def perturb(rpath, wpath, kl_path, Z_path, dth_path, nkl):
# inputs
# Z_path : where to write out the sample normal
# dth_path : where to write out the random rotation
# nkl : where to truncate the K-L expansion
cdim, sdim, x, y, z = read_blade.read_coords(rpath)
# translate blade random amount (rotate about x axis)
random.seed()
dth = random.randn(1)*pi/180*0.1/3.0
for i in range(cdim):
for j in range(sdim):
r = sqrt(y[i,j]**2 + z[i,j]**2)
th = math.atan2(z[i,j],y[i,j])
y[i,j] = r*cos(th+dth)
z[i,j] = r*sin(th+dth)
# write out dth
f = open(dth_path,'w')
f.write('%e\n' % dth)
f.close()
# generate and write out the Z for this mesh
random.seed()
Z = random.randn(nkl) # normal distribution vector
f = open(Z_path,'w')
for i in range(nkl):
f.write('%e\n' % Z[i])
f.close()
# read in PCA modes and singular values
lines = file(kl_path+'S.dat').readlines()
S = array([line.strip().split()[0] for line in lines], float).T
n_modes = len(S)
fp = zeros((cdim,sdim))
for i in range(nkl):
cdim,sdim,V = read_blade.read_mode(kl_path+'V'+str(i+1)+'.dat')
fp += Z[i]*sqrt(S[i])*V
np = read_blade.calcNormals(x,y,z)
xp = x + np[:,:,0]*fp
yp = y + np[:,:,1]*fp
zp = z + np[:,:,2]*fp
# write out the blade surface
f = open(wpath,'w')
f.write('CDIM: %d\n' % cdim)
f.write('SDIM: %d\n' % sdim)
for i in arange(cdim):
for j in arange(sdim):
f.write('%20.8E' * 3 % (xp[i,j],yp[i,j],zp[i,j]))
f.write('\n')
f.close()
# write out to tecplot format
write_tecplot.write_blade_surf(xp,yp,zp,fp,'blade.dat')
def perturb(rpath, wpath, pca_path, scale, mode):
# inputs
# Z_path : where to write out the sample normal
# npca : where to truncate the PCA expansion
cdim, sdim, x, y, z = read_blade.read_coords(rpath)
# perturb using only one of the transformed modes
lines = file(pca_path+'S.dat').readlines()
S = array([line.strip().split()[0] for line in lines], float).T
fp = zeros((cdim,sdim))
cdim,sdim,V = read_blade.read_mode(pca_path+'V'+str(mode)+'.dat')
fp += scale*S[mode-1]*V
np = read_blade.calcNormals(x,y,z)
xp = x + np[:,:,0]*fp
yp = y + np[:,:,1]*fp
zp = z + np[:,:,2]*fp
# write out the blade surface
f = open(wpath,'w')
f.write('CDIM: %d\n' % cdim)
f.write('SDIM: %d\n' % sdim)
for i in arange(cdim):
for j in arange(sdim):
f.write('%20.8E' * 3 % (xp[i,j],yp[i,j],zp[i,j]))
f.write('\n')
f.close()
# test to see how they look
'''
i = 3
cdim,sdim,V = read_blade.read_mode(pca_path+'V'+str(i+1)+'.dat')
s, t = read_blade.xyz2st(x,y,z)
print s[0],s[1]
pylab.contourf(s,t,V.T,50)
pylab.colorbar()
pylab.show()
'''
'''
fig = pylab.figure()
ax = Axes3D(fig)
surf = ax.plot_surface(sg, tg, fp, rstride=1, cstride=1, cmap = cm.jet)
'''
'''
fig = pylab.figure()
ax = Axes3D(fig)
surf = ax.plot_surface(tg, sg, nf[1:-1,1:-1,2], rstride=1, cstride=1, cmap = cm.jet)
# fig.colorbar(surf)
'''
'''
# mean of the Jacobians
meanJ = mean(stackJ,axis=0)
# compute SVD of stacked Jacobian
V,S,UT = linalg.svd(stackJ)
# read in the surface mesh
rpath = '/mnt/pwfiles/ericdow/random_blade/input/rotor37_coarse/blade_surf.dat'
cdim, sdim, x, y, z = read_blade.read_coords(rpath)
# read in the PCA modes
pca_path = '/mnt/pwfiles/ericdow/random_blade/input/rotor37_coarse/pca_modes_gp/'
V_pca = zeros((NZ,cdim,sdim))
for i in range(NZ):
cdim,sdim,V_pca[i,:,:] = read_blade.read_mode(pca_path+'V'+str(i+1)+'.dat')
