/[escript]/trunk/escript/py_src/pdetools.py
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revision 877 by gross, Wed Oct 25 03:06:58 2006 UTC revision 1465 by artak, Wed Apr 2 03:28:25 2008 UTC
# Line 1  Line 1 
1    #
2  # $Id$  # $Id$
3    #
4    #######################################################
5    #
6    #           Copyright 2003-2007 by ACceSS MNRF
7    #       Copyright 2007 by University of Queensland
8    #
9    #                http://esscc.uq.edu.au
10    #        Primary Business: Queensland, Australia
11    #  Licensed under the Open Software License version 3.0
12    #     http://www.opensource.org/licenses/osl-3.0.php
13    #
14    #######################################################
15    #
16    
17  """  """
18  Provides some tools related to PDEs.  Provides some tools related to PDEs.
# Line 32  import escript Line 46  import escript
46  import linearPDEs  import linearPDEs
47  import numarray  import numarray
48  import util  import util
49    import math
50    
51    ##### Added by Artak
52    from Numeric import zeros,Int,Float32,Float64
53    ###################################
54    
55    
56  class TimeIntegrationManager:  class TimeIntegrationManager:
57    """    """
# Line 132  class Projector: Line 152  class Projector:
152      self.__pde.setValue(D = 1.)      self.__pde.setValue(D = 1.)
153      return      return
154    
   def __del__(self):  
     return  
   
155    def __call__(self, input_data):    def __call__(self, input_data):
156      """      """
157      Projects input_data onto a continuous function      Projects input_data onto a continuous function
# Line 142  class Projector: Line 159  class Projector:
159      @param input_data: The input_data to be projected.      @param input_data: The input_data to be projected.
160      """      """
161      out=escript.Data(0.,input_data.getShape(),self.__pde.getFunctionSpaceForSolution())      out=escript.Data(0.,input_data.getShape(),self.__pde.getFunctionSpaceForSolution())
162        self.__pde.setValue(Y = escript.Data(), Y_reduced = escript.Data())
163      if input_data.getRank()==0:      if input_data.getRank()==0:
164          self.__pde.setValue(Y = input_data)          self.__pde.setValue(Y = input_data)
165          out=self.__pde.getSolution()          out=self.__pde.getSolution()
# Line 297  class Locator: Line 315  class Locator:
315         Initializes a Locator to access values in Data objects on the Doamin         Initializes a Locator to access values in Data objects on the Doamin
316         or FunctionSpace where for the sample point which         or FunctionSpace where for the sample point which
317         closest to the given point x.         closest to the given point x.
318    
319           @param where: function space
320           @type where: L{escript.FunctionSpace}
321           @param x: coefficient of the solution.
322           @type x: L{numarray.NumArray} or C{list} of L{numarray.NumArray}
323         """         """
324         if isinstance(where,escript.FunctionSpace):         if isinstance(where,escript.FunctionSpace):
325            self.__function_space=where            self.__function_space=where
326         else:         else:
327            self.__function_space=escript.ContinuousFunction(where)            self.__function_space=escript.ContinuousFunction(where)
328         self.__id=util.length(self.__function_space.getX()-x[:self.__function_space.getDim()]).mindp()         if isinstance(x, list):
329               self.__id=[]
330               for p in x:
331                  self.__id.append(util.length(self.__function_space.getX()-p[:self.__function_space.getDim()]).minGlobalDataPoint())
332           else:
333               self.__id=util.length(self.__function_space.getX()-x[:self.__function_space.getDim()]).minGlobalDataPoint()
334    
335       def __str__(self):       def __str__(self):
336         """         """
337         Returns the coordinates of the Locator as a string.         Returns the coordinates of the Locator as a string.
