+++ /dev/null
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-
+++ /dev/null
-SALOME2 : PYHELLO module (sample)
+++ /dev/null
-# Copyright (C) 2010 CEA/DEN, EDF R&D, OPEN CASCADE
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-
-include $(top_srcdir)/adm_local/unix/make_common_starter.am
-
-ACLOCAL_AMFLAGS = -I adm_local/unix \
- -I ${KERNEL_ROOT_DIR}/salome_adm/unix/config_files \
- -I ${GUI_ROOT_DIR}/adm_local/unix/config_files
-
-SUBDIRS = adm_local src
-
-DISTCLEANFILES = a.out aclocal.m4 configure local-install.sh
-
-EXTRA_DIST += \
- build_configure \
- clean_configure
-
-dist-hook:
- rm -rf `find $(distdir) -name CVS`
-
+++ /dev/null
-# Copyright (C) 2007-2008 CEA/DEN, EDF R&D, OPEN CASCADE
-#
-# Copyright (C) 2003-2007 OPEN CASCADE, EADS/CCR, LIP6, CEA/DEN,
-# CEDRAT, EDF R&D, LEG, PRINCIPIA R&D, BUREAU VERITAS
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-include $(top_srcdir)/adm_local/unix/make_common_starter.am
-
-SUBDIRS = unix
+++ /dev/null
-# Copyright (C) 2007-2008 CEA/DEN, EDF R&D, OPEN CASCADE
-#
-# Copyright (C) 2003-2007 OPEN CASCADE, EADS/CCR, LIP6, CEA/DEN,
-# CEDRAT, EDF R&D, LEG, PRINCIPIA R&D, BUREAU VERITAS
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-
-# ============================================================
-# The following is to avoid PACKAGE_... env variable
-# redefinition compilation warnings
-# ============================================================
-AM_CXXFLAGS = @KERNEL_CXXFLAGS@ -include SALOMEconfig.h
-AM_CPPFLAGS = @KERNEL_CXXFLAGS@ -include SALOMEconfig.h
-
-# ============================================================
-# This file defines the common definitions used in several
-# Makefile. This file must be included, if needed, by the file
-# Makefile.am.
-# ============================================================
-# Standard directory for installation
-#
-salomeincludedir = $(includedir)/salome
-libdir = $(prefix)/lib@LIB_LOCATION_SUFFIX@/salome
-bindir = $(prefix)/bin/salome
-salomescriptdir = $(bindir)
-salomepythondir = $(pythondir)/salome
-salomepyexecdir = $(pyexecdir)/salome
-
-# Directory for installing idl files
-salomeidldir = $(prefix)/idl/salome
-
-# Directory for installing resource files
-salomeresdir = $(prefix)/share/salome/resources/@MODULE_NAME@
-
-# Directories for installing admin files
-admlocaldir = $(prefix)/adm_local
-admlocalunixdir = $(admlocaldir)/unix
-admlocalm4dir = $(admlocaldir)/unix/config_files
-
-# Shared modules installation directory
-sharedpkgpythondir = $(salomepythondir)/shared_modules
-
-# Documentation directory
-docdir = $(datadir)/doc/salome
-
-# common rules
-
-# meta object implementation files generation (moc)
-%_moc.cxx: %.h
- $(MOC) $< -o $@
-
-# translation (*.qm) files generation (lrelease)
-%.qm: %.ts
- $(LRELEASE) $< -qm $@
-
-# resource files generation (qrcc)
-qrc_%.cxx: %.qrc
- $(QRCC) $< -o $@ -name $(*F)
-
-# qt forms files generation (uic)
-ui_%.h: %.ui
- $(UIC) -o $@ $<
-
-# extra distributed files
-EXTRA_DIST = $(MOC_FILES:%_moc.cxx=%.h) $(QRC_FILES:qrc_%.cxx=%.qrc) \
- $(UIC_FILES:ui_%.h=%.ui) $(nodist_salomeres_DATA:%.qm=%.ts)
-
-# customize clean operation
-mostlyclean-local:
- rm -f @builddir@/*_moc.cxx
- rm -f @builddir@/*.qm
- rm -f @builddir@/ui_*.h
- rm -f @builddir@/qrc_*.cxx
-
-# tests
-tests: unittest
-
-unittest: $(UNIT_TEST_PROG)
- @if test "x$(UNIT_TEST_PROG)" != "x"; then \
- $(UNIT_TEST_PROG); \
- fi;
+++ /dev/null
-#!/bin/bash
-# Copyright (C) 2007-2008 CEA/DEN, EDF R&D, OPEN CASCADE
-#
-# Copyright (C) 2003-2007 OPEN CASCADE, EADS/CCR, LIP6, CEA/DEN,
-# CEDRAT, EDF R&D, LEG, PRINCIPIA R&D, BUREAU VERITAS
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-# Tool for updating list of .in file for the SALOME project
-# and regenerating configure script
-# Author :
-# Modified by : Alexander BORODIN (OCN) - autotools usage
-# Date : 10/10/2002
-#
-ORIG_DIR=`pwd`
-CONF_DIR=`echo $0 | sed -e "s,[^/]*$,,;s,/$,,;s,^$,.,"`
-
-########################################################################
-# Test if the KERNEL_ROOT_DIR is set correctly
-
-if test ! -d "${KERNEL_ROOT_DIR}"; then
- echo "failed : KERNEL_ROOT_DIR variable is not correct !"
- exit
-fi
-
-
-########################################################################
-# Test if the GUI_ROOT_DIR is set correctly
-
-if test ! -d "${GUI_ROOT_DIR}"; then
- echo "failed : GUI_ROOT_DIR variable is not correct !"
- exit
-fi
-
-cd ${CONF_DIR}
-ABS_CONF_DIR=`pwd`
-
-########################################################################
-
-# ____________________________________________________________________
-# aclocal creates the aclocal.m4 file from the standard macro and the
-# custom macro embedded in the directory adm_local/unix/config_files
-# and KERNEL config_files directory.
-# output:
-# aclocal.m4
-# autom4te.cache (directory)
-echo "======================================================= aclocal"
-
-aclocal -I adm_local/unix/config_files \
- -I ${KERNEL_ROOT_DIR}/salome_adm/unix/config_files \
- -I ${GUI_ROOT_DIR}/adm_local/unix/config_files || exit 1
-
-# ____________________________________________________________________
-# libtoolize creates some configuration files (ltmain.sh,
-# config.guess and config.sub). It only depends on the libtool
-# version. The files are created in the directory specified with the
-# AC_CONFIG_AUX_DIR(<mydir>) tag (see configure.ac).
-# output:
-# adm_local/unix/config_files/config.guess
-# adm_local/unix/config_files/config.sub
-# adm_local/unix/config_files/ltmain.sh
-echo "==================================================== libtoolize"
-
-libtoolize --force --copy --automake || exit 1
-
-# ____________________________________________________________________
-# autoconf creates the configure script from the file configure.ac (or
-# configure.in if configure.ac doesn't exist)
-# output:
-# configure
-echo "====================================================== autoconf"
-
-autoconf
-
-# ____________________________________________________________________
-# automake creates some scripts used in building process
-# (install-sh, missing, ...). It only depends on the automake
-# version. The files are created in the directory specified with the
-# AC_CONFIG_AUX_DIR(<mydir>) tag (see configure.ac). This step also
-# creates the Makefile.in files from the Makefile.am files.
-# output:
-# adm_local/unix/config_files/compile
-# adm_local/unix/config_files/depcomp
-# adm_local/unix/config_files/install-sh
-# adm_local/unix/config_files/missing
-# adm_local/unix/config_files/py-compile
-# Makefile.in (from Makefile.am)
-echo "====================================================== automake"
-
-automake --copy --gnu --add-missing
+++ /dev/null
-#!/bin/sh
-# Copyright (C) 2007-2008 CEA/DEN, EDF R&D, OPEN CASCADE
-#
-# Copyright (C) 2003-2007 OPEN CASCADE, EADS/CCR, LIP6, CEA/DEN,
-# CEDRAT, EDF R&D, LEG, PRINCIPIA R&D, BUREAU VERITAS
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-rm -rf autom4te.cache aclocal.m4 configure make_config
-find . -name "*~" -print -exec rm {} \;
-find . -name "*.pyc" -print -exec rm {} \;
-#exit
-# ==================== ON SORT AVANT
-
-find bin -name Makefile.in | xargs rm -f
-find doc -name Makefile.in | xargs rm -f
-find idl -name Makefile.in | xargs rm -f
-find resources -name Makefile.in | xargs rm -f
-find salome_adm -name Makefile.in | xargs rm -f
-find src -name Makefile.in | xargs rm -f
-rm -f Makefile.in
+++ /dev/null
-# Copyright (C) 2010 CEA/DEN, 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-
-AC_INIT([DATASSIM module], [0.1], [andre.ribes@edf.fr], [SalomeDATASSIM])
-AC_CONFIG_AUX_DIR(adm_local/unix/config_files)
-AC_CANONICAL_HOST
-AC_CANONICAL_TARGET
-AM_INIT_AUTOMAKE([-Wno-portability])
-
-XVERSION=`echo $VERSION | awk -F. '{printf("0x%02x%02x%02x",$1,$2,$3)}'`
-AC_SUBST(XVERSION)
-
-# set up MODULE_NAME variable for dynamic construction of directories (resources, etc.)
-MODULE_NAME=datassim
-AC_SUBST(MODULE_NAME)
-
-dnl
-dnl Initialize source and build root directories
-dnl
-
-ROOT_BUILDDIR=`pwd`
-ROOT_SRCDIR=`echo $0 | sed -e "s,[[^/]]*$,,;s,/$,,;s,^$,.,"`
-cd $ROOT_SRCDIR
-ROOT_SRCDIR=`pwd`
-cd $ROOT_BUILDDIR
-
-AC_SUBST(ROOT_SRCDIR)
-AC_SUBST(ROOT_BUILDDIR)
-
-echo
-echo Source root directory : $ROOT_SRCDIR
-echo Build root directory : $ROOT_BUILDDIR
-echo
-echo
-
-AC_CHECK_PROG(SHELL,sh)
-AC_SUBST(SHELL)
-
-if test -z "$AR"; then
- AC_CHECK_PROGS(AR,ar xar,:,$PATH)
-fi
-AC_SUBST(AR)
-
-dnl Export the AR macro so that it will be placed in the libtool file
-dnl correctly.
-export AR
-
-echo
-echo ---------------------------------------------
-echo testing make
-echo ---------------------------------------------
-echo
-
-AC_PROG_MAKE_SET
-AC_PROG_INSTALL
-AC_LOCAL_INSTALL
-dnl
-dnl libtool macro check for CC, LD, NM, LN_S, RANLIB, STRIP + for shared libraries
-
-AC_ENABLE_DEBUG(yes)
-AC_DISABLE_PRODUCTION
-
-echo ---------------------------------------------
-echo testing libtool
-echo ---------------------------------------------
-
-dnl first, we set static to no!
-dnl if we want it, use --enable-static
-AC_ENABLE_STATIC(no)
-
-AC_LIBTOOL_DLOPEN
-AC_PROG_LIBTOOL
-
-dnl Fix up the INSTALL macro if it s a relative path. We want the
-dnl full-path to the binary instead.
-case "$INSTALL" in
- *install-sh*)
- INSTALL='\${KERNEL_ROOT_DIR}'/adm_local/unix/config_files/install-sh
- ;;
-esac
-
-echo
-echo ---------------------------------------------
-echo testing python
-echo ---------------------------------------------
-echo
-
-CHECK_PYTHON
-
-AM_PATH_PYTHON(2.4)
-
-echo
-echo ---------------------------------------------
-echo testing QT
-echo ---------------------------------------------
-echo
-
-CHECK_QT
-
-echo
-echo ---------------------------------------------
-echo Testing html generators
-echo ---------------------------------------------
-echo
-
-CHECK_HTML_GENERATORS
-
-echo
-echo ---------------------------------------------
-echo Testing Kernel
-echo ---------------------------------------------
-echo
-
-CHECK_KERNEL
-
-echo
-echo ---------------------------------------------
-echo Testing GUI
-echo ---------------------------------------------
-echo
-
-CHECK_SALOME_GUI
-
-echo
-echo ---------------------------------------------
-echo Summary
-echo ---------------------------------------------
-echo
-
-echo Configure
-variables="python_ok qt_ok doxygen_ok Kernel_ok"
-
-for var in $variables
-do
- printf " %10s : " `echo \$var | sed -e "s,_ok,,"`
- eval echo \$$var
-done
-
-dnl We don t need to say when we re entering directories if we re using
-dnl GNU make becuase make does it for us.
