# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2018 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
"""
Définit les outils généraux élémentaires.
-
- Ce module est destiné à être appelée par AssimilationStudy.
"""
__author__ = "Jean-Philippe ARGAUD"
__all__ = []
import sys
import logging
import copy
+import time
import numpy
-from daCore import Persistence
-from daCore import PlatformInfo
+from functools import partial
+from daCore import Persistence, PlatformInfo, Interfaces
from daCore import Templates
# ==============================================================================
lenghtOfRedundancy = -1,
):
"""
- Les caractéristiques de tolérance peuvent être modifées à la création.
+ Les caractéristiques de tolérance peuvent être modifiées à la création.
"""
- self.__tolerBP = float(toleranceInRedundancy)
- self.__lenghtOR = int(lenghtOfRedundancy)
- self.__initlnOR = self.__lenghtOR
+ self.__tolerBP = float(toleranceInRedundancy)
+ self.__lenghtOR = int(lenghtOfRedundancy)
+ self.__initlnOR = self.__lenghtOR
+ self.__seenNames = []
+ self.__enabled = True
self.clearCache()
def clearCache(self):
"Vide le cache"
- self.__listOPCV = [] # Operator Previous Calculated Points, Results, Point Norms
- # logging.debug("CM Tolerance de determination des doublons : %.2e", self.__tolerBP)
+ self.__listOPCV = []
+ self.__seenNames = []
- def wasCalculatedIn(self, xValue ): #, info="" ):
+ def wasCalculatedIn(self, xValue, oName="" ):
"Vérifie l'existence d'un calcul correspondant à la valeur"
__alc = False
__HxV = None
- for i in range(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1):
- if xValue.size != self.__listOPCV[i][0].size:
- # logging.debug("CM Différence de la taille %s de X et de celle %s du point %i déjà calculé", xValue.shape,i,self.__listOPCP[i].shape)
- continue
- if numpy.linalg.norm(numpy.ravel(xValue) - self.__listOPCV[i][0]) < self.__tolerBP * self.__listOPCV[i][2]:
- __alc = True
- __HxV = self.__listOPCV[i][1]
- # logging.debug("CM Cas%s déja calculé, portant le numéro %i", info, i)
- break
+ if self.__enabled:
+ for i in range(min(len(self.__listOPCV),self.__lenghtOR)-1,-1,-1):
+ if not hasattr(xValue, 'size'):
+ pass
+ elif (str(oName) != self.__listOPCV[i][3]):
+ pass
+ elif (xValue.size != self.__listOPCV[i][0].size):
+ pass
+ elif (numpy.ravel(xValue)[0] - self.__listOPCV[i][0][0]) > (self.__tolerBP * self.__listOPCV[i][2] / self.__listOPCV[i][0].size):
+ pass
+ elif numpy.linalg.norm(numpy.ravel(xValue) - self.__listOPCV[i][0]) < (self.__tolerBP * self.__listOPCV[i][2]):
+ __alc = True
+ __HxV = self.__listOPCV[i][1]
+ break
return __alc, __HxV
- def storeValueInX(self, xValue, HxValue ):
- "Stocke un calcul correspondant à la valeur"
+ def storeValueInX(self, xValue, HxValue, oName="" ):
+ "Stocke pour un opérateur o un calcul Hx correspondant à la valeur x"
if self.__lenghtOR < 0:
- self.__lenghtOR = 2 * xValue.size + 2
+ self.__lenghtOR = 2 * min(xValue.size, 50) + 2 # 2 * xValue.size + 2
self.__initlnOR = self.__lenghtOR
+ self.__seenNames.append(str(oName))
+ if str(oName) not in self.__seenNames: # Etend la liste si nouveau
+ self.__lenghtOR += 2 * min(xValue.size, 50) + 2 # 2 * xValue.size + 2
+ self.__initlnOR += self.__lenghtOR
+ self.__seenNames.append(str(oName))
while len(self.__listOPCV) > self.__lenghtOR:
- # logging.debug("CM Réduction de la liste des cas à %i éléments par suppression du premier", self.__lenghtOR)
self.__listOPCV.pop(0)
self.__listOPCV.append( (
- copy.copy(numpy.ravel(xValue)),
- copy.copy(HxValue),
- numpy.linalg.norm(xValue),
+ copy.copy(numpy.ravel(xValue)), # 0 Previous point
+ copy.copy(HxValue), # 1 Previous value
+ numpy.linalg.norm(xValue), # 2 Norm
+ str(oName), # 3 Operator name
) )
def disable(self):
"Inactive le cache"
self.__initlnOR = self.__lenghtOR
self.__lenghtOR = 0
+ self.__enabled = False
def enable(self):
"Active le cache"
self.__lenghtOR = self.__initlnOR
+ self.__enabled = True
# ==============================================================================
class Operator(object):
NbCallsOfCached = 0
CM = CacheManager()
#
- def __init__(self, fromMethod=None, fromMatrix=None, avoidingRedundancy = True):
+ def __init__(self,
+ name = "GenericOperator",
+ fromMethod = None,
+ fromMatrix = None,
+ avoidingRedundancy = True,
+ inputAsMultiFunction = False,
+ enableMultiProcess = False,
+ extraArguments = None,
+ ):
"""
- On construit un objet de ce type en fournissant à l'aide de l'un des
- deux mots-clé, soit une fonction python, soit une matrice.
+ On construit un objet de ce type en fournissant, à l'aide de l'un des
+ deux mots-clé, soit une fonction ou un multi-fonction python, soit une
+ matrice.
Arguments :
+ - name : nom d'opérateur
- fromMethod : argument de type fonction Python
- fromMatrix : argument adapté au constructeur numpy.matrix
- - avoidingRedundancy : évite ou pas les calculs redondants
+ - avoidingRedundancy : booléen évitant (ou pas) les calculs redondants
+ - inputAsMultiFunction : booléen indiquant une fonction explicitement
+ définie (ou pas) en multi-fonction
+ - extraArguments : arguments supplémentaires passés à la fonction de
+ base et ses dérivées (tuple ou dictionnaire)
"""
+ self.__name = str(name)
self.__NbCallsAsMatrix, self.__NbCallsAsMethod, self.__NbCallsOfCached = 0, 0, 0
- self.__AvoidRC = bool( avoidingRedundancy )
- if fromMethod is not None:
+ self.__AvoidRC = bool( avoidingRedundancy )
+ self.__inputAsMF = bool( inputAsMultiFunction )
+ self.__mpEnabled = bool( enableMultiProcess )
+ self.__extraArgs = extraArguments
+ if fromMethod is not None and self.__inputAsMF:
self.__Method = fromMethod # logtimer(fromMethod)
self.__Matrix = None
self.__Type = "Method"
+ elif fromMethod is not None and not self.__inputAsMF:
+ self.__Method = partial( MultiFonction, _sFunction=fromMethod, _mpEnabled=self.__mpEnabled)
+ self.__Matrix = None
+ self.__Type = "Method"
elif fromMatrix is not None:
self.__Method = None
self.__Matrix = numpy.matrix( fromMatrix, numpy.float )
"Renvoie le type"
return self.__Type
- def appliedTo(self, xValue, HValue = None):
+ def appliedTo(self, xValue, HValue = None, argsAsSerie = False, returnSerieAsArrayMatrix = False):
"""
- Permet de restituer le résultat de l'application de l'opérateur à un
- argument xValue. Cette méthode se contente d'appliquer, son argument
- devant a priori être du bon type.
+ Permet de restituer le résultat de l'application de l'opérateur à une
+ série d'arguments xValue. Cette méthode se contente d'appliquer, chaque
+ argument devant a priori être du bon type.
Arguments :
- - xValue : argument adapté pour appliquer l'opérateur
+ - les arguments par série sont :
+ - xValue : argument adapté pour appliquer l'opérateur
+ - HValue : valeur précalculée de l'opérateur en ce point
+ - argsAsSerie : indique si les arguments sont une mono ou multi-valeur
"""
- if HValue is not None:
- HxValue = numpy.asmatrix( numpy.ravel( HValue ) ).T
- if self.__AvoidRC:
- Operator.CM.storeValueInX(xValue,HxValue)
+ if argsAsSerie:
+ _xValue = xValue
+ _HValue = HValue
else:
- if self.__AvoidRC:
- __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xValue)
+ _xValue = (xValue,)
+ if HValue is not None:
+ _HValue = (HValue,)
else:
- __alreadyCalculated = False
+ _HValue = HValue
+ PlatformInfo.isIterable( _xValue, True, " in Operator.appliedTo" )
+ #
+ if _HValue is not None:
+ assert len(_xValue) == len(_HValue), "Incompatible number of elements in xValue and HValue"
+ _HxValue = []
+ for i in range(len(_HValue)):
+ _HxValue.append( numpy.asmatrix( numpy.ravel( _HValue[i] ) ).T )
+ if self.__AvoidRC:
+ Operator.CM.storeValueInX(_xValue[i],_HxValue[-1],self.__name)
+ else:
+ _HxValue = []
+ _xserie = []
+ _hindex = []
+ for i, xv in enumerate(_xValue):
+ if self.__AvoidRC:
+ __alreadyCalculated, __HxV = Operator.CM.wasCalculatedIn(xv,self.__name)
+ else:
+ __alreadyCalculated = False
+ #
+ if __alreadyCalculated:
+ self.__addOneCacheCall()
+ _hv = __HxV
+ else:
+ if self.__Matrix is not None:
+ self.__addOneMatrixCall()
+ _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 )
#
- if __alreadyCalculated:
- self.__addOneCacheCall()
- HxValue = __HxV
- else:
- if self.__Matrix is not None:
- self.__addOneMatrixCall()
- HxValue = self.__Matrix * xValue
+ if len(_xserie)>0 and self.__Matrix is None:
+ if self.__extraArgs is None:
+ _hserie = self.__Method( _xserie ) # Calcul MF
else:
- self.__addOneMethodCall()
- HxValue = self.__Method( xValue )
- if self.__AvoidRC:
- Operator.CM.storeValueInX(xValue,HxValue)
- #
- return HxValue
-
- def appliedControledFormTo(self, paire ):
+ _hserie = self.__Method( _xserie, self.__extraArgs ) # Calcul MF
+ if not hasattr(_hserie, "pop"):
+ raise TypeError("The user input multi-function doesn't seem to return sequence results, behaving like a mono-function. It has to be checked.")
+ for i in _hindex:
+ _xv = _xserie.pop(0)
+ _hv = _hserie.pop(0)
+ _HxValue[i] = _hv
+ if self.__AvoidRC:
+ Operator.CM.storeValueInX(_xv,_hv,self.__name)
+ #
+ 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, returnSerieAsArrayMatrix = False):
"""
- Permet de restituer le résultat de l'application de l'opérateur à une
- paire (xValue, uValue). Cette méthode se contente d'appliquer, son
+ Permet de restituer le résultat de l'application de l'opérateur à des
+ paires (xValue, uValue). Cette méthode se contente d'appliquer, son
argument devant a priori être du bon type. Si la uValue est None,
on suppose que l'opérateur ne s'applique qu'à xValue.
Arguments :
- - xValue : argument X adapté pour appliquer l'opérateur
- - uValue : argument U adapté pour appliquer l'opérateur
+ - paires : les arguments par paire sont :
+ - xValue : argument X adapté pour appliquer l'opérateur
+ - uValue : argument U adapté pour appliquer l'opérateur
+ - argsAsSerie : indique si l'argument est une mono ou multi-valeur
"""
- assert len(paire) == 2, "Incorrect number of arguments"
- xValue, uValue = paire
+ if argsAsSerie: _xuValue = paires
+ else: _xuValue = (paires,)
+ PlatformInfo.isIterable( _xuValue, True, " in Operator.appliedControledFormTo" )
+ #
if self.__Matrix is not None:
- self.__addOneMatrixCall()
- return self.__Matrix * xValue
- elif uValue is not None:
- self.__addOneMethodCall()
- return self.__Method( (xValue, uValue) )
+ _HxValue = []
+ for paire in _xuValue:
+ _xValue, _uValue = paire
+ _xValue = numpy.matrix(numpy.ravel(_xValue)).T
+ self.__addOneMatrixCall()
+ _HxValue.append( self.__Matrix * _xValue )
else:
- self.__addOneMethodCall()
- return self.__Method( xValue )
+ _xuArgs = []
+ for paire in _xuValue:
+ _xValue, _uValue = paire
+ if _uValue is not None:
+ _xuArgs.append( paire )
+ else:
+ _xuArgs.append( _xValue )
+ self.__addOneMethodCall( len(_xuArgs) )
+ if self.__extraArgs is None:
+ _HxValue = self.__Method( _xuArgs ) # Calcul MF
+ else:
+ _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]
- def appliedInXTo(self, paire ):
+ def appliedInXTo(self, paires, argsAsSerie = False, returnSerieAsArrayMatrix = False):
"""
- Permet de restituer le résultat de l'application de l'opérateur à un
- argument xValue, sachant que l'opérateur est valable en xNominal.
