def process_surface_adjoint(config_filename,
filter_type='LAPLACE',
marker_name='airfoil',
chord_length=1.0):
print('')
print(
'-------------------------------------------------------------------------'
)
print(
'| SU2 Suite (Process Surface Adjoint) |'
)
print(
'-------------------------------------------------------------------------'
)
print('')
# some other defaults
c_clip = 0.01 # percent chord to truncate
fft_copy = 5 # number of times to copy the fft signal
smth_len = 0.05 # percent chord smoothing window length
lapl_len = 1e-4 # laplace smoothing parameter
# read config file
config_data = libSU2.Get_ConfigParams(config_filename)
surface_filename = config_data['SURFACE_ADJ_FILENAME'] + '.csv'
print surface_filename
mesh_filename = config_data['MESH_FILENAME']
gradient = config_data['ADJ_OBJFUNC']
print('Config filename = %s' % config_filename)
print('Surface filename = %s' % surface_filename)
print('Filter Type = %s' % filter_type)
# read adjoint data
adj_data = np.genfromtxt(surface_filename,
dtype=float,
delimiter=',',
skip_header=1)
# read mesh data
mesh_data = libSU2_mesh.Read_Mesh(mesh_filename)
# proces adjoint data
P = map(int, adj_data[:, 0])
X = adj_data[:, 6].copy()
Y = adj_data[:, 7].copy()
Sens = adj_data[:, 1].copy()
PsiRho = adj_data[:, 2].copy()
I = range(0, len(P)) # important - for unsorting durring write
# store in dict by point index
adj_data_dict = dict(zip(P, zip(X, Y, Sens, PsiRho, I)))
# sort airfoil points
iP_sorted, _ = libSU2_mesh.sort_Airfoil(mesh_data, marker_name)
assert (len(iP_sorted) == len(P))
# rebuild airfoil loop
i = 0
for this_P in iP_sorted:
# the adjoint data entry
this_adj_data = adj_data_dict[this_P]
# re-sort
P[i] = this_P
X[i] = this_adj_data[0]
Y[i] = this_adj_data[1]
Sens[i] = this_adj_data[2]
PsiRho[i] = this_adj_data[3]
I[i] = this_adj_data[4]
# next
i = i + 1
#: for each point
# calculate arc length
S = np.sqrt(np.diff(X)**2 + np.diff(Y)**2) / chord_length
S = np.cumsum(np.hstack([0, S]))
# tail trucating, by arc length
I_clip_lo = S < S[0] + c_clip
I_clip_hi = S > S[-1] - c_clip
S_clip = S.copy()
Sens_clip = Sens.copy()
Sens_clip[I_clip_hi] = Sens_clip[I_clip_hi][0]
Sens_clip[I_clip_lo] = Sens_clip[I_clip_lo][-1]
# some edge length statistics
dS_clip = np.diff(S_clip)
min_dS = np.min(dS_clip)
mean_dS = np.mean(dS_clip)
max_dS = np.max(dS_clip)
#print 'min_dS = %.4e ; mean_dS = %.4e ; max_dS = %.4e' % ( min_dS , mean_dS , max_dS )
# --------------------------------------------
# APPLY FILTER
if filter_type == 'FOURIER':
Freq_notch = [1 / max_dS, np.inf] # the notch frequencies
Sens_filter, Frequency, Power = fft_filter(S_clip, Sens_clip,
Freq_notch, fft_copy)
#Sens_filter = smooth(S_clip,Sens_filter, 0.03,'blackman') # post smoothing
elif filter_type == 'WINDOW':
Sens_filter = window(S_clip, Sens_clip, smth_len, 'blackman')
elif filter_type == 'LAPLACE':
Sens_filter = laplace(S_clip, Sens_clip, lapl_len)
elif filter_type == 'SHARPEN':
Sens_smooth = smooth(S_clip, Sens_clip, smth_len / 5,
'blackman') # pre smoothing
Sens_smoother = smooth(S_clip, Sens_smooth, smth_len, 'blackman')
Sens_filter = Sens_smooth + (Sens_smooth - Sens_smoother) # sharpener
else:
raise Exception, 'unknown filter type'
# --------------------------------------------
# PLOTTING
if pylab_imported:
# start plot
fig = plt.figure(gradient)
plt.clf()
#if not fig.axes: # for comparing two filter calls
#plt.subplot(1,1,1)
#ax = fig.axes[0]
#if len(ax.lines) == 4:
#ax.lines.pop(0)
#ax.lines.pop(0)
# SENSITIVITY
plt.plot(S, Sens, color='b') # original
plt.plot(S_clip, Sens_filter, color='r') # filtered
plt.xlim(-0.1, 2.1)
plt.ylim(-5, 5)
plt.xlabel('Arc Length')
plt.ylabel('Surface Sensitivity')
#if len(ax.lines) == 4:
#seq = [2, 2, 7, 2]