-if test "X$GMAKE" = "Xyes"; then
- AC_SUBST(SETX) SETX=":"
-else
- AC_SUBST(SETX) SETX="set -x"
-fi
-echo
-echo ---------------------------------------------
-echo generating Makefiles and configure files
-echo ---------------------------------------------
-echo
-
-AC_OUTPUT_COMMANDS([ \
- chmod +x ./bin/*; \
-])
-
-# This list is initiated using autoscan and must be updated manually
-# when adding a new file <filename>.in to manage. When you execute
-# autoscan, the Makefile list is generated in the output file configure.scan.
-# This could be helpfull to update de configuration.
-AC_OUTPUT([ \
- adm_local/Makefile \
- adm_local/unix/Makefile \
- src/Makefile \
- src/DATASSIM/Makefile \
- src/DATASSIMGUI/Makefile \
- resources/Makefile \
- Makefile \
-])
+++ /dev/null
-# Copyright (C) 2010 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-#
-
-include $(top_srcdir)/adm_local/unix/make_common_starter.am
-
-dist_salomeres_DATA = DATASSIMCatalog.xml
+++ /dev/null
-# Copyright (C) 2010 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-
-include $(top_srcdir)/adm_local/unix/make_common_starter.am
-
-SUBDIRS = daComposant
+++ /dev/null
-# Copyright (C) 2010 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-
-include $(top_srcdir)/adm_local/unix/make_common_starter.am
-
-# Scripts to be installed
-dist_salomescript_SCRIPTS = daCore
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Algorithme variationnel statique (3D-VAR)
-"""
-__author__ = "Jean-Philippe ARGAUD - Mars 2009"
-
-import sys ; sys.path.insert(0, "../daCore")
-import logging
-import Persistence
-from BasicObjects import Algorithm
-import PlatformInfo ; m = PlatformInfo.SystemUsage()
-
-import numpy
-import scipy.optimize
-
-if logging.getLogger().level < 30:
- iprint = 1
- message = scipy.optimize.tnc.MSG_ALL
- disp = 1
-else:
- iprint = -1
- message = scipy.optimize.tnc.MSG_NONE
- disp = 0
-
-# ==============================================================================
-class ElementaryAlgorithm(Algorithm):
- def __init__(self):
- Algorithm.__init__(self)
- self._name = "3DVAR"
- logging.debug("%s Initialisation"%self._name)
-
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None):
- """
- Calcul de l'estimateur 3D-VAR
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- #
- Hm = H["Direct"].appliedTo
- Ht = H["Adjoint"].appliedInXTo
- #
- # Utilisation éventuelle d'un vecteur H(Xb) précalculé
- # ----------------------------------------------------
- if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
- logging.debug("%s Utilisation de HXb"%self._name)
- HXb = H["AppliedToX"]["HXb"]
- else:
- logging.debug("%s Calcul de Hm(Xb)"%self._name)
- HXb = Hm( Xb )
- #
- # Calcul du préconditionnement
- # ----------------------------
- # Bdemi = numpy.linalg.cholesky(B)
- #
- # Calcul de l'innovation
- # ----------------------
- d = Y - HXb
- logging.debug("%s Innovation d = %s"%(self._name, d))
- #
- # Précalcul des inversion appellée dans les fonction-coût et gradient
- # -------------------------------------------------------------------
- BI = B.I
- RI = R.I
- #
- # Définition de la fonction-coût
- # ------------------------------
- def CostFunction(x):
- _X = numpy.asmatrix(x).flatten().T
- logging.info("%s CostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
- _HX = Hm( _X )
- _HX = numpy.asmatrix(_HX).flatten().T
- Jb = 0.5 * (_X - Xb).T * BI * (_X - Xb)
- Jo = 0.5 * (Y - _HX).T * RI * (Y - _HX)
- J = float( Jb ) + float( Jo )
- logging.info("%s CostFunction Jb = %s"%(self._name, Jb))
- logging.info("%s CostFunction Jo = %s"%(self._name, Jo))
- logging.info("%s CostFunction J = %s"%(self._name, J))
- self.StoredVariables["CostFunctionJb"].store( Jb )
- self.StoredVariables["CostFunctionJo"].store( Jo )
- self.StoredVariables["CostFunctionJ" ].store( J )
- return float( J )
- #
- def GradientOfCostFunction(x):
- _X = numpy.asmatrix(x).flatten().T
- logging.info("%s GradientOfCostFunction X = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
- _HX = Hm( _X )
- _HX = numpy.asmatrix(_HX).flatten().T
- GradJb = BI * (_X - Xb)
- GradJo = - Ht( (_X, RI * (Y - _HX)) )
- GradJ = numpy.asmatrix( GradJb ).flatten().T + numpy.asmatrix( GradJo ).flatten().T
- logging.debug("%s GradientOfCostFunction GradJb = %s"%(self._name, numpy.asmatrix( GradJb ).flatten()))
- logging.debug("%s GradientOfCostFunction GradJo = %s"%(self._name, numpy.asmatrix( GradJo ).flatten()))
- logging.debug("%s GradientOfCostFunction GradJ = %s"%(self._name, numpy.asmatrix( GradJ ).flatten()))
- # self.StoredVariables["GradientOfCostFunctionJb"].store( Jb )
- # self.StoredVariables["GradientOfCostFunctionJo"].store( Jo )
- # self.StoredVariables["GradientOfCostFunctionJ" ].store( J )
- return GradJ.A1
- #
- # Point de démarrage de l'optimisation : Xini = Xb
- # ------------------------------------
- if type(Xb) is type(numpy.matrix([])):
- Xini = Xb.A1.tolist()
- else:
- Xini = list(Xb)
- logging.debug("%s Point de démarrage Xini = %s"%(self._name, Xini))
- #
- # Paramètres de pilotage
- # ----------------------
- if Par.has_key("Bounds") and (type(Par["Bounds"]) is type([]) or type(Par["Bounds"]) is type(())) and (len(Par["Bounds"]) > 0):
- Bounds = Par["Bounds"]
- else:
- Bounds = None
- MinimizerList = ["LBFGSB","TNC", "CG", "BFGS"]
- if Par.has_key("Minimizer") and (Par["Minimizer"] in MinimizerList):
- Minimizer = str( Par["Minimizer"] )
- else:
- Minimizer = "LBFGSB"
- logging.debug("%s Minimiseur utilisé = %s"%(self._name, Minimizer))
- if Par.has_key("MaximumNumberOfSteps") and (Par["MaximumNumberOfSteps"] > -1):
- maxiter = int( Par["MaximumNumberOfSteps"] )
- else:
- maxiter = 15000
- logging.debug("%s Nombre maximal de pas d'optimisation = %s"%(self._name, maxiter))
- #
- # Minimisation de la fonctionnelle
- # --------------------------------
- if Minimizer == "LBFGSB":
- Minimum, J_optimal, Informations = scipy.optimize.fmin_l_bfgs_b(
- func = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- bounds = Bounds,
- maxfun = maxiter,
- iprint = iprint,
- )
- logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
- logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, Informations['funcalls']))
- logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, Informations['warnflag']))
- elif Minimizer == "TNC":
- Minimum, nfeval, rc = scipy.optimize.fmin_tnc(
- func = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- bounds = Bounds,
- maxfun = maxiter,
- messages = message,
- )
- logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
- logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, nfeval))
- logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, rc))
- elif Minimizer == "CG":
- Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
- f = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- maxiter = maxiter,
- disp = disp,
- full_output = True,
- )
- logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
- logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, nfeval))
- logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, rc))
- elif Minimizer == "BFGS":
- Minimum, fopt, gopt, Hopt, nfeval, grad_calls, rc = scipy.optimize.fmin_bfgs(
- f = CostFunction,
- x0 = Xini,
- fprime = GradientOfCostFunction,
- args = (),
- maxiter = maxiter,
- disp = disp,
- full_output = True,
- )
- logging.debug("%s %s Minimum = %s"%(self._name, Minimizer, Minimum))
- logging.debug("%s %s Nb of F = %s"%(self._name, Minimizer, nfeval))
- logging.debug("%s %s RetCode = %s"%(self._name, Minimizer, rc))
- else:
- raise ValueError("Error in Minimizer name: %s"%Minimizer)
- #
- # Calcul de l'analyse
- # --------------------
- Xa = numpy.asmatrix(Minimum).T
- logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
- #
- self.StoredVariables["Analysis"].store( Xa.A1 )
- self.StoredVariables["Innovation"].store( d.A1 )
- #
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
- logging.debug("%s Terminé"%self._name)
- #
- return 0
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Algorithme de Kalman simple (BLUE)
-"""
-__author__ = "Jean-Philippe ARGAUD - Mars 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-import logging
-import Persistence
-from BasicObjects import Algorithm
-import PlatformInfo ; m = PlatformInfo.SystemUsage()
-
-# ==============================================================================
-class ElementaryAlgorithm(Algorithm):
- def __init__(self):
- Algorithm.__init__(self)
- self._name = "BLUE"
- logging.debug("%s Initialisation"%self._name)
-
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None):
- """
- Calcul de l'estimateur BLUE (ou Kalman simple, ou Interpolation Optimale)
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- #
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
- #
- # Utilisation éventuelle d'un vecteur H(Xb) précalculé
- # ----------------------------------------------------
- if H["AppliedToX"] is not None and H["AppliedToX"].has_key("HXb"):
- logging.debug("%s Utilisation de HXb"%self._name)
- HXb = H["AppliedToX"]["HXb"]
- else:
- logging.debug("%s Calcul de Hm * Xb"%self._name)
- HXb = Hm * Xb
-
- # Calcul de la matrice de gain dans l'espace le plus petit
- if Y.size <= Xb.size:
- logging.debug("%s Calcul de K dans l'espace des observations"%self._name)
- K = B * Ht * (Hm * B * Ht + R).I
- else:
- logging.debug("%s Calcul de K dans l'espace d'ébauche"%self._name)
- K = (Ht * R.I * Hm + B.I).I * Ht * R.I
- #
- # Calcul de l'innovation et de l'analyse
- # --------------------------------------
- d = Y - HXb
- logging.debug("%s Innovation d = %s"%(self._name, d))
- Xa = Xb + K*d
- logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
- #
- self.StoredVariables["Analysis"].store( Xa.A1 )
- self.StoredVariables["Innovation"].store( d.A1 )
- #
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("MB")))
- logging.debug("%s Terminé"%self._name)
- #
- return 0
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Algorithme de methode d'ensemble simple
-"""
-__author__ = "Sebastien MASSART, Jean-Philippe ARGAUD - Novembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-import logging
-import numpy
-import Persistence
-from BasicObjects import Algorithm
-import PlatformInfo ; m = PlatformInfo.SystemUsage()
-
-# ==============================================================================
-class ElementaryAlgorithm(Algorithm):
- def __init__(self):
- Algorithm.__init__(self)
- self._name = "ENSEMBLEBLUE"
- logging.debug("%s Initialisation"%self._name)
-
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None ):
- """
- Calcul d'une estimation BLUE d'ensemble :
- - génération d'un ensemble d'observations, de même taille que le
- nombre d'ébauches
- - calcul de l'estimateur BLUE pour chaque membre de l'ensemble
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- #
- # Nombre d'ensemble pour l'ébauche
- # --------------------------------
- nb_ens = Xb.stepnumber()
- #
- # Construction de l'ensemble des observations, par génération a partir
- # de la diagonale de R
- # --------------------------------------------------------------------
- DiagonaleR = numpy.diag(R)
- EnsembleY = numpy.zeros([len(Y),nb_ens])
- for npar in range(len(DiagonaleR)) :
- bruit = numpy.random.normal(0,DiagonaleR[npar],nb_ens)
- EnsembleY[npar,:] = Y[npar] + bruit
- EnsembleY = numpy.matrix(EnsembleY)
- #
- # Initialisation des opérateurs d'observation et de la matrice gain
- # -----------------------------------------------------------------
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
-
- K = B * Ht * (Hm * B * Ht + R).I
-
- # Calcul du BLUE pour chaque membre de l'ensemble
- # -----------------------------------------------
- for iens in range(nb_ens):
- d = EnsembleY[:,iens] - Hm * Xb.valueserie(iens)
- Xa = Xb.valueserie(iens) + K*d
-
- self.StoredVariables["Analysis"].store( Xa.A1 )
- self.StoredVariables["Innovation"].store( d.A1 )
- #
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- logging.debug("%s Terminé"%self._name)
- return 0
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Algorithme de Kalman pour un système discret
-
- Remarque : les observations sont exploitées à partir du pas de temps 1, et
- sont utilisées dans Yo comme rangées selon ces indices. Donc le pas 0 n'est
- pas utilisé puisque la première étape de Kalman passe de 0 à 1 avec
- l'observation du pas 1.
