from pygem import FFDParameters, FFD, StlHandler
params = FFDParameters()
params.read_parameters(
filename='PyGem/tests/test_datasets/parameters_test_ffd_sphere.prm')
stl_handler = StlHandler()
mesh_points = stl_handler.parse('PyGem/tests/test_datasets/test_sphere.stl')
fig = stl_handler.plot(plot_file='PyGem/tests/test_datasets/test_sphere.stl')
free_form = FFD(params, mesh_points)
free_form.perform()
new_mesh_points = free_form.modified_mesh_points
stl_handler.write(new_mesh_points, 'test_sphere_mod.stl')
fig = stl_handler.plot(plot_file='test_sphere_mod.stl')
def wheelcase_FitnessFunc(pop):
def downscaleXandZ(value, max_orig, min_orig, max_goal, min_goal):
return (value - min_orig) / (max_orig - min_orig) * (
max_goal - min_goal) + min_goal
# os.system('. /usr/local/stella/OpenFOAM/OpenFOAM-2.4.0/bin/tools/RunFunctions')
fitness_values = []
case_folder = 'domain/wheelcase/hpc1_velo_changed'
home_dir = '/home/sascha/'
# TODO: adapt Fitnessfunction for wheelcase
params_left = FFDParameters()
params_right = FFDParameters()
# stl_handler_wheel = StlHandler()
# stl_handler_velo = StlHandler()
# stl_handler_top = StlHandler()
stl_handler_all = StlHandler()
params_left.read_parameters(
filename='domain/wheelcase/ffd/ffd_config_left.prm'
) # Dummy config file
params_right.read_parameters(
filename='domain/wheelcase/ffd/ffd_config_right.prm')
# pop.to_csv('domain/wheelcase/pop.csv',mode='a+',index=False,header=False)
# WORKAROUND because of OpenFOAM error when faces of mesh intersect -> x and z deformation downscale
# TODO: Teste ob hier die pop Werte schon skalierst sind
# print("pop_before")
# print(pop)
# pop[0] = pop[0].apply(lambda x : downscaleXandZ(x,0,-2,0.2,-0.2))
# pop[1] = pop[1].apply(lambda x : downscaleXandZ(x,0,-2,0.2,-0.2))
# pop[4] = pop[4].apply(lambda x : downscaleXandZ(x,0,-2,0.2,-0.2))
# pop[5] = pop[5].apply(lambda x : downscaleXandZ(x,0,-2,0.2,-0.2))
pop.to_csv('domain/wheelcase/pop.csv',
mode='a+',
index=False,
header=False)
# print("pop")
# print(pop)
# Generate config file for each sample
# print("len(pop)")
# print(len(pop))
for i in range(len(pop)):
# Change displacements (weights) of each sample
sample = pop.iloc[i] # get sample (1x6)
# add penalty if abs(bottom ffd value ) < 1.5
# and abs(top ffd value) < 0.2
if (sample.iloc[2:4].to_numpy().reshape((1, 2))[0, 0] > -1.5
or sample.iloc[2:4].to_numpy().reshape((1, 2))[0, 1] > -0.2):
fitness_values.append(-1)
else:
fitness_values.append(0)
mu_x = []
mu_x_right = []
mu_y = []
mu_y_right = []
mu_z = []
mu_z_right = []
tmp_list = []
x = sample.iloc[:2].to_numpy().reshape((1, 2))
# for 6 genomes
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_x.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([x[0, 0], x[0, 1]])
mu_x.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_x.append(tmp_list.copy())
del tmp_list[:]
# for 18 genomes
# tmp_list.append([0,0])
# tmp_list.append([x[0,0],x[0,1]])
# mu_x.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([0,0])
# tmp_list.append([x[1,0],x[1,1]])
# mu_x.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([0,0])
# tmp_list.append([x[2,0],x[2,1]])
# mu_x.append(tmp_list.copy())
# del tmp_list[:]
# for 36 genomes
# mu_x.append(x[0:2])
# mu_x.append(x[2:4])
# mu_x.append(x[4:6])
# print(mu_x)
# print(mu_x)
params_left.array_mu_x = mu_x