- Cette méthode se contente d'appliquer, son argument devant a priori
- être du bon type. Si l'opérateur est linéaire car c'est une matrice,
- alors il est valable en tout point nominal et il n'est pas nécessaire
- d'utiliser xNominal.
- Arguments : une liste contenant
- - xNominal : argument permettant de donner le point où l'opérateur
- est construit pour etre ensuite appliqué
- - xValue : argument adapté pour appliquer l'opérateur
+ Permet de restituer le résultat de l'application de l'opérateur à une
+ série d'arguments xValue, sachant que l'opérateur est valable en
+ xNominal. Cette méthode se contente d'appliquer, son argument devant a
+ priori être du bon type. Si l'opérateur est linéaire car c'est une
+ matrice, alors il est valable en tout point nominal et xNominal peut
+ être quelconque. Il n'y a qu'une seule paire par défaut, et argsAsSerie
+ permet d'indiquer que l'argument est multi-paires.
+ Arguments :
+ - paires : les arguments par paire sont :
+ - xNominal : série d'arguments permettant de donner le point où
+ l'opérateur est construit pour être ensuite appliqué
+ - xValue : série d'arguments adaptés pour appliquer l'opérateur
+ - argsAsSerie : indique si l'argument est une mono ou multi-valeur
"""
- assert len(paire) == 2, "Incorrect number of arguments"
- xNominal, xValue = paire
+ if argsAsSerie: _nxValue = paires
+ else: _nxValue = (paires,)
+ PlatformInfo.isIterable( _nxValue, True, " in Operator.appliedInXTo" )
+ #
if self.__Matrix is not None:
- self.__addOneMatrixCall()
- return self.__Matrix * xValue
+ _HxValue = []
+ for paire in _nxValue:
+ _xNominal, _xValue = paire
+ _xValue = numpy.matrix(numpy.ravel(_xValue)).T
+ self.__addOneMatrixCall()
+ _HxValue.append( self.__Matrix * _xValue )
else:
- self.__addOneMethodCall()
- return self.__Method( (xNominal, xValue) )
+ self.__addOneMethodCall( len(_nxValue) )
+ if self.__extraArgs is None:
+ _HxValue = self.__Method( _nxValue ) # Calcul MF
+ else:
+ _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]
- def asMatrix(self, ValueForMethodForm = "UnknownVoidValue"):
+ def asMatrix(self, ValueForMethodForm = "UnknownVoidValue", argsAsSerie = False):
"""
Permet de renvoyer l'opérateur sous la forme d'une matrice
"""
if self.__Matrix is not None:
self.__addOneMatrixCall()
- return self.__Matrix
- elif ValueForMethodForm is not "UnknownVoidValue": # Ne pas utiliser "None"
- self.__addOneMethodCall()
- return numpy.matrix( self.__Method( (ValueForMethodForm, None) ) )
+ mValue = [self.__Matrix,]
+ elif not isinstance(ValueForMethodForm,str) or ValueForMethodForm != "UnknownVoidValue": # Ne pas utiliser "None"
+ mValue = []
+ if argsAsSerie:
+ self.__addOneMethodCall( len(ValueForMethodForm) )
+ for _vfmf in ValueForMethodForm:
+ mValue.append( numpy.matrix( self.__Method(((_vfmf, None),)) ) )
+ else:
+ self.__addOneMethodCall()
+ mValue = self.__Method(((ValueForMethodForm, None),))
else:
raise ValueError("Matrix form of the operator defined as a function/method requires to give an operating point.")
+ #
+ if argsAsSerie: return mValue
+ else: return mValue[-1]
def shape(self):
"""
self.__NbCallsAsMatrix += 1 # Decompte local
Operator.NbCallsAsMatrix += 1 # Decompte global
- def __addOneMethodCall(self):
+ def __addOneMethodCall(self, nb = 1):
"Comptabilise un appel"
- self.__NbCallsAsMethod += 1 # Decompte local
- Operator.NbCallsAsMethod += 1 # Decompte global
+ self.__NbCallsAsMethod += nb # Decompte local
+ Operator.NbCallsAsMethod += nb # Decompte global
def __addOneCacheCall(self):
"Comptabilise un appel"
def __init__(self,
name = "GenericFullOperator",
asMatrix = None,
- asOneFunction = None, # Fonction
- asThreeFunctions = None, # Dictionnaire de fonctions
- asScript = None,
+ asOneFunction = None, # 1 Fonction
+ asThreeFunctions = None, # 3 Fonctions in a dictionary
+ asScript = None, # 1 or 3 Fonction(s) by script
asDict = None, # Parameters
appliedInX = None,
+ extraArguments = None,
avoidRC = True,
+ inputAsMF = False,# Fonction(s) as Multi-Functions
scheduledBy = None,
toBeChecked = False,
):
""
- self.__name = str(name)
- self.__check = bool(toBeChecked)
+ self.__name = str(name)
+ self.__check = bool(toBeChecked)
+ self.__extraArgs = extraArguments
#
- self.__FO = {}
+ self.__FO = {}
#
__Parameters = {}
if (asDict is not None) and isinstance(asDict, dict):
__Parameters.update( asDict )
- if "DifferentialIncrement" in asDict:
- __Parameters["withIncrement"] = asDict["DifferentialIncrement"]
- if "CenteredFiniteDifference" in asDict:
- __Parameters["withCenteredDF"] = asDict["CenteredFiniteDifference"]
- if "EnableMultiProcessing" in asDict:
- __Parameters["withmpEnabled"] = asDict["EnableMultiProcessing"]
- if "NumberOfProcesses" in asDict:
- __Parameters["withmpWorkers"] = asDict["NumberOfProcesses"]
+ # Priorité à EnableMultiProcessingInDerivatives=True
+ if "EnableMultiProcessing" in __Parameters and __Parameters["EnableMultiProcessing"]:
+ __Parameters["EnableMultiProcessingInDerivatives"] = True
+ __Parameters["EnableMultiProcessingInEvaluation"] = False
+ if "EnableMultiProcessingInDerivatives" not in __Parameters:
+ __Parameters["EnableMultiProcessingInDerivatives"] = False
+ if __Parameters["EnableMultiProcessingInDerivatives"]:
+ __Parameters["EnableMultiProcessingInEvaluation"] = False
+ if "EnableMultiProcessingInEvaluation" not in __Parameters:
+ __Parameters["EnableMultiProcessingInEvaluation"] = False
+ if "withIncrement" in __Parameters: # Temporaire
+ __Parameters["DifferentialIncrement"] = __Parameters["withIncrement"]
#
if asScript is not None:
__Matrix, __Function = None, None
if asMatrix:
- __Matrix = ImportFromScript(asScript).getvalue( self.__name )
+ __Matrix = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
elif asOneFunction:
- __Function = { "Direct":ImportFromScript(asScript).getvalue( "DirectOperator" ) }
+ __Function = { "Direct":Interfaces.ImportFromScript(asScript).getvalue( "DirectOperator" ) }
__Function.update({"useApproximatedDerivatives":True})
__Function.update(__Parameters)
elif asThreeFunctions:
__Function = {
- "Direct" :ImportFromScript(asScript).getvalue( "DirectOperator" ),
- "Tangent":ImportFromScript(asScript).getvalue( "TangentOperator" ),
- "Adjoint":ImportFromScript(asScript).getvalue( "AdjointOperator" ),
+ "Direct" :Interfaces.ImportFromScript(asScript).getvalue( "DirectOperator" ),
+ "Tangent":Interfaces.ImportFromScript(asScript).getvalue( "TangentOperator" ),
+ "Adjoint":Interfaces.ImportFromScript(asScript).getvalue( "AdjointOperator" ),
}
__Function.update(__Parameters)
else:
if isinstance(__Function, dict) and \
("useApproximatedDerivatives" in __Function) and bool(__Function["useApproximatedDerivatives"]) and \
("Direct" in __Function) and (__Function["Direct"] is not None):
- if "withCenteredDF" not in __Function: __Function["withCenteredDF"] = False
- if "withIncrement" not in __Function: __Function["withIncrement"] = 0.01
- if "withdX" not in __Function: __Function["withdX"] = None
- if "withAvoidingRedundancy" not in __Function: __Function["withAvoidingRedundancy"] = True
- if "withToleranceInRedundancy" not in __Function: __Function["withToleranceInRedundancy"] = 1.e-18
- if "withLenghtOfRedundancy" not in __Function: __Function["withLenghtOfRedundancy"] = -1
- if "withmpEnabled" not in __Function: __Function["withmpEnabled"] = False
- if "withmpWorkers" not in __Function: __Function["withmpWorkers"] = None
- from daNumerics.ApproximatedDerivatives import FDApproximation
- FDA = FDApproximation(
+ if "CenteredFiniteDifference" not in __Function: __Function["CenteredFiniteDifference"] = False
+ if "DifferentialIncrement" not in __Function: __Function["DifferentialIncrement"] = 0.01
+ if "withdX" not in __Function: __Function["withdX"] = None
+ if "withAvoidingRedundancy" not in __Function: __Function["withAvoidingRedundancy"] = avoidRC
+ if "withToleranceInRedundancy" not in __Function: __Function["withToleranceInRedundancy"] = 1.e-18
+ if "withLenghtOfRedundancy" not in __Function: __Function["withLenghtOfRedundancy"] = -1
+ if "NumberOfProcesses" not in __Function: __Function["NumberOfProcesses"] = None
+ if "withmfEnabled" not in __Function: __Function["withmfEnabled"] = inputAsMF
+ from daCore import NumericObjects
+ FDA = NumericObjects.FDApproximation(
+ name = self.__name,
Function = __Function["Direct"],
- centeredDF = __Function["withCenteredDF"],
- increment = __Function["withIncrement"],
+ centeredDF = __Function["CenteredFiniteDifference"],
+ increment = __Function["DifferentialIncrement"],
dX = __Function["withdX"],
+ extraArguments = self.__extraArgs,
avoidingRedundancy = __Function["withAvoidingRedundancy"],
toleranceInRedundancy = __Function["withToleranceInRedundancy"],
lenghtOfRedundancy = __Function["withLenghtOfRedundancy"],
- mpEnabled = __Function["withmpEnabled"],
- mpWorkers = __Function["withmpWorkers"],
+ mpEnabled = __Function["EnableMultiProcessingInDerivatives"],
+ mpWorkers = __Function["NumberOfProcesses"],
+ mfEnabled = __Function["withmfEnabled"],
)
- self.__FO["Direct"] = Operator( fromMethod = FDA.DirectOperator, avoidingRedundancy = avoidRC )
- self.__FO["Tangent"] = Operator( fromMethod = FDA.TangentOperator, avoidingRedundancy = avoidRC )
- self.__FO["Adjoint"] = Operator( fromMethod = FDA.AdjointOperator, avoidingRedundancy = avoidRC )
+ self.__FO["Direct"] = Operator( name = self.__name, fromMethod = FDA.DirectOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
+ self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = FDA.TangentOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
+ self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = FDA.AdjointOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
elif isinstance(__Function, dict) and \
("Direct" in __Function) and ("Tangent" in __Function) and ("Adjoint" in __Function) and \
(__Function["Direct"] is not None) and (__Function["Tangent"] is not None) and (__Function["Adjoint"] is not None):
- self.__FO["Direct"] = Operator( fromMethod = __Function["Direct"], avoidingRedundancy = avoidRC )
- self.__FO["Tangent"] = Operator( fromMethod = __Function["Tangent"], avoidingRedundancy = avoidRC )
- self.__FO["Adjoint"] = Operator( fromMethod = __Function["Adjoint"], avoidingRedundancy = avoidRC )
+ self.__FO["Direct"] = Operator( name = self.__name, fromMethod = __Function["Direct"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
+ self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMethod = __Function["Tangent"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
+ self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMethod = __Function["Adjoint"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, extraArguments = self.__extraArgs )
elif asMatrix is not None:
__matrice = numpy.matrix( __Matrix, numpy.float )
- self.__FO["Direct"] = Operator( fromMatrix = __matrice, avoidingRedundancy = avoidRC )
- self.__FO["Tangent"] = Operator( fromMatrix = __matrice, avoidingRedundancy = avoidRC )
- self.__FO["Adjoint"] = Operator( fromMatrix = __matrice.T, avoidingRedundancy = avoidRC )
+ self.__FO["Direct"] = Operator( name = self.__name, fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF, enableMultiProcess = __Parameters["EnableMultiProcessingInEvaluation"] )
+ self.__FO["Tangent"] = Operator( name = self.__name+"Tangent", fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF )
+ self.__FO["Adjoint"] = Operator( name = self.__name+"Adjoint", fromMatrix = __matrice.T, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF )
del __matrice
else:
- raise ValueError("Improperly defined observation operator, it requires at minima either a matrix, a Direct for approximate derivatives or a Tangent/Adjoint pair.")