-"""
-__author__ = "Jean-Philippe ARGAUD - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-import logging
-import Persistence
-from BasicObjects import Algorithm
-import PlatformInfo ; m = PlatformInfo.SystemUsage()
-
-# ==============================================================================
-class ElementaryAlgorithm(Algorithm):
- def __init__(self):
- Algorithm.__init__(self)
- self._name = "KALMAN"
- logging.debug("%s Initialisation"%self._name)
-
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None):
- """
- Calcul de l'estimateur de Kalman
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- #
- # Opérateur d'observation
- # -----------------------
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
- #
- # Opérateur d'évolution
- # ---------------------
- Mm = M["Direct"].asMatrix()
- Mt = M["Adjoint"].asMatrix()
- #
- duration = Y.stepnumber()
- #
- # Initialisation
- # --------------
- Xn = Xb
- Pn = B
- self.StoredVariables["Analysis"].store( Xn.A1 )
- self.StoredVariables["CovarianceAPosteriori"].store( Pn )
- #
- for step in range(duration-1):
- logging.debug("%s Etape de Kalman %i (i.e. %i->%i) sur un total de %i"%(self._name, step+1, step,step+1, duration-1))
- #
- # Etape de prédiction
- # -------------------
- Xn_predicted = Mm * Xn
- Pn_predicted = Mm * Pn * Mt + Q
- #
- # Etape de correction
- # -------------------
- d = Y.valueserie(step+1) - Hm * Xn_predicted
- K = Pn_predicted * Ht * (Hm * Pn_predicted * Ht + R).I
- Xn = Xn_predicted + K * d
- Pn = Pn_predicted - K * Hm * Pn_predicted
- #
- self.StoredVariables["Analysis"].store( Xn.A1 )
- self.StoredVariables["CovarianceAPosteriori"].store( Pn )
- self.StoredVariables["Innovation"].store( d.A1 )
- #
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- logging.debug("%s Terminé"%self._name)
- #
- return 0
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Algorithme de moindre carres pondérés (analyse sans ebauche)
-"""
-__author__ = "Sophie RICCI, Jean-Philippe ARGAUD - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-import logging
-import Persistence
-from BasicObjects import Algorithm
-import PlatformInfo ; m = PlatformInfo.SystemUsage()
-
-# ==============================================================================
-class ElementaryAlgorithm(Algorithm):
- def __init__(self):
- Algorithm.__init__(self)
- self._name = "LINEARLEASTSQUARES"
-
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None):
- """
- Calcul de l'estimateur au sens des moindres carres sans ebauche
- """
- logging.debug("%s Lancement"%self._name)
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- #
- Hm = H["Direct"].asMatrix()
- Ht = H["Adjoint"].asMatrix()
- #
- K = (Ht * R.I * Hm ).I * Ht * R.I
- Xa = K * Y
- #
- self.StoredVariables["Analysis"].store( Xa.A1 )
- #
- logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("Mo")))
- logging.debug("%s Terminé"%self._name)
- #
- return 0
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
-
+++ /dev/null
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Définit les outils généraux élémentaires.
-
- Ce module est destiné à etre appelée par AssimilationStudy pour constituer
- les objets élémentaires de l'algorithme.
-"""
-__author__ = "Jean-Philippe ARGAUD - Mars 2008"
-
-import os, sys
-import numpy
-import Logging ; Logging.Logging() # A importer en premier
-import Persistence
-from BasicObjects import Operator
-
-# ==============================================================================
-class AssimilationStudy:
- """
- Cette classe sert d'interface pour l'utilisation de l'assimilation de
- données. Elle contient les méthodes ou accesseurs nécessaires à la
- construction d'un calcul d'assimilation.
- """
- def __init__(self, name=""):
- """
- Prévoit de conserver l'ensemble des variables nécssaires à un algorithme
- élémentaire. Ces variables sont ensuite disponibles pour implémenter un
- algorithme élémentaire particulier.
-
- Background............: vecteur Xb
- Observation...........: vecteur Y (potentiellement temporel)
- d'observations
- State.................: vecteur d'état dont une partie est le vecteur de
- contrôle. Cette information n'est utile que si l'on veut faire des
- calculs sur l'état complet, mais elle n'est pas indispensable pour
- l'assimilation.
- Control...............: vecteur X contenant toutes les variables de
- contrôle, i.e. les paramètres ou l'état dont on veut estimer la
- valeur pour obtenir les observations
- ObservationOperator...: opérateur d'observation H
-
- Les observations présentent une erreur dont la matrice de covariance est
- R. L'ébauche du vecteur de contrôle présente une erreur dont la matrice
- de covariance est B.
- """
- self.__name = str(name)
- self.__Xb = None
- self.__Y = None
- self.__B = None
- self.__R = None
- self.__Q = None
- self.__H = {}
- self.__M = {}
- #
- self.__X = Persistence.OneVector()
- self.__Parameters = {}
- self.__StoredDiagnostics = {}
- #
- # Variables temporaires
- self.__algorithm = {}
- self.__algorithmFile = None
- self.__algorithmName = None
- self.__diagnosticFile = None
- #
- # Récupère le chemin du répertoire parent et l'ajoute au path
- # (Cela complète l'action de la classe PathManagement dans PlatformInfo,
- # qui est activée dans Persistence)
- self.__parent = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
- sys.path.insert(0, self.__parent)
- sys.path = list(set(sys.path)) # Conserve en unique exemplaire chaque chemin
-
- # ---------------------------------------------------------
- def setBackground(self,
- asVector = None,
- asPersistentVector = None,
- Scheduler = None,
- ):
- """
- Permet de définir l'estimation a priori :
- - asVector : entrée des données, comme un vecteur compatible avec le
- constructeur de numpy.matrix
- - asPersistentVector : entrée des données, comme un vecteur de type
- persistent contruit avec la classe ad-hoc "Persistence"
- - Scheduler est le contrôle temporel des données
- """
- if asVector is not None:
- if type( asVector ) is type( numpy.matrix([]) ):
- self.__Xb = numpy.matrix( asVector.A1, numpy.float ).T
- else:
- self.__Xb = numpy.matrix( asVector, numpy.float ).T
- elif asPersistentVector is not None:
- self.__Xb = asPersistentVector
- else:
- raise ValueError("Error: improperly defined background")
- return 0
-
- def setBackgroundError(self, asCovariance=None):
- """
- Permet de définir la covariance des erreurs d'ébauche :
- - asCovariance : entrée des données, comme une matrice compatible avec
- le constructeur de numpy.matrix
- """
- self.__B = numpy.matrix( asCovariance, numpy.float )
- return 0
-
- # -----------------------------------------------------------
- def setObservation(self,
- asVector = None,
- asPersistentVector = None,
- Scheduler = None,
- ):
- """
- Permet de définir les observations :
- - asVector : entrée des données, comme un vecteur compatible avec le
- constructeur de numpy.matrix
- - asPersistentVector : entrée des données, comme un vecteur de type
- persistent contruit avec la classe ad-hoc "Persistence"
- - Scheduler est le contrôle temporel des données disponibles
- """
- if asVector is not None:
- if type( asVector ) is type( numpy.matrix([]) ):
- self.__Y = numpy.matrix( asVector.A1, numpy.float ).T
- else:
- self.__Y = numpy.matrix( asVector, numpy.float ).T
- elif asPersistentVector is not None:
- self.__Y = asPersistentVector
- else:
- raise ValueError("Error: improperly defined observations")
- return 0
-
- def setObservationError(self, asCovariance=None):
- """
- Permet de définir la covariance des erreurs d'observations :
- - asCovariance : entrée des données, comme une matrice compatible avec
- le constructeur de numpy.matrix
- """
- self.__R = numpy.matrix( asCovariance, numpy.float )
- return 0
-
- def setObservationOperator(self,
- asFunction = {"Direct":None, "Tangent":None, "Adjoint":None},
- asMatrix = None,
- appliedToX = None,
- ):
- """
- Permet de définir un opérateur d'observation H. L'ordre de priorité des
- définitions et leur sens sont les suivants :
- - si asFunction["Tangent"] et asFunction["Adjoint"] ne sont pas None
- alors on définit l'opérateur à l'aide de fonctions. Si la fonction
- "Direct" n'est pas définie, on prend la fonction "Tangent".
- - si les fonctions ne sont pas disponibles et si asMatrix n'est pas
- None, alors on définit l'opérateur "Direct" et "Tangent" à l'aide de
- la matrice, et l'opérateur "Adjoint" à l'aide de la transposée. La
- matrice fournie doit être sous une forme compatible avec le
- constructeur de numpy.matrix.
- - si l'argument "appliedToX" n'est pas None, alors on définit, pour des
- X divers, l'opérateur par sa valeur appliquée à cet X particulier,
- sous la forme d'un dictionnaire appliedToX[NAME] avec NAME un nom.
- L'opérateur doit néanmoins déjà avoir été défini comme d'habitude.
- """
- if (type(asFunction) is type({})) and (asFunction["Tangent"] is not None) and (asFunction["Adjoint"] is not None):
- if not asFunction.has_key("Direct") or (asFunction["Direct"] is None):
- self.__H["Direct"] = Operator( fromMethod = asFunction["Tangent"] )
- else:
- self.__H["Direct"] = Operator( fromMethod = asFunction["Direct"] )
- self.__H["Tangent"] = Operator( fromMethod = asFunction["Tangent"] )
- self.__H["Adjoint"] = Operator( fromMethod = asFunction["Adjoint"] )
- elif asMatrix is not None:
- mat = numpy.matrix( asMatrix, numpy.float )
- self.__H["Direct"] = Operator( fromMatrix = mat )
- self.__H["Tangent"] = Operator( fromMatrix = mat )
- self.__H["Adjoint"] = Operator( fromMatrix = mat.T )
- else:
- raise ValueError("Error: improperly defined observation operator")
- #
- if appliedToX is not None:
- self.__H["AppliedToX"] = {}
- if type(appliedToX) is not dict:
- raise ValueError("Error: observation operator defined by \"appliedToX\" need a dictionary as argument.")
- for key in appliedToX.keys():
- if type( appliedToX[key] ) is type( numpy.matrix([]) ):
- # Pour le cas où l'on a une vraie matrice
- self.__H["AppliedToX"][key] = numpy.matrix( appliedToX[key].A1, numpy.float ).T
- elif type( appliedToX[key] ) is type( numpy.array([]) ) and len(appliedToX[key].shape) > 1:
- # Pour le cas où l'on a un vecteur représenté en array avec 2 dimensions
- self.__H["AppliedToX"][key] = numpy.matrix( appliedToX[key].reshape(len(appliedToX[key]),), numpy.float ).T
- else:
- self.__H["AppliedToX"][key] = numpy.matrix( appliedToX[key], numpy.float ).T
- else:
- self.__H["AppliedToX"] = None
- #
- return 0
-
- # -----------------------------------------------------------
- def setEvolutionModel(self,
- asFunction = {"Direct":None, "Tangent":None, "Adjoint":None},
- asMatrix = None,
- Scheduler = None,
- ):
- """
- Permet de définir un opérateur d'évolution M. L'ordre de priorité des
- définitions et leur sens sont les suivants :
- - si asFunction["Tangent"] et asFunction["Adjoint"] ne sont pas None
- alors on définit l'opérateur à l'aide de fonctions. Si la fonction
- "Direct" n'est pas définie, on prend la fonction "Tangent".
- - si les fonctions ne sont pas disponibles et si asMatrix n'est pas
- None, alors on définit l'opérateur "Direct" et "Tangent" à l'aide de
- la matrice, et l'opérateur "Adjoint" à l'aide de la transposée. La
- matrice fournie doit être sous une forme compatible avec le
- constructeur de numpy.matrix.
