Source code for gemseo.disciplines.concatenater

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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 - API and implementation and/or documentation
#        :author: Jean-Christophe Giret
"""The concatenation of several input variables into a single one."""
from __future__ import annotations

from typing import Sequence

from numpy import concatenate
from numpy import eye
from numpy import zeros

from gemseo.core.discipline import MDODiscipline
from gemseo.utils.python_compatibility import accumulate

[docs]class Concatenater(MDODiscipline): """Concatenate input variables into a single output variable. These input variables can be scaled before concatenation. Example: >>> from gemseo.api import create_discipline >>> sellar_system_disc = create_discipline('SellarSystem') >>> constraints_names = ['c1', 'c2'] >>> output_name = ['c'] >>> concatenation_disc = create_discipline( ... 'Concatenater', constraints_names, output_name ... ) >>> disciplines = [sellar_system_disc, concatenation_disc] >>> chain = create_discipline('MDOChain', disciplines=disciplines) >>> print(chain.execute()) >>> print(chain.linearize(force_all=True)) """ def __init__( self, input_variables: Sequence[str], output_variable: str, input_coefficients: dict[str, float] = None, ) -> None: """.. # noqa: D205 D212 D415 Args: input_variables: The input variables to concatenate. output_variable: The output variable name. input_coefficients: The coefficients related to the different input variables. """ super().__init__() self.input_grammar.update(input_variables) self.output_grammar.update([output_variable]) self.__output_variable = output_variable self.__coefficients = dict.fromkeys(input_variables, 1.0) if input_coefficients: self.__coefficients.update(input_coefficients) def _run(self) -> None: """Run the discipline.""" input_data = self.get_input_data() self.local_data[self.__output_variable] = concatenate( [ self.__coefficients[input_name] * input_data[input_name] for input_name in self.get_input_data_names() ] ) def _compute_jacobian( self, inputs: Sequence[str] | None = None, outputs: Sequence[str] | None = None, ) -> None: """Compute the jacobian matrix. Args: inputs: The linearization should be performed with respect to inputs list. If None, linearization should be performed wrt all inputs (Default value = None) outputs: The linearization should be performed on outputs list. If None, linearization should be performed on all outputs (Default value = None) """ self._init_jacobian(inputs, outputs, with_zeros=True) names = self.get_input_data_names() sizes = [input.size for input in self.get_all_inputs()] total_size = self.get_inputs_asarray().size # Instead of manually accumulating, we use the accumulate() iterator. jac = self.jac[self.__output_variable] for name, size, start in zip(names, sizes, accumulate(sizes, initial=0)): val = zeros([total_size, size]) val[start : (start + size), :] = self.__coefficients[name] * eye(size) jac[name] = val