Source code for

# 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: Francois Gallard
#        :author: Damien Guenot
"""A parallel coordinates plot of functions and x."""

from __future__ import annotations

from typing import TYPE_CHECKING

import matplotlib
import matplotlib as mpl
from matplotlib import pyplot as plt
from numpy import array
from numpy import ndarray

from import PARULA
from import OptPostProcessor
from import OptPostProcessorOptionType
from gemseo.utils.string_tools import repr_variable

    from import Sequence

    from matplotlib.figure import Figure

[docs] class ParallelCoordinates(OptPostProcessor): """Parallel coordinates plot.""" DEFAULT_FIG_SIZE = (10.0, 5.0)
[docs] @classmethod def parallel_coordinates( cls, y_data: ndarray, x_names: Sequence[str], color_criteria: Sequence[float], ) -> Figure: """Plot the parallel coordinates. Args: y_data: The lines data to plot. x_names: The names of the abscissa. color_criteria: The values of same length as `y_data` to colorize the lines. """ _, n_cols = y_data.shape expected_shape = (len(color_criteria), len(x_names)) if y_data.shape != expected_shape: msg = ( f"The data shape {y_data.shape} is not equal " f"to the expected one {expected_shape}." ) raise ValueError(msg) x_values = list(range(n_cols)) fig = plt.figure(figsize=cls.DEFAULT_FIG_SIZE) axes = plt.gca() c_min, c_max = color_criteria.min(), color_criteria.max() s_m = cmap=PARULA, norm=mpl.colors.Normalize(vmin=c_min, vmax=c_max) ) s_m.set_array([]) color_criteria = (color_criteria - c_min) / (c_max - c_min) for i, y_values in enumerate(y_data): axes.plot(x_values, y_values, c=array(PARULA(color_criteria[i]))) for x_value in x_values: axes.axvline(x_value, linewidth=1, color="black") axes.set_xticks(x_values) axes.set_xticklabels(x_names, rotation=90) axes.grid() fig.colorbar(s_m, ax=axes) return fig
def _plot(self, **options: OptPostProcessorOptionType) -> None: problem = self.optimization_problem variable_history, variable_names, _ = self.database.get_history_array( function_names=problem.get_all_function_name() ) names_to_sizes = self.optimization_problem.design_space.variable_sizes design_names = [ repr_variable(name, i, names_to_sizes[name]) for name in self.optimization_problem.get_design_variable_names() for i in range(names_to_sizes[name]) ] output_dimension = variable_history.shape[1] - len(design_names) design_history = problem.design_space.normalize_vect( variable_history[:, output_dimension:] ) function_names = variable_names[:output_dimension] objective_index = variable_names.index(self._standardized_obj_name) objective_history = variable_history[:, objective_index] if self._change_obj: objective_history = -objective_history variable_history[:, objective_index] = objective_history function_names[objective_index] = self._obj_name obj_name = self._obj_name else: obj_name = self._standardized_obj_name fig = self.parallel_coordinates(design_history, design_names, objective_history) fig.suptitle(f"Design variables history colored by '{obj_name}' value") plt.tight_layout() self._add_figure(fig, "para_coord_des_vars") fig = self.parallel_coordinates( variable_history[:, :output_dimension], function_names, objective_history ) fig.suptitle( f"Objective function and constraints history " f"colored by '{obj_name}' value." ) plt.tight_layout() self._add_figure(fig, "para_coord_funcs")