+ raise ValueError("The %s object is improperly defined or undefined, it requires at minima either a matrix, a Direct operator for approximate derivatives or a Tangent/Adjoint operators pair. Please check your operator input."%self.__name)
#
if __appliedInX is not None:
self.__FO["AppliedInX"] = {}
- if not isinstance(__appliedInX, dict):
- raise ValueError("Error: observation operator defined by \"AppliedInX\" need a dictionary as argument.")
for key in list(__appliedInX.keys()):
if type( __appliedInX[key] ) is type( numpy.matrix([]) ):
# Pour le cas où l'on a une vraie matrice
def __repr__(self):
"x.__repr__() <==> repr(x)"
- return repr(self.__V)
+ return repr(self.__FO)
def __str__(self):
"x.__str__() <==> str(x)"
- return str(self.__V)
+ return str(self.__FO)
# ==============================================================================
class Algorithm(object):
interne à l'objet, mais auquel on accède par la méthode "get".
Les variables prévues sont :
- - CostFunctionJ : fonction-cout globale, somme des deux parties suivantes
- - CostFunctionJb : partie ébauche ou background de la fonction-cout
- - CostFunctionJo : partie observations de la fonction-cout
- - GradientOfCostFunctionJ : gradient de la fonction-cout globale
- - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-cout
- - GradientOfCostFunctionJo : gradient de la partie observations de la fonction-cout
+ - APosterioriCorrelations : matrice de corrélations de la matrice A
+ - APosterioriCovariance : matrice de covariances a posteriori : A
+ - APosterioriStandardDeviations : vecteur des écart-types de la matrice A
+ - APosterioriVariances : vecteur des variances de la matrice A
+ - Analysis : vecteur d'analyse : Xa
+ - BMA : Background moins Analysis : Xa - Xb
+ - CostFunctionJ : fonction-coût globale, somme des deux parties suivantes Jb et Jo
+ - CostFunctionJAtCurrentOptimum : fonction-coût globale à l'état optimal courant lors d'itérations
+ - CostFunctionJb : partie ébauche ou background de la fonction-coût : Jb
+ - CostFunctionJbAtCurrentOptimum : partie ébauche à l'état optimal courant lors d'itérations
+ - CostFunctionJo : partie observations de la fonction-coût : Jo
+ - CostFunctionJoAtCurrentOptimum : partie observations à l'état optimal courant lors d'itérations
+ - CurrentIterationNumber : numéro courant d'itération dans les algorithmes itératifs, à partir de 0
+ - CurrentOptimum : état optimal courant lors d'itérations
- CurrentState : état courant lors d'itérations
- - Analysis : l'analyse Xa
- - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche
- - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant
- - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum
+ - GradientOfCostFunctionJ : gradient de la fonction-coût globale
+ - GradientOfCostFunctionJb : gradient de la partie ébauche de la fonction-coût
+ - GradientOfCostFunctionJo : gradient de la partie observations de la fonction-coût
+ - IndexOfOptimum : index de l'état optimal courant lors d'itérations
- Innovation : l'innovation : d = Y - H(X)
- InnovationAtCurrentState : l'innovation à l'état courant : dn = Y - H(Xn)
- - SigmaObs2 : indicateur de correction optimale des erreurs d'observation
- - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche
+ - JacobianMatrixAtBackground : matrice jacobienne à l'état d'ébauche
+ - JacobianMatrixAtCurrentState : matrice jacobienne à l'état courant
+ - JacobianMatrixAtOptimum : matrice jacobienne à l'optimum
+ - KalmanGainAtOptimum : gain de Kalman à l'optimum
- MahalanobisConsistency : indicateur de consistance des covariances
- - OMA : Observation moins Analysis : Y - Xa
+ - OMA : Observation moins Analyse : Y - Xa
- OMB : Observation moins Background : Y - Xb
- - AMB : Analysis moins Background : Xa - Xb
- - APosterioriCovariance : matrice A
- - APosterioriVariances : variances de la matrice A
- - APosterioriStandardDeviations : écart-types de la matrice A
- - APosterioriCorrelations : correlations de la matrice A
+ - ForecastState : état prédit courant lors d'itérations
- Residu : dans le cas des algorithmes de vérification
+ - SampledStateForQuantiles : échantillons d'états pour l'estimation des quantiles
+ - SigmaBck2 : indicateur de correction optimale des erreurs d'ébauche
+ - SigmaObs2 : indicateur de correction optimale des erreurs d'observation
+ - SimulatedObservationAtBackground : l'état observé H(Xb) à l'ébauche
+ - SimulatedObservationAtCurrentOptimum : l'état observé H(X) à l'état optimal courant
+ - SimulatedObservationAtCurrentState : l'état observé H(X) à l'état courant
+ - SimulatedObservationAtOptimum : l'état observé H(Xa) à l'optimum
+ - SimulationQuantiles : états observés H(X) pour les quantiles demandés
On peut rajouter des variables à stocker dans l'initialisation de
l'algorithme élémentaire qui va hériter de cette classe
"""
self._name = str( name )
self._parameters = {"StoreSupplementaryCalculations":[]}
self.__required_parameters = {}
- self.__required_inputs = {"RequiredInputValues":{"mandatory":(), "optional":()}}
+ self.__required_inputs = {
+ "RequiredInputValues":{"mandatory":(), "optional":()},
+ "ClassificationTags":[],
+ }
+ self.__variable_names_not_public = {"nextStep":False} # Duplication dans AlgorithmAndParameters
+ self.__canonical_parameter_name = {} # Correspondance "lower"->"correct"
+ self.__canonical_stored_name = {} # Correspondance "lower"->"correct"
#
self.StoredVariables = {}
+ self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations")
+ self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance")
+ self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations")
+ self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances")
+ self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
+ self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA")
self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ")
- self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
- self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
self.StoredVariables["CostFunctionJAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJAtCurrentOptimum")
+ self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
self.StoredVariables["CostFunctionJbAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJbAtCurrentOptimum")
+ self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
self.StoredVariables["CostFunctionJoAtCurrentOptimum"] = Persistence.OneScalar(name = "CostFunctionJoAtCurrentOptimum")
+ self.StoredVariables["CurrentEnsembleState"] = Persistence.OneMatrix(name = "CurrentEnsembleState")
+ self.StoredVariables["CurrentIterationNumber"] = Persistence.OneIndex(name = "CurrentIterationNumber")
+ self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum")
+ self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState")
+ self.StoredVariables["ForecastState"] = Persistence.OneVector(name = "ForecastState")
self.StoredVariables["GradientOfCostFunctionJ"] = Persistence.OneVector(name = "GradientOfCostFunctionJ")
self.StoredVariables["GradientOfCostFunctionJb"] = Persistence.OneVector(name = "GradientOfCostFunctionJb")
self.StoredVariables["GradientOfCostFunctionJo"] = Persistence.OneVector(name = "GradientOfCostFunctionJo")
- self.StoredVariables["CurrentState"] = Persistence.OneVector(name = "CurrentState")
- self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
- self.StoredVariables["IndexOfOptimum"] = Persistence.OneIndex(name = "IndexOfOptimum")
- self.StoredVariables["CurrentOptimum"] = Persistence.OneVector(name = "CurrentOptimum")
- self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground")
- self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState")
- self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum")
- self.StoredVariables["SimulatedObservationAtCurrentOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentOptimum")
+ self.StoredVariables["IndexOfOptimum"] = Persistence.OneIndex(name = "IndexOfOptimum")
self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation")
+ self.StoredVariables["InnovationAtCurrentAnalysis"] = Persistence.OneVector(name = "InnovationAtCurrentAnalysis")
self.StoredVariables["InnovationAtCurrentState"] = Persistence.OneVector(name = "InnovationAtCurrentState")
- self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2")
- self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2")
+ self.StoredVariables["JacobianMatrixAtBackground"] = Persistence.OneMatrix(name = "JacobianMatrixAtBackground")
+ self.StoredVariables["JacobianMatrixAtCurrentState"] = Persistence.OneMatrix(name = "JacobianMatrixAtCurrentState")
+ self.StoredVariables["JacobianMatrixAtOptimum"] = Persistence.OneMatrix(name = "JacobianMatrixAtOptimum")
+ self.StoredVariables["KalmanGainAtOptimum"] = Persistence.OneMatrix(name = "KalmanGainAtOptimum")
self.StoredVariables["MahalanobisConsistency"] = Persistence.OneScalar(name = "MahalanobisConsistency")
self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA")
self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB")
- self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA")
- self.StoredVariables["APosterioriCovariance"] = Persistence.OneMatrix(name = "APosterioriCovariance")
- self.StoredVariables["APosterioriVariances"] = Persistence.OneVector(name = "APosterioriVariances")
- self.StoredVariables["APosterioriStandardDeviations"] = Persistence.OneVector(name = "APosterioriStandardDeviations")
- self.StoredVariables["APosterioriCorrelations"] = Persistence.OneMatrix(name = "APosterioriCorrelations")
- self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles")
self.StoredVariables["Residu"] = Persistence.OneScalar(name = "Residu")
+ self.StoredVariables["SampledStateForQuantiles"] = Persistence.OneMatrix(name = "SampledStateForQuantiles")
+ self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2")
+ self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2")
+ self.StoredVariables["SimulatedObservationAtBackground"] = Persistence.OneVector(name = "SimulatedObservationAtBackground")
+ self.StoredVariables["SimulatedObservationAtCurrentAnalysis"]= Persistence.OneVector(name = "SimulatedObservationAtCurrentAnalysis")
+ self.StoredVariables["SimulatedObservationAtCurrentOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentOptimum")
+ self.StoredVariables["SimulatedObservationAtCurrentState"] = Persistence.OneVector(name = "SimulatedObservationAtCurrentState")
+ self.StoredVariables["SimulatedObservationAtOptimum"] = Persistence.OneVector(name = "SimulatedObservationAtOptimum")
+ self.StoredVariables["SimulationQuantiles"] = Persistence.OneMatrix(name = "SimulationQuantiles")
+ #
+ for k in self.StoredVariables:
+ self.__canonical_stored_name[k.lower()] = k
+ #
+ for k, v in self.__variable_names_not_public.items():
+ self.__canonical_parameter_name[k.lower()] = k
+ self.__canonical_parameter_name["algorithm"] = "Algorithm"
+ self.__canonical_parameter_name["storesupplementarycalculations"] = "StoreSupplementaryCalculations"
- def _pre_run(self, Parameters, Xb=None, Y=None, R=None, B=None, Q=None ):
+ def _pre_run(self, Parameters, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None ):
"Pré-calcul"
logging.debug("%s Lancement", self._name)
- logging.debug("%s Taille mémoire utilisée de %.0f Mio", self._name, self._m.getUsedMemory("Mio"))
- #
- # Mise a jour de self._parameters avec Parameters
- self.__setParameters(Parameters)
- #
- # Corrections et complements
- def __test_vvalue( argument, variable, argname):
+ logging.debug("%s Taille mémoire utilisée de %.0f Mio"%(self._name, self._m.getUsedMemory("Mio")))
+ self._getTimeState(reset=True)
+ #
+ # Mise a jour des paramètres internes avec le contenu de Parameters, en
+ # reprenant les valeurs par défauts pour toutes celles non définies
+ self.__setParameters(Parameters, reset=True)
+ for k, v in self.__variable_names_not_public.items():
+ if k not in self._parameters: self.__setParameters( {k:v} )
+ #
+ # Corrections et compléments des vecteurs
+ def __test_vvalue(argument, variable, argname, symbol=None):
+ if symbol is None: symbol = variable
if argument is None:
if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
- raise ValueError("%s %s vector %s has to be properly defined!"%(self._name,argname,variable))
+ raise ValueError("%s %s vector %s is not set and has to be properly defined!"%(self._name,argname,symbol))
elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
- logging.debug("%s %s vector %s is not set, but is optional."%(self._name,argname,variable))
+ logging.debug("%s %s vector %s is not set, but is optional."%(self._name,argname,symbol))
else:
- logging.debug("%s %s vector %s is not set, but is not required."%(self._name,argname,variable))
+ logging.debug("%s %s vector %s is not set, but is not required."%(self._name,argname,symbol))
else:
- logging.debug("%s %s vector %s is set, and its size is %i."%(self._name,argname,variable,numpy.array(argument).size))
