputs of the discipline
from some of its

It uses closures to generate functions instances from a discipline execution.
"""

def __init__(
self,
discipline: MDODiscipline,
) -> None:
"""
Args:
discipline: The discipline from which the generator builds the functions.
"""
self.discipline = discipline

[docs]    def get_function(
self,
input_names: Sequence[str],
output_names: Sequence[str],
default_inputs: Mapping[str, ndarray] | None = None,
differentiable: bool = True,
) -> MDOFunction:
"""Build a function from a discipline input and output lists.

Args:
input_names: The names of the inputs of the discipline
to be inputs of the function.
output_names: The names of outputs of the discipline
to be returned by the function.
default_inputs: The default values of the inputs.
If None,
use the default values of the inputs
specified by the discipline.
differentiable: If True, then inputs and outputs are added
to the variables to be differentiated.

Returns:
The function.

Raises:
ValueError: If a given input (or output) name is not the name
of an input (or output) variable of the discipline.
"""
if isinstance(input_names, str):
input_names = [input_names]

if isinstance(output_names, str):
output_names = [output_names]

if input_names is None:
input_names = self.discipline.get_input_data_names()
if output_names is None:
output_names = self.discipline.get_output_data_names()

if not self.discipline.is_all_inputs_existing(input_names):
raise ValueError(
"Some elements of {} are not inputs of the discipline {}; "
"available inputs are: {}.".format(
input_names,
self.discipline.name,
self.discipline.get_input_data_names(),
)
)

if not self.discipline.is_all_outputs_existing(output_names):
raise ValueError(
"Some elements of {} are not outputs of the discipline {}; "
"available outputs are: {}.".format(
output_names,
self.discipline.name,
", ".join(self.discipline.get_output_data_names()),
)
)

# adds inputs and outputs to the list of variables to be
# differentiated
if differentiable: