# -*- 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
"""
A parallel coordinates plot of functions and x
**********************************************
"""
from __future__ import absolute_import, division, unicode_literals
from os.path import splitext
import matplotlib as mpl
from future import standard_library
from matplotlib import pyplot
from numpy import array
from gemseo.post.core.colormaps import PARULA
from gemseo.post.opt_post_processor import OptPostProcessor
standard_library.install_aliases()
from gemseo import LOGGER
[docs]class ParallelCoordinates(OptPostProcessor):
"""
The **ParallelCoordinates** post processing
builds parallel coordinates plots among design
variables, outputs functions and constraints
x- and y- figure sizes can be changed in option.
It is possible either to save the plot, to show the plot or both.
"""
def _run(self, figsize_x=10, figsize_y=2, **options):
"""Visualizes the ScatterPlotMatrix
:param options: plotting options according to associated json file
:param figsize_x: X size of the figure Default value = 10
:param figsize_y: Y size of the figure Default value = 2
"""
self._plot(figsize_x, figsize_y, **options)
[docs] @staticmethod
def parallel_coordinates(y_data, x_names, color_criteria, figsize_x, figsize_y):
"""Plots parallel coordinates
:param y_data: the lines data to plot
:type y_data: array
:param x_names: names of the abscissa
:type x_names: list(str)
:param color_criteria: the list of values of same length
as y_data to colorize the lines
:type color_criteria: list(float)
: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
"""
n_x, n_cols = y_data.shape
assert n_cols == len(x_names)
assert n_x == len(color_criteria)
x_values = list(range(n_cols))
fig = pyplot.figure(figsize=(figsize_x, figsize_y))
main_ax = pyplot.gca()
c_max = color_criteria.max()
c_min = color_criteria.min()
color_criteria_n = (color_criteria - c_min) / (c_max - c_min)
for i in range(n_x):
color = array(PARULA(color_criteria_n[i])).flatten()
main_ax.plot(x_values, y_data[i, :], c=color)
norm = mpl.colors.Normalize(vmin=c_min, vmax=c_max)
cax, _ = mpl.colorbar.make_axes(main_ax)
mpl.colorbar.ColorbarBase(cax, norm=norm, cmap=PARULA)
for i in x_values:
main_ax.axvline(i, linewidth=1, color="black")
main_ax.set_xticks(x_values)
main_ax.set_xticklabels(x_names)
main_ax.grid()
return fig
def _plot(
self,
figsize_x,
figsize_y,
show=False,
save=False,
file_path="para_coord_funcs",
extension="pdf",
):
"""
Plots the ScatterPlotMatrix graph
: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()
design_variables = self.opt_problem.get_design_variable_names()
vals, vname, _ = self.database.get_history_array(all_funcs, add_dv=True)
n_x = len(self.database.get_x_by_iter(0))
x_names = []
for d_v in design_variables:
dv_size = self.opt_problem.design_space.variables_sizes[d_v]
if dv_size == 1:
x_names.append(d_v)
else:
for i in range(dv_size):
x_names.append(d_v + "_" + str(i))
func_names = vname[: len(vname) - n_x]
obj_name = self.opt_problem.get_objective_name()
obj_val = vals[:, vname.index(obj_name)]
x_vals = vals[:, vals.shape[1] - len(x_names) :]
x_vals = self.opt_problem.design_space.normalize_vect(x_vals)
fig = self.parallel_coordinates(x_vals, x_names, obj_val, figsize_x, figsize_y)
fig.suptitle(
"Design variables history colored" + " by '" + obj_name + "' value"
)
root = splitext(file_path)[0]
self._save_and_show(
fig,
save=save,
show=show,
file_path=root + "para_coord_des_vars",
extension=extension,
)
func_vals = vals[:, : vals.shape[1] - len(x_names)]
fig = self.parallel_coordinates(
func_vals, func_names, obj_val, figsize_x, figsize_y
)
fig.suptitle(
"Objective function and constraints history"
+ " colored by '"
+ obj_name
+ "' value"
)
self._save_and_show(
fig,
file_path=root + "para_coord_funcs",
save=save,
show=show,
extension=extension,
)