+ logging.debug("%s %s vector %s is set, and its size is %i."%(self._name,argname,symbol,numpy.array(argument).size))
+ return 0
__test_vvalue( Xb, "Xb", "Background or initial state" )
__test_vvalue( Y, "Y", "Observation" )
- def __test_cvalue( argument, variable, argname):
+ __test_vvalue( U, "U", "Control" )
+ #
+ # Corrections et compléments des covariances
+ def __test_cvalue(argument, variable, argname, symbol=None):
+ if symbol is None: symbol = variable
if argument is None:
if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
- raise ValueError("%s %s error covariance matrix %s has to be properly defined!"%(self._name,argname,variable))
+ raise ValueError("%s %s error covariance matrix %s is not set and has to be properly defined!"%(self._name,argname,symbol))
elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
- logging.debug("%s %s error covariance matrix %s is not set, but is optional."%(self._name,argname,variable))
+ logging.debug("%s %s error covariance matrix %s is not set, but is optional."%(self._name,argname,symbol))
else:
- logging.debug("%s %s error covariance matrix %s is not set, but is not required."%(self._name,argname,variable))
+ logging.debug("%s %s error covariance matrix %s is not set, but is not required."%(self._name,argname,symbol))
else:
- logging.debug("%s %s error covariance matrix %s is set."%(self._name,argname,variable))
- __test_cvalue( R, "R", "Observation" )
+ logging.debug("%s %s error covariance matrix %s is set."%(self._name,argname,symbol))
+ return 0
__test_cvalue( B, "B", "Background" )
+ __test_cvalue( R, "R", "Observation" )
__test_cvalue( Q, "Q", "Evolution" )
#
+ # Corrections et compléments des opérateurs
+ def __test_ovalue(argument, variable, argname, symbol=None):
+ if symbol is None: symbol = variable
+ if argument is None or (isinstance(argument,dict) and len(argument)==0):
+ if variable in self.__required_inputs["RequiredInputValues"]["mandatory"]:
+ raise ValueError("%s %s operator %s is not set and has to be properly defined!"%(self._name,argname,symbol))
+ elif variable in self.__required_inputs["RequiredInputValues"]["optional"]:
+ logging.debug("%s %s operator %s is not set, but is optional."%(self._name,argname,symbol))
+ else:
+ logging.debug("%s %s operator %s is not set, but is not required."%(self._name,argname,symbol))
+ else:
+ logging.debug("%s %s operator %s is set."%(self._name,argname,symbol))
+ return 0
+ __test_ovalue( HO, "HO", "Observation", "H" )
+ __test_ovalue( EM, "EM", "Evolution", "M" )
+ __test_ovalue( CM, "CM", "Control Model", "C" )
+ #
+ # Corrections et compléments des bornes
if ("Bounds" in self._parameters) and isinstance(self._parameters["Bounds"], (list, tuple)) and (len(self._parameters["Bounds"]) > 0):
logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
else:
self._parameters["Bounds"] = None
#
+ # Corrections et compléments de l'initialisation en X
+ if "InitializationPoint" in self._parameters:
+ if Xb is not None:
+ if self._parameters["InitializationPoint"] is not None and hasattr(self._parameters["InitializationPoint"],'size'):
+ if self._parameters["InitializationPoint"].size != numpy.ravel(Xb).size:
+ raise ValueError("Incompatible size %i of forced initial point that have to replace the background of size %i" \
+ %(self._parameters["InitializationPoint"].size,numpy.ravel(Xb).size))
+ # Obtenu par typecast : numpy.ravel(self._parameters["InitializationPoint"])
+ else:
+ self._parameters["InitializationPoint"] = numpy.ravel(Xb)
+ else:
+ if self._parameters["InitializationPoint"] is None:
+ raise ValueError("Forced initial point can not be set without any given Background or required value")
+ #
+ # Correction pour pallier a un bug de TNC sur le retour du Minimum
+ if "Minimizer" in self._parameters and self._parameters["Minimizer"] == "TNC":
+ self.setParameterValue("StoreInternalVariables",True)
+ #
+ # Verbosité et logging
if logging.getLogger().level < logging.WARNING:
self._parameters["optiprint"], self._parameters["optdisp"] = 1, 1
if PlatformInfo.has_scipy:
_EI = numpy.diag(1./numpy.sqrt(numpy.diag(_A)))
_C = numpy.dot(_EI, numpy.dot(_A, _EI))
self.StoredVariables["APosterioriCorrelations"].store( _C )
- if _oH is not None:
+ if _oH is not None and "Direct" in _oH and "Tangent" in _oH and "Adjoint" in _oH:
logging.debug("%s Nombre d'évaluation(s) de l'opérateur d'observation direct/tangent/adjoint.: %i/%i/%i", self._name, _oH["Direct"].nbcalls(0),_oH["Tangent"].nbcalls(0),_oH["Adjoint"].nbcalls(0))
logging.debug("%s Nombre d'appels au cache d'opérateur d'observation direct/tangent/adjoint..: %i/%i/%i", self._name, _oH["Direct"].nbcalls(3),_oH["Tangent"].nbcalls(3),_oH["Adjoint"].nbcalls(3))
logging.debug("%s Taille mémoire utilisée de %.0f Mio", self._name, self._m.getUsedMemory("Mio"))
+ logging.debug("%s Durées d'utilisation CPU de %.1fs et elapsed de %.1fs", self._name, self._getTimeState()[0], self._getTimeState()[1])
logging.debug("%s Terminé", self._name)
return 0
+ def _toStore(self, key):
+ "True if in StoreSupplementaryCalculations, else False"
+ return key in self._parameters["StoreSupplementaryCalculations"]
+
def get(self, key=None):
"""
Renvoie l'une des variables stockées identifiée par la clé, ou le
des classes de persistance.
"""
if key is not None:
- return self.StoredVariables[key]
+ return self.StoredVariables[self.__canonical_stored_name[key.lower()]]
else:
return self.StoredVariables
def __contains__(self, key=None):
"D.__contains__(k) -> True if D has a key k, else False"
- return key in self.StoredVariables
+ if key is None or key.lower() not in self.__canonical_stored_name:
+ return False
+ else:
+ return self.__canonical_stored_name[key.lower()] in self.StoredVariables
def keys(self):
"D.keys() -> list of D's keys"
def pop(self, k, d):
"D.pop(k[,d]) -> v, remove specified key and return the corresponding value"
- if hasattr(self, "StoredVariables"):
- return self.StoredVariables.pop(k, d)
+ if hasattr(self, "StoredVariables") and k.lower() in self.__canonical_stored_name:
+ return self.StoredVariables.pop(self.__canonical_stored_name[k.lower()], d)
else:
try:
msg = "'%s'"%k
"""
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.
"minval" : minval,
"maxval" : maxval,
"listval" : listval,
+ "listadv" : listadv,
"message" : message,
}
+ self.__canonical_parameter_name[name.lower()] = name
logging.debug("%s %s (valeur par défaut = %s)", self._name, message, self.setParameterValue(name))
def getRequiredParameters(self, noDetails=True):
"""
Renvoie la valeur d'un paramètre requis de manière contrôlée
"""
- default = self.__required_parameters[name]["default"]
- typecast = self.__required_parameters[name]["typecast"]
- minval = self.__required_parameters[name]["minval"]
- maxval = self.__required_parameters[name]["maxval"]
- listval = self.__required_parameters[name]["listval"]
+ __k = self.__canonical_parameter_name[name.lower()]
+ default = self.__required_parameters[__k]["default"]
+ typecast = self.__required_parameters[__k]["typecast"]
+ 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
else: __val = typecast( default )
else:
if typecast is None: __val = value
- else: __val = typecast( value )
+ else:
+ try:
+ __val = typecast( value )
+ except:
+ raise ValueError("The value '%s' for the parameter named '%s' can not be correctly evaluated with type '%s'."%(value, __k, typecast))
#
if minval is not None and (numpy.array(__val, float) < minval).any():
- raise ValueError("The parameter named \"%s\" of value \"%s\" can not be less than %s."%(name, __val, minval))
+ 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."%(name, __val, maxval))
- if listval is not None:
+ raise ValueError("The parameter named '%s' of value '%s' can not be greater than %s."%(__k, __val, maxval))
+ 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:
- raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%(v, name, listval))
- elif __val not in listval:
- raise ValueError("The value \"%s\" of the parameter named \"%s\" is not allowed, it has to be in the list %s."%( __val, name,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 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
def requireInputArguments(self, mandatory=(), optional=()):
"""
- Permet d'imposer des arguments requises en entrée
+ Permet d'imposer des arguments de calcul requis en entrée.
"""
self.__required_inputs["RequiredInputValues"]["mandatory"] = tuple( mandatory )
self.__required_inputs["RequiredInputValues"]["optional"] = tuple( optional )
- def __setParameters(self, fromDico={}):
+ def getInputArguments(self):
"""
- Permet de stocker les paramètres reçus dans le dictionnaire interne.
+ Permet d'obtenir les listes des arguments de calcul requis en entrée.
"""
- self._parameters.update( fromDico )
- for k in self.__required_parameters.keys():
- if k in fromDico.keys():
- self._parameters[k] = self.setParameterValue(k,fromDico[k])
- else:
- self._parameters[k] = self.setParameterValue(k)
- logging.debug("%s %s : %s", self._name, self.__required_parameters[k]["message"], self._parameters[k])
-
-# ==============================================================================
-class Diagnostic(object):
- """
- Classe générale d'interface de type diagnostic
-
- Ce template s'utilise de la manière suivante : il sert de classe "patron" en
- même temps que l'une des classes de persistance, comme "OneScalar" par
- exemple.
-
- Une classe élémentaire de diagnostic doit implémenter ses deux méthodes, la
- méthode "_formula" pour écrire explicitement et proprement la formule pour
- l'écriture mathématique du calcul du diagnostic (méthode interne non
- publique), et "calculate" pour activer la précédente tout en ayant vérifié
- et préparé les données, et pour stocker les résultats à chaque pas (méthode
- externe d'activation).
- """
- def __init__(self, name = "", parameters = {}):
- "Initialisation"
- self.name = str(name)
- self.parameters = dict( parameters )
+ return self.__required_inputs["RequiredInputValues"]["mandatory"], self.__required_inputs["RequiredInputValues"]["optional"]
- def _formula(self, *args):
+ def setAttributes(self, tags=()):
"""
- Doit implémenter l'opération élémentaire de diagnostic sous sa forme
- mathématique la plus naturelle possible.
+ Permet d'adjoindre des attributs comme les tags de classification.
+ Renvoie la liste actuelle dans tous les cas.
"""
- raise NotImplementedError("Diagnostic mathematical formula has not been implemented!")
+ self.__required_inputs["ClassificationTags"].extend( tags )
+ return self.__required_inputs["ClassificationTags"]
- def calculate(self, *args):
+ def __setParameters(self, fromDico={}, reset=False):
"""
- Active la formule de calcul avec les arguments correctement rangés
+ Permet de stocker les paramètres reçus dans le dictionnaire interne.
"""
- raise NotImplementedError("Diagnostic activation method has not been implemented!")
+ self._parameters.update( fromDico )
+ __inverse_fromDico_keys = {}
+ for k in fromDico.keys():
+ if k.lower() in self.__canonical_parameter_name:
+ __inverse_fromDico_keys[self.__canonical_parameter_name[k.lower()]] = k
+ #~ __inverse_fromDico_keys = dict([(self.__canonical_parameter_name[k.lower()],k) for k in fromDico.keys()])
+ __canonic_fromDico_keys = __inverse_fromDico_keys.keys()
+ for k in self.__required_parameters.keys():
+ if k in __canonic_fromDico_keys:
+ self._parameters[k] = self.setParameterValue(k,fromDico[__inverse_fromDico_keys[k]])
+ elif reset:
+ self._parameters[k] = self.setParameterValue(k)
+ else:
+ pass
+ logging.debug("%s %s : %s", self._name, self.__required_parameters[k]["message"], self._parameters[k])
-# ==============================================================================
-class DiagnosticAndParameters(object):
- """
- Classe générale d'interface d'interface de type diagnostic
- """
- def __init__(self,
- name = "GenericDiagnostic",
- asDiagnostic = None,
- asIdentifier = None,
- asDict = None,
- asScript = None,
- asUnit = None,
- asBaseType = None,
- asExistingDiags = None,
- ):
+ def _getTimeState(self, reset=False):
"""
+ Initialise ou restitue le temps de calcul (cpu/elapsed) à la seconde
"""
- self.__name = str(name)
- self.__D = None
- self.__I = None
- self.__P = {}
- self.__U = ""
- self.__B = None
- self.__E = tuple(asExistingDiags)
- self.__TheDiag = None
- #
- if asScript is not None:
- __Diag = ImportFromScript(asScript).getvalue( "Diagnostic" )
- __Iden = ImportFromScript(asScript).getvalue( "Identifier" )
- __Dict = ImportFromScript(asScript).getvalue( self.__name, "Parameters" )
- __Unit = ImportFromScript(asScript).getvalue( "Unit" )
- __Base = ImportFromScript(asScript).getvalue( "BaseType" )
+ if reset:
+ self.__initial_cpu_time = time.process_time()
+ self.__initial_elapsed_time = time.perf_counter()
+ return 0., 0.
