def variable_sweep(problem):
number_of_points = 5
outputs = carpet_plot(problem, number_of_points, 0,
0) #run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0, :],
inputs[1, :],
objective,
20,
linewidths=2,
cmap='jet')
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel('fuel burn (kg)')
CS_const = plt.contour(inputs[0, :],
inputs[1, :],
constraints[0, :, :],
cmap='jet')
plt.clabel(CS_const, inline=1, fontsize=10)
cbar = plt.colorbar(CS_const)
cbar.ax.set_ylabel('fuel margin')
plt.xlabel('Wing Area (m^2)')
plt.ylabel('Cruise Altitude (km)')
plt.show(block=True)
return
def variable_sweep(problem, color_label, bar_label, xlabel, ylabel, title):
number_of_points = 5
outputs = carpet_plot(problem, number_of_points, 0,
0) # run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0, :],
inputs[1, :],
objective,
20,
linewidths=2,
cmap='hot')
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel(color_label)
# cbar.ax.set_ylabel('fuel burn (kg)')
if bar_label != "unknown":
CS_const = plt.contour(inputs[0, :], inputs[1, :],
constraints[0, :, :])
plt.clabel(CS_const, inline=1, fontsize=10)
cbar = plt.colorbar(CS_const)
# cbar.ax.set_ylabel('fuel margin')
cbar.ax.set_ylabel(bar_label)
# plt.xlabel('wing area (m^2)')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# plt.ylabel('cruise_speed (km)')
'''
#now plot optimization path (note that these data points were post-processed into a plottable format)
wing_1 = [95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.2149828]
alt_1 = [11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.52829047]
wing_2 = [128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891]
alt_2 = [13.8, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334]
opt_1 = plt.plot(wing_1, alt_1, label='optimization path 1')
init_1 = plt.plot(wing_1[0], alt_1[0], 'ko')
final_1 = plt.plot(wing_1[-1], alt_1[-1], 'kx')
opt_2 = plt.plot(wing_2, alt_2, 'k--', label='optimization path 2')
init_2 = plt.plot(wing_2[0], alt_2[0], 'ko', label= 'initial points')
final_2 = plt.plot(wing_2[-1], alt_2[-1], 'kx', label= 'final points')
'''
plt.legend(loc='upper left')
plt.savefig(title + ".eps")
plt.show()
return
def variable_sweep(problem, color_label, bar_label, xlabel, ylabel, title):
number_of_points = 5
outputs = carpet_plot(problem, number_of_points, 0, 0) # run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0, :], inputs[1, :], objective, 20, linewidths=2, cmap='hot')
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel(color_label)
# cbar.ax.set_ylabel('fuel burn (kg)')
if bar_label != "unknown":
CS_const = plt.contour(inputs[0, :], inputs[1, :], constraints[0, :, :])
plt.clabel(CS_const, inline=1, fontsize=10)
cbar = plt.colorbar(CS_const)
# cbar.ax.set_ylabel('fuel margin')
cbar.ax.set_ylabel(bar_label)
# plt.xlabel('wing area (m^2)')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# plt.ylabel('cruise_speed (km)')
'''
#now plot optimization path (note that these data points were post-processed into a plottable format)
wing_1 = [95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.2149828]
alt_1 = [11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.52829047]
wing_2 = [128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891]
alt_2 = [13.8, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334]
opt_1 = plt.plot(wing_1, alt_1, label='optimization path 1')
init_1 = plt.plot(wing_1[0], alt_1[0], 'ko')
final_1 = plt.plot(wing_1[-1], alt_1[-1], 'kx')
opt_2 = plt.plot(wing_2, alt_2, 'k--', label='optimization path 2')
init_2 = plt.plot(wing_2[0], alt_2[0], 'ko', label= 'initial points')
final_2 = plt.plot(wing_2[-1], alt_2[-1], 'kx', label= 'final points')
'''
plt.legend(loc='upper left')
plt.savefig(title + ".eps")
plt.show()
return
def variable_sweep(problem):
number_of_points = 5
outputs = carpet_plot(problem, number_of_points, 0, 0) #run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0,:],inputs[1,:], objective, 20, linewidths=2)
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel('fuel burn (kg)')
CS_const = plt.contour(inputs[0,:],inputs[1,:], constraints[0,:,:])
plt.clabel(CS_const, inline=1, fontsize=10)
cbar = plt.colorbar(CS_const)
cbar.ax.set_ylabel('fuel margin')
plt.xlabel('Wing Area (m^2)')
