From: Jean-Philippe ARGAUD Date: Sat, 29 Dec 2018 10:39:56 +0000 (+0100) Subject: Adding multi-functions input capabilities (3) X-Git-Tag: V9_3_0.1-prealpha1~20 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=7c22942c737b2433d6da59a4e43c9a837d71b918;p=modules%2Fadao.git Adding multi-functions input capabilities (3) --- diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py index c99697f..eb9c3c8 100644 --- a/src/daComposant/daCore/BasicObjects.py +++ b/src/daComposant/daCore/BasicObjects.py @@ -31,6 +31,7 @@ import sys import logging import copy import numpy +from functools import partial from daCore import Persistence from daCore import PlatformInfo from daCore import Interfaces @@ -107,21 +108,28 @@ class Operator(object): NbCallsOfCached = 0 CM = CacheManager() # - def __init__(self, fromMethod=None, fromMatrix=None, avoidingRedundancy = True): + def __init__(self, fromMethod=None, fromMatrix=None, avoidingRedundancy = True, inputAsMultiFunction = False): """ - 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 : - fromMethod : argument de type fonction Python - fromMatrix : argument adapté au constructeur numpy.matrix - avoidingRedundancy : évite ou pas les calculs redondants + - inputAsMultiFunction : fonction explicitement définie ou pas en multi-fonction """ self.__NbCallsAsMatrix, self.__NbCallsAsMethod, self.__NbCallsOfCached = 0, 0, 0 self.__AvoidRC = bool( avoidingRedundancy ) - if fromMethod is not None: + self.__inputAsMF = bool( inputAsMultiFunction ) + 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) + self.__Matrix = None + self.__Type = "Method" elif fromMatrix is not None: self.__Method = None self.__Matrix = numpy.matrix( fromMatrix, numpy.float ) @@ -293,6 +301,7 @@ class FullOperator(object): asDict = None, # Parameters appliedInX = None, avoidRC = True, + inputAsMF = False,# Fonction(s) as Multi-Functions scheduledBy = None, toBeChecked = False, ): @@ -386,6 +395,7 @@ class FullOperator(object): if "withLenghtOfRedundancy" not in __Function: __Function["withLenghtOfRedundancy"] = -1 if "withmpEnabled" not in __Function: __Function["withmpEnabled"] = False if "withmpWorkers" not in __Function: __Function["withmpWorkers"] = None + if "withmfEnabled" not in __Function: __Function["withmfEnabled"] = inputAsMF from daNumerics.ApproximatedDerivatives import FDApproximation FDA = FDApproximation( Function = __Function["Direct"], @@ -397,21 +407,22 @@ class FullOperator(object): lenghtOfRedundancy = __Function["withLenghtOfRedundancy"], mpEnabled = __Function["withmpEnabled"], mpWorkers = __Function["withmpWorkers"], + 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( fromMethod = FDA.DirectOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF) + self.__FO["Tangent"] = Operator( fromMethod = FDA.TangentOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) + self.__FO["Adjoint"] = Operator( fromMethod = FDA.AdjointOperator, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) 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( fromMethod = __Function["Direct"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) + self.__FO["Tangent"] = Operator( fromMethod = __Function["Tangent"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) + self.__FO["Adjoint"] = Operator( fromMethod = __Function["Adjoint"], avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) 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( fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) + self.__FO["Tangent"] = Operator( fromMatrix = __matrice, avoidingRedundancy = avoidRC, inputAsMultiFunction = inputAsMF ) + self.__FO["Adjoint"] = Operator( 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.")