def run_test1():
fhigh = tf.ForresterND(0.65, 0.9, 2.0)
flow = tf.ForresterND(0.7, 1.0, 0.0)
xl = np.array([0,0],dtype=float)
xu = np.array([1,1],dtype=float)
xDOE = get_DOE_data()
npts = 50 # for plot
nprobes = 5 #FFD level for probe
jnTarget = 0.1
dTarget = 0.2
minDistance = 0.02
X1, X2 = t.get_2d_grid(npts, xl, xu)
X = t.get_ffd_2d(npts, xl, xu)
xprobe = t.get_ffd_2d(nprobes, xl, xu)
# ---------------------
yl = np.array([flow(xi) for xi in xDOE])
model = mt.RbfMod(xDOE, yl)
Yl = np.array([flow(xi) for xi in X])
Yl = np.reshape(Yl,X1.shape)
bnds = np.array([[-0.01,0.01],[1.01,1.01]])
for i in range(25):
yprobe = np.array([objective(xi, xDOE, yl, jnTarget, dTarget) for xi in xprobe])
match = True
idx = 0
while match:
x0idx = np.argsort(yprobe)[idx]
x0 = xprobe[x0idx]
rslt = minimize(objective,x0,method='SLSQP',bounds=bnds,args=(xDOE,yl,jnTarget, dTarget,))
xnew = t.move_pt_to_bounds(rslt.x,xl,xu)
fnew = objective(xnew, xDOE,yl,jnTarget, dTarget)
for xx in xDOE:
if (xx==xnew).all():
match = True
idx += 1
break
else:
match = False
print '%.4f\t%.4f\t%.4f\t%d'%(xnew[0],xnew[1],fnew, idx)
xDOE = np.vstack([xDOE,xnew])
yl = np.array([flow(xi) for xi in xDOE])
model = mt.RbfMod(xDOE, yl)
Ym = np.array([model(xi) for xi in X])
Ym = np.reshape(Ym,X1.shape)
Yobj = np.array([objective(xi, xDOE, yl, jnTarget, dTarget) for xi in X])
Yobj = np.reshape(Yobj,X1.shape)
yobj = np.array([objective(xi, xDOE, yl, jnTarget, dTarget) for xi in xDOE])
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
ax1.plot_wireframe(X1,X2,Yl,color='r')
ax1.plot_wireframe(X1,X2,Ym,color='b')
ax1.plot(xDOE[:,0],xDOE[:,1],yl,'ro')
ax1.set_xlabel('X1')
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111, projection='3d')
ax2.hold(True)
ax2.plot_wireframe(X1,X2,Yobj,color='r')
ax2.plot(xDOE[:,0],xDOE[:,1],yobj,'ro')
ax2.set_xlabel('X1')
plt.show()
def run_test1():
flow = tf.ForresterND(0.7, 1.0, 0.0)
xl = np.array([0,0],dtype=float)
xu = np.array([1,1],dtype=float)
xDOE = get_DOE_data()
xDOEinit = get_DOE_data()
npts = 50 # for plot
nprobes = 7 #FFD level for probe
jnTarget = 0.1 # for normalization
minDistance = 0.05
maxDistance = 0.50
X1, X2 = t.get_2d_grid(npts, xl, xu)
X = t.get_ffd_2d(npts, xl, xu)
xprobe = t.get_ffd_2d(nprobes, xl, xu)
# ---------------------
yl = np.array([flow(xi) for xi in xDOE])
model = mt.RbfMod(xDOE, yl)
Yl = np.array([flow(xi) for xi in X])
Yl = np.reshape(Yl,X1.shape)
bnds = np.array([[-0.01,0.01],[1.01,1.01]])
tol = 0.5
err = tol+1.0
while err>=tol:
yprobe = np.array([objective(xi, xDOE, yl) for xi in xprobe])
x0idx = np.argsort(yprobe)[0]
x0 = xprobe[x0idx]
rslt = minimize(objective,x0,method='SLSQP',bounds=bnds,args=(xDOE,yl,))
xnew = t.move_pt_to_bounds(rslt.x,xl,xu) #SLSQP bugfix
fnew = objective(xnew, xDOE,yl)
match = True
jnbest = 0
while match:
for xx in xDOE:
if (xx==xnew).all():
match = True
for xi in xDOE:
d = np.linalg.norm(xi-xnew)
if minDistance<= d <=maxDistance:
if objective(xi, xDOE, yl)<=jnbest:
xmove = xi
xnew = move_pt_dir(xnew, xmove)
break
else:
match = False
print '%d\t%.4f\t%.4f\t%.4f\t%.4f'%(len(xDOE),xnew[0],xnew[1],-fnew,err)
xDOE = np.vstack([xDOE,xnew])
yl = np.array([flow(xi) for xi in xDOE])
jnavg = np.array([objective(xi, xDOE, yl) for xi in xDOE])
err = -np.average(jnavg)
ylinit = np.array([flow(xi) for xi in xDOEinit])
model = mt.RbfMod(xDOE, yl)
Ym = np.array([model(xi) for xi in X])
Ym = np.reshape(Ym,X1.shape)
Yobj = np.array([objective(xi, xDOE, yl) for xi in X])
Yobj = np.reshape(Yobj,X1.shape)
yobj = np.array([objective(xi, xDOE, yl) for xi in xDOE])
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
ax1.plot_wireframe(X1,X2,Yl,color='r')
ax1.plot_wireframe(X1,X2,Ym,color='b')
ax1.plot(xDOE[:,0],xDOE[:,1],yl,'ro')
ax1.plot(xDOEinit[:,0],xDOEinit[:,1],ylinit,'bs')
ax1.set_xlabel('X1')
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111, projection='3d')
ax2.hold(True)
ax2.plot_wireframe(X1,X2,Yobj,color='r')
ax2.plot(xDOE[:,0],xDOE[:,1],yobj,'ro')
ax2.set_xlabel('X1')
plt.show()
def main():
#flow = tf.ForresterND(1.0, 0.9, 2.0)
flow = tf.stybtang
#flow = tf.ForresterND(0.7, 1.0, 0.0)
xDOEinit = get_DOE_data('LHS30.txt')
xDOEinit = xDOEinit*10.0-5.0
xDOE0 = t.get_ffd_2d(11, [-5.0,-5.0], [5.0, 5.0])
nTry = 10
nFoldSamples = 1
k = 10
#xDOE0 = get_DOE_data('LHS11.txt')
# xDOE0 = np.vstack([xDOE0,[[0,0],[0,1],[1,0],[1,1]]])
xl = np.array([-5.0,-5],dtype=float)
xu = np.array([5,5.0],dtype=float)
minDistance = 0.1
tol = 0.01
runBudget = 75
npts = 50 # for plot only
# larger design space: slsqp bug
xDOE = xDOEinit
# plot only
ylinit = np.array([flow(xi) for xi in xDOEinit])
X1, X2 = t.get_2d_grid(npts, xl, xu)
X = t.get_ffd_2d(npts, xl, xu)
yl = np.array([flow(xi) for xi in xDOE])
model = mt.RbfMod(xDOE, yl)
Ym0 = np.array([model(xi) for xi in X])
Ym0 = np.reshape(Ym0,X1.shape)
# main body
err = tol+1
itr = 0
yl = np.array([flow(xi) for xi in xDOE])
bnds = np.array([[-0.005,-0.005],[1.005,1.005]])
#for zz in range(10):
npts = len(xDOE)
while err>tol and runBudget>npts:
itr += 1
jn0 = np.array([objective(xi, xDOE, yl, None, nFoldSamples) for xi in xDOE0])
xsort = xDOE0[np.argsort(jn0)]
xopt = np.zeros([nTry,xDOE.shape[1]])
pointFound = False
i = 0
while not pointFound:
x0 = xsort[i]
i += 1
rslt = minimize(objective,x0,method='SLSQP',bounds=bnds,args=(xDOE,yl,None,nFoldSamples,k,minDistance,))
xopt = t.move_pt_to_bounds(rslt.x,xl,xu) #SLSQP bugfix
#fopt = get_jn(xopt, xDOE,yl,None,nFoldSamples)
pointFound = check_point(xopt, xDOE, minDistance)
#xopt = filter_results(xopt,fopt,0.1)
xDOE = mt.remove_duplicates(np.vstack([xopt,xDOE]))
npts = len(xDOE)
yl = np.array([flow(xi) for xi in xDOE])
err = t.RRMSE(xDOE,yl)
#err = np.average(np.array([get_jn(xi, xDOE, yl,None,nFoldSamples) for xi in xDOE]))