- """
- if (type(asFunction) is type({})) and (asFunction["Tangent"] is not None) and (asFunction["Adjoint"] is not None):
- if not asFunction.has_key("Direct") or (asFunction["Direct"] is None):
- self.__M["Direct"] = Operator( fromMethod = asFunction["Tangent"] )
- else:
- self.__M["Direct"] = Operator( fromMethod = asFunction["Direct"] )
- self.__M["Tangent"] = Operator( fromMethod = asFunction["Tangent"] )
- self.__M["Adjoint"] = Operator( fromMethod = asFunction["Adjoint"] )
- elif asMatrix is not None:
- matrice = numpy.matrix( asMatrix, numpy.float )
- self.__M["Direct"] = Operator( fromMatrix = matrice )
- self.__M["Tangent"] = Operator( fromMatrix = matrice )
- self.__M["Adjoint"] = Operator( fromMatrix = matrice.T )
- else:
- raise ValueError("Error: improperly defined evolution operator")
- return 0
-
- def setEvolutionError(self, asCovariance=None):
- """
- Permet de définir la covariance des erreurs de modèle :
- - asCovariance : entrée des données, comme une matrice compatible avec
- le constructeur de numpy.matrix
- """
- self.__Q = numpy.matrix( asCovariance, numpy.float )
- return 0
-
- # -----------------------------------------------------------
- def setControls (self, asVector = None ):
- """
- Permet de définir la valeur initiale du vecteur X contenant toutes les
- variables de contrôle, i.e. les paramètres ou l'état dont on veut
- estimer la valeur pour obtenir les observations. C'est utile pour un
- algorithme itératif/incrémental
- - asVector : entrée des données, comme un vecteur compatible avec le
- constructeur de numpy.matrix.
- """
- if asVector is not None:
- self.__X.store( asVector )
- return 0
-
- # -----------------------------------------------------------
- def setAlgorithm(self, choice = None ):
- """
- Permet de sélectionner l'algorithme à utiliser pour mener à bien l'étude
- d'assimilation. L'argument est un champ caractère se rapportant au nom
- d'un fichier contenu dans "../daAlgorithms" et réalisant l'opération
- d'assimilation sur les arguments (Xb,Y,H,R,B,Xa).
- """
- if choice is None:
- raise ValueError("Error: algorithm choice has to be given")
- if self.__algorithmName is not None:
- raise ValueError("Error: algorithm choice has already been done as \"%s\", it can't be changed."%self.__algorithmName)
- daDirectory = "daAlgorithms"
- #
- # 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 algorithm 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.__algorithmFile = __import__(str(choice), globals(), locals(), [])
- self.__algorithmName = str(choice)
- sys.path = sys_path_tmp ; del sys_path_tmp
- except ImportError, 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
- # -------------------------------------------------
- self.__algorithm = self.__algorithmFile.ElementaryAlgorithm()
- return 0
-
- def setAlgorithmParameters(self, asDico=None):
- """
- Permet de définir les paramètres de l'algorithme, sous la forme d'un
- dictionnaire.
- """
- self.__Parameters = dict( asDico )
- return 0
-
- # -----------------------------------------------------------
- def setDiagnostic(self, choice = None, name = "", unit = "", basetype = None, parameters = {} ):
- """
- 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, 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 self.__StoredDiagnostics.has_key(name):
- raise ValueError("A diagnostic with the same name already exists")
- else:
- self.__StoredDiagnostics[name] = self.__diagnosticFile.ElementaryDiagnostic(
- name = name,
- unit = unit,
- basetype = basetype,
- parameters = parameters )
- return 0
-
- # -----------------------------------------------------------
- def shape_validate(self):
- """
- Validation de la correspondance correcte des tailles des variables et
- des matrices s'il y en a.
- """
- if self.__Xb is None: __Xb_shape = (0,)
- elif hasattr(self.__Xb,"shape"):
- if type(self.__Xb.shape) is tuple: __Xb_shape = self.__Xb.shape
- else: __Xb_shape = self.__Xb.shape()
- else: raise TypeError("Xb has no attribute of shape: problem !")
- #
- if self.__Y is None: __Y_shape = (0,)
- elif hasattr(self.__Y,"shape"):
- if type(self.__Y.shape) is tuple: __Y_shape = self.__Y.shape
- else: __Y_shape = self.__Y.shape()
- else: raise TypeError("Y has no attribute of shape: problem !")
- #
- if self.__B is None: __B_shape = (0,0)
- elif hasattr(self.__B,"shape"):
- if type(self.__B.shape) is tuple: __B_shape = self.__B.shape
- else: __B_shape = self.__B.shape()
- else: raise TypeError("B has no attribute of shape: problem !")
- #
- if self.__R is None: __R_shape = (0,0)
- elif hasattr(self.__R,"shape"):
- if type(self.__R.shape) is tuple: __R_shape = self.__R.shape
- else: __R_shape = self.__R.shape()
- else: raise TypeError("R has no attribute of shape: problem !")
- #
- if self.__Q is None: __Q_shape = (0,0)
- elif hasattr(self.__Q,"shape"):
- if type(self.__Q.shape) is tuple: __Q_shape = self.__Q.shape
- else: __Q_shape = self.__Q.shape()
- else: raise TypeError("Q has no attribute of shape: problem !")
- #
- if len(self.__H) == 0: __H_shape = (0,0)
- elif type(self.__H) is type({}): __H_shape = (0,0)
- elif hasattr(self.__H["Direct"],"shape"):
- if type(self.__H["Direct"].shape) is tuple: __H_shape = self.__H["Direct"].shape
- else: __H_shape = self.__H["Direct"].shape()
- else: raise TypeError("H has no attribute of shape: problem !")
- #
- if len(self.__M) == 0: __M_shape = (0,0)
- elif type(self.__M) is type({}): __M_shape = (0,0)
- elif hasattr(self.__M["Direct"],"shape"):
- if type(self.__M["Direct"].shape) is tuple: __M_shape = self.__M["Direct"].shape
- else: __M_shape = self.__M["Direct"].shape()
- else: raise TypeError("M has no attribute of shape: problem !")
- #
- # Vérification des conditions
- # ---------------------------
- if not( len(__Xb_shape) == 1 or min(__Xb_shape) == 1 ):
- raise ValueError("Shape characteristic of Xb is incorrect: \"%s\""%(__Xb_shape,))
- if not( len(__Y_shape) == 1 or min(__Y_shape) == 1 ):
- raise ValueError("Shape characteristic of Y is incorrect: \"%s\""%(__Y_shape,))
- #
- if not( min(__B_shape) == max(__B_shape) ):
- raise ValueError("Shape characteristic of B is incorrect: \"%s\""%(__B_shape,))
- if not( min(__R_shape) == max(__R_shape) ):
- raise ValueError("Shape characteristic of R is incorrect: \"%s\""%(__R_shape,))
- if not( min(__Q_shape) == max(__Q_shape) ):
- raise ValueError("Shape characteristic of Q is incorrect: \"%s\""%(__Q_shape,))
- if not( min(__M_shape) == max(__M_shape) ):
- raise ValueError("Shape characteristic of M is incorrect: \"%s\""%(__M_shape,))
- #
- if len(self.__H) > 0 and not(type(self.__H) is type({})) and not( __H_shape[1] == max(__Xb_shape) ):
- raise ValueError("Shape characteristic of H \"%s\" and X \"%s\" are incompatible"%(__H_shape,__Xb_shape))
- if len(self.__H) > 0 and not(type(self.__H) is type({})) and not( __H_shape[0] == max(__Y_shape) ):
- raise ValueError("Shape characteristic of H \"%s\" and Y \"%s\" are incompatible"%(__H_shape,__Y_shape))
- if len(self.__H) > 0 and not(type(self.__H) is type({})) and len(self.__B) > 0 and not( __H_shape[1] == __B_shape[0] ):
- raise ValueError("Shape characteristic of H \"%s\" and B \"%s\" are incompatible"%(__H_shape,__B_shape))
- if len(self.__H) > 0 and not(type(self.__H) is type({})) and len(self.__R) > 0 and not( __H_shape[0] == __R_shape[1] ):
- raise ValueError("Shape characteristic of H \"%s\" and R \"%s\" are incompatible"%(__H_shape,__R_shape))
- #
- if len(self.__B) > 0 and not( __B_shape[1] == max(__Xb_shape) ):
- raise ValueError("Shape characteristic of B \"%s\" and Xb \"%s\" are incompatible"%(__B_shape,__Xb_shape))
- #
- if len(self.__R) > 0 and not( __R_shape[1] == max(__Y_shape) ):
- raise ValueError("Shape characteristic of R \"%s\" and Y \"%s\" are incompatible"%(__R_shape,__Y_shape))
- #
- if len(self.__M) > 0 and not(type(self.__M) is type({})) and not( __M_shape[1] == max(__Xb_shape) ):
- raise ValueError("Shape characteristic of M \"%s\" and X \"%s\" are incompatible"%(__M_shape,__Xb_shape))
- #
- return 1
-
- # -----------------------------------------------------------
- def analyze(self):
- """
- Permet de lancer le calcul d'assimilation.
-
- Le nom de la méthode à activer est toujours "run". Les paramètres en
- arguments de la méthode sont fixés. En sortie, on obtient les résultats
- dans la variable de type dictionnaire "StoredVariables", qui contient en
- particulier des objets de Persistence pour les analyses, OMA...
- """
- self.shape_validate()
- #
- self.__algorithm.run(
- Xb = self.__Xb,
- Y = self.__Y,
- H = self.__H,
- M = self.__M,
- R = self.__R,
- B = self.__B,
- Q = self.__Q,
- Par = self.__Parameters,
- )
- return 0
-
- # -----------------------------------------------------------
- def get(self, key=None):
- """
- Renvoie les résultats disponibles après l'exécution de la méthode
- d'assimilation, ou les diagnostics disponibles. Attention, quand un
- diagnostic porte le même nom qu'un variable stockée, c'est la variable
- stockée qui est renvoyée, et le diagnostic est inatteignable.
- """
- if key is not None:
- if self.__algorithm.has_key(key):
- return self.__algorithm.get( key )
- elif self.__StoredDiagnostics.has_key(key):
- return self.__StoredDiagnostics[key]
- else:
- raise ValueError("The requested key \"%s\" does not exists as a diagnostic or as a stored variable."%key)
- else:
- allvariables = self.__algorithm.get()
- allvariables.update( self.__StoredDiagnostics )
- return allvariables
-
- def get_available_algorithms(self):
- """
- Renvoie la liste des algorithmes identifiés par les chaînes de
- caractères
- """
- files = []
- for directory in sys.path:
- if os.path.isdir(os.path.join(directory,"daAlgorithms")):
- for fname in os.listdir(os.path.join(directory,"daAlgorithms")):
- root, ext = os.path.splitext(fname)
- if ext == '.py' and root != '__init__':
- files.append(root)
- files.sort()
- return files
-
- def get_available_diagnostics(self):
- """
- Renvoie la liste des diagnostics identifiés par les chaînes de
- caractères
- """
- files = []
- for directory in sys.path:
- if os.path.isdir(os.path.join(directory,"daDiagnostics")):
- for fname in os.listdir(os.path.join(directory,"daDiagnostics")):
- root, ext = os.path.splitext(fname)
- if ext == '.py' and root != '__init__':
- files.append(root)
- files.sort()
- return files
-
- # -----------------------------------------------------------
- def get_algorithms_main_path(self):
- """
- Renvoie le chemin pour le répertoire principal contenant les algorithmes
- dans un sous-répertoire "daAlgorithms"
- """
- return self.__parent
-
- def add_algorithms_path(self, asPath=None):
- """
- Ajoute au chemin de recherche des algorithmes un répertoire dans lequel
- se trouve un sous-répertoire "daAlgorithms"
-
- Remarque : si le chemin a déjà été ajouté pour les diagnostics, il n'est
- pas indispensable de le rajouter ici.
- """
- if not os.path.isdir(asPath):
- raise ValueError("The given "+asPath+" argument must exist as a directory")
- if not os.path.isdir(os.path.join(asPath,"daAlgorithms")):
- raise ValueError("The given \""+asPath+"\" argument must contain a subdirectory named \"daAlgorithms\"")
- if not os.path.isfile(os.path.join(asPath,"daAlgorithms","__init__.py")):
- raise ValueError("The given \""+asPath+"/daAlgorithms\" path must contain a file named \"__init__.py\"")
- sys.path.insert(0, os.path.abspath(asPath))
- sys.path = list(set(sys.path)) # Conserve en unique exemplaire chaque chemin
- return 1
-
- def get_diagnostics_main_path(self):
- """
- Renvoie le chemin pour le répertoire principal contenant les diagnostics
- dans un sous-répertoire "daDiagnostics"
- """
- return self.__parent
-
- def add_diagnostics_path(self, asPath=None):
- """
- Ajoute au chemin de recherche des algorithmes un répertoire dans lequel
- se trouve un sous-répertoire "daDiagnostics"
-
- Remarque : si le chemin a déjà été ajouté pour les algorithmes, il n'est
- pas indispensable de le rajouter ici.
