/[escript]/trunk/escript/py_src/pdetools.py
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revision 877 by gross, Wed Oct 25 03:06:58 2006 UTC revision 1330 by gross, Mon Oct 22 04:54:49 2007 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  class TimeIntegrationManager:  class TimeIntegrationManager:
52    """    """
# Line 132  class Projector: Line 147  class Projector:
147      self.__pde.setValue(D = 1.)      self.__pde.setValue(D = 1.)
148      return      return
149    
   def __del__(self):  
     return  
   
150    def __call__(self, input_data):    def __call__(self, input_data):
151      """      """
152      Projects input_data onto a continuous function      Projects input_data onto a continuous function
# Line 142  class Projector: Line 154  class Projector:
154      @param input_data: The input_data to be projected.      @param input_data: The input_data to be projected.
155      """      """
156      out=escript.Data(0.,input_data.getShape(),self.__pde.getFunctionSpaceForSolution())      out=escript.Data(0.,input_data.getShape(),self.__pde.getFunctionSpaceForSolution())
157        self.__pde.setValue(Y = escript.Data(), Y_reduced = escript.Data())
158      if input_data.getRank()==0:      if input_data.getRank()==0:
159          self.__pde.setValue(Y = input_data)          self.__pde.setValue(Y = input_data)
160          out=self.__pde.getSolution()          out=self.__pde.getSolution()
# Line 297  class Locator: Line 310  class Locator:
310         Initializes a Locator to access values in Data objects on the Doamin         Initializes a Locator to access values in Data objects on the Doamin
311         or FunctionSpace where for the sample point which         or FunctionSpace where for the sample point which
312         closest to the given point x.         closest to the given point x.
313    
314           @param where: function space
315           @type where: L{escript.FunctionSpace}
316           @param x: coefficient of the solution.
317           @type x: L{numarray.NumArray} or C{list} of L{numarray.NumArray}
318         """         """
319         if isinstance(where,escript.FunctionSpace):         if isinstance(where,escript.FunctionSpace):
320            self.__function_space=where            self.__function_space=where
321         else:         else:
322            self.__function_space=escript.ContinuousFunction(where)            self.__function_space=escript.ContinuousFunction(where)
323         self.__id=util.length(self.__function_space.getX()-x[:self.__function_space.getDim()]).mindp()         if isinstance(x, list):
324               self.__id=[]
325               for p in x:
326                  self.__id.append(util.length(self.__function_space.getX()-p[:self.__function_space.getDim()]).minGlobalDataPoint())
327           else:
328               self.__id=util.length(self.__function_space.getX()-x[:self.__function_space.getDim()]).minGlobalDataPoint()
329    
330       def __str__(self):       def __str__(self):
331         """         """
332         Returns the coordinates of the Locator as a string.         Returns the coordinates of the Locator as a string.
333         """         """
334         return "<Locator %s>"%str(self.getX())         x=self.getX()
335           if instance(x,list):
336              out="["
337              first=True
338              for xx in x:
339                if not first:
340                    out+=","
341                else:
342                    first=False
343                out+=str(xx)
344              out+="]>"
345           else:
346              out=str(x)
347           return out
348    
349         def getX(self):
350            """
351        Returns the exact coordinates of the Locator.
352        """
353            return self(self.getFunctionSpace().getX())
354    
355       def getFunctionSpace(self):       def getFunctionSpace(self):
356          """          """
# Line 316  class Locator: Line 358  class Locator:
358      """      """
359          return self.__function_space          return self.__function_space
360    
361       def getId(self):       def getId(self,item=None):
362          """          """
363      Returns the identifier of the location.      Returns the identifier of the location.
364      """      """
365          return self.__id          if item == None:
366               return self.__id
367            else:
368               if isinstance(self.__id,list):
369                  return self.__id[item]
370               else:
371                  return self.__id
372    
      def getX(self):  
         """  
     Returns the exact coordinates of the Locator.  