338         """         """
339         return "<Locator %s>"%str(self.getX())         x=self.getX()
340           if instance(x,list):
341              out="["
342              first=True
343              for xx in x:
344                if not first:
345                    out+=","
346                else:
347                    first=False
348                out+=str(xx)
349              out+="]>"
350           else:
351              out=str(x)
352           return out
353    
354         def getX(self):
355            """
356        Returns the exact coordinates of the Locator.
357        """
358            return self(self.getFunctionSpace().getX())
359    
360       def getFunctionSpace(self):       def getFunctionSpace(self):
361          """          """
# Line 316  class Locator: Line 363  class Locator:
363      """      """
364          return self.__function_space          return self.__function_space
365    
366       def getId(self):       def getId(self,item=None):
367          """          """
368      Returns the identifier of the location.      Returns the identifier of the location.
369      """      """
370          return self.__id          if item == None:
371               return self.__id
372            else:
373               if isinstance(self.__id,list):
374                  return self.__id[item]
375               else:
376                  return self.__id
377    
      def getX(self):  
         """  
     Returns the exact coordinates of the Locator.  
     """  
         return self(self.getFunctionSpace().getX())  
378    
379       def __call__(self,data):       def __call__(self,data):
380          """          """
# Line 342  class Locator: Line 390  class Locator:
390      """      """
391          if isinstance(data,escript.Data):          if isinstance(data,escript.Data):
392             if data.getFunctionSpace()==self.getFunctionSpace():             if data.getFunctionSpace()==self.getFunctionSpace():
393               #out=data.convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1])               dat=data
              out=data.convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1],self.getId()[2])  
394             else:             else:
395               #out=data.interpolate(self.getFunctionSpace()).convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1])               dat=data.interpolate(self.getFunctionSpace())
396               out=data.interpolate(self.getFunctionSpace()).convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1],self.getId()[2])             id=self.getId()
397             if data.getRank()==0:             r=data.getRank()
398                return out[0]             if isinstance(id,list):
399                   out=[]
400                   for i in id:
401                      o=data.getValueOfGlobalDataPoint(*i)
402                      if data.getRank()==0:
403                         out.append(o[0])
404                      else:
405                         out.append(o)
406                   return out
407             else:             else:
408                return out               out=data.getValueOfGlobalDataPoint(*id)
409                 if data.getRank()==0:
410                    return out[0]
411                 else:
412                    return out
413          else:          else:
414             return data             return data
415    
416    class SolverSchemeException(Exception):
417       """
418       exceptions thrown by solvers
419       """
420       pass
421    
422    class IndefinitePreconditioner(SolverSchemeException):
423       """
424       the preconditioner is not positive definite.
425       """
426       pass
427    class MaxIterReached(SolverSchemeException):
428       """
429       maxium number of iteration steps is reached.
430       """
431       pass
432    class IterationBreakDown(SolverSchemeException):
433       """
434       iteration scheme econouters an incurable breakdown.
435       """
436       pass
437    class NegativeNorm(SolverSchemeException):
438       """
439       a norm calculation returns a negative norm.
440       """
441       pass
442    
443    class IterationHistory(object):
444       """
445       The IterationHistory class is used to define a stopping criterium. It keeps track of the
446       residual norms. The stoppingcriterium indicates termination if the residual norm has been reduced by
447       a given tolerance.
448       """
449       def __init__(self,tolerance=math.sqrt(util.EPSILON),verbose=False):
450          """
451          Initialization
452    
453          @param tolerance: tolerance
454          @type tolerance: positive C{float}
455          @param verbose: switches on the printing out some information
456          @type verbose: C{bool}
457          """
458          if not tolerance>0.:
459              raise ValueError,"tolerance needs to be positive."
460          self.tolerance=tolerance
461          self.verbose=verbose
462          self.history=[]
463       def stoppingcriterium(self,norm_r,r,x):
464           """
465           returns True if the C{norm_r} is C{tolerance}*C{norm_r[0]} where C{norm_r[0]}  is the residual norm at the first call.
466    
467          
468           @param norm_r: current residual norm
469           @type norm_r: non-negative C{float}
470           @param r: current residual (not used)
471           @param x: current solution approximation (not used)
472           @return: C{True} is the stopping criterium is fullfilled. Otherwise C{False} is returned.
473           @rtype: C{bool}
474    
475           """
476           self.history.append(norm_r)
477           if self.verbose: print "iter: %s:  inner(rhat,r) = %e"%(len(self.history)-1, self.history[-1])
478           return self.history[-1]<=self.tolerance * self.history[0]
479    
480    def PCG(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100):
481       """
482       Solver for
483    
484       M{Ax=b}
485    
486       with a symmetric and positive definite operator A (more details required!).
