# -*- 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 - API and implementation and/or documentation
# :author: Francois Gallard
# :author: Damien Guenot
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Plot the derivatives of the functions
*************************************
"""
from __future__ import absolute_import, division, unicode_literals
from future import standard_library
from matplotlib import pyplot
from numpy import arange, atleast_2d
from gemseo.post.opt_post_processor import OptPostProcessor
standard_library.install_aliases()
from gemseo import LOGGER
[docs]class GradientSensitivity(OptPostProcessor):
"""
The **GradientSensitivity** post processing
builds histograms of derivatives of objective and constraints
The plot method considers the derivatives at the last iteration.
The iteration can be changed in option. The x- and y- figure sizes
can also be modified in option.
It is possible either to save the plot, to show the plot or both.
"""
def _plot(
self,
iteration=-1,
figsize_x=10,
figsize_y=10,
save=False,
show=False,
file_path="gradient_sensitivity",
extension="pdf",
):
"""
Plots the GradientSensitivity graph
:param iteration: the iteration to plot sensitivities, if negative,
use optimum
:type iteration: int
:param figsize_x: size of figure in horizontal direction (inches)
:type figsize_x: int
:param figsize_y: size of figure in vertical direction (inches)
:type figsize_y: int
:param show: if True, displays the plot windows
:type show: bool
:param save: if True, exports plot to pdf
:type save: bool
:param file_path: the base paths of the files to export
:type file_path: str
:param extension: file extension
:type extension: str
"""
all_funcs = self.opt_problem.get_all_functions_names()
if iteration == -1:
x_ref = self.opt_problem.solution.x_opt
else:
x_ref = self.opt_problem.database.get_x_by_iter(iteration)
grad_dict = {}
for func in all_funcs:
grad = self.database.get_f_of_x("@" + func, x_ref)
if grad is not None:
if len(grad.shape) == 1:
grad_dict[func] = grad
else:
n_f, _ = grad.shape
for i in range(n_f):
grad_dict[func + "_" + str(i)] = grad[i, :]
fig = self.__generate_subplots(x_ref, grad_dict, figsize_x, figsize_y)
self._save_and_show(
fig, save=save, show=show, file_path=file_path, extension=extension
)
@staticmethod
def __generate_subplots(x_ref, grad_dict, figsize_x=10, figsize_y=10):
"""
Generates the gradient sub plots from the data
:param x_ref: reference value for x
:param grad_dict : dict of gradients to plot
:param figsize_x : size of figure in horizontal direction (inches)
:param figsize_y : size of figure in vertical direction (inches)
"""
n_funcs = len(grad_dict)
if n_funcs == 0:
raise ValueError("No gradients to plot at current iteration !")
nrows = n_funcs // 2
if 2 * nrows < n_funcs:
nrows += 1
ncols = 2
fig, axes = pyplot.subplots(
nrows=nrows,
ncols=2,
sharex=True,
sharey=False,
figsize=(figsize_x, figsize_y),
)
i = 0
j = -1
axes = atleast_2d(axes)
n_subplots = len(axes) * len(axes[0])
abscissa = arange(len(x_ref))
x_labels = [r"$x_{" + str(x_id) + "}$" for x_id in abscissa]
for func, grad in sorted(grad_dict.items()):
j += 1
if j == ncols:
j = 0
i += 1
axe = axes[i][j]
axe.bar(abscissa, grad, color="blue", align="center")
axe.set_title(func)
axe.set_xticklabels(x_labels, fontsize=14)
axe.set_xticks(abscissa)
# Update y labels spacing
vis_labels = [
label for label in axe.get_yticklabels() if label.get_visible() is True
]
pyplot.setp(vis_labels[::2], visible=False)
if len(grad_dict) < n_subplots:
# xlabel must be written with the same fontsize on the 2 columns
j += 1
# if j == ncols: Seems impossible to reach
# j = 0
# i += 1
axe = axes[i][j]
axe.set_xticklabels(x_labels, fontsize=14)
axe.set_xticks(abscissa)
fig.suptitle(
"Derivatives of objective and constraints"
+ " with respect to design variables",
fontsize=14,
)
return fig