else:
- __Diag = asDiagnostic
- __Iden = asIdentifier
- __Dict = asDict
- __Unit = asUnit
- __Base = asBaseType
- #
- if __Diag is not None:
- self.__D = str(__Diag)
- if __Iden is not None:
- self.__I = str(__Iden)
+ self.__cpu_time = time.process_time() - self.__initial_cpu_time
+ 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:
- self.__I = str(__Diag)
- if __Dict is not None:
- self.__P.update( dict(__Dict) )
- if __Unit is None or __Unit == "None":
- self.__U = ""
- if __Base is None or __Base == "None":
- self.__B = None
- #
- self.__setDiagnostic( self.__D, self.__I, self.__U, self.__B, self.__P, self.__E )
-
- def get(self):
- "Renvoie l'objet"
- return self.__TheDiag
-
- def __setDiagnostic(self, __choice = None, __name = "", __unit = "", __basetype = None, __parameters = {}, __existings = () ):
- """
- Permet de sélectionner un diagnostic a effectuer
- """
- if __choice is None:
- raise ValueError("Error: diagnostic choice has to be given")
- __daDirectory = "daDiagnostics"
- #
- # Recherche explicitement le fichier complet
- # ------------------------------------------
- __module_path = None
- for directory in sys.path:
- if os.path.isfile(os.path.join(directory, __daDirectory, str(__choice)+'.py')):
- __module_path = os.path.abspath(os.path.join(directory, __daDirectory))
- if __module_path is None:
- raise ImportError("No diagnostic module named \"%s\" was found in a \"%s\" subdirectory\n The search path is %s"%(__choice, __daDirectory, sys.path))
- #
- # Importe le fichier complet comme un module
- # ------------------------------------------
- try:
- __sys_path_tmp = sys.path ; sys.path.insert(0,__module_path)
- self.__diagnosticFile = __import__(str(__choice), globals(), locals(), [])
- sys.path = __sys_path_tmp ; del __sys_path_tmp
- except ImportError as e:
- raise ImportError("The module named \"%s\" was found, but is incorrect at the import stage.\n The import error message is: %s"%(__choice,e))
- #
- # Instancie un objet du type élémentaire du fichier
- # -------------------------------------------------
- if __name in __existings:
- raise ValueError("A default input with the same name \"%s\" already exists."%str(__name))
+ __SC, __SR = False, ""
+ if withReason:
+ return __SC, __SR
else:
- self.__TheDiag = self.__diagnosticFile.ElementaryDiagnostic(
- name = __name,
- unit = __unit,
- basetype = __basetype,
- parameters = __parameters )
- return 0
+ return __SC
# ==============================================================================
class AlgorithmAndParameters(object):
#
self.updateParameters( asDict, asScript )
#
- if asScript is not None:
- __Algo = ImportFromScript(asScript).getvalue( "Algorithm" )
+ if asAlgorithm is None and asScript is not None:
+ __Algo = Interfaces.ImportFromScript(asScript).getvalue( "Algorithm" )
else:
__Algo = asAlgorithm
#
self.__P.update( {"Algorithm":self.__A} )
#
self.__setAlgorithm( self.__A )
+ #
+ self.__variable_names_not_public = {"nextStep":False} # Duplication dans Algorithm
def updateParameters(self,
asDict = None,
asScript = None,
):
"Mise a jour des parametres"
- if asScript is not None:
- __Dict = ImportFromScript(asScript).getvalue( self.__name, "Parameters" )
+ if asDict is None and asScript is not None:
+ __Dict = Interfaces.ImportFromScript(asScript).getvalue( self.__name, "Parameters" )
else:
__Dict = asDict
#
"Permet de lancer le calcul d'assimilation"
if FileName is None or not os.path.exists(FileName):
raise ValueError("a YACS file name has to be given for YACS execution.\n")
- if not PlatformInfo.has_salome or not PlatformInfo.has_yacs or not PlatformInfo.has_adao:
- raise ImportError("Unable to get SALOME, YACS or ADAO environnement variables. Please launch SALOME before executing.\n")
+ else:
+ __file = os.path.abspath(FileName)
+ logging.debug("The YACS file name is \"%s\"."%__file)
+ if not PlatformInfo.has_salome or \
+ not PlatformInfo.has_yacs or \
+ not PlatformInfo.has_adao:
+ raise ImportError("\n\n"+\
+ "Unable to get SALOME, YACS or ADAO environnement variables.\n"+\
+ "Please load the right environnement before trying to use it.\n")
#
import pilot
import SALOMERuntime
xmlLoader = loader.YACSLoader()
xmlLoader.registerProcCataLoader()
try:
- catalogAd = r.loadCatalog("proc", os.path.abspath(FileName))
+ catalogAd = r.loadCatalog("proc", __file)
+ r.addCatalog(catalogAd)
except:
pass
- r.addCatalog(catalogAd)
try:
- p = xmlLoader.load(os.path.abspath(FileName))
+ p = xmlLoader.load(__file)
except IOError as ex:
print("The YACS XML schema file can not be loaded: %s"%(ex,))
elif key in self.__P:
return self.__P[key]
else:
- return self.__P
+ allvariables = self.__P
+ for k in self.__variable_names_not_public: allvariables.pop(k, None)
+ return allvariables
def pop(self, k, d):
"Necessaire pour le pickling"
"Renvoie la liste des paramètres requis selon l'algorithme"
return self.__algorithm.getRequiredParameters(noDetails)
+ def getAlgorithmInputArguments(self):
+ "Renvoie la liste des entrées requises selon l'algorithme"
+ return self.__algorithm.getInputArguments()
+
+ def getAlgorithmAttributes(self):
+ "Renvoie la liste des attributs selon l'algorithme"
+ return self.__algorithm.setAttributes()
+
def setObserver(self, __V, __O, __I, __S):
if self.__algorithm is None \
or isinstance(self.__algorithm, dict) \
return self.__algorithm.StoredVariables[ __V ].hasDataObserver()
def keys(self):
- return list(self.__algorithm.keys()) + list(self.__P.keys())
+ __allvariables = list(self.__algorithm.keys()) + list(self.__P.keys())
+ for k in self.__variable_names_not_public:
+ if k in __allvariables: __allvariables.remove(k)
+ return __allvariables
def __contains__(self, key=None):
"D.__contains__(k) -> True if D has a key k, else False"
#
return 1
+# ==============================================================================
+class RegulationAndParameters(object):
+ """
+ Classe générale d'interface d'action pour la régulation et ses paramètres
+ """
+ def __init__(self,
+ name = "GenericRegulation",
+ asAlgorithm = None,
+ asDict = None,
+ asScript = None,
+ ):
+ """
+ """
+ self.__name = str(name)
+ self.__P = {}
+ #
+ if asAlgorithm is None and asScript is not None:
+ __Algo = Interfaces.ImportFromScript(asScript).getvalue( "Algorithm" )
+ else:
+ __Algo = asAlgorithm
+ #
+ if asDict is None and asScript is not None:
+ __Dict = Interfaces.ImportFromScript(asScript).getvalue( self.__name, "Parameters" )
+ else:
+ __Dict = asDict
+ #
+ if __Dict is not None:
+ self.__P.update( dict(__Dict) )
+ #
+ if __Algo is not None:
+ self.__P.update( {"Algorithm":str(__Algo)} )
+
+ def get(self, key = None):
+ "Vérifie l'existence d'une clé de variable ou de paramètres"
+ if key in self.__P:
+ return self.__P[key]
+ else:
+ return self.__P
+
# ==============================================================================
class DataObserver(object):
"""
else:
raise ValueError("setting an observer has to be done over a variable name or a list of variable names.")
#
- if asString is not None:
- __FunctionText = asString
- elif (asTemplate is not None) and (asTemplate in Templates.ObserverTemplates):
- __FunctionText = Templates.ObserverTemplates[asTemplate]
- elif asScript is not None:
- __FunctionText = ImportFromScript(asScript).getstring()
- else:
- __FunctionText = ""
- __Function = ObserverF(__FunctionText)
- #
if asObsObject is not None:
self.__O = asObsObject
else:
+ __FunctionText = str(UserScript('Observer', asTemplate, asString, asScript))
+ __Function = Observer2Func(__FunctionText)
self.__O = __Function.getfunc()
#
for k in range(len(self.__V)):
"x.__str__() <==> str(x)"
return str(self.__V)+"\n"+str(self.__O)
+# ==============================================================================
+class UserScript(object):
+ """
+ Classe générale d'interface de type texte de script utilisateur
+ """
+ def __init__(self,
+ name = "GenericUserScript",
+ asTemplate = None,
+ asString = None,
+ asScript = None,
+ ):
+ """
+ """
+ self.__name = str(name)
+ #
+ if asString is not None:
+ self.__F = asString
+ elif self.__name == "UserPostAnalysis" and (asTemplate is not None) and (asTemplate in Templates.UserPostAnalysisTemplates):
+ self.__F = Templates.UserPostAnalysisTemplates[asTemplate]
+ elif self.__name == "Observer" and (asTemplate is not None) and (asTemplate in Templates.ObserverTemplates):
+ self.__F = Templates.ObserverTemplates[asTemplate]
+ elif asScript is not None:
+ self.__F = Interfaces.ImportFromScript(asScript).getstring()
+ else:
+ self.__F = ""
+
+ def __repr__(self):
+ "x.__repr__() <==> repr(x)"
+ return repr(self.__F)
+
+ def __str__(self):
+ "x.__str__() <==> str(x)"
+ return str(self.__F)
+
+# ==============================================================================
+class ExternalParameters(object):
+ """
+ Classe générale d'interface de type texte de script utilisateur
+ """
+ def __init__(self,
+ name = "GenericExternalParameters",
+ asDict = None,
+ asScript = None,
+ ):
+ """
+ """
+ self.__name = str(name)
+ self.__P = {}
+ #
+ self.updateParameters( asDict, asScript )
+
+ def updateParameters(self,
+ asDict = None,
+ asScript = None,
+ ):
+ "Mise a jour des parametres"
+ if asDict is None and asScript is not None:
+ __Dict = Interfaces.ImportFromScript(asScript).getvalue( self.__name, "ExternalParameters" )
+ else:
+ __Dict = asDict
+ #
+ if __Dict is not None:
+ self.__P.update( dict(__Dict) )
+
+ def get(self, key = None):
+ if key in self.__P:
+ return self.__P[key]
+ else:
+ return list(self.__P.keys())
+
+ def keys(self):
+ return list(self.__P.keys())
+
+ def pop(self, k, d):
+ return self.__P.pop(k, d)
+
+ def items(self):
+ return self.__P.items()
+
+ def __contains__(self, key=None):
+ "D.__contains__(k) -> True if D has a key k, else False"
+ return key in self.__P
+
# ==============================================================================
class State(object):
"""
asVector = None,
asPersistentVector = None,
asScript = None,
+ asDataFile = None,
+ colNames = None,
+ colMajor = False,
scheduledBy = None,
toBeChecked = False,
):
nommée "name", la variable est de type "asVector" (par défaut) ou
"asPersistentVector" selon que l'une de ces variables est placée à
"True".
+ - asDataFile : si un ou plusieurs fichiers valides sont donnés
+ contenant des valeurs en colonnes, elles-mêmes nommées "colNames"
+ (s'il n'y a pas de nom de colonne indiquée, on cherche une colonne
+ nommée "name"), on récupère les colonnes et on les range ligne après
+ ligne (colMajor=False, par défaut) ou colonne après colonne
+ (colMajor=True). La variable résultante est de type "asVector" (par
+ défaut) ou "asPersistentVector" selon que l'une de ces variables est
+ placée à "True".