     """  
         return self(self.getFunctionSpace().getX())  
373    
374       def __call__(self,data):       def __call__(self,data):
375          """          """
# Line 342  class Locator: Line 385  class Locator:
385      """      """
386          if isinstance(data,escript.Data):          if isinstance(data,escript.Data):
387             if data.getFunctionSpace()==self.getFunctionSpace():             if data.getFunctionSpace()==self.getFunctionSpace():
388               #out=data.convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1])               dat=data
              out=data.convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1],self.getId()[2])  
389             else:             else:
390               #out=data.interpolate(self.getFunctionSpace()).convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1])               dat=data.interpolate(self.getFunctionSpace())
391               out=data.interpolate(self.getFunctionSpace()).convertToNumArrayFromDPNo(self.getId()[0],self.getId()[1],self.getId()[2])             id=self.getId()
392             if data.getRank()==0:             r=data.getRank()
393                return out[0]             if isinstance(id,list):
394                   out=[]
395                   for i in id:
396                      o=data.getValueOfGlobalDataPoint(*i)
397                      if data.getRank()==0:
398                         out.append(o[0])
399                      else:
400                         out.append(o)
401                   return out
402             else:             else:
403                return out               out=data.getValueOfGlobalDataPoint(*id)
404                 if data.getRank()==0:
405                    return out[0]
406                 else:
407                    return out
408          else:          else:
409             return data             return data
410    
411    class SolverSchemeException(Exception):
412       """
413       exceptions thrown by solvers
414       """
415       pass
416    
417    class IndefinitePreconditioner(SolverSchemeException):
418       """
419       the preconditioner is not positive definite.
420       """
421       pass
422    class MaxIterReached(SolverSchemeException):
423       """
424       maxium number of iteration steps is reached.
425       """
426       pass
427    class IterationBreakDown(SolverSchemeException):
428       """
429       iteration scheme econouters an incurable breakdown.
430       """
431       pass
432    class NegativeNorm(SolverSchemeException):
433       """
434       a norm calculation returns a negative norm.
435       """
436       pass
437    
438    class IterationHistory(object):
439       """
440       The IterationHistory class is used to define a stopping criterium. It keeps track of the
441       residual norms. The stoppingcriterium indicates termination if the residual norm has been reduced by
442       a given tolerance.
443       """
444       def __init__(self,tolerance=math.sqrt(util.EPSILON),verbose=False):
445          """
446          Initialization
447    
448          @param tolerance: tolerance
449          @type tolerance: positive C{float}
450          @param verbose: switches on the printing out some information
451          @type verbose: C{bool}
452          """
453          if not tolerance>0.:
454              raise ValueError,"tolerance needs to be positive."
455          self.tolerance=tolerance
456          self.verbose=verbose
457          self.history=[]
458       def stoppingcriterium(self,norm_r,r,x):
459           """
460           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.
461    
462          
463           @param norm_r: current residual norm
464           @type norm_r: non-negative C{float}
465           @param r: current residual (not used)
466           @param x: current solution approximation (not used)
467           @return: C{True} is the stopping criterium is fullfilled. Otherwise C{False} is returned.
468           @rtype: C{bool}
469    
470           """
471           self.history.append(norm_r)
472           if self.verbose: print "iter: %s:  inner(rhat,r) = %e"%(len(self.history)-1, self.history[-1])
473           return self.history[-1]<=self.tolerance * self.history[0]
474    
475    def PCG(b, Aprod, Msolve, bilinearform, stoppingcriterium, x=None, iter_max=100):
476       """
477       Solver for
478    
479       M{Ax=b}
480    
481       with a symmetric and positive definite operator A (more details required!).
482       It uses the conjugate gradient method with preconditioner M providing an approximation of A.
483    
484       The iteration is terminated if the C{stoppingcriterium} function return C{True}.
485    
486       For details on the preconditioned conjugate gradient method see the book:
487    
488       Templates for the Solution of Linear Systems by R. Barrett, M. Berry,
489       T.F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo,
490       C. Romine, and H. van der Vorst.
491    
492       @param b: the right hand side of the liner system. C{b} is altered.
493       @type b: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
494       @param Aprod: returns the value Ax
495       @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}.
496       @param Msolve: solves Mx=r
497       @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
498    type like argument C{x}.
499       @param bilinearform: inner product C{<x,r>}
500       @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}.
501       @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.
502       @type stoppingcriterium: function that returns C{True} or C{False}
503       @param x: an initial guess for the solution. If no C{x} is given 0*b is used.
504       @type x: any object supporting inplace add (x+=y) and scaling (x=scalar*y)
505       @param iter_max: maximum number of iteration steps.
506       @type iter_max: C{int}
507       @return: the solution approximation and the corresponding residual
508       @rtype: C{tuple}
509       @warning: C{b} and C{x} are altered.
510       """
511       iter=0
512       if x==None:
513          x=0*b
514       else:
515          b += (-1)*Aprod(x)
516       r=b
517       rhat=Msolve(r)
518       d = rhat
519       rhat_dot_r = bilinearform(rhat, r)
520       if rhat_dot_r<0: raise NegativeNorm,"negative norm."
