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 - initial API and implementation and/or initial
#                           documentation
#        :author: Matthias De Lozzo
"""Draw surfaces from a :class:`.Dataset`.

A :class:`.Surfaces` plot represents samples
of a functional variable :math:`z(x,y)` discretized over a 2D mesh.
Both evaluations of :math:`z` and mesh are stored in a :class:`.Dataset`,
:math:`z` as a parameter and the mesh as a metadata.
from __future__ import annotations

from typing import Sequence

import matplotlib.pyplot as plt
import matplotlib.tri as mtri
from matplotlib.axes import Axes
from matplotlib.figure import Figure

from gemseo.core.dataset import Dataset
from import DatasetPlot

[docs]class Surfaces(DatasetPlot): """Plot surfaces y_i over the mesh x.""" def __init__( self, dataset: Dataset, mesh: str, variable: str, samples: Sequence[int] | None = None, add_points: bool = False, fill: bool = True, levels: int | Sequence[int] = None, ) -> None: """ Args: mesh: The name of the dataset metadata corresponding to the mesh. variable: The name of the variable for the x-axis. samples: The indices of the samples to plot. If None, plot all samples. add_points: If True then display the samples over the surface plot. fill: Whether to generate a filled contour plot. levels: Either the number of contour lines or the values of the contour lines in increasing order. If ``None``, select them automatically. """ super().__init__( dataset, mesh=mesh, variable=variable, samples=samples, add_points=add_points, fill=fill, levels=levels, ) def _plot( self, fig: None | Figure = None, axes: None | Axes = None, ) -> list[Figure]: mesh = self._param.mesh variable = self._param.variable samples = self._param.samples x_data = self.dataset.metadata[mesh][:, 0] y_data = self.dataset.metadata[mesh][:, 1] if samples is not None: samples = self.dataset[variable][samples, :] else: samples = self.dataset[variable] options = {"cmap": self.colormap} levels = self._param.levels if levels is not None: options["levels"] = levels figs = [] for sample, sample_name in zip(samples, self.dataset.row_names): fig = plt.figure(figsize=self.fig_size) axes = fig.add_subplot(1, 1, 1) triangle = mtri.Triangulation(x_data, y_data) if self._param.fill: tcf = axes.tricontourf(triangle, sample, **options) else: tcf = axes.tricontour(triangle, sample, **options) if self._param.add_points: axes.scatter(x_data, y_data, color=self.color or None) axes.set_xlabel(self.xlabel) axes.set_ylabel(self.ylabel) axes.set_title(f"{self.title or self.zlabel or variable} - {sample_name}") fig.colorbar(tcf) figs.append(fig) return figs