plt.ylabel('Cruise Altitude (km)')
plt.legend(loc='upper left')
plt.show(block=True)
return
def variable_sweep(problem):
from matplotlib import rcParams
rcParams['font.family'] = 'times new roman'
# rcParams['font.times-new-roman'] = ['times new roman']
number_of_points = 5
outputs = carpet_plot(problem, number_of_points, 0,
0) #run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0, :], inputs[1, :], objective, 20, linewidths=2)
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel('Fuel Burn (kg)')
CS_const = plt.contour(inputs[0, :],
inputs[1, :],
constraints[-1, :, :],
cmap=plt.get_cmap('hot'))
plt.clabel(CS_const, inline=1, fontsize=12, family='times new roman')
cbar = plt.colorbar(CS_const)
# plt.FontProperties(family='times new roman', style='italic', size=12)
cbar.ax.set_ylabel('BOW (kg)')
# font = matplotlib.font_manager.FontProperties(family='times new roman', style='italic', size=12)
# CS_const.font_manager.FontProperties.set_family(family='times new roman')
plt.xlabel('Wing Area (m^2)')
plt.ylabel('Aspect Ratio (-)')
plt.legend(loc='upper left')
# plt.show(block=True)
plt.show()
number_of_points = 5
outputs = carpet_plot(
problem, number_of_points, 0, 0, sweep_index_0=1,
sweep_index_1=3) # run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0, :], inputs[1, :], objective, 20, linewidths=2)
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel('Fuel Burn (kg)')
CS_const = plt.contour(inputs[0, :],
inputs[1, :],
constraints[-1, :, :],
cmap=plt.get_cmap('hot'))
plt.clabel(CS_const, inline=1, fontsize=10)
cbar = plt.colorbar(CS_const)
cbar.ax.set_ylabel('BOW (kg)')
plt.xlabel('AR (-)')
plt.ylabel('Sweep Angle (Deg)')
plt.legend(loc='upper left')
plt.show()
number_of_points = 5
outputs = carpet_plot(
problem, number_of_points, 0, 0, sweep_index_0=2,
sweep_index_1=3) # run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0, :], inputs[1, :], objective, 20, linewidths=2)
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel('Fuel Burn (kg)')
CS_const = plt.contour(inputs[0, :],
inputs[1, :],
constraints[-1, :, :],
cmap=plt.get_cmap('hot'))
plt.clabel(CS_const, inline=1, fontsize=10)
cbar = plt.colorbar(CS_const)
cbar.ax.set_ylabel('BOW (kg)')
plt.xlabel('t/c (-)')
plt.ylabel('Sweep Angle (Deg)')
plt.legend(loc='upper left')
plt.show(block=True)
return
def variable_sweep(problem):
number_of_points = 5
outputs = carpet_plot(problem, number_of_points, 0,
0) #run carpet plot, suppressing default plots
inputs = outputs.inputs
objective = outputs.objective
constraints = outputs.constraint_val
plt.figure(0)
CS = plt.contourf(inputs[0, :], inputs[1, :], objective, 20, linewidths=2)
cbar = plt.colorbar(CS)
cbar.ax.set_ylabel('fuel burn (kg)')
CS_const = plt.contour(inputs[0, :], inputs[1, :], constraints[0, :, :])
plt.clabel(CS_const, inline=1, fontsize=10)
cbar = plt.colorbar(CS_const)
cbar.ax.set_ylabel('fuel margin')
plt.xlabel('wing area (m^2)')
plt.ylabel('cruise_altitude (km)')
wing_1 = np.array([
0.95, 0.9500000149011611, 0.95, 0.95, 0.9500000149011611, 0.95, 0.95,
0.9500000149011611, 0.95, 1.0830895945429801, 1.0830896094441413,
1.0830895945429801, 1.0830895945429801, 1.0830896094441413,
1.0830895945429801, 1.0830895945429801, 1.0830896094441413,
1.0830895945429801, 1.021757391325076, 1.0217574062262371,
1.021757391325076, 1.021757391325076, 1.0217574062262371,
1.021757391325076, 1.021757391325076, 1.0217574062262371,
1.021757391325076, 0.923430766276824, 0.9234307811779852,
0.923430766276824, 0.923430766276824, 0.9234307811779852,
0.923430766276824, 0.923430766276824, 0.9234307811779852,
0.923430766276824, 0.9458241453484385, 0.9458241602495997,
0.9458241453484385, 0.9458241453484385, 0.9458241602495997,
0.9458241453484385, 0.9458241453484385, 0.9458241602495997,
0.9458241453484385, 0.943053052966747, 0.9430530678679082,
0.943053052966747, 0.943053052966747, 0.9430530678679082,
0.943053052966747, 0.943053052966747, 0.9430530678679082,
0.943053052966747, 0.90000000000000002, 0.90000000000000002,
0.90000000000000002, 0.90000000000000002, 0.90000000000000002, 1.0,
1.0, 1.0, 1.0, 1.0, 1.1000000000000001, 1.1000000000000001,
1.1000000000000001, 1.1000000000000001, 1.1000000000000001, 1.2, 1.2,
1.2, 1.2, 1.2, 1.3, 1.3, 1.3, 1.3, 1.3, 0.94305305
]) * 100.