"""
self.__name = str(name)
self.__check = bool(toBeChecked)
if asScript is not None:
__Vector, __Series = None, None
if asPersistentVector:
- __Series = ImportFromScript(asScript).getvalue( self.__name )
+ __Series = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
+ else:
+ __Vector = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
+ elif asDataFile is not None:
+ __Vector, __Series = None, None
+ if asPersistentVector:
+ if colNames is not None:
+ __Series = Interfaces.ImportFromFile(asDataFile).getvalue( colNames )[1]
+ else:
+ __Series = Interfaces.ImportFromFile(asDataFile).getvalue( [self.__name,] )[1]
+ if bool(colMajor) and not Interfaces.ImportFromFile(asDataFile).getformat() == "application/numpy.npz":
+ __Series = numpy.transpose(__Series)
+ elif not bool(colMajor) and Interfaces.ImportFromFile(asDataFile).getformat() == "application/numpy.npz":
+ __Series = numpy.transpose(__Series)
else:
- __Vector = ImportFromScript(asScript).getvalue( self.__name )
+ if colNames is not None:
+ __Vector = Interfaces.ImportFromFile(asDataFile).getvalue( colNames )[1]
+ else:
+ __Vector = Interfaces.ImportFromFile(asDataFile).getvalue( [self.__name,] )[1]
+ if bool(colMajor):
+ __Vector = numpy.ravel(__Vector, order = "F")
+ else:
+ __Vector = numpy.ravel(__Vector, order = "C")
else:
__Vector, __Series = asVector, asPersistentVector
#
self.size = self.__V.size
elif __Series is not None:
self.__is_series = True
- if isinstance(__Series, (tuple, list, numpy.ndarray, numpy.matrix)):
+ if isinstance(__Series, (tuple, list, numpy.ndarray, numpy.matrix, str)):
self.__V = Persistence.OneVector(self.__name, basetype=numpy.matrix)
+ if isinstance(__Series, str): __Series = eval(__Series)
for member in __Series:
self.__V.store( numpy.matrix( numpy.asmatrix(member).A1, numpy.float ).T )
- import sys ; sys.stdout.flush()
else:
self.__V = __Series
if isinstance(self.__V.shape, (tuple, list)):
self.shape = (self.shape[0],1)
self.size = self.shape[0] * self.shape[1]
else:
- raise ValueError("The %s object is improperly defined, it requires at minima either a vector, a list/tuple of vectors or a persistent object. Please check your vector input."%self.__name)
+ raise ValueError("The %s object is improperly defined or undefined, it requires at minima either a vector, a list/tuple of vectors or a persistent object. Please check your vector input."%self.__name)
#
if scheduledBy is not None:
self.__T = scheduledBy
if asScript is not None:
__Matrix, __Scalar, __Vector, __Object = None, None, None, None
if asEyeByScalar:
- __Scalar = ImportFromScript(asScript).getvalue( self.__name )
+ __Scalar = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
elif asEyeByVector:
- __Vector = ImportFromScript(asScript).getvalue( self.__name )
+ __Vector = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
elif asCovObject:
- __Object = ImportFromScript(asScript).getvalue( self.__name )
+ __Object = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
else:
- __Matrix = ImportFromScript(asScript).getvalue( self.__name )
+ __Matrix = Interfaces.ImportFromScript(asScript).getvalue( self.__name )
else:
__Matrix, __Scalar, __Vector, __Object = asCovariance, asEyeByScalar, asEyeByVector, asCovObject
#
if __Scalar is not None:
- if numpy.matrix(__Scalar).size != 1:
- raise ValueError(' The diagonal multiplier given to define a sparse matrix is not a unique scalar value.\n Its actual measured size is %i. Please check your scalar input.'%numpy.matrix(__Scalar).size)
+ if isinstance(__Scalar, str):
+ __Scalar = __Scalar.replace(";"," ").replace(","," ").split()
+ if len(__Scalar) > 0: __Scalar = __Scalar[0]
+ if numpy.array(__Scalar).size != 1:
+ raise ValueError(' The diagonal multiplier given to define a sparse matrix is not a unique scalar value.\n Its actual measured size is %i. Please check your scalar input.'%numpy.array(__Scalar).size)
self.__is_scalar = True
self.__C = numpy.abs( float(__Scalar) )
self.shape = (0,0)
self.size = 0
elif __Vector is not None:
+ if isinstance(__Vector, str):
+ __Vector = __Vector.replace(";"," ").replace(","," ").split()
self.__is_vector = True
- self.__C = numpy.abs( numpy.array( numpy.ravel( numpy.matrix(__Vector, float ) ) ) )
+ self.__C = numpy.abs( numpy.array( numpy.ravel( __Vector ), dtype=float ) )
self.shape = (self.__C.size,self.__C.size)
self.size = self.__C.size**2
elif __Matrix is not None:
elif __Object is not None:
self.__is_object = True
self.__C = __Object
- for at in ("getT","getI","diag","trace","__add__","__sub__","__neg__","__mul__","__rmul__"):
+ for at in ("getT","getI","diag","trace","__add__","__sub__","__neg__","__matmul__","__mul__","__rmatmul__","__rmul__"):
if not hasattr(self.__C,at):
raise ValueError("The matrix given for %s as an object has no attribute \"%s\". Please check your object input."%(self.__name,at))
if hasattr(self.__C,"shape"):
def getI(self):
"Inversion"
if self.ismatrix():
- return Covariance(self.__name+"I", asCovariance = self.__C.I )
+ return Covariance(self.__name+"I", asCovariance = numpy.linalg.inv(self.__C) )
elif self.isvector():
return Covariance(self.__name+"I", asEyeByVector = 1. / self.__C )
elif self.isscalar():
return Covariance(self.__name+"I", asEyeByScalar = 1. / self.__C )
- elif self.isobject():
+ elif self.isobject() and hasattr(self.__C,"getI"):
return Covariance(self.__name+"I", asCovObject = self.__C.getI() )
else:
return None # Indispensable
return Covariance(self.__name+"T", asEyeByVector = self.__C )
elif self.isscalar():
return Covariance(self.__name+"T", asEyeByScalar = self.__C )
- elif self.isobject():
+ elif self.isobject() and hasattr(self.__C,"getT"):
return Covariance(self.__name+"T", asCovObject = self.__C.getT() )
+ else:
+ raise AttributeError("the %s covariance matrix has no getT attribute."%(self.__name,))
def cholesky(self):
"Décomposition de Cholesky"
return Covariance(self.__name+"C", asEyeByScalar = numpy.sqrt( self.__C ) )
elif self.isobject() and hasattr(self.__C,"cholesky"):
return Covariance(self.__name+"C", asCovObject = self.__C.cholesky() )
+ else:
+ raise AttributeError("the %s covariance matrix has no cholesky attribute."%(self.__name,))
def choleskyI(self):
"Inversion de la décomposition de Cholesky"
if self.ismatrix():
- return Covariance(self.__name+"H", asCovariance = numpy.linalg.cholesky(self.__C).I )
+ return Covariance(self.__name+"H", asCovariance = numpy.linalg.inv(numpy.linalg.cholesky(self.__C)) )
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,"choleskyI"):
return Covariance(self.__name+"H", asCovObject = self.__C.choleskyI() )
+ else:
+ raise AttributeError("the %s covariance matrix has no choleskyI attribute."%(self.__name,))
+
+ def sqrtm(self):
+ "Racine carrée matricielle"
+ if self.ismatrix():
+ import scipy
+ return Covariance(self.__name+"C", asCovariance = numpy.real(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,"sqrtm"):
+ return Covariance(self.__name+"C", asCovObject = self.__C.sqrtm() )
+ else:
+ raise AttributeError("the %s covariance matrix has no sqrtm attribute."%(self.__name,))
+
+ def sqrtmI(self):
+ "Inversion de la racine carrée matricielle"
+ if self.ismatrix():
+ import scipy
+ return Covariance(self.__name+"H", asCovariance = numpy.linalg.inv(numpy.real(scipy.linalg.sqrtm(self.__C))) )
+ 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,"sqrtmI"):
+ return Covariance(self.__name+"H", asCovObject = self.__C.sqrtmI() )
+ else:
+ raise AttributeError("the %s covariance matrix has no sqrtmI attribute."%(self.__name,))
def diag(self, msize=None):
"Diagonale de la matrice"
raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,))
else:
return self.__C * numpy.ones(int(msize))
- elif self.isobject():
+ elif self.isobject() and hasattr(self.__C,"diag"):
return self.__C.diag()
-
- def asfullmatrix(self, msize=None):
- "Matrice pleine"
- if self.ismatrix():
- return self.__C
- elif self.isvector():
- return numpy.matrix( numpy.diag(self.__C), float )
- elif self.isscalar():
- if msize is None:
- raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,))
- else:
- return numpy.matrix( self.__C * numpy.eye(int(msize)), float )
- elif self.isobject() and hasattr(self.__C,"asfullmatrix"):
- return self.__C.asfullmatrix()
+ else:
+ raise AttributeError("the %s covariance matrix has no diag attribute."%(self.__name,))
def trace(self, msize=None):
"Trace de la matrice"
return self.__C * int(msize)
elif self.isobject():
return self.__C.trace()
+ else:
+ raise AttributeError("the %s covariance matrix has no trace attribute."%(self.__name,))
+
+ def asfullmatrix(self, msize=None):
+ "Matrice pleine"
+ if self.ismatrix():
+ return numpy.asarray(self.__C)
+ elif self.isvector():
+ return numpy.asarray( numpy.diag(self.__C), float )
+ elif self.isscalar():
+ if msize is None:
+ raise ValueError("the size of the %s covariance matrix has to be given in case of definition as a scalar over the diagonal."%(self.__name,))
+ else:
+ return numpy.asarray( self.__C * numpy.eye(int(msize)), float )
+ elif self.isobject() and hasattr(self.__C,"asfullmatrix"):
+ return self.__C.asfullmatrix()
+ else:
+ raise AttributeError("the %s covariance matrix has no asfullmatrix attribute."%(self.__name,))
+
+ def assparsematrix(self):
+ "Valeur sparse"
+ return self.__C
def getO(self):
return self
"x.__neg__() <==> -x"
return - self.__C
+ def __matmul__(self, other):
+ "x.__mul__(y) <==> x@y"
+ if self.ismatrix() and isinstance(other, (int, float)):
+ return numpy.asarray(self.__C) * other
+ elif self.ismatrix() and isinstance(other, (list, numpy.matrix, numpy.ndarray, tuple)):
+ if numpy.ravel(other).size == self.shape[1]: # Vecteur
+ return numpy.ravel(self.__C @ numpy.ravel(other))
+ elif numpy.asarray(other).shape[0] == self.shape[1]: # Matrice
+ return numpy.asarray(self.__C) @ numpy.asarray(other)
+ else:
+ raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.asarray(other).shape,self.__name))
+ elif self.isvector() and isinstance(other, (list, numpy.matrix, numpy.ndarray, tuple)):
+ if numpy.ravel(other).size == self.shape[1]: # Vecteur
+ return numpy.ravel(self.__C) * numpy.ravel(other)
+ elif numpy.asarray(other).shape[0] == self.shape[1]: # Matrice
+ return numpy.ravel(self.__C).reshape((-1,1)) * numpy.asarray(other)
+ else:
+ raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.ravel(other).shape,self.__name))
+ elif self.isscalar() and isinstance(other,numpy.matrix):
+ return numpy.asarray(self.__C * other)
+ elif self.isscalar() and isinstance(other, (list, numpy.ndarray, tuple)):
+ if len(numpy.asarray(other).shape) == 1 or numpy.asarray(other).shape[1] == 1 or numpy.asarray(other).shape[0] == 1:
+ return self.__C * numpy.ravel(other)
+ else:
+ return self.__C * numpy.asarray(other)
+ elif self.isobject():
+ return self.__C.__matmul__(other)
+ else:
+ raise NotImplementedError("%s covariance matrix __matmul__ method not available for %s type!"%(self.__name,type(other)))
+
def __mul__(self, other):
"x.__mul__(y) <==> x*y"
- if self.ismatrix() and isinstance(other,numpy.matrix):
+ if self.ismatrix() and isinstance(other, (int, numpy.matrix, float)):
return self.__C * other
elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)):
if numpy.ravel(other).size == self.shape[1]: # Vecteur
else:
raise NotImplementedError("%s covariance matrix __mul__ method not available for %s type!"%(self.__name,type(other)))
+ def __rmatmul__(self, other):
+ "x.__rmul__(y) <==> y@x"
+ if self.ismatrix() and isinstance(other, (int, numpy.matrix, float)):
+ return other * self.__C
+ elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)):
+ if numpy.ravel(other).size == self.shape[1]: # Vecteur
+ return numpy.asmatrix(numpy.ravel(other)) * self.__C
+ elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice
+ return numpy.asmatrix(other) * self.__C
+ else:
+ raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.asmatrix(other).shape,self.shape,self.__name))
+ elif self.isvector() and isinstance(other,numpy.matrix):
+ if numpy.ravel(other).size == self.shape[0]: # Vecteur
+ return numpy.asmatrix(numpy.ravel(other) * self.__C)
+ elif numpy.asmatrix(other).shape[1] == self.shape[0]: # Matrice
+ return numpy.asmatrix(numpy.array(other) * self.__C)
+ else:
+ raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.ravel(other).shape,self.shape,self.__name))
+ elif self.isscalar() and isinstance(other,numpy.matrix):
+ return other * self.__C
+ elif self.isobject():
+ return self.__C.__rmatmul__(other)
+ else:
+ raise NotImplementedError("%s covariance matrix __rmatmul__ method not available for %s type!"%(self.__name,type(other)))
+