487       It uses the conjugate gradient method with preconditioner M providing an approximation of A.
488    
489       The iteration is terminated if the C{stoppingcriterium} function return C{True}.
490    
491       For details on the preconditioned conjugate gradient method see the book:
492    
493       Templates for the Solution of Linear Systems by R. Barrett, M. Berry,
494       T.F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo,
495       C. Romine, and H. van der Vorst.
496    
497       @param b: the right hand side of the liner system. C{b} is altered.
498       @type b: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
499       @param Aprod: returns the value Ax
500       @type Aprod: function C{Aprod(x)} where C{x} is of the same object like argument C{x}. The returned object needs to be of the same type like argument C{b}.
501       @param Msolve: solves Mx=r
502       @type Msolve: function C{Msolve(r)} where C{r} is of the same type like argument C{b}. The returned object needs to be of the same
503    type like argument C{x}.
504       @param bilinearform: inner product C{<x,r>}
505       @type bilinearform: function C{bilinearform(x,r)} where C{x} is of the same type like argument C{x} and C{r} is . The returned value is a C{float}.
506       @param stoppingcriterium: function which returns True if a stopping criterium is meet. C{stoppingcriterium} has the arguments C{norm_r}, C{r} and C{x} giving the current norm of the residual (=C{sqrt(bilinearform(Msolve(r),r)}), the current residual and the current solution approximation. C{stoppingcriterium} is called in each iteration step.
507       @type stoppingcriterium: function that returns C{True} or C{False}
508       @param x: an initial guess for the solution. If no C{x} is given 0*b is used.
509       @type x: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
510       @param iter_max: maximum number of iteration steps.
511       @type iter_max: C{int}
512       @return: the solution approximation and the corresponding residual
513       @rtype: C{tuple}
514       @warning: C{b} and C{x} are altered.
515       """
516       iter=0
517       if x==None:
518          x=0*b
519       else:
520          b += (-1)*Aprod(x)
521       r=b
522       rhat=Msolve(r)
523       d = rhat
524       rhat_dot_r = bilinearform(rhat, r)
525       if rhat_dot_r<0: raise NegativeNorm,"negative norm."
526    
527       while not stoppingcriterium(math.sqrt(rhat_dot_r),r,x):
528           iter+=1
529           if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
530    
531           q=Aprod(d)
532           alpha = rhat_dot_r / bilinearform(d, q)
533           x += alpha * d
534           r += (-alpha) * q
535    
536           rhat=Msolve(r)
537           rhat_dot_r_new = bilinearform(rhat, r)
538           beta = rhat_dot_r_new / rhat_dot_r
539           rhat+=beta * d
540           d=rhat
541    
542           rhat_dot_r = rhat_dot_r_new
543           if rhat_dot_r<0: raise NegativeNorm,"negative norm."
544    
545       return x,r
546    
547    
548    ############################
549    # Added by Artak
550    #################################3
551    
552    #Apply a sequence of k Givens rotations, used within gmres codes
553    # vrot=givapp(c, s, vin, k)
554    def givapp(c,s,vin,k):
555        vrot=vin
556        for i in range(k+1):
557            w1=c[i]*vrot[i]-s[i]*vrot[i+1]
558        w2=s[i]*vrot[i]+c[i]*vrot[i+1]
559            vrot[i:i+2]=w1,w2
560        return vrot
561    
562    def GMRES(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100):
563    
564       from numarray import dot
565      
566       v0=b
567       iter=0
568       if x==None:
569          x=0*b
570       else:
571          b += (-1.)*Aprod(x)
572       r=b
573    
574       rhat=Msolve(r)
575      
576       rhat_dot_r = bilinearform(rhat, rhat)
577       norm_r=math.sqrt(rhat_dot_r)
578    
579       if rhat_dot_r<0: raise NegativeNorm,"negative norm."