521    
522       while not stoppingcriterium(math.sqrt(rhat_dot_r),r,x):
523           iter+=1
524           if iter  >= iter_max: raise MaxIterReached,"maximum number of %s steps reached."%iter_max
525    
526           q=Aprod(d)
527           alpha = rhat_dot_r / bilinearform(d, q)
528           x += alpha * d
529           r += (-alpha) * q
530    
531           rhat=Msolve(r)
532           rhat_dot_r_new = bilinearform(rhat, r)
533           beta = rhat_dot_r_new / rhat_dot_r
534           rhat+=beta * d
535           d=rhat
536    
537           rhat_dot_r = rhat_dot_r_new
538           if rhat_dot_r<0: raise NegativeNorm,"negative norm."
539    
540       return x,r
541    
542  class SaddlePointProblem(object):  class SaddlePointProblem(object):
543     """     """
544     This implements a solver for a saddlepoint problem     This implements a solver for a saddlepoint problem
# Line 363  class SaddlePointProblem(object): Line 548  class SaddlePointProblem(object):
548    
549     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:
550    
551     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})}
552     M{Q_g (p^{k+1}-p^{k}) =   g(u^{k+1})}     M{Q_g (p^{k+1}-p^{k}) =   g(u^{k+1})}
553    
554     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 560  class SaddlePointProblem(object):
560         """         """
561         initializes the problem         initializes the problem
562    
563         @parm verbose: switches on the printing out some information         @param verbose: switches on the printing out some information
564         @type verbose: C{bool}         @type verbose: C{bool}
565         @note: this method may be overwritten by a particular saddle point problem         @note: this method may be overwritten by a particular saddle point problem
566         """         """
567           if not isinstance(verbose,bool):
568                raise TypeError("verbose needs to be of type bool.")
569         self.__verbose=verbose         self.__verbose=verbose
570         self.relaxation=1.         self.relaxation=1.
571    
# Line 386  class SaddlePointProblem(object): Line 573  class SaddlePointProblem(object):
573         """         """
574         prints text if verbose has been set         prints text if verbose has been set
575    
576         @parm text: a text message         @param text: a text message
577         @type text: C{str}         @type text: C{str}
578         """         """
579         if self.__verbose: print "%s: %s"%(str(self),text)         if self.__verbose: print "%s: %s"%(str(self),text)
# Line 539  class SaddlePointProblem(object): Line 726  class SaddlePointProblem(object):
726              norm_u_new = util.Lsup(u_new)              norm_u_new = util.Lsup(u_new)
727              p_new=p+self.relaxation*g_new              p_new=p+self.relaxation*g_new
728              norm_p_new = util.sqrt(self.inner(p_new,p_new))              norm_p_new = util.sqrt(self.inner(p_new,p_new))
729              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))
730    
731              if self.iter>1:              if self.iter>1:
732                 dg2=g_new-g                 dg2=g_new-g
# Line 551  class SaddlePointProblem(object): Line 738  class SaddlePointProblem(object):
738                 tol_eq_f=tolerance_u*norm_df2/norm_f*norm_u_new                 tol_eq_f=tolerance_u*norm_df2/norm_f*norm_u_new
739                 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:
740                     converged=True                     converged=True
                    break  
741              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
742          self.trace("convergence after %s steps."%self.iter)          self.trace("convergence after %s steps."%self.iter)
743          return u,p          return u,p
# Line 582  class SaddlePointProblem(object): Line 768  class SaddlePointProblem(object):
768  #  #
769  #      return u,p  #      return u,p
770                        
771  # vim: expandtab shiftwidth=4:  def MaskFromBoundaryTag(function_space,*tags):
772       """
773       create a mask on the given function space which one for samples
774       that touch the boundary tagged by tags.
775    
776       usage: m=MaskFromBoundaryTag(Solution(domain),"left", "right")
777    
778       @param function_space: a given function space
779       @type function_space: L{escript.FunctionSpace}
780       @param tags: boundray tags
781       @type tags: C{str}
782       @return: a mask which marks samples used by C{function_space} that are touching the
783                boundary tagged by any of the given tags.
784       @rtype: L{escript.Data} of rank 0
785       """
786       pde=linearPDEs.LinearPDE(function_space.getDomain(),numEquations=1, numSolutions=1)
787       d=escript.Scalar(0.,escript.FunctionOnBoundary(function_space.getDomain()))
788       for t in tags: d.setTaggedValue(t,1.)
789       pde.setValue(y=d)
790       out=util.whereNonZero(pde.getRightHandSide())
791       if out.getFunctionSpace() == function_space:
792          return out
793       else:
794          return util.whereNonZero(util.interpolate(out,function_space))
795    

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