def __rmul__(self, other):
"x.__rmul__(y) <==> y*x"
- if self.ismatrix() and isinstance(other,numpy.matrix):
+ if self.ismatrix() and isinstance(other, (int, numpy.matrix, float)):
return other * self.__C
+ elif self.ismatrix() and isinstance(other, (list, numpy.ndarray, tuple)):
+ if numpy.ravel(other).size == self.shape[1]: # Vecteur
+ return numpy.asmatrix(numpy.ravel(other)) * self.__C
+ elif numpy.asmatrix(other).shape[0] == self.shape[1]: # Matrice
+ return numpy.asmatrix(other) * self.__C
+ else:
+ raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.asmatrix(other).shape,self.shape,self.__name))
elif self.isvector() and isinstance(other,numpy.matrix):
if numpy.ravel(other).size == self.shape[0]: # Vecteur
return numpy.asmatrix(numpy.ravel(other) * self.__C)
elif numpy.asmatrix(other).shape[1] == self.shape[0]: # Matrice
return numpy.asmatrix(numpy.array(other) * self.__C)
else:
- raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(self.shape,numpy.ravel(other).shape,self.__name))
+ raise ValueError("operands could not be broadcast together with shapes %s %s in %s matrix"%(numpy.ravel(other).shape,self.shape,self.__name))
elif self.isscalar() and isinstance(other,numpy.matrix):
return other * self.__C
elif self.isobject():
return self.shape[0]
# ==============================================================================
-class ObserverF(object):
+class Observer2Func(object):
"""
Creation d'une fonction d'observateur a partir de son texte
"""
self.__objname = str(__objname)
self.__logSerie = []
self.__switchoff = False
- self.__viewers = self.__loaders = {
- "TUI":_TUIViewer,
- "DCT":_DCTViewer,
- "SCD":_SCDViewer,
- "YACS":_YACSViewer,
+ self.__viewers = {
+ "TUI" :Interfaces._TUIViewer,
+ "SCD" :Interfaces._SCDViewer,
+ "YACS":Interfaces._YACSViewer,
+ }
+ self.__loaders = {
+ "TUI" :Interfaces._TUIViewer,
+ "COM" :Interfaces._COMViewer,
}
if __addViewers is not None:
self.__viewers.update(dict(__addViewers))
if not __switchoff:
self.__switchoff = False
- def dump(self, __filename=None, __format="TUI"):
- "Restitution normalisée des commandes (par les *GenericCaseViewer)"
+ def dump(self, __filename=None, __format="TUI", __upa=""):
+ "Restitution normalisée des commandes"
if __format in self.__viewers:
__formater = self.__viewers[__format](self.__name, self.__objname, self.__logSerie)
else:
raise ValueError("Dumping as \"%s\" is not available"%__format)
- return __formater.dump(__filename)
+ return __formater.dump(__filename, __upa)
- def load(self, __filename=None, __format="TUI"):
+ def load(self, __filename=None, __content=None, __object=None, __format="TUI"):
"Chargement normalisé des commandes"
if __format in self.__loaders:
__formater = self.__loaders[__format]()
else:
raise ValueError("Loading as \"%s\" is not available"%__format)
- return __formater.load(__filename)
+ return __formater.load(__filename, __content, __object)
# ==============================================================================
-class GenericCaseViewer(object):
- """
- Gestion des commandes de creation d'une vue de cas
- """
- def __init__(self, __name="", __objname="case", __content=None):
- "Initialisation et enregistrement de l'entete"
- self._name = str(__name)
- self._objname = str(__objname)
- self._lineSerie = []
- self._switchoff = False
- self._numobservers = 2
- self._content = __content
- self._missing = """raise ValueError("This case requires beforehand to import or define the variable named <%s>. When corrected, remove this command, correct and uncomment the following one.")\n# """
- def _append(self):
- "Transformation de commande individuelle en enregistrement"
- raise NotImplementedError()
- def _extract(self):
- "Transformation d'enregistrement en commande individuelle"
- raise NotImplementedError()
- def _finalize(self):
- "Enregistrement du final"
- pass
- def _addLine(self, line=""):
- "Ajoute un enregistrement individuel"
- self._lineSerie.append(line)
- def dump(self, __filename=None):
- "Restitution normalisée des commandes"
- self._finalize()
- __text = "\n".join(self._lineSerie)
- __text +="\n"
- if __filename is not None:
- __file = os.path.abspath(__filename)
- __fid = open(__file,"w")
- __fid.write(__text)
- __fid.close()
- return __text
- def load(self, __filename=None):
- "Chargement normalisé des commandes"
- if os.path.exists(__filename):
- self._content = open(__filename, 'r').read()
- __commands = self._extract(self._content)
- return __commands
-
- # --> Inutile d'accrocher l'interpretation au cas
- # def _interpret(self):
- # "Interprétation d'une commande"
- # raise NotImplementedError()
- # def execCase(self, __filename=None):
- # "Exécution normalisée des commandes"
- # if os.path.exists(__filename):
- # self._content = open(__filename, 'r').read()
- # __retcode = self._interpret(self._content)
- # return __retcode
-
-class _TUIViewer(GenericCaseViewer):
- """
- Etablissement des commandes d'un cas TUI
- """
- def __init__(self, __name="", __objname="case", __content=None):
- "Initialisation et enregistrement de l'entete"
- GenericCaseViewer.__init__(self, __name, __objname, __content)
- self._addLine("# -*- coding: utf-8 -*-")
- self._addLine("#\n# Python script for ADAO TUI\n#")
- self._addLine("from numpy import array, matrix")
- self._addLine("import adaoBuilder")
- self._addLine("%s = adaoBuilder.New('%s')"%(self._objname, self._name))
- if self._content is not None:
- for command in self._content:
- self._append(*command)
- def _append(self, __command=None, __keys=None, __local=None, __pre=None, __switchoff=False):
- "Transformation d'une commande individuelle en un enregistrement"
- if __command is not None and __keys is not None and __local is not None:
- __text = ""
- if __pre is not None:
- __text += "%s = "%__pre
- __text += "%s.%s( "%(self._objname,str(__command))
- if "self" in __keys: __keys.remove("self")
- if __command not in ("set","get") and "Concept" in __keys: __keys.remove("Concept")
- for k in __keys:
- __v = __local[k]
- if __v is None: continue
- if k == "Checked" and not __v: continue
- if k == "Stored" and not __v: continue
- if k == "AvoidRC" and __v: continue
- if k == "noDetails": continue
- if isinstance(__v,Persistence.Persistence): __v = __v.values()
- if callable(__v): __text = self._missing%__v.__name__+__text
- if isinstance(__v,dict):
- for val in __v.values():
- if callable(val): __text = self._missing%val.__name__+__text
- numpy.set_printoptions(precision=15,threshold=1000000,linewidth=1000*15)
- __text += "%s=%s, "%(k,repr(__v))
- numpy.set_printoptions(precision=8,threshold=1000,linewidth=75)
- __text.rstrip(", ")
- __text += ")"
- self._addLine(__text)
- def _extract(self, __content=""):
- "Transformation un enregistrement en une commande individuelle"
- __is_case = False
- __commands = []
- __content = __content.replace("\r\n","\n")
- for line in __content.split("\n"):
- if "adaoBuilder.New" in line and "=" in line:
- self._objname = line.split("=")[0].strip()
- __is_case = True
- if not __is_case:
- continue
- else:
- if self._objname+".set" in line:
- __commands.append( line.replace(self._objname+".","",1) )
- return __commands
-
- # def _interpret(self, __content=""):
- # "Interprétation d'une commande"
- # __content = __content.replace("\r\n","\n")
- # exec(__content)
- # return 0
-
-class _DCTViewer(GenericCaseViewer):
- """
- Etablissement des commandes d'un cas DCT
- """
- def __init__(self, __name="", __objname="case", __content=None):
- "Initialisation et enregistrement de l'entete"
- GenericCaseViewer.__init__(self, __name, __objname, __content)
- self._observerIndex = 0
- self._addLine("# -*- coding: utf-8 -*-")
- self._addLine("#\n# Python script for ADAO DCT\n#")
- self._addLine("from numpy import array, matrix")
- self._addLine("#")
- self._addLine("%s = {}"%__objname)
- if self._content is not None:
- for command in self._content:
- self._append(*command)
- def _append(self, __command=None, __keys=None, __local=None, __pre=None, __switchoff=False):
- "Transformation d'une commande individuelle en un enregistrement"
- if __command is not None and __keys is not None and __local is not None:
- __text = ""
- if "execute" in __command: return
- if "self" in __keys: __keys.remove("self")
- if __command in ("set","get") and "Concept" in __keys:
- __key = __local["Concept"]
- __keys.remove("Concept")
- else:
- __key = __command.replace("set","").replace("get","")
- if "Observer" in __key and 'Variable' in __keys:
- self._observerIndex += 1
- __key += "_%i"%self._observerIndex
- __text += "%s['%s'] = {"%(self._objname,str(__key))
- for k in __keys:
- __v = __local[k]
- if __v is None: continue
- if k == "Checked" and not __v: continue
- if k == "Stored" and not __v: continue
- if k == "AvoidRC" and __v: continue
- if k == "noDetails": continue
- if isinstance(__v,Persistence.Persistence): __v = __v.values()
- if callable(__v): __text = self._missing%__v.__name__+__text
- if isinstance(__v,dict):
- for val in __v.values():
- if callable(val): __text = self._missing%val.__name__+__text
- numpy.set_printoptions(precision=15,threshold=1000000,linewidth=1000*15)
- __text += "'%s':%s, "%(k,repr(__v))
- numpy.set_printoptions(precision=8,threshold=1000,linewidth=75)
- __text.rstrip(", ").rstrip()
- __text += "}"
- if __text[-2:] == "{}": return # Supprime les *Debug et les variables
- self._addLine(__text)
- def _extract(self, __content=""):
- "Transformation un enregistrement en une commande individuelle"
- __is_case = False
- __commands = []
- __content = __content.replace("\r\n","\n")
- exec(__content)
- self._objdata = None
- __getlocals = locals()
- for k in __getlocals:
- try:
- if 'AlgorithmParameters' in __getlocals[k] and type(__getlocals[k]) is dict:
- self._objname = k
- self._objdata = __getlocals[k]
- except:
- continue
- if self._objdata is None:
- raise ValueError("Impossible to load given content as a ADAO DCT one (no 'AlgorithmParameters' key found).")
- for k in self._objdata:
- if 'Observer_' in k:
- __command = k.split('_',1)[0]
- else:
- __command = k
- __arguments = ["%s = %s"%(k,repr(v)) for k,v in self._objdata[k].items()]
- __commands.append( "set( Concept='%s', %s )"%(__command, ", ".join(__arguments)))
- __commands.sort() # Pour commencer par 'AlgorithmParameters'
- return __commands
-
-class _SCDViewer(GenericCaseViewer):
+def MultiFonction(
+ __xserie,
+ _extraArguments = None,
+ _sFunction = lambda x: x,
+ _mpEnabled = False,
+ _mpWorkers = None,
+ ):
"""
- Etablissement des commandes d'un cas SCD (Study Config Dictionary)
+ Pour une liste ordonnée de vecteurs en entrée, renvoie en sortie la liste
+ correspondante de valeurs de la fonction en argument
"""
- def __init__(self, __name="", __objname="case", __content=None):
- "Initialisation et enregistrement de l'entete"
- GenericCaseViewer.__init__(self, __name, __objname, __content)
- self._addLine("# -*- coding: utf-8 -*-")
- self._addLine("#\n# Input for ADAO converter to YACS\n#")
- self._addLine("from numpy import array, matrix")
- self._addLine("#")
- self._addLine("study_config = {}")
- self._addLine("study_config['StudyType'] = 'ASSIMILATION_STUDY'")
- self._addLine("study_config['Name'] = '%s'"%self._name)
- self._addLine("observers = {}")
- self._addLine("study_config['Observers'] = observers")
- self._addLine("#")
- self._addLine("inputvariables_config = {}")
- self._addLine("inputvariables_config['Order'] =['adao_default']")
- self._addLine("inputvariables_config['adao_default'] = -1")
- self._addLine("study_config['InputVariables'] = inputvariables_config")
- self._addLine("#")
- self._addLine("outputvariables_config = {}")
- self._addLine("outputvariables_config['Order'] = ['adao_default']")
- self._addLine("outputvariables_config['adao_default'] = -1")
- self._addLine("study_config['OutputVariables'] = outputvariables_config")
- if __content is not None:
- for command in __content:
- self._append(*command)
- def _append(self, __command=None, __keys=None, __local=None, __pre=None, __switchoff=False):
- "Transformation d'une commande individuelle en un enregistrement"
- if __command == "set": __command = __local["Concept"]
- else: __command = __command.replace("set", "", 1)
- #
- __text = None
- if __command in (None, 'execute', 'executePythonScheme', 'executeYACSScheme', 'get'):
- return
- elif __command in ['Debug', 'setDebug']:
- __text = "#\nstudy_config['Debug'] = '1'"
- elif __command in ['NoDebug', 'setNoDebug']:
- __text = "#\nstudy_config['Debug'] = '0'"
- elif __command in ['Observer', 'setObserver']:
- __obs = __local['Variable']
- self._numobservers += 1
- __text = "#\n"