580      
581       h=zeros((iter_max,iter_max),Float32)
582       c=zeros(iter_max,Float32)
583       s=zeros(iter_max,Float32)
584       g=zeros(iter_max,Float32)
585       v=[]
586    
587       v.append(rhat/norm_r)
588       rho=norm_r
589       g[0]=rho
590    
591       while not stoppingcriterium(rho,rho,norm_r):
592    
593        if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
594    
595        
596        vhat=Aprod(v[iter])
597        p=Msolve(vhat)  
598    
599        v.append(p)
600    
601        v_norm1=math.sqrt(bilinearform(v[iter+1], v[iter+1]))  
602    
603    # Modified Gram-Schmidt
604        for j in range(iter+1):
605          h[j][iter]=bilinearform(v[j],v[iter+1])  
606          v[iter+1]+=(-1.)*h[j][iter]*v[j]
607          
608        h[iter+1][iter]=math.sqrt(bilinearform(v[iter+1],v[iter+1]))
609        v_norm2=h[iter+1][iter]
610    
611    
612    # Reorthogonalize if needed
613        if v_norm1 + 0.001*v_norm2 == v_norm1:   #Brown/Hindmarsh condition (default)
614         for j in range(iter+1):
615            hr=bilinearform(v[j],v[iter+1])
616                h[j][iter]=h[j][iter]+hr #vhat
617                v[iter+1] +=(-1.)*hr*v[j]
618    
619         v_norm2=math.sqrt(bilinearform(v[iter+1], v[iter+1]))  
620         h[iter+1][iter]=v_norm2
621    
622    #   watch out for happy breakdown
623            if v_norm2 != 0:
624             v[iter+1]=v[iter+1]/h[iter+1][iter]
625    
626    #   Form and store the information for the new Givens rotation
627        if iter > 0 :
628            hhat=[]
629            for i in range(iter+1) : hhat.append(h[i][iter])
630            hhat=givapp(c[0:iter],s[0:iter],hhat,iter-1);
631                for i in range(iter+1) : h[i][iter]=hhat[i]
632    
633        mu=math.sqrt(h[iter][iter]*h[iter][iter]+h[iter+1][iter]*h[iter+1][iter])
634        if mu!=0 :
635            c[iter]=h[iter][iter]/mu
636            s[iter]=-h[iter+1][iter]/mu
637            h[iter][iter]=c[iter]*h[iter][iter]-s[iter]*h[iter+1][iter]
638            h[iter+1][iter]=0.0
639            g[iter:iter+2]=givapp(c[iter],s[iter],g[iter:iter+2],0)
640    
641    # Update the residual norm
642            rho=abs(g[iter+1])
643        iter+=1
644    
645    # At this point either iter > iter_max or rho < tol.
646    # It's time to compute x and leave.        
647    
648       if iter > 0 :
649         y=zeros(iter,Float32)  
650         y[iter-1] = g[iter-1] / h[iter-1][iter-1]
651         if iter > 1 :  
652            i=iter-2  
653            while i>=0 :
654              y[i] = ( g[i] - dot(h[i][i+1:iter], y[i+1:iter])) / h[i][i]
655              i=i-1
656         xhat=v[iter-1]*y[iter-1]
657         for i in range(iter-1):
658        xhat += v[i]*y[i]
659       else : xhat=v[0]
660        
661       x += xhat
662    
663       return x
664        
665    #############################################
666    
667    class ArithmeticTuple(object):
668       """
669       tuple supporting inplace update x+=y and scaling x=a*y where x,y is an ArithmeticTuple and a is a float.
670    
671       example of usage:
672    
673       from esys.escript import Data
674       from numarray import array
675       a=Data(...)
676       b=array([1.,4.])
677       x=ArithmeticTuple(a,b)
678       y=5.*x
679    
680       """
681       def __init__(self,*args):
682           """
683           initialize object with elements args.