#ax.lines[0].set_dashes(seq)
#ax.lines[1].set_dashes(seq)
plot_filename = os.path.splitext(surface_filename)[0] + '.png'
plt.savefig('Sens_' + plot_filename, dpi=300)
# zoom in
plt.ylim(-0.4, 0.4)
plt.savefig('Sens_zoom_' + plot_filename, dpi=300)
# SPECTRAL
if filter_type == 'FOURIER':
plt.figure('SPECTRAL')
plt.clf()
plt.plot(Frequency, Power)
#plt.xlim(0,Freq_notch[0]+10)
plt.xlim(0, 200)
plt.ylim(0, 0.15)
plt.xlabel('Frequency (1/C)')
plt.ylabel('Surface Sensitivity Spectal Power')
plt.savefig('Spectral_' + plot_filename, dpi=300)
#: if spectral plot
#: if plot
# --------------------------------------------
# SAVE SURFACE FILE
# reorder back to input surface points
Sens_out = np.zeros(len(S))
Sens_out[I] = Sens_filter # left over from sort
adj_data[:, 1] = Sens_out
# get surface header
surface_orig = open(surface_filename, 'r')
header = surface_orig.readline()
surface_orig.close()
# get list of prefix names
prefix_names = libSU2.get_AdjointPrefix(None)
prefix_names = prefix_names.values()
# add filter prefix, before adjoint prefix
surface_filename_split = surface_filename.rstrip('.csv').split('_')
if surface_filename_split[-1] in prefix_names:
surface_filename_split = surface_filename_split[0:-1] + [
'filtered'
] + [surface_filename_split[-1]]
else:
surface_filename_split = surface_filename_split + ['filtered']
surface_filename_new = '_'.join(surface_filename_split) + '.csv'
# write filtered surface file (only updates Sensitivity)
surface_new = open(surface_filename_new, 'w')
surface_new.write(header)
for row in adj_data:
for i, value in enumerate(row):
if i > 0:
surface_new.write(', ')
if i == 0:
surface_new.write('%i' % value)
else:
surface_new.write('%.16e' % value)
surface_new.write('\n')
surface_new.close()
print('')
print(
'----------------- Exit Success (Process Surface Adjoint) ----------------'
)
print('')
return
def process_surface_adjoint( config_filename ,
filter_type='LAPLACE' ,
marker_name='airfoil' ,
chord_length=1.0 ):
print('')
print('-------------------------------------------------------------------------')
print('| SU2 Suite (Process Surface Adjoint) |')
print('-------------------------------------------------------------------------')
print('')
# some other defaults
c_clip = 0.01 # percent chord to truncate
fft_copy = 5 # number of times to copy the fft signal
smth_len = 0.05 # percent chord smoothing window length
lapl_len = 1e-4 # laplace smoothing parameter
# read config file
config_data = libSU2.Get_ConfigParams(config_filename)
surface_filename = config_data['SURFACE_ADJ_FILENAME'] + '.csv'
print surface_filename
mesh_filename = config_data['MESH_FILENAME']
gradient = config_data['OBJECTIVE_FUNCTION']
print('Config filename = %s' % config_filename)
print('Surface filename = %s' % surface_filename)
print('Filter Type = %s' % filter_type)
# read adjoint data
adj_data = np.genfromtxt( surface_filename ,
dtype = float ,
delimiter = ',' ,
skip_header = 1 )
# read mesh data
mesh_data = libSU2_mesh.Read_Mesh(mesh_filename)
# proces adjoint data
P = map(int, adj_data[:,0] )
X = adj_data[:,6].copy()
Y = adj_data[:,7].copy()
Sens = adj_data[:,1].copy()
PsiRho = adj_data[:,2].copy()
I = range(0,len(P)) # important - for unsorting durring write
# store in dict by point index
adj_data_dict = dict( zip( P , zip(X,Y,Sens,PsiRho,I) ) )
# sort airfoil points
iP_sorted,_ = libSU2_mesh.sort_Airfoil(mesh_data,marker_name)
assert(len(iP_sorted) == len(P))
# rebuild airfoil loop
i = 0
for this_P in iP_sorted:
# the adjoint data entry
this_adj_data = adj_data_dict[this_P]
# re-sort
P[i] = this_P
X[i] = this_adj_data[0]
Y[i] = this_adj_data[1]
Sens[i] = this_adj_data[2]
PsiRho[i] = this_adj_data[3]
I[i] = this_adj_data[4]
# next
i = i+1
#: for each point
