mma and scipy as options on the same footing

Ccore/ccore.pro

@@ -387,7 +387,7 @@ PostOperation {
 
       CreateDir["res"];
       //Print[js, OnElementsOf Vol_S_Mag, File "res/js.pos"];
-      Print[b, OnElementsOf Vol_Mag, File "res/b.pos"];
+      //Print[b, OnElementsOf Vol_Mag, File "res/b.pos"];
 
       Print[az, OnElementsOf Vol_Mag, File "res/az.pos"];
       Echo[ Str["k= PostProcessing.NbViews-1;",
@@ -501,10 +501,10 @@ PostOperation{
 	    File "tmp.geo", LastTimeStepOnly] ;
 
       //Print[az, OnElementsOf Vol_Mag, File "res/azstar.pos"];
-      Echo[ Str["k= PostProcessing.NbViews-1;",
-		"View[k].IntervalsType = 3;",
-		"View[0].NbIso = 30;"], 
-	    File "tmp.geo", LastTimeStepOnly] ;
+      // Echo[ Str["k= PostProcessing.NbViews-1;",
+      // 		"View[k].IntervalsType = 3;",
+      // 		"View[0].NbIso = 30;"], 
+      // 	    File "tmp.geo", LastTimeStepOnly] ;
 
       Print[ Lie_az, OnElementsOf Vol_Mag, File "res/Lie_az.pos" ];
       Echo[ Str["k= PostProcessing.NbViews-1;",

Ccore/ccore_common.pro

@@ -6,6 +6,10 @@ ParamIndex =
 	       Choices {0="Optimize on D and E", 
 			1="D: air gap length", 
 	       		2="E: C-core thickness"} ];
+Optimizer = 
+  DefineNumber[0, Name "Optimization/Optimizer",
+	       Choices {0="scipy", 
+			1="MMA"}];
 StepsInRange = 
   DefineNumber[20, Name "Optimization/Steps in range", Visible ParamIndex];
 

Ccore/shp.py

@@ -8,6 +8,7 @@ import conveks
 import onelab
 from scipy.optimize import minimize
 
+modelName = 'ccore'
 
 c = onelab.client(__file__)
 
@@ -27,8 +28,6 @@ if(not len(mygetdp)):
 
 c.sendInfo('Will use gmsh={0} and getdp={1}'.format(mygmsh, mygetdp))
 
-modelName = 'ccore'
-
 # append correct path to model file names
 file_geo = c.getPath(modelName+'.geo')
 file_msh = c.getPath(modelName+'.msh')
@@ -46,10 +45,6 @@ c.sendCommand('Solver.AutoMesh = -1;')
 # dry getdp run (without -solve or -pos option) to get model parameters in the GUI
 c.runSubClient('myGetDP', mygetdp)
 
-# define optimization parameters as Onelab parameter (editable in the GUI)
-maxIter = c.defineNumber('Optimization/00Max iterations', value=100)
-maxChange = c.defineNumber('Optimization/01Max change', value=1e-0)
-
 # end of check phase. We're done if we do not start the optimization
 if c.action == 'check' :
    exit(0)
@@ -145,13 +140,22 @@ def test(n):
 # List of design parameters with format
 # [ "Onelab name", value, Min, Max, "variable name" ]
 designParameters={
-    0 : ['Optimization/Parameters/D', 0.01, 0.001, 0.050, 'D'],
+    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=1e-1,  visible=(Optimizer=='mma'))
+
 
 if index >= 0: # parameter sweep and optimisation over one selected design parameter
     x[0] = designParameters[index];
@@ -162,40 +166,28 @@ if index >= 0: # parameter sweep and optimisation over one selected design param
     print res
 
 else: # optimisation over full parameter space
+
+    if Optimizer=='scipy':
         for label, var in designParameters.iteritems():
             x[label] = var
         values = [designParameters[i][1] for i in range(len(designParameters))]
         bounds = [(designParameters[i][2], designParameters[i][3]) for i in range(len(designParameters))]
-    ## print x
-    ## print values, bounds
-    ## print fobj(values)
-    ## print grad(values)
-    ## exit(1)
 
         res = minimize(fobj, values, jac=grad, bounds=bounds, callback=callbackF,\
                 method = 'L-BFGS-B', tol=None, options={'ftol': 1e-5, 'gtol': 1e-3, 'disp':True} )
         print res
 
-    
-    
-exit(1)
-
-# number of design variables and number of constraints
-numVariables = len(x.keys())
-
-# initial value of the variables in the design space,
-# the lower bound for design variables,
-# as well as the upper bound for design variables.
+    elif Optimizer=='mma':
+        numVariables = len(designParameters.keys())
         initialPoint = np.zeros(numVariables)
         lowerBound = np.zeros(numVariables)
         upperBound = np.zeros(numVariables)
-for label, var in x.iteritems():
+        for label, var in designParameters.iteritems():
+            x[label] = var
             initialPoint[label] = var[1]
             lowerBound[label]   = var[2]
             upperBound[label]   = var[3]
   
-
-  
         # Initialize the MMA optimizer
         conveks.mma.initialize(initialPoint, lowerBound, upperBound)
 
@@ -212,17 +204,11 @@ conveks.mma.option.setNumber('SubProblem.asymptotesRmin', 0.001) # def 0.001
         # 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 > 1e-6 :
+        while it <= maxIter and c.getString(c.name+'/Action') != 'stop' and change > 1e-4 :
             c.setNumber('Optimization/01Current iteration', value=it, readOnly=1,
                          attributes={'Highlight':'LightYellow'})
 
-    # get (copy of) current point
             xFromMMA = np.array(conveks.mma.getCurrentPoint())
-    print 'xFromMMA = ', xFromMMA
-    
-    # get the value of the objective function and of the constraints
-    # as well as their sensitivity with respect to design variables at `x'
             objective = fobj(xFromMMA)
             grad_objective = grad(xFromMMA)
             constraints = np.array([np.sum(xFromMMA)/100.0-1.0])
@@ -232,7 +218,7 @@ while it <= maxIter and c.getString(c.name+'/Action') != 'stop': #and change > 1
             objective *= fscale
             grad_objective *= fscale
 
-    print it, xFromMMA, objective, grad_objective
+            print it, change, xFromMMA, objective, grad_objective
             c.sendInfo('Optimization: it. {}, obj. {}, constr. {}'.format(it,objective,constraints[0]))
 
             # call MMA and update the current point
@@ -244,3 +230,4 @@ while it <= maxIter and c.getString(c.name+'/Action') != 'stop': #and change > 1
         # This should be called at the end
         conveks.mma.finalize()
 
+