684    
685           @param args: tuple of object that support implace add (x+=y) and scaling (x=a*y)
686           """
687           self.__items=list(args)
688    
689       def __len__(self):
690           """
691           number of items
692    
693           @return: number of items
694           @rtype: C{int}
695           """
696           return len(self.__items)
697    
698       def __getitem__(self,index):
699           """
700           get an item
701    
702           @param index: item to be returned
703           @type index: C{int}
704           @return: item with index C{index}
705           """
706           return self.__items.__getitem__(index)
707    
708       def __mul__(self,other):
709           """
710           scaling from the right
711    
712           @param other: scaling factor
713           @type other: C{float}
714           @return: itemwise self*other
715           @rtype: L{ArithmeticTuple}
716           """
717           out=[]
718           for i in range(len(self)):
719               out.append(self[i]*other)
720           return ArithmeticTuple(*tuple(out))
721    
722       def __rmul__(self,other):
723           """
724           scaling from the left
725    
726           @param other: scaling factor
727           @type other: C{float}
728           @return: itemwise other*self
729           @rtype: L{ArithmeticTuple}
730           """
731           out=[]
732           for i in range(len(self)):
733               out.append(other*self[i])
734           return ArithmeticTuple(*tuple(out))
735    
736    #########################
737    # Added by Artak
738    #########################
739       def __div__(self,other):
740           """
741           dividing from the right
742    
743           @param other: scaling factor
744           @type other: C{float}
745           @return: itemwise self/other
746           @rtype: L{ArithmeticTuple}
747           """
748           out=[]
749           for i in range(len(self)):
750               out.append(self[i]/other)
751           return ArithmeticTuple(*tuple(out))
752    
753       def __rdiv__(self,other):
754           """
755           dividing from the left
756    
757           @param other: scaling factor
758           @type other: C{float}
759           @return: itemwise other/self
760           @rtype: L{ArithmeticTuple}
761           """
762           out=[]
763           for i in range(len(self)):
764               out.append(other/self[i])
765           return ArithmeticTuple(*tuple(out))
766      
767    ##########################################33
768    
769       def __iadd__(self,other):
770           """
771           in-place add of other to self
772    
773           @param other: increment
774           @type other: C{ArithmeticTuple}
775           """
776           if len(self) != len(other):
777               raise ValueError,"tuple length must match."
778           for i in range(len(self)):
779               self.__items[i]+=other[i]
780           return self
781    
782    class HomogeneousSaddlePointProblem(object):
783          """
784          This provides a framwork for solving homogeneous saddle point problem of the form
785    
786                 Av+B^*p=f
787                 Bv    =0
788    
789          for the unknowns v and p and given operators A and B and given right hand side f.
790          B^* is the adjoint operator of B is the given inner product.
791    
792          """
793          def __init__(self,**kwargs):
794            self.setTolerance()
795            self.setToleranceReductionFactor()
796    
797          def initialize(self):
798            """
799            initialize the problem (overwrite)
800            """
801            pass
802          def B(self,v):
803             """
804             returns Bv (overwrite)
805             @rtype: equal to the type of p
806    
807             @note: boundary conditions on p should be zero!
808             """
809             pass
810    
811          def inner(self,p0,p1):
812             """
813             returns inner product of two element p0 and p1  (overwrite)
814            
815             @type p0: equal to the type of p
816             @type p1: equal to the type of p
817             @rtype: C{float}
818    
819             @rtype: equal to the type of p
820             """
821             pass
822    
823          def solve_A(self,u,p):
824             """
825             solves Av=f-Au-B^*p with accuracy self.getReducedTolerance() (overwrite)
826    
827             @rtype: equal to the type of v
828             @note: boundary conditions on v should be zero!
829             """
830             pass
831    
832          def solve_prec(self,p):
833             """
834             provides a preconditioner for BA^{-1}B^* with accuracy self.getReducedTolerance() (overwrite)
835    
836             @rtype: equal to the type of p
837             """
838             pass
839    
840          def stoppingcriterium(self,Bv,v,p):
841             """
842             returns a True if iteration is terminated. (overwrite)
843    
844             @rtype: C{bool}
845             """
846             pass
847                
848          def __inner(self,p,r):
849             return self.inner(p,r[1])
850    
851          def __inner_p(self,p1,p2):
852             return self.inner(p1,p2)
853    
854          def __stoppingcriterium(self,norm_r,r,p):
855              return self.stoppingcriterium(r[1],r[0],p)
856    
857          def __stoppingcriterium_GMRES(self,norm_r,rho,r):
858              return self.stoppingcriterium_GMRES(rho,r)
859    
860          def setTolerance(self,tolerance=1.e-8):
861                  self.__tol=tolerance
862          def getTolerance(self):
863                  return self.__tol
864          def setToleranceReductionFactor(self,reduction=0.01):
865                  self.__reduction=reduction
866          def getSubProblemTolerance(self):
867                  return self.__reduction*self.getTolerance()
868    
869          def solve(self,v,p,max_iter=20, verbose=False, show_details=False, solver='GMRES'):
870                  """
871                  solves the saddle point problem using initial guesses v and p.