- """
- if not os.path.isdir(asPath):
- raise ValueError("The given "+asPath+" argument must exist as a directory")
- if not os.path.isdir(os.path.join(asPath,"daDiagnostics")):
- raise ValueError("The given \""+asPath+"\" argument must contain a subdirectory named \"daDiagnostics\"")
- if not os.path.isfile(os.path.join(asPath,"daDiagnostics","__init__.py")):
- raise ValueError("The given \""+asPath+"/daDiagnostics\" path must contain a file named \"__init__.py\"")
- sys.path.insert(0, os.path.abspath(asPath))
- sys.path = list(set(sys.path)) # Conserve en unique exemplaire chaque chemin
- return 1
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- ADD = AssimilationStudy("Ma premiere etude BLUE")
-
- ADD.setBackground (asVector = [0, 1, 2])
- ADD.setBackgroundError (asCovariance = "1 0 0;0 1 0;0 0 1")
- ADD.setObservation (asVector = [0.5, 1.5, 2.5])
- ADD.setObservationError (asCovariance = "1 0 0;0 1 0;0 0 1")
- ADD.setObservationOperator(asMatrix = "1 0 0;0 1 0;0 0 1")
-
- ADD.setAlgorithm(choice="Blue")
-
- ADD.analyze()
-
- print "Nombre d'analyses :", ADD.get("Analysis").stepnumber()
- print "Analyse résultante :", ADD.get("Analysis").valueserie(0)
- print "Innovation :", ADD.get("Innovation").valueserie(0)
- print
-
- print "Algorithmes disponibles :", ADD.get_available_algorithms()
- # print " Chemin des algorithmes :", ADD.get_algorithms_main_path()
- print "Diagnostics disponibles :", ADD.get_available_diagnostics()
- # print " Chemin des diagnostics :", ADD.get_diagnostics_main_path()
- print
-
- ADD.setDiagnostic("RMS", "Ma RMS")
-
- liste = ADD.get().keys()
- liste.sort()
- print "Variables et diagnostics disponibles :", liste
- print
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Définit les outils généraux élémentaires.
-
- Ce module est destiné à etre appelée par AssimilationStudy pour constituer
- les objets élémentaires de l'algorithme.
-"""
-__author__ = "Jean-Philippe ARGAUD - Mars 2008"
-
-import numpy
-import Persistence
-
-# ==============================================================================
-class Operator:
- """
- Classe générale d'interface de type opérateur
- """
- def __init__(self, fromMethod=None, fromMatrix=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 matrice.
- Arguments :
- - fromMethod : argument de type fonction Python
- - fromMatrix : argument adapté au constructeur numpy.matrix
- """
- if fromMethod is not None:
- self.__Method = fromMethod
- self.__Matrix = None
- elif fromMatrix is not None:
- self.__Method = None
- self.__Matrix = numpy.matrix( fromMatrix, numpy.float )
- else:
- self.__Method = None
- self.__Matrix = None
-
- def appliedTo(self, xValue):
- """
- 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.
- Arguments :
- - xValue : argument adapté pour appliquer l'opérateur
- """
- if self.__Matrix is not None:
- return self.__Matrix * xValue
- else:
- return self.__Method( xValue )
-
- def appliedInXTo(self, (xNominal, xValue) ):
- """
- 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
- """
- if self.__Matrix is not None:
- return self.__Matrix * xValue
- else:
- return self.__Method( (xNominal, xValue) )
-
- def asMatrix(self):
- """
- Permet de renvoyer l'opérateur sous la forme d'une matrice
- """
- if self.__Matrix is not None:
- return self.__Matrix
- else:
- raise ValueError("Matrix form of the operator is not available")
-
- def shape(self):
- """
- Renvoie la taille sous forme numpy si l'opérateur est disponible sous
- la forme d'une matrice
- """
- if self.__Matrix is not None:
- return self.__Matrix.shape
- else:
- raise ValueError("Matrix form of the operator is not available, nor the shape")
-
-# ==============================================================================
-class Algorithm:
- """
- Classe générale d'interface de type algorithme
-
- Elle donne un cadre pour l'écriture d'une classe élémentaire d'algorithme
- d'assimilation, en fournissant un container (dictionnaire) de variables
- persistantes initialisées, et des méthodes d'accès à ces variables stockées.
-
- Une classe élémentaire d'algorithme doit implémenter la méthode "run".
- """
- def __init__(self):
- """
- L'initialisation présente permet de fabriquer des variables de stockage
- disponibles de manière générique dans les algorithmes élémentaires. Ces
- variables de stockage sont ensuite conservées dans un dictionnaire
- interne à l'objet, mais auquel on accède par la méthode "get".
-
- Les variables prévues sont :
- - Analysis : l'analyse
- - Innovation : l'innovation : d = Y - H Xb
- - SigmaObs2 : correction optimale des erreurs d'observation
- - SigmaBck2 : correction optimale des erreurs d'ébauche
- - OMA : Observation moins Analysis : Y - Xa
- - OMB : Observation moins Background : Y - Xb
- - AMB : Analysis moins Background : Xa - Xb
- - CovarianceAPosteriori : matrice A
- On peut rajouter des variables à stocker dans l'initialisation de
- l'algorithme élémentaire qui va hériter de cette classe
- """
- self.StoredVariables = {}
- self.StoredVariables["CostFunctionJ"] = Persistence.OneScalar(name = "CostFunctionJ")
- self.StoredVariables["CostFunctionJb"] = Persistence.OneScalar(name = "CostFunctionJb")
- self.StoredVariables["CostFunctionJo"] = Persistence.OneScalar(name = "CostFunctionJo")
- self.StoredVariables["GradientOfCostFunctionJ"] = Persistence.OneScalar(name = "GradientOfCostFunctionJ")
- self.StoredVariables["GradientOfCostFunctionJb"] = Persistence.OneScalar(name = "GradientOfCostFunctionJb")
- self.StoredVariables["GradientOfCostFunctionJo"] = Persistence.OneScalar(name = "GradientOfCostFunctionJo")
- self.StoredVariables["Analysis"] = Persistence.OneVector(name = "Analysis")
- self.StoredVariables["Innovation"] = Persistence.OneVector(name = "Innovation")
- self.StoredVariables["SigmaObs2"] = Persistence.OneScalar(name = "SigmaObs2")
- self.StoredVariables["SigmaBck2"] = Persistence.OneScalar(name = "SigmaBck2")
- self.StoredVariables["OMA"] = Persistence.OneVector(name = "OMA")
- self.StoredVariables["OMB"] = Persistence.OneVector(name = "OMB")
- self.StoredVariables["BMA"] = Persistence.OneVector(name = "BMA")
- self.StoredVariables["CovarianceAPosteriori"] = Persistence.OneMatrix(name = "CovarianceAPosteriori")
- self._name = None
-
- def get(self, key=None):
- """
- Renvoie l'une des variables stockées identifiée par la clé, ou le
- dictionnaire de l'ensemble des variables disponibles en l'absence de
- clé. Ce sont directement les variables sous forme objet qui sont
- renvoyées, donc les méthodes d'accès à l'objet individuel sont celles
- des classes de persistance.
- """
- if key is not None:
- return self.StoredVariables[key]
- else:
- return self.StoredVariables
-
- def has_key(self, key=None):
- """
- Vérifie si l'une des variables stockées est identifiée par la clé.
- """
- return self.StoredVariables.has_key(key)
-
- def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Par=None):
- """
- Doit implémenter l'opération élémentaire de calcul d'assimilation sous
- sa forme mathématique la plus naturelle possible.
- """
- raise NotImplementedError("Mathematical assimilation calculation has not been implemented!")
-
-# ==============================================================================
-class Diagnostic:
- """
- 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 = {}):
- self.name = str(name)
- self.parameters = dict( parameters )
-
- def _formula(self, *args):
- """
- Doit implémenter l'opération élémentaire de diagnostic sous sa forme
- mathématique la plus naturelle possible.
- """
- raise NotImplementedError("Diagnostic mathematical formula has not been implemented!")
-
- def calculate(self, *args):
- """
- Active la formule de calcul avec les arguments correctement rangés
- """
- raise NotImplementedError("Diagnostic activation method has not been implemented!")
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Ce module permet de mettre en place un logging utilisable partout dans
- l'application, par défaut à la console, et si nécessaire dans un fichier.
-
- Il doit être appelé en premier dans AssimilationStudy (mais pas directement
- dans les applications utilisateurs), en l'important et en instanciant un
- objet :
- import Logging ; Logging.Logging()
-
- Par défaut, seuls les messages du niveau WARNING ou au-delà sont disponibles
- (donc les simples messages d'info ne sont pas disponibles), ce que l'on peut
- changer à l'instanciation avec le mot-clé "level" :
- import Logging ; Logging.Logging(level=20)
-
- On peut éventuellement demander à l'objet de sortir aussi les messages dans
- un fichier (noms par défaut : AssimilationStudy.log, niveau NOTSET) :
- import Logging ; Logging.Logging().setLogfile()
-
- Si on veut changer le nom du fichier ou le niveau global de message, il faut
- récupérer l'instance et appliquer les méthodes :
- import Logging
- log = Logging.Logging()
- import logging
- log.setLevel(logging.DEBUG)
- log.setLogfile(filename="toto.log", filemode="a", level=logging.WARNING)
- et on change éventuellement le niveau avec :
- log.setLogfileLevel(logging.INFO)
-
- Ensuite, n'importe où dans les applications, il suffit d'utiliser le module
- "logging" (avec un petit "l") :
- import logging
- log = logging.getLogger(NAME) # Avec rien (recommandé) ou un nom NAME
- log.critical("...")
- log.error("...")
- log.warning("...")
- log.info("...")
- log.debug("...")
- ou encore plus simplement :
- import logging
- logging.info("...")
-
- Dans une application, à n'importe quel endroit et autant de fois qu'on veut,
- on peut changer le niveau global de message en utilisant par exemple :
- import logging
- logging.setLevel(logging.DEBUG)
-
- On rappelle les niveaux (attributs de "logging") et leur ordre :
- NOTSET=0 < DEBUG=10 < INFO=20 < WARNING=30 < ERROR=40 < CRITICAL=50
-"""
-__author__ = "Jean-Philippe ARGAUD - Octobre 2008"
-
-import os
-import sys
-import logging
-from PlatformInfo import PlatformInfo
-
-LOGFILE = os.path.join(os.path.abspath(os.curdir),"AssimilationStudy.log")
-
-# ==============================================================================
-class Logging:
- def __init__(self, level=logging.WARNING):
- """
- Initialise un logging à la console pour TOUS les niveaux de messages.
- """
- logging.basicConfig(
- format = '%(levelname)-8s %(message)s',
- level = level,
- stream = sys.stdout,
- )
- self.__logfile = None
- #
- # Initialise l'affichage de logging
- # ---------------------------------
- p = PlatformInfo()
- #
- logging.info( "--------------------------------------------------" )
- logging.info( "Lancement de "+p.getName()+" "+p.getVersion() )
- logging.info( "--------------------------------------------------" )
- logging.info( "Versions logicielles :" )
- logging.info( "- Python "+p.getPythonVersion() )
- logging.info( "- Numpy "+p.getNumpyVersion() )
- logging.info( "- Scipy "+p.getScipyVersion() )
- logging.info( "" )
-
- def setLogfileLevel(self, level=logging.NOTSET ):
- """
- Permet de changer globalement le niveau des messages disponibles.
- """
- logging.getLogger().setLevel(level)
-
- def setLogfile(self, filename=LOGFILE, filemode="w", level=logging.NOTSET):
- """
- Permet de disposer des messages dans un fichier EN PLUS de la console.
- """
- if self.__logfile is not None:
- # Supprime le précédent mode de stockage fichier s'il exsitait
- logging.getLogger().removeHandler(self.__logfile)
- self.__logfile = logging.FileHandler(filename, filemode)
- self.__logfile.setLevel(level)
- self.__logfile.setFormatter(
- logging.Formatter('%(asctime)s %(levelname)-8s %(message)s',
- '%d %b %Y %H:%M:%S'))
- logging.getLogger().addHandler(self.__logfile)
-
- def setLogfileLevel(self, level=logging.NOTSET ):
- """
- Permet de changer le niveau des messages stockés en fichier. Il ne sera
- pris en compte que s'il est supérieur au niveau global.
- """
- self.__logfile.setLevel(level)
-
- def getLevel(self):
- """
- Renvoie le niveau de Logging sous forme texte
- """
- return logging.getLevelName( logging.getLogger().getEffectiveLevel() )
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- import os.path
-
- l = Logging(level = logging.NOTSET)
-
- logging.info("Message numéro 1 uniquement disponible sur console")
-
- l.setLogfile(level = logging.WARNING)
- if not os.path.isfile(LOGFILE):
- raise ValueError("Le fichier de log \"%s\" n'a pas pu être créé."%LOGFILE)
-
- logging.info("Message numéro 2 uniquement disponible sur console")
- logging.warning("Message numéro 3 conjointement disponible sur console et fichier")
-
- l.setLogfileLevel(logging.INFO)
-
- logging.info("Message numéro 4 conjointement disponible sur console et fichier")
-
- print
- print " Le logging a été correctement initialisé. Le fichier suivant"
- print " %s"%os.path.basename(LOGFILE)
- print " a été correctement créé, et peut être effacé après vérification."