V_pca_tmp = zeros((NZ,cdim*sdim))
for i in range(NZ):
V_pca_tmp[i,:] = reshape(V_pca[i,:,:],(cdim*sdim))
# transform the modes
V_trans_tmp = dot(dot(UT.T,eye(NZ)*S_pca),V_pca_tmp)
V_trans = zeros((NZ,cdim,sdim))
for i in range(NZ):
V_trans[i,:,:] = reshape(V_trans_tmp[i,:],(cdim,sdim))
# write out the transformed modes
for imode in range(V_trans.shape[0]):
fname = 'V'+str(imode+1)+'.dat'
pylab.xlabel('K-L mode index (i)')
pylab.ylabel('Average |dJ/dZ_i|')
pylab.title('Jacobian of speed-line shift with '+str(M)+' nearest neighbors')
# compute SVD of stacked Jacobian
V,S,UT = linalg.svd(stackJ)
# read in the surface mesh
rpath = '/mnt/pwfiles/ericdow/random_blade/input/rotor37_coarse/blade_surf.dat'
cdim, sdim, x, y, z = read_blade.read_coords(rpath)
# read in the K-L modes
kl_path = '/mnt/pwfiles/ericdow/random_blade/input/rotor37_coarse/kl_modes/'
V_kl = zeros((NZ,cdim,sdim))
for i in range(NZ):
cdim,sdim,V_kl[i,:,:] = read_blade.read_mode(kl_path+'V'+str(i+1)+'.dat')
V_kl_tmp = zeros((NZ,cdim*sdim))
for i in range(NZ):
V_kl_tmp[i,:] = reshape(V_kl[i,:,:],(cdim*sdim))
# transform the modes
V_trans_tmp = dot(dot(UT,eye(NZ)*S_kl),V_kl_tmp)
V_trans = zeros((NZ,cdim,sdim))
for i in range(NZ):
V_trans[i,:,:] = reshape(V_trans_tmp[i,:],(cdim,sdim))
'''
# write out the transformed modes
for imode in range(V_trans.shape[0]):
def perturb(rpath, wpath, pca_path, Z_path, npca):
# inputs
# Z_path : where to write out the sample normal
# npca : where to truncate the PCA expansion
cdim, sdim, x, y, z = read_blade.read_coords(rpath)
# generate and write out the Z for this mesh
random.seed()
Z = random.randn(npca) # normal distribution vector
f = open(Z_path,'w')
for i in range(npca):
f.write('%e\n' % Z[i])
f.close()
################################
# SCALE PCA MODES BY CHORD RATIO
################################
# r5_chord = 0.859 # inches at hub
# r37_chord = 2.17 # inches at hub
# scale = r37_chord/r5_chord
scale = 1.0
# read in PCA modes and singular values
lines = file(pca_path+'S.dat').readlines()
S = array([line.strip().split()[0] for line in lines], float).T
n_modes = len(S)
# add the mean
cdim,sdim,fp = read_blade.read_mode(pca_path+'mean.dat')
# add the PCA modes
for i in range(npca):
cdim,sdim,V = read_blade.read_mode(pca_path+'V'+str(i+1)+'.dat')
fp += scale*Z[i]*S[i]*V
np = read_blade.calcNormals(x,y,z)
xp = x + np[:,:,0]*fp
yp = y + np[:,:,1]*fp
zp = z + np[:,:,2]*fp
# write out the blade surface
f = open(wpath,'w')
f.write('CDIM: %d\n' % cdim)
f.write('SDIM: %d\n' % sdim)
for i in arange(cdim):
for j in arange(sdim):
f.write('%20.8E' * 3 % (xp[i,j],yp[i,j],zp[i,j]))
f.write('\n')
f.close()
# write out to tecplot format
# write_tecplot.write_blade_surf(xp,yp,zp,fp,'blade.dat')
# test to see how they look
'''
i = 3
cdim,sdim,V = read_blade.read_mode(pca_path+'V'+str(i+1)+'.dat')
s, t = read_blade.xyz2st(x,y,z)
print s[0],s[1]
pylab.contourf(s,t,V.T,50)
pylab.colorbar()
pylab.show()
'''
'''
fig = pylab.figure()
ax = Axes3D(fig)
surf = ax.plot_surface(sg, tg, fp, rstride=1, cstride=1, cmap = cm.jet)
'''
'''
fig = pylab.figure()
ax = Axes3D(fig)
surf = ax.plot_surface(tg, sg, nf[1:-1,1:-1,2], rstride=1, cstride=1, cmap = cm.jet)
# fig.colorbar(surf)
'''
'''