872    
873                  @param max_iter: maximum number of iteration steps.
874                  """
875                  self.verbose=verbose
876                  self.show_details=show_details and self.verbose
877    
878              # Az=f is solved as A(z-v)=f-Av (z-v = 0 on fixed_u_mask)      
879    
880              
881              self.__z=v+self.solve_A(v,p*0)
882    
883                  Bz=self.B(self.__z)
884                  #
885              #   solve BA^-1B^*p = Bz
886                  #
887                  #   note that the residual r=Bz-BA^-1B^*p = B(z-A^-1B^*p) = Bv
888                  #
889                  #   with                    Av=Az-B^*p = f - B^*p (v=z on fixed_u_mask)
890                  #                           A(v-z)=Az-B^*p-Az = f -Az - B^*p (v-z=0 on fixed_u_mask)
891                  #
892                  self.iter=0
893              if solver=='GMRES':      
894                    if self.verbose: print "enter GMRES method (iter_max=%s)"%max_iter
895                    p=GMRES(Bz,self.__Aprod_GMRES,self.__Msolve_GMRES,self.__inner_p,self.__stoppingcriterium_GMRES,iter_max=max_iter, x=p*1)
896            u=v+self.solve_A(v,p)
897        
898                  else:
899                    if self.verbose: print "enter PCG method (iter_max=%s)"%max_iter
900                    p,r=PCG(ArithmeticTuple(self.__z*1.,Bz),self.__Aprod,self.__Msolve,self.__inner,self.__stoppingcriterium,iter_max=max_iter, x=p*1)
901                u=r[0]  
902                    u=v+self.solve_A(v,p)     # Lutz to check !!!!!
903    
904              return u,p
905    
906          def __Msolve(self,r):
907              return self.solve_prec(r[1])
908    
909          def __Msolve_GMRES(self,r):
910              return self.solve_prec(r)
911    
912    
913          def __Aprod(self,p):
914              # return BA^-1B*p
915              #solve Av =-B^*p as Av =f-Az-B^*p
916              v=self.solve_A(self.__z,p)
917              return ArithmeticTuple(v, self.B(v))
918    
919          def __Aprod_GMRES(self,p):
920              # return BA^-1B*p
921              #solve Av =-B^*p as Av =f-Az-B^*p
922          v=self.solve_A(self.__z,p)
923              return self.B(v)
924    
925  class SaddlePointProblem(object):  class SaddlePointProblem(object):
926     """     """
927     This implements a solver for a saddlepoint problem     This implements a solver for a saddlepoint problem
# Line 363  class SaddlePointProblem(object): Line 931  class SaddlePointProblem(object):
931    
932     for u and p. The problem is solved with an inexact Uszawa scheme for p:     for u and p. The problem is solved with an inexact Uszawa scheme for p:
933    
934     M{Q_f (u^{k+1}-u^{k}) = - f(u^{k},p^{k})     M{Q_f (u^{k+1}-u^{k}) = - f(u^{k},p^{k})}
935     M{Q_g (p^{k+1}-p^{k}) =   g(u^{k+1})}     M{Q_g (p^{k+1}-p^{k}) =   g(u^{k+1})}
936    
937     where Q_f is an approximation of the Jacobiean A_f of f with respect to u  and Q_f is an approximation of     where Q_f is an approximation of the Jacobiean A_f of f with respect to u  and Q_f is an approximation of
# Line 375  class SaddlePointProblem(object): Line 943  class SaddlePointProblem(object):
943         """         """
944         initializes the problem         initializes the problem
945    
946         @parm verbose: switches on the printing out some information         @param verbose: switches on the printing out some information
947         @type verbose: C{bool}         @type verbose: C{bool}
948         @note: this method may be overwritten by a particular saddle point problem         @note: this method may be overwritten by a particular saddle point problem
949         """         """
950           if not isinstance(verbose,bool):
951                raise TypeError("verbose needs to be of type bool.")