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Définit des outils de persistence et d'enregistrement de séries de valeurs
- pour analyse ultérieure ou utilisation de calcul.
-"""
-__author__ = "Jean-Philippe ARGAUD - Mars 2008"
-
-import numpy
-
-from PlatformInfo import PathManagement ; PathManagement()
-
-# ==============================================================================
-class Persistence:
- """
- Classe générale de persistence définissant les accesseurs nécessaires
- (Template)
- """
- def __init__(self, name="", unit="", basetype=str):
- """
- name : nom courant
- unit : unité
- basetype : type de base de l'objet stocké à chaque pas
-
- La gestion interne des données est exclusivement basée sur les variables
- initialisées ici (qui ne sont pas accessibles depuis l'extérieur des
- objets comme des attributs) :
- __step : numérotation par défaut du pas courant
- __basetype : le type de base de chaque valeur, sous la forme d'un type
- permettant l'instanciation ou le casting Python
- __steps : les pas de stockage. Par défaut, c'est __step
- __values : les valeurs de stockage. Par défaut, c'est None
- """
- self.__name = str(name)
- self.__unit = str(unit)
- #
- self.__step = -1
- self.__basetype = basetype
- #
- self.__steps = []
- self.__values = []
-
- def basetype(self, basetype=None):
- """
- Renvoie ou met en place le type de base des objets stockés
- """
- if basetype is None:
- return self.__basetype
- else:
- self.__basetype = basetype
-
- def store(self, value=None, step=None):
- """
- Stocke une valeur à un pas. Une instanciation est faite avec le type de
- base pour stocker l'objet. Si le pas n'est pas fournit, on utilise
- l'étape de stockage comme valeur de pas.
- """
- if value is None: raise ValueError("Value argument required")
- self.__step += 1
- if step is not None:
- self.__steps.append(step)
- else:
- self.__steps.append(self.__step)
- #
- self.__values.append(self.__basetype(value))
-
- def shape(self):
- """
- Renvoie la taille sous forme numpy du dernier objet stocké. Si c'est un
- objet numpy, renvoie le shape. Si c'est un entier, un flottant, un
- complexe, renvoie 1. Si c'est une liste ou un dictionnaire, renvoie la
- longueur. Par défaut, renvoie 1.
- """
- if len(self.__values) > 0:
- if self.__basetype in [numpy.matrix, numpy.array]:
- return self.__values[-1].shape
- elif self.__basetype in [int, float]:
- return (1,)
- elif self.__basetype in [list, dict]:
- return (len(self.__values[-1]),)
- else:
- return (1,)
- else:
- raise ValueError("Object has no shape before its first storage")
-
- def __len__(self):
- """
- Renvoie le nombre d'éléments dans un séquence ou la plus grande
- dimension d'une matrice
- """
- return max( self.shape() )
-
- # ---------------------------------------------------------
- def stepserie(self, item=None, step=None):
- """
- Renvoie par défaut toute la liste des pas de temps. Si l'argument "step"
- existe dans la liste des pas de stockage effectués, renvoie ce pas
- "step". Si l'argument "item" est correct, renvoie le pas stockée au
- numéro "item".
- """
- if step is not None and step in self.__steps:
- return step
- elif item is not None and item < len(self.__steps):
- return self.__steps[item]
- else:
- return self.__steps
-
- def valueserie(self, item=None, step=None):
- """
- Renvoie par défaut toute la liste des valeurs/objets. Si l'argument
- "step" existe dans la liste des pas de stockage effectués, renvoie la
- valeur stockée à ce pas "step". Si l'argument "item" est correct,
- renvoie la valeur stockée au numéro "item".
- """
- if step is not None and step in self.__steps:
- index = self.__steps.index(step)
- return self.__values[index]
- elif item is not None and item < len(self.__values):
- return self.__values[item]
- else:
- return self.__values
-
- def stepnumber(self):
- """
- Renvoie le nombre de pas de stockage.
- """
- return len(self.__steps)
-
- # ---------------------------------------------------------
- def mean(self):
- """
- Renvoie la valeur moyenne des données à chaque pas. Il faut que le type
- de base soit compatible avec les types élémentaires numpy.
- """
- try:
- return [numpy.matrix(item).mean() for item in self.__values]
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def std(self, ddof=0):
- """
- Renvoie l'écart-type des données à chaque pas. Il faut que le type de
- base soit compatible avec les types élémentaires numpy.
-
- ddof : c'est le nombre de degrés de liberté pour le calcul de
- l'écart-type, qui est dans le diviseur. Inutile avant Numpy 1.1
- """
- try:
- if numpy.version.version >= '1.1.0':
- return [numpy.matrix(item).std(ddof=ddof) for item in self.__values]
- else:
- return [numpy.matrix(item).std() for item in self.__values]
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def sum(self):
- """
- Renvoie la somme des données à chaque pas. Il faut que le type de
- base soit compatible avec les types élémentaires numpy.
- """
- try:
- return [numpy.matrix(item).sum() for item in self.__values]
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def min(self):
- """
- Renvoie le minimum des données à chaque pas. Il faut que le type de
- base soit compatible avec les types élémentaires numpy.
- """
- try:
- return [numpy.matrix(item).min() for item in self.__values]
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def max(self):
- """
- Renvoie le maximum des données à chaque pas. Il faut que le type de
- base soit compatible avec les types élémentaires numpy.
- """
- try:
- return [numpy.matrix(item).max() for item in self.__values]
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def plot(self, item=None, step=None,
- steps = None,
- title = "",
- xlabel = "",
- ylabel = "",
- ltitle = None,
- geometry = "600x400",
- filename = "",
- persist = False,
- pause = True,
- ):
- """
- Renvoie un affichage de la valeur à chaque pas, si elle est compatible
- avec un affichage Gnuplot (donc essentiellement un vecteur). Si
- l'argument "step" existe dans la liste des pas de stockage effectués,
- renvoie l'affichage de la valeur stockée à ce pas "step". Si l'argument
- "item" est correct, renvoie l'affichage de la valeur stockée au numéro
- "item". Par défaut ou en l'absence de "step" ou "item", renvoie un
- affichage successif de tous les pas.
-
- Arguments :
- - step : valeur du pas à afficher
- - item : index de la valeur à afficher
- - steps : liste unique des pas de l'axe des X, ou None si c'est
- la numérotation par défaut
- - title : base du titre général, qui sera automatiquement
- complétée par la mention du pas
- - xlabel : label de l'axe des X
- - ylabel : label de l'axe des Y
- - ltitle : titre associé au vecteur tracé
- - geometry : taille en pixels de la fenêtre et position du coin haut
- gauche, au format X11 : LxH+X+Y (défaut : 600x400)
- - filename : base de nom de fichier Postscript pour une sauvegarde,
- qui est automatiquement complétée par le numéro du
- fichier calculé par incrément simple de compteur
- - persist : booléen indiquant que la fenêtre affichée sera
- conservée lors du passage au dessin suivant
- Par défaut, persist = False
- - pause : booléen indiquant une pause après chaque tracé, et
- attendant un Return
- Par défaut, pause = True
- """
- import os
- #
- # Vérification de la disponibilité du module Gnuplot
- try:
- import Gnuplot
- self.__gnuplot = Gnuplot
- except:
- raise ImportError("The Gnuplot module is required to plot the object.")
- #
- # Vérification et compléments sur les paramètres d'entrée
- if persist:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -persist -geometry '+geometry
- else:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -geometry '+geometry
- if ltitle is None:
- ltitle = ""
- self.__g = self.__gnuplot.Gnuplot() # persist=1
- self.__g('set terminal '+self.__gnuplot.GnuplotOpts.default_term)
- self.__g('set style data lines')
- self.__g('set grid')
- self.__g('set autoscale')
- self.__g('set xlabel "'+str(xlabel).encode('ascii','replace')+'"')
- self.__g('set ylabel "'+str(ylabel).encode('ascii','replace')+'"')
- #
- # Tracé du ou des vecteurs demandés
- indexes = []
- if step is not None and step in self.__steps:
- indexes.append(self.__steps.index(step))
- elif item is not None and item < len(self.__values):
- indexes.append(item)
- else:
- indexes = indexes + range(len(self.__values))
- #
- i = -1
- for index in indexes:
- self.__g('set title "'+str(title).encode('ascii','replace')+' (pas '+str(index)+')"')
- if ( type(steps) is type([]) ) or ( type(steps) is type(numpy.array([])) ):
- Steps = list(steps)
- else:
- Steps = range(len(self.__values[index]))
- #
- self.__g.plot( self.__gnuplot.Data( Steps, self.__values[index], title=ltitle ) )
- #
- if filename != "":
- i += 1
- stepfilename = "%s_%03i.ps"%(filename,i)
- if os.path.isfile(stepfilename):
- raise ValueError("Error: a file with this name \"%s\" already exists."%stepfilename)
- self.__g.hardcopy(filename=stepfilename, color=1)
- if pause:
- raw_input('Please press return to continue...\n')
-
- # ---------------------------------------------------------
- def stepmean(self):
- """
- Renvoie la moyenne sur toutes les valeurs sans tenir compte de la
- longueur des pas. Il faut que le type de base soit compatible avec
- les types élémentaires numpy.
- """
- try:
- return numpy.matrix(self.__values).mean()
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def stepstd(self, ddof=0):
- """
- Renvoie l'écart-type de toutes les valeurs sans tenir compte de la
- longueur des pas. Il faut que le type de base soit compatible avec
- les types élémentaires numpy.
-
- ddof : c'est le nombre de degrés de liberté pour le calcul de
- l'écart-type, qui est dans le diviseur. Inutile avant Numpy 1.1
- """
- try:
- if numpy.version.version >= '1.1.0':
- return numpy.matrix(self.__values).std(ddof=ddof)
- else:
- return numpy.matrix(self.__values).std()
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def stepsum(self):
- """
- Renvoie la somme de toutes les valeurs sans tenir compte de la
- longueur des pas. Il faut que le type de base soit compatible avec
- les types élémentaires numpy.
- """
- try:
- return numpy.matrix(self.__values).sum()
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def stepmin(self):
- """
- Renvoie le minimum de toutes les valeurs sans tenir compte de la
- longueur des pas. Il faut que le type de base soit compatible avec
- les types élémentaires numpy.
- """
- try:
- return numpy.matrix(self.__values).min()
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def stepmax(self):
- """
- Renvoie le maximum de toutes les valeurs sans tenir compte de la
- longueur des pas. Il faut que le type de base soit compatible avec
- les types élémentaires numpy.
- """
- try:
- return numpy.matrix(self.__values).max()
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- def cumsum(self):
- """
- Renvoie la somme cumulée de toutes les valeurs sans tenir compte de la
- longueur des pas. Il faut que le type de base soit compatible avec
- les types élémentaires numpy.
- """
- try:
- return numpy.matrix(self.__values).cumsum(axis=0)
- except:
- raise TypeError("Base type is incompatible with numpy")
-
- # On pourrait aussi utiliser les autres attributs d'une "matrix", comme
- # "tofile", "min"...
-
- def stepplot(self,
- steps = None,
- title = "",
- xlabel = "",
- ylabel = "",
- ltitle = None,
- geometry = "600x400",
- filename = "",
- persist = False,
- pause = True,
- ):
- """
- Renvoie un affichage unique pour l'ensemble des valeurs à chaque pas, si
- elles sont compatibles avec un affichage Gnuplot (donc essentiellement
- un vecteur). Si l'argument "step" existe dans la liste des pas de
- stockage effectués, renvoie l'affichage de la valeur stockée à ce pas
- "step". Si l'argument "item" est correct, renvoie l'affichage de la
- valeur stockée au numéro "item".
-
- Arguments :
- - steps : liste unique des pas de l'axe des X, ou None si c'est
- la numérotation par défaut
- - title : base du titre général, qui sera automatiquement
- complétée par la mention du pas
- - xlabel : label de l'axe des X
- - ylabel : label de l'axe des Y
- - ltitle : titre associé au vecteur tracé
- - geometry : taille en pixels de la fenêtre et position du coin haut
- gauche, au format X11 : LxH+X+Y (défaut : 600x400)
- - filename : nom de fichier Postscript pour une sauvegarde,
- - persist : booléen indiquant que la fenêtre affichée sera
- conservée lors du passage au dessin suivant
- Par défaut, persist = False
- - pause : booléen indiquant une pause après chaque tracé, et
- attendant un Return
- Par défaut, pause = True
- """
- import os
- #
- # Vérification de la disponibilité du module Gnuplot
- try:
- import Gnuplot
- self.__gnuplot = Gnuplot
- except:
- raise ImportError("The Gnuplot module is required to plot the object.")
