Merge branch 'master' of https://gitlab.onelab.info/conveks/tutorials

Ccore/shape.plt

+#set terminal pdf
+
+set grid
+set ytics nomirror
+set y2tics
+set style data lines
+
+set xlabel "tau"
+set y2label "w"
+set ylabel "dw/dtau"
+
+set output "aaa.pdf"
+plot "aaa" u 2:3 axis x1y2 t"w",\
+     "aaa" u 2:4 t"dwdt",\
+     "aaa" u 2:5 w linesp t"dwdt FD"
+
+pause -1
+
+set output "optishape1.pdf"
+plot "Torque_linTH.txt" u 2:3 axis x1y2 t"w",\
+     "Torque_linTH.txt" u 2:4 t"dwdt",\
+     "Torque_linTH.txt" u 2:5 w linesp t"dwdt FD"
+
+pause -1
+
+set output "optishape2.pdf"
+plot "LieDevSmoother.txt" u 2:3 axis x1y2 t"w",\
+     "LieDevSmoother.txt" u 2:4 t"dwdt",\
+     "LieDevSmoother.txt" u 2:5 w linesp t"dwdt FD"
+
+pause -1
+
+set output "optishape3.pdf"
+plot "Torque_linTH2.txt" u 2:3 axis x1y2 t"w",\
+     "Torque_linTH2.txt" u 2:4 t"dwdt",\
+     "Torque_linTH2.txt" u 2:5 w linesp t"dwdt FD"
+
+pause -1
+
+set output "optishape4.pdf"
+plot "LieDevSmoother2.txt" u 2:3 axis x1y2 t"w",\
+     "LieDevSmoother2.txt" u 2:4 t"dwdt",\
+     "LieDevSmoother2.txt" u 2:5 w linesp t"dwdt FD"
+
+pause -1
+
+set output "optishape5.pdf"
+plot "Torque_TH.txt" u 2:3 axis x1y2 t"w",\
+     "Torque_TH.txt" u 2:4 t"dwdt",\
+     "Torque_TH.txt" u 2:5 w linesp t"dwdt FD"
+
+pause -1
+