952         self.__verbose=verbose         self.__verbose=verbose
953         self.relaxation=1.         self.relaxation=1.
954    
# Line 386  class SaddlePointProblem(object): Line 956  class SaddlePointProblem(object):
956         """         """
957         prints text if verbose has been set         prints text if verbose has been set
958    
959         @parm text: a text message         @param text: a text message
960         @type text: C{str}         @type text: C{str}
961         """         """
962         if self.__verbose: print "%s: %s"%(str(self),text)         if self.__verbose: print "%s: %s"%(str(self),text)
# Line 539  class SaddlePointProblem(object): Line 1109  class SaddlePointProblem(object):
1109              norm_u_new = util.Lsup(u_new)              norm_u_new = util.Lsup(u_new)
1110              p_new=p+self.relaxation*g_new              p_new=p+self.relaxation*g_new
1111              norm_p_new = util.sqrt(self.inner(p_new,p_new))              norm_p_new = util.sqrt(self.inner(p_new,p_new))
1112              self.trace("%s th step: f/u = %s, g/p = %s, relaxation = %s."%(self.iter,norm_f_new/norm_u_new, norm_g_new/norm_p_new, self.relaxation))              self.trace("%s th step: f = %s, f/u = %s, g = %s, g/p = %s, relaxation = %s."%(self.iter, norm_f_new ,norm_f_new/norm_u_new, norm_g_new, norm_g_new/norm_p_new, self.relaxation))
1113    
1114              if self.iter>1:              if self.iter>1:
1115                 dg2=g_new-g                 dg2=g_new-g
# Line 551  class SaddlePointProblem(object): Line 1121  class SaddlePointProblem(object):
1121                 tol_eq_f=tolerance_u*norm_df2/norm_f*norm_u_new                 tol_eq_f=tolerance_u*norm_df2/norm_f*norm_u_new
1122                 if norm_g_new <= tol_eq_g and norm_f_new <= tol_eq_f:                 if norm_g_new <= tol_eq_g and norm_f_new <= tol_eq_f:
1123                     converged=True                     converged=True
                    break  
1124              f, norm_f, u, norm_u, g, norm_g, p, norm_p = f_new, norm_f_new, u_new, norm_u_new, g_new, norm_g_new, p_new, norm_p_new              f, norm_f, u, norm_u, g, norm_g, p, norm_p = f_new, norm_f_new, u_new, norm_u_new, g_new, norm_g_new, p_new, norm_p_new
1125          self.trace("convergence after %s steps."%self.iter)          self.trace("convergence after %s steps."%self.iter)
1126          return u,p          return u,p
# Line 582  class SaddlePointProblem(object): Line 1151  class SaddlePointProblem(object):
1151  #  #
1152  #      return u,p  #      return u,p
1153                        
1154  # vim: expandtab shiftwidth=4:  def MaskFromBoundaryTag(function_space,*tags):
1155       """
1156       create a mask on the given function space which one for samples
1157       that touch the boundary tagged by tags.
1158    
1159       usage: m=MaskFromBoundaryTag(Solution(domain),"left", "right")
1160    
1161       @param function_space: a given function space
1162       @type function_space: L{escript.FunctionSpace}
1163       @param tags: boundray tags
1164       @type tags: C{str}
1165       @return: a mask which marks samples used by C{function_space} that are touching the
1166                boundary tagged by any of the given tags.
1167       @rtype: L{escript.Data} of rank 0
1168       """
1169       pde=linearPDEs.LinearPDE(function_space.getDomain(),numEquations=1, numSolutions=1)
1170       d=escript.Scalar(0.,escript.FunctionOnBoundary(function_space.getDomain()))
1171       for t in tags: d.setTaggedValue(t,1.)
1172       pde.setValue(y=d)
1173       out=util.whereNonZero(pde.getRightHandSide())
1174       if out.getFunctionSpace() == function_space:
1175          return out
1176       else:
1177          return util.whereNonZero(util.interpolate(out,function_space))
1178    
1179    
1180    

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