- #
- # Vérification et compléments sur les paramètres d'entrée
- if persist:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -persist -geometry '+geometry
- else:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -geometry '+geometry
- if ltitle is None:
- ltitle = ""
- if ( type(steps) is type([]) ) or ( type(steps) is type(numpy.array([])) ):
- Steps = list(steps)
- else:
- Steps = range(len(self.__values[0]))
- self.__g = self.__gnuplot.Gnuplot() # persist=1
- self.__g('set terminal '+self.__gnuplot.GnuplotOpts.default_term)
- self.__g('set style data lines')
- self.__g('set grid')
- self.__g('set autoscale')
- self.__g('set title "'+str(title).encode('ascii','replace') +'"')
- self.__g('set xlabel "'+str(xlabel).encode('ascii','replace')+'"')
- self.__g('set ylabel "'+str(ylabel).encode('ascii','replace')+'"')
- #
- # Tracé du ou des vecteurs demandés
- indexes = range(len(self.__values))
- self.__g.plot( self.__gnuplot.Data( Steps, self.__values[indexes.pop(0)], title=ltitle+" (pas 0)" ) )
- for index in indexes:
- self.__g.replot( self.__gnuplot.Data( Steps, self.__values[index], title=ltitle+" (pas %i)"%index ) )
- #
- if filename != "":
- self.__g.hardcopy(filename=filename, color=1)
- if pause:
- raw_input('Please press return to continue...\n')
-
-# ==============================================================================
-class OneScalar(Persistence):
- """
- Classe définissant le stockage d'une valeur unique réelle (float) par pas
-
- Le type de base peut être changé par la méthode "basetype", mais il faut que
- le nouveau type de base soit compatible avec les types par éléments de
- numpy. On peut même utiliser cette classe pour stocker des vecteurs/listes
- ou des matrices comme dans les classes suivantes, mais c'est déconseillé
- pour conserver une signification claire des noms.
- """
- def __init__(self, name="", unit="", basetype = float):
- Persistence.__init__(self, name, unit, basetype)
-
-class OneVector(Persistence):
- """
- Classe définissant le stockage d'une liste (list) de valeurs homogènes par
- hypothèse par pas. Pour éviter les confusions, ne pas utiliser la classe
- "OneVector" pour des données hétérogènes, mais bien "OneList".
- """
- def __init__(self, name="", unit="", basetype = list):
- Persistence.__init__(self, name, unit, basetype)
-
-class OneMatrix(Persistence):
- """
- Classe définissant le stockage d'une matrice de valeurs (numpy.matrix) par
- pas
- """
- def __init__(self, name="", unit="", basetype = numpy.matrix):
- Persistence.__init__(self, name, unit, basetype)
-
-class OneList(Persistence):
- """
- Classe définissant le stockage d'une liste de valeurs potentiellement
- hétérogènes (list) par pas. Pour éviter les confusions, ne pas utiliser la
- classe "OneVector" pour des données hétérogènes, mais bien "OneList".
- """
- def __init__(self, name="", unit="", basetype = list):
- Persistence.__init__(self, name, unit, basetype)
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- print "======> Un flottant"
- OBJET_DE_TEST = OneScalar("My float", unit="cm")
- OBJET_DE_TEST.store( 5.)
- OBJET_DE_TEST.store(-5.)
- OBJET_DE_TEST.store( 1.)
- print "Les pas de stockage :", OBJET_DE_TEST.stepserie()
- print "Les valeurs :", OBJET_DE_TEST.valueserie()
- print "La 2ème valeur :", OBJET_DE_TEST.valueserie(1)
- print "La dernière valeur :", OBJET_DE_TEST.valueserie(-1)
- print "Valeurs par pas :"
- print " La moyenne :", OBJET_DE_TEST.mean()
- print " L'écart-type :", OBJET_DE_TEST.std()
- print " La somme :", OBJET_DE_TEST.sum()
- print " Le minimum :", OBJET_DE_TEST.min()
- print " Le maximum :", OBJET_DE_TEST.max()
- print "Valeurs globales :"
- print " La moyenne :", OBJET_DE_TEST.stepmean()
- print " L'écart-type :", OBJET_DE_TEST.stepstd()
- print " La somme :", OBJET_DE_TEST.stepsum()
- print " Le minimum :", OBJET_DE_TEST.stepmin()
- print " Le maximum :", OBJET_DE_TEST.stepmax()
- print " La somme cumulée :", OBJET_DE_TEST.cumsum()
- print "Taille \"shape\" :", OBJET_DE_TEST.shape()
- print "Taille \"len\" :", len(OBJET_DE_TEST)
- del OBJET_DE_TEST
- print
-
- print "======> Un entier"
- OBJET_DE_TEST = OneScalar("My int", unit="cm", basetype=int)
- OBJET_DE_TEST.store( 5 )
- OBJET_DE_TEST.store(-5 )
- OBJET_DE_TEST.store( 1.)
- print "Les pas de stockage :", OBJET_DE_TEST.stepserie()
- print "Les valeurs :", OBJET_DE_TEST.valueserie()
- print "La 2ème valeur :", OBJET_DE_TEST.valueserie(1)
- print "La dernière valeur :", OBJET_DE_TEST.valueserie(-1)
- print "Valeurs par pas :"
- print " La moyenne :", OBJET_DE_TEST.mean()
- print " L'écart-type :", OBJET_DE_TEST.std()
- print " La somme :", OBJET_DE_TEST.sum()
- print " Le minimum :", OBJET_DE_TEST.min()
- print " Le maximum :", OBJET_DE_TEST.max()
- print "Valeurs globales :"
- print " La moyenne :", OBJET_DE_TEST.stepmean()
- print " L'écart-type :", OBJET_DE_TEST.stepstd()
- print " La somme :", OBJET_DE_TEST.stepsum()
- print " Le minimum :", OBJET_DE_TEST.stepmin()
- print " Le maximum :", OBJET_DE_TEST.stepmax()
- print " La somme cumulée :", OBJET_DE_TEST.cumsum()
- print "Taille \"shape\" :", OBJET_DE_TEST.shape()
- print "Taille \"len\" :", len(OBJET_DE_TEST)
- del OBJET_DE_TEST
- print
-
- print "======> Un booléen"
- OBJET_DE_TEST = OneScalar("My bool", unit="", basetype=bool)
- OBJET_DE_TEST.store( True )
- OBJET_DE_TEST.store( False )
- OBJET_DE_TEST.store( True )
- print "Les pas de stockage :", OBJET_DE_TEST.stepserie()
- print "Les valeurs :", OBJET_DE_TEST.valueserie()
- print "La 2ème valeur :", OBJET_DE_TEST.valueserie(1)
- print "La dernière valeur :", OBJET_DE_TEST.valueserie(-1)
- print "Taille \"shape\" :", OBJET_DE_TEST.shape()
- print "Taille \"len\" :", len(OBJET_DE_TEST)
- del OBJET_DE_TEST
- print
-
- print "======> Un vecteur de flottants"
- OBJET_DE_TEST = OneVector("My float vector", unit="cm")
- OBJET_DE_TEST.store( (5 , -5) )
- OBJET_DE_TEST.store( (-5, 5 ) )
- OBJET_DE_TEST.store( (1., 1.) )
- print "Les pas de stockage :", OBJET_DE_TEST.stepserie()
- print "Les valeurs :", OBJET_DE_TEST.valueserie()
- print "La 2ème valeur :", OBJET_DE_TEST.valueserie(1)
- print "La dernière valeur :", OBJET_DE_TEST.valueserie(-1)
- print "Valeurs par pas :"
- print " La moyenne :", OBJET_DE_TEST.mean()
- print " L'écart-type :", OBJET_DE_TEST.std()
- print " La somme :", OBJET_DE_TEST.sum()
- print " Le minimum :", OBJET_DE_TEST.min()
- print " Le maximum :", OBJET_DE_TEST.max()
- print "Valeurs globales :"
- print " La moyenne :", OBJET_DE_TEST.stepmean()
- print " L'écart-type :", OBJET_DE_TEST.stepstd()
- print " La somme :", OBJET_DE_TEST.stepsum()
- print " Le minimum :", OBJET_DE_TEST.stepmin()
- print " Le maximum :", OBJET_DE_TEST.stepmax()
- print " La somme cumulée :", OBJET_DE_TEST.cumsum()
- print "Taille \"shape\" :", OBJET_DE_TEST.shape()
- print "Taille \"len\" :", len(OBJET_DE_TEST)
- del OBJET_DE_TEST
- print
-
- print "======> Une liste hétérogène"
- OBJET_DE_TEST = OneList("My list", unit="bool/cm")
- OBJET_DE_TEST.store( (True , -5) )
- OBJET_DE_TEST.store( (False, 5 ) )
- OBJET_DE_TEST.store( (True , 1.) )
- print "Les pas de stockage :", OBJET_DE_TEST.stepserie()
- print "Les valeurs :", OBJET_DE_TEST.valueserie()
- print "La 2ème valeur :", OBJET_DE_TEST.valueserie(1)
- print "La dernière valeur :", OBJET_DE_TEST.valueserie(-1)
- print "Valeurs par pas : attention, on peut les calculer car True=1, False=0, mais cela n'a pas de sens"
- print " La moyenne :", OBJET_DE_TEST.mean()
- print " L'écart-type :", OBJET_DE_TEST.std()
- print " La somme :", OBJET_DE_TEST.sum()
- print " Le minimum :", OBJET_DE_TEST.min()
- print " Le maximum :", OBJET_DE_TEST.max()
- print "Valeurs globales : attention, on peut les calculer car True=1, False=0, mais cela n'a pas de sens"
- print " La moyenne :", OBJET_DE_TEST.stepmean()
- print " L'écart-type :", OBJET_DE_TEST.stepstd()
- print " La somme :", OBJET_DE_TEST.stepsum()
- print " Le minimum :", OBJET_DE_TEST.stepmin()
- print " Le maximum :", OBJET_DE_TEST.stepmax()
- print " La somme cumulée :", OBJET_DE_TEST.cumsum()
- print "Taille \"shape\" :", OBJET_DE_TEST.shape()
- print "Taille \"len\" :", len(OBJET_DE_TEST)
- del OBJET_DE_TEST
- print
-
- print "======> Utilisation directe de la classe Persistence"
- OBJET_DE_TEST = Persistence("My object", unit="", basetype=int )
- OBJET_DE_TEST.store( 1 )
- OBJET_DE_TEST.store( 3 )
- OBJET_DE_TEST.store( 7 )
- print "Les pas de stockage :", OBJET_DE_TEST.stepserie()
- print "Les valeurs :", OBJET_DE_TEST.valueserie()
- print "La 2ème valeur :", OBJET_DE_TEST.valueserie(1)
- print "La dernière valeur :", OBJET_DE_TEST.valueserie(-1)
- print "Taille \"shape\" :", OBJET_DE_TEST.shape()
- print "Taille \"len\" :", len(OBJET_DE_TEST)
- del OBJET_DE_TEST
- print
-
- print "======> Affichage d'objets stockés"
- OBJET_DE_TEST = Persistence("My object", unit="", basetype=numpy.array)
- D = OBJET_DE_TEST
- vect1 = [1, 2, 1, 2, 1]
- vect2 = [-3, -3, 0, -3, -3]
- vect3 = [-1, 1, -5, 1, -1]
- vect4 = 100*[0.29, 0.97, 0.73, 0.01, 0.20]
- print "Stockage de 3 vecteurs de longueur identique"
- D.store(vect1)
- D.store(vect2)
- D.store(vect3)
- print "Affichage de l'ensemble du stockage sur une même image"
- D.stepplot(
- title = "Tous les vecteurs",
- filename="vecteurs.ps",
- xlabel = "Axe X",
- ylabel = "Axe Y",
- pause = False )
- print "Stockage d'un quatrième vecteur de longueur différente"
- D.store(vect4)
- print "Affichage séparé du dernier stockage"
- D.plot(
- item = 3,
- title = "Vecteurs",
- filename = "vecteur",
- xlabel = "Axe X",
- ylabel = "Axe Y",
- pause = False )
- print "Les images ont été stockées en fichiers Postscript"
- print "Taille \"shape\" du dernier objet stocké",OBJET_DE_TEST.shape()
- print "Taille \"len\" du dernier objet stocké",len(OBJET_DE_TEST)
- del OBJET_DE_TEST
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Informations sur le code et la plateforme, et mise à jour des chemins
-
- La classe "PlatformInfo" permet de récupérer les informations générales sur
- le code et la plateforme sous forme de strings, ou d'afficher directement
- les informations disponibles par les méthodes. L'impression directe d'un
- objet de cette classe affiche les informations minimales. Par exemple :
- print PlatformInfo()
- print PlatformInfo().getVersion()
- created = PlatformInfo().getDate()
-
- La classe "PathManagement" permet de mettre à jour les chemins système pour
- ajouter les outils numériques, matrices... On l'utilise en instanciant
- simplement cette classe, sans meme récupérer d'objet :
- PathManagement()
-"""
-__author__ = "Jean-Philippe ARGAUD - Mars 2008"
-
-import os
-
-# ==============================================================================
-class PlatformInfo:
- """
- Rassemblement des informations sur le code et la plateforme
- """
- def getName(self):
- "Retourne le nom de l'application"
- import version
- return version.name
-
- def getVersion(self):
- "Retourne le numéro de la version"
- import version
- return version.version
-
- def getDate(self):
- "Retourne la date de création de la version"
- import version
- return version.date
-
- def getPythonVersion(self):
- "Retourne la version de python utilisée"
- import sys
- return ".".join(map(str,sys.version_info[0:3]))
-
- def getNumpyVersion(self):
- "Retourne la version de numpy utilisée"
- import numpy.version
- return numpy.version.version
-
- def getScipyVersion(self):
- "Retourne la version de scipy utilisée"
- import scipy.version
- return scipy.version.version
-
- def getCurrentMemorySize(self):
- "Retourne la taille mémoire courante utilisée"
- return 1
-
- def __str__(self):
- import version
- return "%s %s (%s)"%(version.name,version.version,version.date)
-
-# ==============================================================================
-class PathManagement:
- """
- Mise à jour du path système pour les répertoires d'outils
- """
- def __init__(self):
- import os, sys
- parent = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
- self.__paths = {}
- self.__paths["daExternals"] = os.path.join(parent,"daExternals")
- self.__paths["daMatrices"] = os.path.join(parent,"daMatrices")
- self.__paths["daNumerics"] = os.path.join(parent,"daNumerics")
- #
- for v in self.__paths.values():
- sys.path.insert(0, v )
- #
- # Conserve en unique exemplaire chaque chemin
- sys.path = list(set(sys.path))
- del parent
-
- def getpaths(self):
- """
- Renvoie le dictionnaire des chemins ajoutés
- """
- return self.__paths
-
-# ==============================================================================
-class SystemUsage:
- """
- Permet de récupérer les différentes tailles mémoires du process courant
- """
- #
- # Le module resource renvoie 0 pour les tailles mémoire. On utilise donc
- # plutôt : http://code.activestate.com/recipes/286222/ et les infos de
- # http://www.redhat.com/docs/manuals/enterprise/RHEL-4-Manual/en-US/Reference_Guide/s2-proc-meminfo.html
- #
- _proc_status = '/proc/%d/status' % os.getpid()
- _memo_status = '/proc/meminfo'
- _scale = {
- 'o': 1.0,
- 'ko': 1024.0, 'mo': 1024.0*1024.0,
- 'Ko': 1024.0, 'Mo': 1024.0*1024.0,
- 'B': 1.0,
- 'kB': 1024.0, 'mB': 1024.0*1024.0,
- 'KB': 1024.0, 'MB': 1024.0*1024.0,
- }
- _max_mem = 0
- _max_rss = 0
- _max_sta = 0
- #
- def _VmA(self, VmKey, unit):
- try:
- t = open(self._memo_status)
- v = t.read()
- t.close()
- except:
- return 0.0 # non-Linux?