print '%d\t%d\t%.4e'%(itr, len(xDOE), err)
yl = np.array([flow(xi) for xi in xDOE])
model = mt.RbfMod(xDOE, yl)
Ym1 = np.array([model(xi) for xi in X])
Ym1 = np.reshape(Ym1,X1.shape)
Yl = np.array([flow(xi) for xi in X])
Yl = np.reshape(Yl,X1.shape)
Yobj = np.array([objective(xi, xDOE, yl,None,nFoldSamples) for xi in X])
Yobj = np.reshape(Yobj,X1.shape)
yobj = np.array([get_jn(xi, xDOE, yl,None,nFoldSamples) for xi in xDOE])
Yobj2 = np.array([get_jn(xi, xDOE, yl,None,nFoldSamples) for xi in X])
Yobj2 = np.reshape(Yobj2,X1.shape)
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
ax1.plot_wireframe(X1,X2,Yl,color='r')
#ax1.plot_wireframe(X1,X2,Ym0,color='b')
ax1.plot_wireframe(X1,X2,Ym1,color='b')
ax1.plot(xDOE[:,0],xDOE[:,1],yl,'ro')
ax1.plot(xDOEinit[:,0],xDOEinit[:,1],ylinit,'bs')
ax1.set_xlabel('X1')
ax1.set_ylabel('X2')
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111, projection='3d')
ax2.hold(True)
ax2.plot_wireframe(X1,X2,Yobj,color='r')
ax2.plot(xDOE[:,0],xDOE[:,1],yobj,'ro')
ax2.plot_wireframe(X1,X2,Yobj2,color='b')
ax2.set_xlabel('X1')
#ax2.set_zlim([0,16])
lvls = np.linspace(-100,250,20)
fig3 = plt.figure(3)
ax3 = fig3.add_subplot(111)
ax3.hold(True)
ax3.plot(xDOE[:,0],xDOE[:,1],'ro')
ax3.plot(xDOEinit[:,0],xDOEinit[:,1],'bs')
CS = ax3.contourf(X1,X2, Yl, levels=lvls)
plt.colorbar(CS, ticks=lvls, format='%.4f')
ax3.set_xlabel('X1')
ax3.set_ylabel('X2')
fig4 = plt.figure(4)
ax4 = fig4.add_subplot(111)
ax4.hold(True)
ax4.plot(xDOE[:,0],xDOE[:,1],'ro')
ax4.plot(xDOEinit[:,0],xDOEinit[:,1],'bs')
CS1 = ax4.contourf(X1,X2, Ym1, levels=lvls)
plt.colorbar(CS1, ticks=lvls, format='%.4f')
ax4.set_xlabel('X1')
ax4.set_ylabel('X2')
#ax3.clabel(CS, inline=1, fontsize=10)
plt.show()
def create_aero_db():
af = MyTools.airfoil.airfoil.Airfoil()
af.read_txt('Clark-Y.txt')
#af.create_naca4()
#
flow = AeroModel('ClarkY-adaptive.txt')
flow.plot()
# fhigh = AeroModel('aero_data_jfoil.txt')
#flow.plot()
#fhigh.plot()
#fhigh = CFDrun(af)
#fhigh.modelCL.rbf.epsilon = 1e-8
#flow.plot()
#fhigh.plot()
# def func(x):
# cl0, cd0, cm0 = flow(x)
# cl1, cd1, cm1 = fhigh(x)
# return cl1-cl0, cd1-cd0, cm1-cm0
func = CFDrun(af)
func.path = 'ClarkY-adaptive.txt'
# func = AeroModel('ClarkY-C81.txt')
# func.plot()
# fhigh = AeroModel('ClarkY-adaptive.txt')
# fhigh.plot()
Mach = np.array([0.2, 0.8])
alpha = np.array([-20.0, 20.0])
DOEpath = 'LHS30.txt'
tol = np.array([0.05, 0.001, 0.01])
runBudget = 100
minDistance = 0.025
kFold = 1
pOut = 5
xDOE0 = t.get_ffd_2d(51, [-1.0, -1], [1.,1.]) # trial initial points
nptsPlot = 50
# --- ---
bnds = np.array([[-0.0001,-0.0001],[1.0001,1.0001]])
_xl = np.array([-1.0, -1.0])
_xu = np.array([1.0, 1.0])
xl = np.array([Mach[0], alpha[0]])
xu = np.array([Mach[1], alpha[1]])
norm = mt.Normalization(xl, xu)
xDOE = mt.read_tabulated_data_without_header(DOEpath)
#xDOE = t.get_ffd_2d(3,[-1.0, -1], [1.,1.])
