# Source code for gemseo.post.dataset.surfaces

# -*- coding: utf-8 -*-
# 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
"""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 division, unicode_literals

from typing import List, Mapping, Optional, Sequence

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

from gemseo.post.dataset.dataset_plot import DatasetPlot

[docs]class Surfaces(DatasetPlot):
"""Plot surfaces y_i over the mesh x."""

def _plot(
self,
properties,  # type: Mapping
mesh,  # type: str
variable,  # type: str
samples=None,  # type:Optional[Sequence[int]]
add_points=False,  # type: bool
):  # type: (...) -> List[Figure]
"""
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.
"""
color = properties.get(self.COLOR) or "blue"
colormap = properties.get(self.COLORMAP) or "Blues"
x_data = self.dataset.metadata[mesh][:, 0]
y_data = self.dataset.metadata[mesh][:, 1]
if samples is not None:
outputs = self.dataset[variable][variable][samples, :]
else:
outputs = self.dataset[variable][variable]

sample = 0
fig = []
for z_data, variable_component in zip(outputs, self.dataset.row_names):
fig.append(plt.figure())
axes = fig[-1].add_subplot(1, 1, 1)
triangle = mtri.Triangulation(x_data, y_data)
tcf = axes.tricontourf(triangle, z_data, cmap=colormap)
if add_points:
axes.scatter(x_data, y_data, color=color)
axes.set_title("{} - {}".format(variable, variable_component))
fig[-1].colorbar(tcf)
fig[-1] = plt.gcf()
sample += 1
return fig