alt_1 = [
11., 11., 11.00000015, 11., 11., 11.00000015, 11., 11., 11.00000015,
9., 9., 9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015, 9., 9.,
9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015,
9., 9., 9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015, 9., 9.,
9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015,
9., 9., 9.00000015, 9., 10.25, 11.5, 12.75, 14., 9., 10.25, 11.5,
12.75, 14., 9., 10.25, 11.5, 12.75, 14., 9., 10.25, 11.5, 12.75, 14.,
9., 10.25, 11.5, 12.75, 14., 9.
]
wing_2 = np.array([
1.28, 1.2800000149011612, 1.28, 1.28, 1.2800000149011612, 1.28, 1.28,
1.2800000149011612, 1.28, 1.3, 1.3000000149011612, 1.3, 1.3,
1.3000000149011612, 1.3, 1.3, 1.3000000149011612, 1.3,
1.2512983213829585, 1.2512983362841197, 1.2512983213829585,
1.2512983213829585, 1.2512983362841197, 1.2512983213829585,
1.2512983213829585, 1.2512983362841197, 1.2512983213829585,
1.2027018036450188, 1.20270181854618, 1.2027018036450188,
1.2027018036450188, 1.20270181854618, 1.2027018036450188,
1.2027018036450188, 1.20270181854618, 1.2027018036450188,
0.9696195074510734, 0.9696195223522346, 0.9696195074510734,
0.9696195074510734, 0.9696195223522346, 0.9696195074510734,
0.9696195074510734, 0.9696195223522346, 0.9696195074510734,
0.9356100057984995, 0.9356100206996607, 0.9356100057984995,
0.9356100057984995, 0.9356100206996607, 0.9356100057984995,
0.9356100057984995, 0.9356100206996607, 0.9356100057984995,
0.9442290521247874, 0.9442290670259486, 0.9442290521247874,
0.9442290521247874, 0.9442290670259486, 0.9442290521247874,
0.9442290521247874, 0.9442290670259486, 0.9442290521247874,
0.90000000000000002, 0.90000000000000002, 0.90000000000000002,
0.90000000000000002, 0.90000000000000002, 1.0, 1.0, 1.0, 1.0, 1.0,
1.1000000000000001, 1.1000000000000001, 0.94422905
]) * 100.
alt_2 = [
13.8, 13.8, 13.80000015, 13.8, 13.8, 13.80000015, 13.8, 13.8,
13.80000015, 10.86578142, 10.86578142, 10.86578157, 10.86578142,
10.86578142, 10.86578157, 10.86578142, 10.86578142, 10.86578157, 9.,
9., 9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015, 9.26299784,
9.26299784, 9.26299799, 9.26299784, 9.26299784, 9.26299799, 9.26299784,
9.26299784, 9.26299799, 9.21358943, 9.21358943, 9.21358957, 9.21358943,
9.21358943, 9.21358957, 9.21358943, 9.21358943, 9.21358957, 9., 9.,
9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015, 9., 9., 9.00000015,
9., 9., 9.00000015, 9., 9., 9.00000015, 9., 10.25, 11.5, 12.75, 14.,
9., 10.25, 11.5, 12.75, 14., 9., 10.25, 9.
]
#now plot optimization path (note that these data points were post-processed into a plottable format)
#wing_1 = [95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.2149828]
#alt_1 = [11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.52829047]
#wing_2 = [128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891]
#alt_2 = [13.8, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334]
'''
opt_1 = plt.plot(wing_1, alt_1, label='optimization path 1')
init_1 = plt.plot(wing_1[0], alt_1[0], 'ko')
final_1 = plt.plot(wing_1[-1], alt_1[-1], 'kx')
opt_2 = plt.plot(wing_2, alt_2, 'k--', label='optimization path 2')
init_2 = plt.plot(wing_2[0], alt_2[0], 'ko', label= 'initial points')
final_2 = plt.plot(wing_2[-1], alt_2[-1], 'kx', label= 'final points')
'''
plt.legend(loc='upper left')
plt.show(block=True)
return
def variable_sweep(problem):
number_of_points=2
outputs=carpet_plot(problem, number_of_points, plot_obj = 0, plot_const = 0) #run carpet plot, suppressing default plots
return outputs
def variable_sweep(problem):
number_of_points=2
#run carpet plot, suppressing default plots
outputs=carpet_plot(problem, number_of_points, plot_obj = 0, plot_const = 0)
return outputs
