Source code for RCAIDE.Framework.Optimization.Common.generate_line_plot
# RCAIDE/Framework/Optimization/Common/generate_line_plot.py
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# IMPORT
# -----------------------------------------------------------------------------------------------------------------
from RCAIDE.Framework.Core import Data
from RCAIDE.Framework.Optimization.Common.generate_carpet_plot import _fmt, _axis_label
import numpy as np
import matplotlib.pyplot as plt
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# generate_line_plot
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[docs]
def generate_line_plot(problem,
design_input_1_index = 0,
number_of_points = 5,
plot_objective = True,
plot_constraint = True,
unit_labels = None):
"""Sweeps one design variable across its bounds and produces line plots
of the objective and constraints.
Assumptions:
N/A
Source:
N/A
Inputs:
problem [Nexus] optimization problem
design_input_1_index [int] column index of the design variable to sweep
number_of_points [int] number of evaluation points along the sweep
plot_objective [bool] if True, plot the objective vs the swept variable
plot_constraint [bool] if True, plot each constraint vs the swept variable
unit_labels [list] optional unit strings aligned with problem.inputs rows,
e.g. ['m²', 'km', 'nmi']. If None, tag name only is shown.
Outputs:
outputs.inputs [array] (2, number_of_points) swept variable values
outputs.objective [array] (number_of_points,) objective values
outputs.constraint_val [array] (n_constraints, number_of_points) constraint values
Properties Used:
N/A
"""
idx0 = design_input_1_index
opt_prob = problem.optimization_problem
inp = opt_prob.inputs
names = inp[:, 0]
con_names = opt_prob.constraints[:, 0]
obj_name = opt_prob.objective[0][0]
n_con = len(con_names)
units = unit_labels if unit_labels is not None else [None] * len(names)
x = np.linspace(float(inp[idx0, 2]), float(inp[idx0, 3]), number_of_points)
obj = np.zeros(number_of_points)
con = np.zeros((n_con, number_of_points))
for i in range(number_of_points):
inp[idx0, 1] = x[i]
obj[i] = problem.objective()[0]
con[:, i] = problem.all_constraints()
x_lbl = _axis_label(names[idx0], units[idx0])
con_lbls = [_fmt(n) for n in con_names]
if plot_objective:
fig, ax = plt.subplots()
ax.plot(x, obj, lw=2)
ax.set_xlabel(x_lbl)
ax.set_ylabel(_fmt(obj_name))
fig.tight_layout()
if plot_constraint:
for i in range(n_con):
fig, ax = plt.subplots()
ax.plot(x, con[i], lw=2)
ax.axhline(0, color='gray', linewidth=1, linestyle='--')
ax.set_xlabel(x_lbl)
ax.set_ylabel(con_lbls[i])
fig.tight_layout()
plt.show(block=True)
outputs = Data()
outputs.inputs = np.vstack([x, np.zeros_like(x)])
outputs.objective = obj
outputs.constraint_val = con
return outputs