Ccore/shape.py

+# Open this file with Gmsh
+# - interactively with File->Open->shp.py,
+# - in the command line with 'gmsh shape.py'
+
+
+import numpy as np
+import onelab
+import conveks
+from scipy.optimize import minimize
+
+modelName = 'ccore'
+
+c = onelab.client(__file__)
+
+# get gmsh and getdp locations from Gmsh options
+mygmsh = c.getString('General.ExecutableFileName')
+mygetdp = ''
+for s in range(9):
+   n = c.getString('Solver.Name' + str(s))
+   if(n == 'GetDP'):
+      mygetdp = c.getString('Solver.Executable' + str(s))
+      break
+if(not len(mygetdp)):
+   c.sendError('This appears to be the first time you are trying to run GetDP')
+   c.sendError('Please run a GetDP model interactively once with Gmsh to ' +
+               'initialize the solver location')
+   exit(0)
+
+c.sendInfo('Will use gmsh={0} and getdp={1}'.format(mygmsh, mygetdp))
+
+# append correct path to model file names
+file_geo = c.getPath(modelName+'.geo')
+file_msh = c.getPath(modelName+'.msh')
+file_pro = c.getPath(modelName+'.pro')
+
+# build command strings with appropriate parameters and options
+mygmsh += ' -p 0 -v 2 -parametric ' + file_geo + ' '
+mygetdp += ' -p 0 ' + file_pro + ' -msh ' + file_msh + ' '
+
+# load geometry in GUI to have something to look at
+c.openProject(file_geo)
+
+c.sendCommand('Solver.AutoMesh = -1;')
+
+# dry getdp run (without -solve or -pos option) to get model parameters in the GUI
+c.runSubClient('myGetDP', mygetdp)
+
+# end of check phase. We're done if we do not start the optimization
+if c.action == 'check' :
+   exit(0)
+
+## def readSimpleTable(path):
+##     with open(path) as FileObj:
+##         return np.array([float(lines.split()[-1]) for lines in FileObj])
+
+def setNodeTable(var, nodes, val):
+    data = np.ravel(np.column_stack((nodes, val)))
+    data = np.insert(data, 0, float(len(nodes)))
+    c.setNumber(var, values=data, visible=0)
+
+def getVelocityField(xvar, perturb=1e-7):
+    for id, var in xvar.iteritems():
+        c.runNonBlockingSubClient('myGmsh', mygmsh \
+          + ' -setnumber PerturbMesh 1'\
+          + ' -setnumber Perturbation '+str(perturb)\
+          + ' -setnumber VelocityTag '+str(id)\
+          + ' -setnumber '+var[-1]+' '+str(var[1]+perturb)\
+          + ' -run')
+    c.waitOnSubClients()
+    # send the x,y,z components of each velocity field
+    for label, var in x.iteritems():
+        d = c.getNumbers('Optimization/Results/velocity_'+str(label))
+        if label==0:
+            nodes = np.array(d[1::4])
+        setNodeTable('Optimization/Results/velocity_'+str(label)+'_1',nodes,np.array(d[2::4]))
+        setNodeTable('Optimization/Results/velocity_'+str(label)+'_2',nodes,np.array(d[3::4]))
+        setNodeTable('Optimization/Results/velocity_'+str(label)+'_3',nodes,np.array(d[4::4]))
+        c.setNumber(var[0],value=x[label][1])
+
+def fobj(xFromOpti):
+    # send the current point to GetDP model
+    for label, var in x.iteritems():
+        x[label][1] = xFromOpti[label]
+        c.setNumber(var[0],value=x[label][1])
+
+    # reload the geometry in GUI to see the geometrical changes
+    c.openProject(file_geo)
+
+    c.runSubClient('myGmsh', mygmsh + ' -2')
+    c.runSubClient('myGetDP', mygetdp + '-solve GetPerformances' \
+                   + ' - setnumber OuterIteration ' + str(0)) # it !!
+    return c.getNumber('Optimization/Results/w')
+
+
+def grad(xFromOpti):
+    # send the current point to GetDP model
+    for label, var in x.iteritems():
+        x[label][1] = xFromOpti[label]
+        c.setNumber(var[0],value=x[label][1])
+        
+    # as well as their sensitivity with respect to design variables at `x'
+    getVelocityField(x)
+    for dv, var in x.iteritems():
+        c.runNonBlockingSubClient('myGetDP', mygetdp \
+          + ' -setnumber VelocityTag '+str(dv)\
+          + ' -solve GetGradient_wrt_dv')
+    c.waitOnSubClients()
+    return np.asarray([c.getNumber('Optimization/Results/dwdtau_'+str(dv)) for dv, var in x.iteritems()])
+    
+Nfeval = 1
+def callbackF(Xi):
+    global Nfeval
+    print '{0:4d} {1: 3.6f} {2: 3.6f} {3: 3.6f}'.format(Nfeval, Xi[0], fobj(Xi), grad(Xi)[0])
+    Nfeval += 1
+
+def test(n):
+    tab = np.zeros((n+1,4))
+    for step in range(n+1):
+        xstep = x[0][2] + (x[0][3]-x[0][2])/float(n)*step
+        f,g,d = fobj([xstep]), grad([xstep])[0], 0
+        tab[step] = [xstep, f, g, d]
+        if step >= 2 :
+            tab[step-1][3] = (tab[step][1]-tab[step-2][1])/(tab[step][0]-tab[step-2][0])
+        if c.getString(c.name+'/Action') == 'stop':
+            exit(1)
+    print
+    for step in range(n+1):
+        ind1, ind2 = max(step-1,0), min(step+1,n)
+        tab[step][3] = (tab[ind2][1]-tab[ind1][1])/(tab[ind2][0]-tab[ind1][0])
+        print "%4d" % step,
+        for item in tab[step] : print "%3.6f" % item,
+        print
+    
+    return tab
+
+# List of design parameters with format
+# [ "Onelab name", value, Min, Max, "variable name" ]
+designParameters={
+    0 : ['Optimization/Parameters/D', 0.02, 0.001, 0.050, 'D'],
+    1 : ['Optimization/Parameters/E', 0.01 , 0.01, 0.15 , 'E']
+}
+Optimizers={
+    0 : 'scipy',
+    1 : 'mma'
+}
+
+x = {}
+index = int(c.getNumber('Optimization/Sweep on parameter'))-1
+Optimizer = Optimizers[int(c.getNumber('Optimization/Optimizer'))]
+
+# define optimization parameters as Onelab parameter (editable in the GUI)
+maxIter = c.defineNumber('Optimization/00Max iterations', value=100, visible=(Optimizer=='mma'))
+maxChange = c.defineNumber('Optimization/01Max change', value=1.0e-3,  visible=(Optimizer=='mma'))
+
+if index >= 0: # parameter sweep and optimisation over one selected design parameter
+    x[0] = designParameters[index][:] # make a copy
+    test(int(c.getNumber('Optimization/Steps in range')))
+else: # optimisation over full parameter space
+    x = designParameters.copy()
+
+# initial value of the variables in the design space,
+# the lower bound for design variables,
+# as well as the upper bound for design variables.
+numVariables = 1 if index >= 0 else len(designParameters)
+initialPoint = np.zeros(numVariables)
+lowerBound = np.zeros(numVariables)
+upperBound = np.zeros(numVariables)
+if index >= 0: # single parameter
+    x[0] = designParameters[index]
+    initialPoint[0] = designParameters[index][1]
+    lowerBound[0] = designParameters[index][2]
+    upperBound[0] = designParameters[index][3]
+else: # all the parameters
+    for label, var in designParameters.iteritems():
+        x[label] = var
+        initialPoint[label] = var[1]
+        lowerBound[label] = var[2]
+        upperBound[label] = var[3]
+
+if Optimizer=='scipy':
+    bounds = [(lowerBound[i], upperBound[i]) for i in range(numVariables)]
+    res = minimize(fobj, initialPoint.tolist(), jac=grad, bounds=bounds, \
+        callback=callbackF, method = 'L-BFGS-B', tol=None, \
+        options={'ftol': 1e-5, 'gtol': 1e-3, 'disp':True} )
+    print 'Scipy result:', res
+
+elif Optimizer=='mma':
+    # Initialize the MMA optimizer
+    conveks.mma.initialize(initialPoint, lowerBound, upperBound)
+
+    # Set some options for MMA
+    conveks.mma.option.setNumber('General.Verbosity', 0) 
+
+    # Get iteration count (here it will be 1 - could be different in case of restart)
+    it = conveks.mma.getOuterIteration()
+    change = 1.0
+    
+    while it <= maxIter and c.getString(c.name+'/Action') != 'stop' and change > maxChange:
+        c.setNumber('Optimization/01Current iteration', value=it, readOnly=1,
+            attributes={'Highlight':'LightYellow'})
+
+        xFromMMA = np.array(conveks.mma.getCurrentPoint())
+        objective = fobj(xFromMMA)
+        grad_objective = grad(xFromMMA)
+        # since mma is tailored for constrained optimization,
+        # we add a dummy constrained which is always fulfilled 
+        constraints = np.array([np.sum(xFromMMA)/10.0-1.0])
+        grad_constraints = np.ones(numVariables)/10.0
+    
+        print it, change, xFromMMA, objective, grad_objective
+        c.sendInfo('Optimization: it. {}, obj. {}, constr. {}, ch. {}'.format(it,objective,constraints[0],change))
+
+        # call MMA and update the current point
+        #conveks.mma.setMoveLimits(lowerBound, upperBound, 1e-0) # parametre interessant
+        conveks.mma.updateCurrentPoint(constraints,grad_objective,grad_constraints)
+        change = conveks.mma.getDesignChange()
+        it = conveks.mma.getOuterIteration()
+
+    print 'Mma result: ', conveks.mma.getCurrentPoint()
+
+    # This should be called at the end
+    conveks.mma.finalize()