# for right side: different coords
# Reihenfolge
# 2,3,0,1,6,7,4,5,10,11,8,9
# for 36 genomes
# tmp_list.append([x[1,0],x[1,1]])
# tmp_list.append([x[0,0],x[0,1]])
# mu_x_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([x[3,0],x[3,1]])
# tmp_list.append([x[2,0],x[2,1]])
# mu_x_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([x[5,0],x[5,1]])
# tmp_list.append([x[4,0],x[4,1]])
# mu_x_right.append(tmp_list.copy())
# del tmp_list[:]
# for 6 genomes
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_x_right.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([x[0, 0], x[0, 1]])
tmp_list.append([0, 0])
mu_x_right.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_x_right.append(tmp_list.copy())
del tmp_list[:]
# for 18 genomes
# tmp_list.append([x[0,0],x[0,1]])
# tmp_list.append([0,0])
# mu_x_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([x[1,0],x[1,1]])
# tmp_list.append([0,0])
# mu_x_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([x[2,0],x[2,1]])
# tmp_list.append([0,0])
# mu_x_right.append(tmp_list.copy())
# del tmp_list[:]
params_right.array_mu_x = mu_x_right
y = sample.iloc[2:4].to_numpy().reshape((1, 2))
# for 36 genomes
# mu_y.append(y[0:2])
# mu_y.append(y[2:4])
# mu_y.append(y[4:6])
# for 6 genomes
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_y.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([y[0, 0], y[0, 1]])
mu_y.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_y.append(tmp_list.copy())
del tmp_list[:]
# for 18 genomes
# tmp_list.append([0,0])
# tmp_list.append([y[0,0],y[0,1]])
# mu_y.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([0,0])
# tmp_list.append([y[1,0],y[1,1]])
# mu_y.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([0,0])
# tmp_list.append([y[2,0],y[2,1]])
# mu_y.append(tmp_list.copy())
# del tmp_list[:]
params_left.array_mu_y = mu_y
# for 6 genomes
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_y_right.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([-y[0, 0], -y[0, 1]])
tmp_list.append([0, 0])
mu_y_right.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_y_right.append(tmp_list.copy())
del tmp_list[:]
# for 18 genomes
# tmp_list.append([-y[0,0],-y[0,1]])
# tmp_list.append([0,0])
# mu_y_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([-y[1,0],-y[1,1]])
# tmp_list.append([0,0])
# mu_y_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([-y[2,0],-y[2,1]])
# tmp_list.append([0,0])
# mu_y_right.append(tmp_list.copy())
# del tmp_list[:]
params_right.array_mu_y = mu_y_right
z = sample.iloc[4:6].to_numpy().reshape((1, 2))
# mu_z.append(z[0:2])
# mu_z.append(z[2:4])
# mu_z.append(z[4:6])
# for 6 genomes
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_z.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([z[0, 0], z[0, 1]])
mu_z.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_z.append(tmp_list.copy())
del tmp_list[:]
# for 18 genomes
# tmp_list.append([0,0])
# tmp_list.append([z[0,0],z[0,1]])
# mu_z.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([0,0])
# tmp_list.append([z[1,0],z[1,1]])
# mu_z.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([0,0])
# tmp_list.append([z[2,0],z[2,1]])
# mu_z.append(tmp_list.copy())
# del tmp_list[:]
params_left.array_mu_z = mu_z
# for 6 genomes
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_z_right.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([z[0, 0], z[0, 1]])
tmp_list.append([0, 0])
mu_z_right.append(tmp_list.copy())
