# 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.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
r"""Draw a variable versus two others from a :class:`.Dataset`.
A :class:`.ZvsXY` plot represents the variable :math:`z` with respect to
:math:`x` and :math:`y` as a surface plot, based on a set of points
:points :math:`\{x_i,y_i,z_i\}_{1\leq i \leq n}`. This interpolation is
relies on the Delaunay triangulation of :math:`\{x_i,y_i\}_{1\leq i \leq n}`
"""
from __future__ import annotations
from typing import Iterable
from typing import Sequence
import matplotlib.tri as mtri
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from gemseo.datasets.dataset import Dataset
from gemseo.post.dataset.dataset_plot import DatasetPlot
from gemseo.post.dataset.dataset_plot import VariableType
[docs]class ZvsXY(DatasetPlot):
"""Plot surface z versus x,y."""
def __init__(
self,
dataset: Dataset,
x: VariableType,
y: VariableType,
z: VariableType,
add_points: bool = False,
fill: bool = True,
levels: int | Sequence[int] | None = None,
other_datasets: Iterable[Dataset] | None = None,
) -> None:
"""
Args:
x: The name of the variable on the x-axis,
with its optional component if not ``0``,
e.g. ``("foo", 3)`` for the fourth component of the variable ``"foo"``.
y: The name of the variable on the y-axis,
with its optional component if not ``0``,
e.g. ``("bar", 3)`` for the fourth component of the variable ``"bar"``.
z: The name of the variable on the z-axis,
with its optional component if not ``0``,
e.g. ``("baz", 3)`` for the fourth component of the variable ``"baz"``.
add_points: Whether to display the entries of the dataset as points
above the surface.
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.
other_datasets: Additional datasets to be plotted as points
above the surface.
""" # noqa: D205, D212, D415
super().__init__(
dataset=dataset,
x=self._force_variable_to_tuple(x),
y=self._force_variable_to_tuple(y),
z=self._force_variable_to_tuple(z),
add_points=add_points,
other_datasets=other_datasets,
fill=fill,
levels=levels,
)
def _plot(
self,
fig: None | Figure = None,
axes: None | Axes = None,
) -> list[Figure]:
other_datasets = self._param.other_datasets
x, x_comp = self._param.x
y, y_comp = self._param.y
z, z_comp = self._param.z
n_series = 1
if other_datasets:
n_series += len(other_datasets)
self._set_color(n_series)
x_data = self.dataset.get_view(variable_names=x).to_numpy()[:, x_comp]
y_data = self.dataset.get_view(variable_names=y).to_numpy()[:, y_comp]
z_data = self.dataset.get_view(variable_names=z).to_numpy()[:, z_comp]
fig, axes = self._get_figure_and_axes(fig, axes)
grid = mtri.Triangulation(x_data, y_data)
levels = self._param.levels
options = {"cmap": self.colormap}
if levels is not None:
options["levels"] = levels
if self._param.fill:
tcf = axes.tricontourf(grid, z_data, **options)
else:
tcf = axes.tricontour(grid, z_data, **options)
if self._param.add_points:
axes.scatter(x_data, y_data, color=self.color[0])
if other_datasets:
for index, dataset in enumerate(other_datasets):
x_data = dataset.get_view(variable_names=x).to_numpy()[:, x_comp]
y_data = dataset.get_view(variable_names=y).to_numpy()[:, y_comp]
axes.scatter(x_data, y_data, color=self.color[index + 1])
if not self.xlabel:
self.xlabel = self._get_component_name(
x, x_comp, self.dataset.variable_names_to_n_components
)
if not self.ylabel:
self.ylabel = self._get_component_name(
y, y_comp, self.dataset.variable_names_to_n_components
)
if not self.zlabel:
self.zlabel = self._get_component_name(
z, z_comp, self.dataset.variable_names_to_n_components
)
if not self.title:
self.title = self.zlabel
axes.set_xlabel(self.xlabel)
axes.set_ylabel(self.ylabel)
axes.set_title(self.title)
fig.colorbar(tcf, ax=axes)
return [fig]