Lbracket/topo.py

@@ -3,8 +3,8 @@
 #
 
 import numpy as np
-import conveks
 import onelab
+import conveks
 
 c = onelab.client(__file__)
 
@@ -92,13 +92,7 @@ conveks.mma.initialize(x, lowerBound, upperBound)
 
 # Set appropriate options for MMA
 conveks.mma.option.setNumber('General.Verbosity', 4)
-conveks.mma.option.setNumber('General.SaveHistory', 0)
-conveks.mma.option.setNumber('SubProblem.isRobustMoveLimit', 1)
-conveks.mma.option.setNumber('SubProblem.isRobustAsymptotes', 1)
-conveks.mma.option.setNumber('SubProblem.type', 0)
-conveks.mma.option.setNumber('SubProblem.addConvexity', 1)
-conveks.mma.option.setNumber('SubProblem.asymptotesRmax', 100.0)
-conveks.mma.option.setNumber('SubProblem.asymptotesRmin', 0.001)
+conveks.mma.option.setNumber('SubProblem.move', 0.1)
 
 # Get iteration count (here it will be 1 - could be different in case of restart)
 it = conveks.mma.getOuterIteration()
@@ -112,7 +106,7 @@ while change > maxChange and it <= maxIter and c.getString('topo/Action') != 'st
                 attributes={'Highlight':'LightYellow'})
 
     # get (copy of) current point
-    x = np.array(conveks.mma.getCurrentPoint())
+    x = conveks.mma.getCurrentPoint()
 
     if filtering:
         setElementTable('Optimization/Results/filterInput', elements, x)
@@ -160,7 +154,7 @@ while change > maxChange and it <= maxIter and c.getString('topo/Action') != 'st
     c.sendInfo('Optimization: it. {}, obj. {}, constr. {}, change {}'.format(it,objective,constraints[0], change))
 
