X-Git-Url: http://git.salome-platform.org/gitweb/?a=blobdiff_plain;f=src%2FdaComposant%2FdaCore%2FBasicObjects.py;h=0cc2cb04fd55367082218c5480a1033ae341fe4f;hb=8f27741af02e5f1125f56475f0bb80e2fe709bf9;hp=d3b060fa44357cdfca3d90726f38749ce4d0f630;hpb=59c9144c3b491494cb2beeb10093f232a5e13f2a;p=modules%2Fadao.git diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py index d3b060f..0cc2cb0 100644 --- a/src/daComposant/daCore/BasicObjects.py +++ b/src/daComposant/daCore/BasicObjects.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- # -# Copyright (C) 2008-2020 EDF R&D +# Copyright (C) 2008-2021 EDF R&D # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public @@ -179,7 +179,7 @@ class Operator(object): "Renvoie le type" return self.__Type - def appliedTo(self, xValue, HValue = None, argsAsSerie = False): + def appliedTo(self, xValue, HValue = None, argsAsSerie = False, returnSerieAsArrayMatrix = False): """ Permet de restituer le résultat de l'application de l'opérateur à une série d'arguments xValue. Cette méthode se contente d'appliquer, chaque @@ -203,13 +203,13 @@ class Operator(object): # if _HValue is not None: assert len(_xValue) == len(_HValue), "Incompatible number of elements in xValue and HValue" - HxValue = [] + _HxValue = [] for i in range(len(_HValue)): - HxValue.append( numpy.asmatrix( numpy.ravel( _HValue[i] ) ).T ) + _HxValue.append( numpy.asmatrix( numpy.ravel( _HValue[i] ) ).T ) if self.__AvoidRC: - Operator.CM.storeValueInX(_xValue[i],HxValue[-1],self.__name) + Operator.CM.storeValueInX(_xValue[i],_HxValue[-1],self.__name) else: - HxValue = [] + _HxValue = [] _xserie = [] _hindex = [] for i, xv in enumerate(_xValue): @@ -224,13 +224,14 @@ class Operator(object): else: if self.__Matrix is not None: self.__addOneMatrixCall() - _hv = self.__Matrix * xv + _xv = numpy.matrix(numpy.ravel(xv)).T + _hv = self.__Matrix * _xv else: self.__addOneMethodCall() _xserie.append( xv ) _hindex.append( i ) _hv = None - HxValue.append( _hv ) + _HxValue.append( _hv ) # if len(_xserie)>0 and self.__Matrix is None: if self.__extraArgs is None: @@ -242,14 +243,17 @@ class Operator(object): for i in _hindex: _xv = _xserie.pop(0) _hv = _hserie.pop(0) - HxValue[i] = _hv + _HxValue[i] = _hv if self.__AvoidRC: Operator.CM.storeValueInX(_xv,_hv,self.__name) # - if argsAsSerie: return HxValue - else: return HxValue[-1] + if returnSerieAsArrayMatrix: + _HxValue = numpy.stack([numpy.ravel(_hv) for _hv in _HxValue], axis=1) + # + if argsAsSerie: return _HxValue + else: return _HxValue[-1] - def appliedControledFormTo(self, paires, argsAsSerie = False): + def appliedControledFormTo(self, paires, argsAsSerie = False, returnSerieAsArrayMatrix = False): """ Permet de restituer le résultat de l'application de l'opérateur à des paires (xValue, uValue). Cette méthode se contente d'appliquer, son @@ -266,30 +270,33 @@ class Operator(object): PlatformInfo.isIterable( _xuValue, True, " in Operator.appliedControledFormTo" ) # if self.__Matrix is not None: - HxValue = [] + _HxValue = [] for paire in _xuValue: _xValue, _uValue = paire + _xValue = numpy.matrix(numpy.ravel(_xValue)).T self.__addOneMatrixCall() - HxValue.append( self.__Matrix * _xValue ) + _HxValue.append( self.__Matrix * _xValue ) else: - HxValue = [] + _xuArgs = [] for paire in _xuValue: - _xuValue = [] _xValue, _uValue = paire if _uValue is not None: - _xuValue.append( paire ) + _xuArgs.append( paire ) else: - _xuValue.append( _xValue ) - self.__addOneMethodCall( len(_xuValue) ) + _xuArgs.append( _xValue ) + self.__addOneMethodCall( len(_xuArgs) ) if self.__extraArgs is None: - HxValue = self.__Method( _xuValue ) # Calcul MF + _HxValue = self.__Method( _xuArgs ) # Calcul MF else: - HxValue = self.__Method( _xuValue, self.__extraArgs ) # Calcul MF + _HxValue = self.__Method( _xuArgs, self.__extraArgs ) # Calcul MF + # + if returnSerieAsArrayMatrix: + _HxValue = numpy.stack([numpy.ravel(_hv) for _hv in _HxValue], axis=1) # - if argsAsSerie: return HxValue - else: return HxValue[-1] + if argsAsSerie: return _HxValue + else: return _HxValue[-1] - def appliedInXTo(self, paires, argsAsSerie = False): + def appliedInXTo(self, paires, argsAsSerie = False, returnSerieAsArrayMatrix = False): """ Permet de restituer le résultat de l'application de l'opérateur à une série d'arguments xValue, sachant que l'opérateur est valable en @@ -310,20 +317,24 @@ class Operator(object): PlatformInfo.isIterable( _nxValue, True, " in Operator.appliedInXTo" ) # if self.__Matrix is not None: - HxValue = [] + _HxValue = [] for paire in _nxValue: _xNominal, _xValue = paire + _xValue = numpy.matrix(numpy.ravel(_xValue)).T self.__addOneMatrixCall() - HxValue.append( self.__Matrix * _xValue ) + _HxValue.append( self.__Matrix * _xValue ) else: self.__addOneMethodCall( len(_nxValue) ) if self.__extraArgs is None: - HxValue = self.