del tmp_list[:]
tmp_list.append([0, 0])
tmp_list.append([0, 0])
mu_z_right.append(tmp_list.copy())
del tmp_list[:]
# for 18 genomes
# tmp_list.append([z[0,0],z[0,1]])
# tmp_list.append([0,0])
# mu_z_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([z[1,0],z[1,1]])
# tmp_list.append([0,0])
# mu_z_right.append(tmp_list.copy())
# del tmp_list[:]
# tmp_list.append([z[2,0],z[2,1]])
# tmp_list.append([0,0])
# mu_z_right.append(tmp_list.copy())
# del tmp_list[:]
params_right.array_mu_z = mu_z_right
params_left.write_parameters(filename='configs/ffd_config_left_' +
str(i) + '.prm')
params_right.write_parameters(filename='configs/ffd_config_right_' +
str(i) + '.prm')
print("config files generated")
# print("fitness values")
# print(fitness_values)
# Create wheelcase stl based on each config file
for j in range(len(pop)):
params_left.read_parameters(filename='configs/ffd_config_left_' +
str(j) + '.prm')
params_right.read_parameters(filename='configs/ffd_config_right_' +
str(j) + '.prm')
mesh_points_all = stl_handler_all.parse(
filename='/home/sascha/SAIL/domain/wheelcase/ffd/combined_180.stl')
free_form = FFD(params_left, mesh_points_all)
free_form.perform() # Perform FFD
new_mesh_points = free_form.modified_mesh_points # Save new mesh of wheelcase
free_form = FFD(params_right, new_mesh_points)
free_form.perform()
mesh_points_final = free_form.modified_mesh_points
stl_handler_all.write(mesh_points_final,
'stls/all_deformed_' + str(j) + '.stl')
print("slts generated")
# config and stls correct
# execute openFoam for each stl (combine with top and velo stls)
# long running loop (!)
print("start openfoam")
for k in range(len(pop)):
copy_cmd = "cp stls/all_deformed_" + str(
k) + ".stl domain/wheelcase/hpc1_velo_changed/constant/triSurface"
rename_cmd = "mv domain/wheelcase/hpc1_velo_changed/constant/triSurface/all_deformed_" + str(
k
) + ".stl domain/wheelcase/hpc1_velo_changed/constant/triSurface/all_deformed.stl"
print("cp: " + str(os.system(copy_cmd)))
print("cp executed")
print("renaming: " + str(os.system(rename_cmd)))
print("renaming executed")
os.chdir("/home/sascha/SAIL/domain/wheelcase/hpc1_velo_changed")
os.system("./Allrun")
data_force = np.loadtxt(
'/home/sascha/SAIL/domain/wheelcase/hpc1_velo_changed/postProcessing/forceCoeffs1/0/forceCoeffs.dat'
)
df_force = pd.DataFrame(data=data_force)
cD = df_force.iloc[:, 2]
cD.to_csv('drag' + str(k) + '.csv')
# fitness is mean over the last 100 timesteps (inverted because lower cD (drag) is better)
fitness = np.negative(
cD.iloc[100:200].mean()) # OpenFOAM 200 timesteps -> controlDict
print("FITNESS IS: " + str(fitness))
fitness_values[k] += fitness
remove_wheelcase_cmd = "rm /home/sascha/SAIL/domain/wheelcase/hpc1_velo_changed/constant/triSurface/all_deformed.stl"
os.system("./Allclean")
os.system(remove_wheelcase_cmd)
os.chdir("/home/sascha/SAIL")
print("wheelcase_turned.stl removed from hpc folder")
print("end openfoam")
# print("fitness values after")
# print(fitness_values)
remove_stls_cmd = "rm -r " + home_dir + "SAIL/stls/*"
remove_configs_cmd = "rm -r " + home_dir + "SAIL/configs/*"
os.system(remove_stls_cmd)
os.system(remove_configs_cmd)
# extract Fitness values (postProcessing folder)
pd.DataFrame(data=fitness_values).to_csv('domain/wheelcase/fitness.csv',
mode='a+',
index=False,
header=False)
df_fitness = pd.DataFrame(data=fitness_values).transpose()
return df_fitness