# Source code for gemseo.post.topology_view

# 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.
"""View of the solution of a 2D topology optimization problem."""
from __future__ import annotations

from typing import Iterable

from matplotlib import colors
from matplotlib import pyplot as plt

from gemseo.post.opt_post_processor import OptPostProcessor

[docs]class TopologyView(OptPostProcessor):
"""Visualization of the solution of a 2D topology optimization problem."""

DEFAULT_FIG_SIZE = (11.0, 6.0)

def _plot(
self,
n_x: int,
n_y: int,
observable: str = None,
iterations: int | Iterable[int] | None = None,
) -> None:
"""Plot the design variable or an observable field patch plot.

Args:
n_x: The number of elements in the horizontal direction.
n_y: The number of elements in the vertical direction.
observable: The name of the observable to be plotted.
It should be of size n_x*n_y.
iterations: The iterations of the optimization history.
If None, the last iteration is taken.
"""
if iterations is None:
iterations = [len(self.database)]
elif isinstance(iterations, int):
iterations = [iterations]
for iteration in iterations:
plt.ion()  # Ensure that redrawing is possible
design = self.database.get_x_vect(iteration)
fig, ax = plt.subplots()
if observable:
data = (
-self.database.get_function_value(observable, design)
.reshape((n_x, n_y))
.T
)
else:
data = -design.reshape((n_x, n_y)).T

im = ax.imshow(
data,
cmap="gray",
interpolation="none",
norm=colors.Normalize(vmin=-1, vmax=0),
)
im.set_array(data)
plt.axis("off")