# calculate arc length
S = np.sqrt( np.diff(X)**2 + np.diff(Y)**2 ) / chord_length
S = np.cumsum( np.hstack([ 0 , S ]) )
# tail trucating, by arc length
I_clip_lo = S < S[0] + c_clip
I_clip_hi = S > S[-1] - c_clip
S_clip = S.copy()
Sens_clip = Sens.copy()
Sens_clip[I_clip_hi] = Sens_clip[I_clip_hi][0]
Sens_clip[I_clip_lo] = Sens_clip[I_clip_lo][-1]
# some edge length statistics
dS_clip = np.diff(S_clip)
min_dS = np.min ( dS_clip )
mean_dS = np.mean( dS_clip )
max_dS = np.max ( dS_clip )
#print 'min_dS = %.4e ; mean_dS = %.4e ; max_dS = %.4e' % ( min_dS , mean_dS , max_dS )
# --------------------------------------------
# APPLY FILTER
if filter_type == 'FOURIER':
Freq_notch = [ 1/max_dS, np.inf ] # the notch frequencies
Sens_filter,Frequency,Power = fft_filter( S_clip,Sens_clip, Freq_notch, fft_copy )
#Sens_filter = smooth(S_clip,Sens_filter, 0.03,'blackman') # post smoothing
elif filter_type == 'WINDOW':
Sens_filter = window( S_clip, Sens_clip, smth_len, 'blackman' )
elif filter_type == 'LAPLACE':
Sens_filter = laplace( S_clip, Sens_clip, lapl_len )
elif filter_type == 'SHARPEN':
Sens_smooth = smooth( S_clip, Sens_clip , smth_len/5, 'blackman' ) # pre smoothing
Sens_smoother = smooth( S_clip, Sens_smooth, smth_len , 'blackman' )
Sens_filter = Sens_smooth + (Sens_smooth - Sens_smoother) # sharpener
else:
raise Exception, 'unknown filter type'
# --------------------------------------------
# PLOTTING
if pylab_imported:
# start plot
fig = plt.figure(gradient)
plt.clf()
#if not fig.axes: # for comparing two filter calls
#plt.subplot(1,1,1)
#ax = fig.axes[0]
#if len(ax.lines) == 4:
#ax.lines.pop(0)
#ax.lines.pop(0)
# SENSITIVITY
plt.plot(S ,Sens ,color='b') # original
plt.plot(S_clip,Sens_filter,color='r') # filtered
plt.xlim(-0.1,2.1)
plt.ylim(-5,5)
plt.xlabel('Arc Length')
plt.ylabel('Surface Sensitivity')
#if len(ax.lines) == 4:
#seq = [2, 2, 7, 2]
#ax.lines[0].set_dashes(seq)
#ax.lines[1].set_dashes(seq)
plot_filename = os.path.splitext(surface_filename)[0] + '.png'
plt.savefig('Sens_'+plot_filename,dpi=300)
# zoom in
plt.ylim(-0.4,0.4)
plt.savefig('Sens_zoom_'+plot_filename,dpi=300)
# SPECTRAL
if filter_type == 'FOURIER':
plt.figure('SPECTRAL')
plt.clf()
plt.plot(Frequency,Power)
#plt.xlim(0,Freq_notch[0]+10)
plt.xlim(0,200)
plt.ylim(0,0.15)
plt.xlabel('Frequency (1/C)')
plt.ylabel('Surface Sensitivity Spectal Power')
plt.savefig('Spectral_'+plot_filename,dpi=300)
#: if spectral plot
#: if plot
# --------------------------------------------
# SAVE SURFACE FILE
# reorder back to input surface points
Sens_out = np.zeros(len(S))
Sens_out[I] = Sens_filter # left over from sort
adj_data[:,1] = Sens_out
# get surface header
surface_orig = open(surface_filename,'r')
header = surface_orig.readline()
surface_orig.close()
# get list of prefix names
prefix_names = libSU2.get_AdjointPrefix(None)
prefix_names = prefix_names.values()
# add filter prefix, before adjoint prefix
surface_filename_split = surface_filename.rstrip('.csv').split('_')
if surface_filename_split[-1] in prefix_names:
surface_filename_split = surface_filename_split[0:-1] + ['filtered'] + [surface_filename_split[-1]]
else:
surface_filename_split = surface_filename_split + ['filtered']
surface_filename_new = '_'.join(surface_filename_split) + '.csv'
# write filtered surface file (only updates Sensitivity)
surface_new = open(surface_filename_new,'w')
surface_new.write(header)
for row in adj_data:
for i,value in enumerate(row):
if i > 0:
surface_new.write(', ')
if i == 0:
surface_new.write('%i' % value )
else:
surface_new.write('%.16e' % value )
surface_new.write('\n')
surface_new.close()
print('')
print('----------------- Exit Success (Process Surface Adjoint) ----------------')
print('')
return