#get_aero_data(xDOE[10], af, norm)
data = np.zeros([len(xDOE),3])
dataNew = np.zeros(3)
for i,xx in enumerate(xDOE):
data[i,0], data[i,1], data[i,2] = func(xx)
xreal = func.norm.denormalize(xx)
out2 = '%.6f\t%.6f\t%.8f\t%.8f\t%.8f\n'%(xreal[0],xreal[1],data[i,0],data[i,1],data[i,2])
fid2 = open('ClarkY-ad.txt','at')
fid2.write(out2)
fid2.close()
# data = mt.read_tabulated_data_without_header(func.path)[:,2:]
itr = 0
err = tol+1.0
npts = len(xDOE)
while np.all(err>tol) and runBudget>npts:
itr += 1
dataSetIdx = itr%3
ds = data[:,dataSetIdx]
jn0 = np.array([objective(xi, xDOE, ds, None, pOut, kFold) for xi in xDOE0])
xsort = xDOE0[np.argsort(jn0)]
#xopt = np.zeros([nTrial,xDOE.shape[1]])
pointFound = False
i = 0
while not pointFound:
x0 = xsort[i]
i += 0
rslt = minimize(objective,x0,method='SLSQP',bounds=bnds,
args=(xDOE,ds,None,pOut, kFold,minDistance,))
xopt = t.move_pt_to_bounds(rslt.x,_xl,_xu) #SLSQP bugfix
pointFound = check_point(xopt, xDOE, minDistance)
xDOE = mt.remove_duplicates(np.vstack([xopt,xDOE]))
npts = len(xDOE)
dataNew[0], dataNew[1], dataNew[2] = func(xopt)
data = np.vstack([dataNew, data])
err1 = t.RRMSE(xDOE,data[:,0])
err2 = t.RRMSE(xDOE,data[:,1])
err3 = t.RRMSE(xDOE,data[:,2])
# err1 = np.average(np.array([get_jn(xi, xDOE, data[:,0],None, pOut, kFold) for xi in xDOE]))
# err2 = np.average(np.array([get_jn(xi, xDOE, data[:,1],None, pOut, kFold) for xi in xDOE]))
# err3 = np.average(np.array([get_jn(xi, xDOE, data[:,2],None, pOut, kFold) for xi in xDOE]))
err = np.array([err1,err2,err3])
out = '%d\t%d\t%.4e\t%.4e\t%.4e\n'%(itr, len(xDOE), err[0], err[1], err[2])
xreal = func.norm.denormalize(xDOE[0])
out2 = '%.6f\t%.6f\t%.8f\t%.8f\t%.8f\n'%(xreal[0],xreal[1],dataNew[0],dataNew[1],dataNew[2])
print out
fid1 = open('aDOE_results.txt','at')
fid1.write(out)
fid1.close()
fid2 = open('ClarkY-ad.txt','at')
fid2.write(out2)
fid2.close()
#--- plot area
iz = 0
X1, X2 = t.get_2d_grid(nptsPlot, [-1.0, -1.0], [1.0, 1.0])
X = t.get_ffd_2d(nptsPlot, [-1.0, -1.0], [1.0, 1.0])
X1r, X2r = t.get_2d_grid(nptsPlot, xl, xu)
#Yl = np.array([func(xi)[iz] for xi in X])
#Yl = np.reshape(Yl,X1.shape)
xDOEreal = np.zeros(xDOE.shape)
for i,xd in enumerate(xDOE):
xDOEreal[i] = norm.denormalize(xd)
Yobj = np.array([objective(xi, xDOE, data[:,iz],None,1, kFold) for xi in X])
Yobj = np.reshape(Yobj,X1.shape)
yobj = np.array([objective(xi, xDOE, data[:,iz],None,1, kFold) for xi in xDOE])
model = mt.RbfMod(xDOE, data[:,iz])
Ym1 = np.array([model(xi) for xi in X])
Ym1 = np.reshape(Ym1,X1.shape)
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
#.plot_wireframe(X1r,X2r,Yl,color='r')
#ax1.plot_wireframe(X1,X2,Ym0,color='b')
ax1.plot_wireframe(X1r,X2r,Ym1,color='b')
ax1.plot(xDOEreal[:,0],xDOEreal[:,1],data[:,iz],'ro')
#ax1.plot(xDOEinit[:,0],xDOEinit[:,1],ylinit,'bs')
ax1.set_xlabel('X1')
ax1.set_ylabel('X2')
ax1.set_zlim([-1,1])
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111, projection='3d')
ax2.hold(True)
ax2.plot_wireframe(X1,X2,Yobj,color='r')
ax2.plot(xDOE[:,0],xDOE[:,1],yobj,'ro')
ax2.set_xlabel('X1')
#lvls = np.linspace(-30,20,20)
fig3 = plt.figure(3)
ax3 = fig3.add_subplot(111)
ax3.hold(True)
ax3.plot(xDOE[:,0],xDOE[:,1],'ro')
#ax3.plot(xDOEinit[:,0],xDOEinit[:,1],'bs')
#CS = ax3.contourf(X1,X2, Yl, levels=lvls)
#plt.colorbar(CS, ticks=lvls, format='%.4f')
ax3.set_xlabel('X1')
ax3.set_ylabel('X2')
fig4 = plt.figure(4)
ax4 = fig4.add_subplot(111)
ax4.hold(True)
ax4.plot(xDOE[:,0],xDOE[:,1],'ro')
#ax4.plot(xDOEinit[:,0],xDOEinit[:,1],'bs')
#CS1 = ax4.contourf(X1,X2, Ym1, levels=lvls)
#plt.colorbar(CS1, ticks=lvls, format='%.4f')
ax4.set_xlabel('X1')
ax4.set_ylabel('X2')
#ax3.clabel(CS, inline=1, fontsize=10)
plt.show()