__Method( _nxValue ) # Calcul MF + _HxValue = self.__Method( _nxValue ) # Calcul MF else: - HxValue = self.__Method( _nxValue, self.__extraArgs ) # Calcul MF + _HxValue = self.__Method( _nxValue, self.__extraArgs ) # Calcul MF + # + if returnSerieAsArrayMatrix: + _HxValue = numpy.stack([numpy.ravel(_hv) for _hv in _HxValue], axis=1) # - if argsAsSerie: return HxValue - else: return HxValue[-1] + if argsAsSerie: return _HxValue + else: return _HxValue[-1] def asMatrix(self, ValueForMethodForm = "UnknownVoidValue", argsAsSerie = False): """ @@ -842,7 +853,7 @@ class Algorithm(object): """ raise NotImplementedError("Mathematical assimilation calculation has not been implemented!") - def defineRequiredParameter(self, name = None, default = None, typecast = None, message = None, minval = None, maxval = None, listval = None): + def defineRequiredParameter(self, name = None, default = None, typecast = None, message = None, minval = None, maxval = None, listval = None, listadv = None): """ Permet de définir dans l'algorithme des paramètres requis et leurs caractéristiques par défaut. @@ -856,6 +867,7 @@ class Algorithm(object): "minval" : minval, "maxval" : maxval, "listval" : listval, + "listadv" : listadv, "message" : message, } self.__canonical_parameter_name[name.lower()] = name @@ -881,6 +893,7 @@ class Algorithm(object): minval = self.__required_parameters[__k]["minval"] maxval = self.__required_parameters[__k]["maxval"] listval = self.__required_parameters[__k]["listval"] + listadv = self.__required_parameters[__k]["listadv"] # if value is None and default is None: __val = None @@ -899,12 +912,14 @@ class Algorithm(object): raise ValueError("The parameter named '%s' of value '%s' can not be less than %s."%(__k, __val, minval)) if maxval is not None and (numpy.array(__val, float) > maxval).any(): raise ValueError("The parameter named '%s' of value '%s' can not be greater than %s."%(__k, __val, maxval)) - if listval is not None: + if listval is not None or listadv is not None: if typecast is list or typecast is tuple or isinstance(__val,list) or isinstance(__val,tuple): for v in __val: - if v not in listval: + if listval is not None and v in listval: continue + elif listadv is not None and v in listadv: continue + else: raise ValueError("The value '%s' is not allowed for the parameter named '%s', it has to be in the list %s."%(v, __k, listval)) - elif __val not in listval: + elif not (listval is not None and __val in listval) and not (listadv is not None and __val in listadv): raise ValueError("The value '%s' is not allowed for the parameter named '%s', it has to be in the list %s."%( __val, __k,listval)) # return __val @@ -963,6 +978,20 @@ class Algorithm(object): self.__elapsed_time = time.perf_counter() - self.__initial_elapsed_time return self.__cpu_time, self.__elapsed_time + def _StopOnTimeLimit(self, X=None, withReason=False): + "Stop criteria on time limit: True/False [+ Reason]" + c, e = self._getTimeState() + if "MaximumCpuTime" in self._parameters and c > self._parameters["MaximumCpuTime"]: + __SC, __SR = True, "Reached maximum CPU time (%.1fs > %.1fs)"%(c, self._parameters["MaximumCpuTime"]) + elif "MaximumElapsedTime" in self._parameters and e > self._parameters["MaximumElapsedTime"]: + __SC, __SR = True, "Reached maximum elapsed time (%.1fs > %.1fs)"%(e, self._parameters["MaximumElapsedTime"]) + else: + __SC, __SR = False, "" + if withReason: + return __SC, __SR + else: + return __SC + # ============================================================================== class AlgorithmAndParameters(object): """ @@ -1826,6 +1855,30 @@ class Covariance(object): elif self.isobject() and hasattr(self.__C,"choleskyI"): return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() ) + def sqrtm(self): + "Racine carrée matricielle" + if self.ismatrix(): + import scipy + return Covariance(self.__name+"C", asCovariance = scipy.linalg.sqrtm(self.__C) ) + elif self.isvector(): + return Covariance(self.__name+"C", asEyeByVector = numpy.sqrt( self.__C ) ) + elif self.isscalar(): + return Covariance(self.__name+"C", asEyeByScalar = numpy.sqrt( self.__C ) ) + elif self.isobject() and hasattr(self.__C,"sqrt"): + return Covariance(self.__name+"C", asCovObject = self.__C.sqrt() ) + + def sqrtmI(self): + "Inversion de la racine carrée matricielle" + if self.ismatrix(): + import scipy + return Covariance(self.__name+"H", asCovariance = scipy.linalg.sqrtm(self.__C).I ) + elif self.isvector(): + return Covariance(self.__name+"H", asEyeByVector = 1.0 / numpy.sqrt( self.__C ) ) + elif self.isscalar(): + return Covariance(self.__name+"H", asEyeByScalar = 1.0 / numpy.sqrt( self.__C ) ) + elif self.isobject() and hasattr(self.__C,"sqrtI"): + return Covariance(self.__name+"H", asCovObject = self.__C.sqrtI() ) + def diag(self, msize=None): "Diagonale de la matrice" if self.ismatrix():