     # call MMA and update the current point
-    conveks.mma.setMoveLimits(lowerBound, upperBound, 0.1)
+    #conveks.mma.setMoveLimits(lowerBound, upperBound, 0.1)
     conveks.mma.updateCurrentPoint(constraints, grad_objective, grad_constraints)
     change = conveks.mma.getDesignChange()
     it = conveks.mma.getOuterIteration()

Team25/shape.py

@@ -3,8 +3,8 @@
 #
 
 import numpy as np
-import conveks
 import onelab
+import conveks
 
 c = onelab.client(__file__)
 
@@ -106,13 +106,6 @@ conveks.mma.initialize(initialPoint, lowerBound, upperBound)
 
 # Set some options for MMA
 conveks.mma.option.setNumber('General.Verbosity', 4)
-conveks.mma.option.setNumber('General.SaveHistory', 0)
-conveks.mma.option.setNumber('SubProblem.isRobustMoveLimit', 1)
-conveks.mma.option.setNumber('SubProblem.isRobustAsymptotes', 1)
-conveks.mma.option.setNumber('SubProblem.type', 0)
-conveks.mma.option.setNumber('SubProblem.addConvexity', 0)
-conveks.mma.option.setNumber('SubProblem.asymptotesRmax', 100.0)
-conveks.mma.option.setNumber('SubProblem.asymptotesRmin', 0.001)
 
 # Get iteration count (here it will be 1 - could be different in case of restart)
 it = conveks.mma.getOuterIteration()
@@ -124,7 +117,7 @@ while it <= maxIter and c.getString('shape/Action') != 'stop':
         attributes={'Highlight':'LightYellow'})
 
     # get (copy of) current point
-    xFromMMA = np.array(conveks.mma.getCurrentPoint())
+    xFromMMA = conveks.mma.getCurrentPoint()
 
     # send the current point to GetDP model
     for label, var in x.iteritems():
@@ -137,30 +130,23 @@ while it <= maxIter and c.getString('shape/Action') != 'stop':
     # mesh the geometry
     c.runSubClient('myGmsh', mygmsh + ' -2')
 
-    # generate the velocity field of each design variable
-    getVelocityField(x)
-
     # compute objective function and constraints
     c.runSubClient('myGetDP', mygetdp + '-solve GetPerformances')
+    objective = np.sum(readSimpleTable(c.getPath('res_opt/w.txt')))
+    constraints = np.array([np.sum(xFromMMA)/np.sum(upperBound)-1.0])
 
-    # as well as their sensitivity with respect to design variables at `x'
+    # generate the velocity field of each design variable
+    getVelocityField(x)
+
+    # compute the sensitivity with respect to design variables at `x'
     for dv, var in x.iteritems():
         c.runNonBlockingSubClient('myGetDP', mygetdp \
           + ' -setnumber VelocityTag '+str(dv)\
           + ' -solve GetGradient_wrt_dv')
     c.waitOnSubClients()
-
-    # get the value of the objective function and of the constraints
-    # as well as their sensitivity with respect to design variables at `x'
-    objective = np.sum(readSimpleTable(c.getPath('res_opt/w.txt')))
-    constraints = np.array([np.sum(xFromMMA)/100.0-1.0])
     grad_objective = np.asarray([np.sum(readSimpleTable(c.getPath('res_opt/Grad_w_wrt_dv_'+str(dv)+'.txt')))\
         for dv in xrange(numVariables)])
-    grad_constraints = np.ones(numVariables)/100.0
-
-    if it == 1: fscale = 1.0 / objective
-    objective *= fscale
-    grad_objective *= fscale
+    grad_constraints = np.ones(numVariables)/np.sum(upperBound)
 
     print('*'*50)
     print('iteration: ', it)
@@ -171,7 +157,7 @@ while it <= maxIter and c.getString('shape/Action') != 'stop':
     c.sendInfo('Optimization: it. {}, obj. {}, constr. {}'.format(it,objective,constraints[0]))
 
     # call MMA and update the current point
-    conveks.mma.setMoveLimits(lowerBound, upperBound, 1.0e-4)
+    conveks.mma.setMoveLimits(lowerBound, upperBound, 1.0e-3)
     conveks.mma.updateCurrentPoint(constraints,grad_objective,grad_constraints)
     change = conveks.mma.getDesignChange()
     it = conveks.mma.getOuterIteration()