- __text += "observers['%s'] = {}\n"%__obs
- if __local['String'] is not None:
- __text += "observers['%s']['nodetype'] = '%s'\n"%(__obs, 'String')
- __text += "observers['%s']['String'] = \"\"\"%s\"\"\"\n"%(__obs, __local['String'])
- if __local['Script'] is not None:
- __text += "observers['%s']['nodetype'] = '%s'\n"%(__obs, 'Script')
- __text += "observers['%s']['Script'] = \"%s\"\n"%(__obs, __local['Script'])
- if __local['Template'] is not None and __local['Template'] in Templates.ObserverTemplates:
- __text += "observers['%s']['nodetype'] = '%s'\n"%(__obs, 'String')
- __text += "observers['%s']['String'] = \"\"\"%s\"\"\"\n"%(__obs, Templates.ObserverTemplates[__local['Template']])
- if __local['Info'] is not None:
- __text += "observers['%s']['info'] = \"\"\"%s\"\"\"\n"%(__obs, __local['Info'])
- else:
- __text += "observers['%s']['info'] = \"\"\"%s\"\"\"\n"%(__obs, __obs)
- __text += "observers['%s']['number'] = %s"%(__obs, self._numobservers)
- elif __local is not None: # __keys is not None and
- numpy.set_printoptions(precision=15,threshold=1000000,linewidth=1000*15)
- __text = "#\n"
- __text += "%s_config = {}\n"%__command
- if 'self' in __local: __local.pop('self')
- __to_be_removed = []
- for __k,__v in __local.items():
- if __v is None: __to_be_removed.append(__k)
- for __k in __to_be_removed:
- __local.pop(__k)
- for __k,__v in __local.items():
- if __k == "Concept": continue
- if __k in ['ScalarSparseMatrix','DiagonalSparseMatrix','Matrix','OneFunction','ThreeFunctions'] and 'Script' in __local: continue
- if __k == 'Algorithm':
- __text += "study_config['Algorithm'] = %s\n"%(repr(__v))
- elif __k == 'Script':
- __k = 'Vector'
- __f = 'Script'
- __v = "'"+repr(__v)+"'"
- for __lk in ['ScalarSparseMatrix','DiagonalSparseMatrix','Matrix']:
- if __lk in __local and __local[__lk]: __k = __lk
- if __command == "AlgorithmParameters": __k = "Dict"
- if 'OneFunction' in __local and __local['OneFunction']:
- __text += "%s_ScriptWithOneFunction = {}\n"%(__command,)
- __text += "%s_ScriptWithOneFunction['Function'] = ['Direct', 'Tangent', 'Adjoint']\n"%(__command,)
- __text += "%s_ScriptWithOneFunction['Script'] = {}\n"%(__command,)
- __text += "%s_ScriptWithOneFunction['Script']['Direct'] = %s\n"%(__command,__v)
- __text += "%s_ScriptWithOneFunction['Script']['Tangent'] = %s\n"%(__command,__v)
- __text += "%s_ScriptWithOneFunction['Script']['Adjoint'] = %s\n"%(__command,__v)
- __text += "%s_ScriptWithOneFunction['DifferentialIncrement'] = 1e-06\n"%(__command,)
- __text += "%s_ScriptWithOneFunction['CenteredFiniteDifference'] = 0\n"%(__command,)
- __k = 'Function'
- __f = 'ScriptWithOneFunction'
- __v = '%s_ScriptWithOneFunction'%(__command,)
- if 'ThreeFunctions' in __local and __local['ThreeFunctions']:
- __text += "%s_ScriptWithFunctions = {}\n"%(__command,)
- __text += "%s_ScriptWithFunctions['Function'] = ['Direct', 'Tangent', 'Adjoint']\n"%(__command,)
- __text += "%s_ScriptWithFunctions['Script'] = {}\n"%(__command,)
- __text += "%s_ScriptWithFunctions['Script']['Direct'] = %s\n"%(__command,__v)
- __text += "%s_ScriptWithFunctions['Script']['Tangent'] = %s\n"%(__command,__v)
- __text += "%s_ScriptWithFunctions['Script']['Adjoint'] = %s\n"%(__command,__v)
- __k = 'Function'
- __f = 'ScriptWithFunctions'
- __v = '%s_ScriptWithFunctions'%(__command,)
- __text += "%s_config['Type'] = '%s'\n"%(__command,__k)
- __text += "%s_config['From'] = '%s'\n"%(__command,__f)
- __text += "%s_config['Data'] = %s\n"%(__command,__v)
- __text = __text.replace("''","'")
- elif __k in ('Stored', 'Checked'):
- if bool(__v):
- __text += "%s_config['%s'] = '%s'\n"%(__command,__k,int(bool(__v)))
- elif __k in ('AvoidRC', 'noDetails'):
- if not bool(__v):
- __text += "%s_config['%s'] = '%s'\n"%(__command,__k,int(bool(__v)))
- else:
- if __k == 'Parameters': __k = "Dict"
- if isinstance(__v,Persistence.Persistence): __v = __v.values()
- if callable(__v): __text = self._missing%__v.__name__+__text
- if isinstance(__v,dict):
- for val in __v.values():
- if callable(val): __text = self._missing%val.__name__+__text
- __text += "%s_config['Type'] = '%s'\n"%(__command,__k)
- __text += "%s_config['From'] = '%s'\n"%(__command,'String')
- __text += "%s_config['Data'] = \"\"\"%s\"\"\"\n"%(__command,repr(__v))
- __text += "study_config['%s'] = %s_config"%(__command,__command)
- numpy.set_printoptions(precision=8,threshold=1000,linewidth=75)
- if __switchoff:
- self._switchoff = True
- if __text is not None: self._addLine(__text)
- if not __switchoff:
- self._switchoff = False
- def _finalize(self):
- self.__loadVariablesByScript()
- self._addLine("#")
- self._addLine("Analysis_config = {}")
- self._addLine("Analysis_config['From'] = 'String'")
- self._addLine("Analysis_config['Data'] = \"\"\"import numpy")
- self._addLine("xa=numpy.ravel(ADD.get('Analysis')[-1])")
- self._addLine("print 'Analysis:',xa\"\"\"")
- self._addLine("study_config['UserPostAnalysis'] = Analysis_config")
- def __loadVariablesByScript(self):
- __ExecVariables = {} # Necessaire pour recuperer la variable
- exec("\n".join(self._lineSerie), __ExecVariables)
- study_config = __ExecVariables['study_config']
- self.__hasAlgorithm = bool(study_config['Algorithm'])
- if not self.__hasAlgorithm and \
- "AlgorithmParameters" in study_config and \
- isinstance(study_config['AlgorithmParameters'], dict) and \
- "From" in study_config['AlgorithmParameters'] and \
- "Data" in study_config['AlgorithmParameters'] and \
- study_config['AlgorithmParameters']['From'] == 'Script':
- __asScript = study_config['AlgorithmParameters']['Data']
- __var = ImportFromScript(__asScript).getvalue( "Algorithm" )
- __text = "#\nstudy_config['Algorithm'] = '%s'"%(__var,)
- self._addLine(__text)
- if self.__hasAlgorithm and \
- "AlgorithmParameters" in study_config and \
- isinstance(study_config['AlgorithmParameters'], dict) and \
- "From" not in study_config['AlgorithmParameters'] and \
- "Data" not in study_config['AlgorithmParameters']:
- __text = "#\n"
- __text += "AlgorithmParameters_config['Type'] = 'Dict'\n"
- __text += "AlgorithmParameters_config['From'] = 'String'\n"
- __text += "AlgorithmParameters_config['Data'] = '{}'\n"
- self._addLine(__text)
- del study_config
-
-class _XMLViewer(GenericCaseViewer):
- """
- Etablissement des commandes de creation d'un cas XML
- """
- def __init__(self, __name="", __objname="case", __content=None):
- "Initialisation et enregistrement de l'entete"
- GenericCaseViewer.__init__(self, __name, __objname, __content)
- raise NotImplementedError()
-
-class _YACSViewer(GenericCaseViewer):
- """
- Etablissement des commandes de creation d'un cas YACS
- """
- def __init__(self, __name="", __objname="case", __content=None):
- "Initialisation et enregistrement de l'entete"
- GenericCaseViewer.__init__(self, __name, __objname, __content)
- self.__internalSCD = _SCDViewer(__name, __objname, __content)
- self._append = self.__internalSCD._append
- def dump(self, __filename=None, __convertSCDinMemory=True):
- "Restitution normalisée des commandes"
- self.__internalSCD._finalize()
- # -----
- if __filename is None:
- raise ValueError("A file name has to be given for YACS XML output.")
- # -----
- if not PlatformInfo.has_salome or \
- not PlatformInfo.has_adao:
- raise ImportError("\n\n"+\
- "Unable to get SALOME or ADAO environnement variables.\n"+\
- "Please load the right environnement before trying to use it.\n")
- elif __convertSCDinMemory:
- __file = os.path.abspath(__filename)
- __SCDdump = self.__internalSCD.dump()
- if os.path.isfile(__file) or os.path.islink(__file):
- os.remove(__file)
- from daYacsSchemaCreator.run import create_schema_from_content
- create_schema_from_content(__SCDdump, __file)
+ # Vérifications et définitions initiales
+ # logging.debug("MULTF Internal multifonction calculations begin with function %s"%(_sFunction.__name__,))
+ if not PlatformInfo.isIterable( __xserie ):
+ raise TypeError("MultiFonction not iterable unkown input type: %s"%(type(__xserie),))
+ if _mpEnabled:
+ if (_mpWorkers is None) or (_mpWorkers is not None and _mpWorkers < 1):
+ __mpWorkers = None
else:
- __file = os.path.abspath(__filename)
- __SCDfile = __file[:__file.rfind(".")] + '_SCD.py'
- __SCDdump = self.__internalSCD.dump(__SCDfile)
- if os.path.isfile(__file) or os.path.islink(__file):
- os.remove(__file)
- __converterExe = os.path.join(os.environ["ADAO_ROOT_DIR"], "bin/salome", "AdaoYacsSchemaCreator.py")
- __args = ["python", __converterExe, __SCDfile, __file]
- import subprocess
- __p = subprocess.Popen(__args)
- (__stdoutdata, __stderrdata) = __p.communicate()
- __p.terminate()
- os.remove(__SCDfile)
- # -----
- if not os.path.exists(__file):
- # logging.debug("-- Error YacsSchemaCreator with convert SCD in memory=%s --"%__convertSCDinMemory)
- # logging.debug("-- Content of the file : --")
- # logging.debug(__SCDdump)
- __msg = "An error occured during the ADAO YACS Schema build for\n"
- __msg += "the target output file:\n"
- __msg += " %s\n"%__file
- __msg += "See errors details in your launching terminal log.\n"
- raise ValueError(__msg)
- # -----
- __fid = open(__file,"r")
- __text = __fid.read()
- __fid.close()
- return __text
-
-# ==============================================================================
-class ImportFromScript(object):
- """
- Obtention d'une variable nommee depuis un fichier script importe
- """
- def __init__(self, __filename=None):
- "Verifie l'existence et importe le script"
- self.__filename = __filename.rstrip(".py")
- if self.__filename is None:
- raise ValueError("The name of the file, containing the variable to be read, has to be specified.")
- if not os.path.isfile(str(self.__filename)+".py"):
- raise ValueError("The file containing the variable to be imported doesn't seem to exist. Please check the file. The given file name is:\n \"%s\""%self.__filename)
- self.__scriptfile = __import__(self.__filename, globals(), locals(), [])
- self.__scriptstring = open(self.__filename+".py",'r').read()
- def getvalue(self, __varname=None, __synonym=None ):
- "Renvoie la variable demandee"
- if __varname is None:
- raise ValueError("The name of the variable to be read has to be specified. Please check the content of the file and the syntax.")
- if not hasattr(self.__scriptfile, __varname):
- if __synonym is None:
- raise ValueError("The imported script file \"%s\" doesn't contain the mandatory variable \"%s\" to be read. Please check the content of the file and the syntax."%(str(self.__filename)+".py",__varname))
- elif not hasattr(self.__scriptfile, __synonym):
- raise ValueError("The imported script file \"%s\" doesn't contain the mandatory variable \"%s\" to be read. Please check the content of the file and the syntax."%(str(self.__filename)+".py",__synonym))
- else:
- return getattr(self.__scriptfile, __synonym)
+ __mpWorkers = int(_mpWorkers)
+ try:
+ import multiprocessing
+ __mpEnabled = True
+ except ImportError:
+ __mpEnabled = False
+ else:
+ __mpEnabled = False
+ __mpWorkers = None
+ #
+ # Calculs effectifs
+ if __mpEnabled:
+ _jobs = __xserie
+ # logging.debug("MULTF Internal multiprocessing calculations begin : evaluation of %i point(s)"%(len(_jobs),))
+ import multiprocessing
+ with multiprocessing.Pool(__mpWorkers) as pool:
+ __multiHX = pool.map( _sFunction, _jobs )
+ pool.close()
+ pool.join()
+ # logging.debug("MULTF Internal multiprocessing calculation end")
+ else:
+ # logging.debug("MULTF Internal monoprocessing calculation begin")
+ __multiHX = []
+ if _extraArguments is None:
+ for __xvalue in __xserie:
+ __multiHX.append( _sFunction( __xvalue ) )
+ elif _extraArguments is not None and isinstance(_extraArguments, (list, tuple, map)):
+ for __xvalue in __xserie:
+ __multiHX.append( _sFunction( __xvalue, *_extraArguments ) )
+ elif _extraArguments is not None and isinstance(_extraArguments, dict):
+ for __xvalue in __xserie:
+ __multiHX.append( _sFunction( __xvalue, **_extraArguments ) )
else:
- return getattr(self.__scriptfile, __varname)
- def getstring(self):
- "Renvoie le script complet"
- return self.__scriptstring
+ raise TypeError("MultiFonction extra arguments unkown input type: %s"%(type(_extraArguments),))
+ # logging.debug("MULTF Internal monoprocessing calculation end")
+ #
+ # logging.debug("MULTF Internal multifonction calculations end")
+ return __multiHX
# ==============================================================================
def CostFunction3D(_x,
# ==============================================================================
if __name__ == "__main__":
- print('\n AUTODIAGNOSTIC \n')
+ print('\n AUTODIAGNOSTIC\n')
