# Source code for gemseo.post.dataset.zvsxy

# 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: 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,
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),
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)

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]