- i = v.index(VmKey) # get VmKey line e.g. 'VmRSS: 9999 kB\n ...'
- v = v[i:].split(None, 3) # whitespace
- if len(v) < 3:
- return 0.0 # invalid format?
- # convert Vm value to bytes
- mem = float(v[1]) * self._scale[v[2]]
- return mem / self._scale[unit]
- #
- def getAvailablePhysicalMemory(self, unit="o"):
- "Renvoie la mémoire physique utilisable en octets"
- return self._VmA('MemTotal:', unit)
- #
- def getAvailableSwapMemory(self, unit="o"):
- "Renvoie la mémoire swap utilisable en octets"
- return self._VmA('SwapTotal:', unit)
- #
- def getAvailableMemory(self, unit="o"):
- "Renvoie la mémoire totale (physique+swap) utilisable en octets"
- return self._VmA('MemTotal:', unit) + self._VmA('SwapTotal:', unit)
- #
- def getUsableMemory(self, unit="o"):
- """Renvoie la mémoire utilisable en octets
- Rq : il n'est pas sûr que ce décompte soit juste...
- """
- return self._VmA('MemFree:', unit) + self._VmA('SwapFree:', unit) + \
- self._VmA('Cached:', unit) + self._VmA('SwapCached:', unit)
- #
- def _VmB(self, VmKey, unit):
- try:
- t = open(self._proc_status)
- v = t.read()
- t.close()
- except:
- return 0.0 # non-Linux?
- i = v.index(VmKey) # get VmKey line e.g. 'VmRSS: 9999 kB\n ...'
- v = v[i:].split(None, 3) # whitespace
- if len(v) < 3:
- return 0.0 # invalid format?
- # convert Vm value to bytes
- mem = float(v[1]) * self._scale[v[2]]
- return mem / self._scale[unit]
- #
- def getUsedMemory(self, unit="o"):
- "Renvoie la mémoire totale utilisée en octets"
- mem = self._VmB('VmSize:', unit)
- self._max_mem = max(self._max_mem, mem)
- return mem
- #
- def getUsedResident(self, unit="o"):
- "Renvoie la mémoire résidente utilisée en octets"
- mem = self._VmB('VmRSS:', unit)
- self._max_rss = max(self._max_rss, mem)
- return mem
- #
- def getUsedStacksize(self, unit="o"):
- "Renvoie la taille du stack utilisé en octets"
- mem = self._VmB('VmStk:', unit)
- self._max_sta = max(self._max_sta, mem)
- return mem
- #
- def getMaxUsedMemory(self):
- "Renvoie la mémoire totale maximale mesurée"
- return self._max_mem
- #
- def getMaxUsedResident(self):
- "Renvoie la mémoire résidente maximale mesurée"
- return self._max_rss
- #
- def getMaxUsedStacksize(self):
- "Renvoie la mémoire du stack maximale mesurée"
- return self._max_sta
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- print PlatformInfo()
- print
- p = PlatformInfo()
- print "Les caractéristiques détaillées des applications et outils sont :"
- print " - Application.......:",p.getName()
- print " - Version...........:",p.getVersion()
- print " - Date Application..:",p.getDate()
- print " - Python............:",p.getPythonVersion()
- print " - Numpy.............:",p.getNumpyVersion()
- print " - Scipy.............:",p.getScipyVersion()
- print
-
- p = PathManagement()
- print "Les chemins ajoutés au système pour des outils :"
- for k,v in p.getpaths().items():
- print " %12s : %s"%(k,os.path.basename(v))
- print
-
- m = SystemUsage()
- print "La mémoire disponible est la suivante :"
- print " - mémoire totale....: %4.1f Mo"%m.getAvailableMemory("Mo")
- print " - mémoire physique..: %4.1f Mo"%m.getAvailablePhysicalMemory("Mo")
- print " - mémoire swap......: %4.1f Mo"%m.getAvailableSwapMemory("Mo")
- print " - utilisable........: %4.1f Mo"%m.getUsableMemory("Mo")
- print "L'usage mémoire de cette exécution est le suivant :"
- print " - mémoire totale....: %4.1f Mo"%m.getUsedMemory("Mo")
- print " - mémoire résidente.: %4.1f Mo"%m.getUsedResident("Mo")
- print " - taille de stack...: %4.1f Mo"%m.getUsedStacksize("Mo")
- print "Création d'un objet range(1000000) et mesure mémoire"
- x = range(1000000)
- print " - mémoire totale....: %4.1f Mo"%m.getUsedMemory("Mo")
- print "Destruction de l'objet et mesure mémoire"
- del x
- print " - mémoire totale....: %4.1f Mo"%m.getUsedMemory("Mo")
- print "L'usage mémoire maximal de cette exécution est le suivant :"
- print " - mémoire totale....: %4.1f Mo"%m.getMaxUsedMemory()
- print " - mémoire résidente.: %4.1f Mo"%m.getMaxUsedResident()
- print " - taille de stack...: %4.1f Mo"%m.getMaxUsedStacksize()
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-name = "Data Assimilation Package"
-version = "0.2.0"
-date = "lundi 23 septembre 2009, 11:11:11 (UTC+0200)"
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic qui effectue le test d egalite des moyennes de 2 vecteurs
- dependants au sens du test de Student.
- Ce diagnostic utilise le calcul de la p-value pour le test de Student
- pour 2 vecteurs dependants
- En input : la tolerance
- En output : le resultat du diagnostic est une reponse booleenne au test :
- True si les moyennes sont egales au sens du Test de Student
- False dans le cas contraire.
-"""
-__author__ = "Sophie RICCI - Octobre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from ComputeStudent import DependantVectors
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- """
- Diagnostic qui effectueIndependantVectorsEqualVariance le test d egalite des moyennes de 2 vecteurs
- dependants au sens du test de Student.
- Ce diagnostic utilise le calcul de la p-value pour le test de Student
- pour 2 vecteurs dependants
- En input : la tolerance
- En output : le resultat du diagnostic est une reponse booleenne au test :
- True si les moyennes sont egales au sens du Test de Student
- False dans le cas contraire.
- """
- def __init__(self, name="", unit="", basetype = None, parameters = {} ):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
- if not self.parameters.has_key("tolerance"):
- raise ValueError("A parameter named \"tolerance\" is required.")
-
- def formula(self, V1, V2):
- """
- Effectue le calcul de la p-value de Student pour deux vecteurs.
- """
- [aire, Q, reponse, message] = DependantVectors(
- vector1 = V1,
- vector2 = V2,
- tolerance = self.parameters["tolerance"] )
- logging.info( message )
- answerStudentTest = False
- if (aire < (100.*self.parameters["tolerance"])) :
- answerStudentTest = False
- else:
- answerStudentTest = True
- return answerStudentTest
-
- def calculate(self, vector1 = None, vector2 = None, step = None):
- """
- Active la formule de calcul
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Student value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size, or the vector types are incompatible")
- value = self.formula( V1, V2 )
- self.store( value = value, step = step)
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- print " Test d'égalite des moyennes au sens de Student pour deux vecteurs"
- print " dépendants."
- print
- #
- # Initialisation des inputs et appel du diagnostic
- # --------------------------------------------------------------------
- tolerance = 0.05
- D = ElementaryDiagnostic("ComputeMeanStudent_DependVect", parameters = {
- "tolerance":tolerance,
- })
- #
- # Tirage de l'echantillon aleatoire
- # --------------------------------------------------------------------
- x1 = numpy.array(([-0.23262176, 1.36065207, 0.32988102, 0.24400551, -0.66765848, -0.19088483, -0.31082575, 0.56849814, 1.21453443, 0.99657516]))
- x2 = numpy.array(([-0.23, 1.36, 0.32, 0.24, -0.66, -0.19, -0.31, 0.56, 1.21, 0.99]))
- #
- # Calcul
- # --------------------------------------------------------------------
- D.calculate(x1, x2)
- #
- if D.valueserie(0) :
- print " L'hypothèse d'égalité des moyennes est valide."
- print
- else :
- raise ValueError("The egality of the means is NOT valid")
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic qui effectue le test d egalite des moyennes de 2 vecteurs
- independants supposes de variances differentes au sens du test de Student.
- En input : la tolerance
- En output : le resultat du diagnostic est une reponse booleenne au test :
- True si les moyennes sont egales au sens du Test de Student
- False dans le cas contraire.
-"""
-__author__ = "Sophie RICCI - Octobre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from ComputeStudent import IndependantVectorsDifferentVariance
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- """
- Diagnostic qui effectue le test d egalite des moyennes de 2 vecteurs
- independants supposes de variances differentes au sens du test de Student.
- En input : la tolerance
- En output : le resultat du diagnostic est une reponse booleenne au test :
- True si les moyennes sont egales au sens du Test de Student
- False dans le cas contraire.
- """
- def __init__(self, name="", unit="", basetype = None, parameters = {} ):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
- if not self.parameters.has_key("tolerance"):
- raise ValueError("A parameter named \"tolerance\" is required.")
-
- def formula(self, V1, V2):
- """
- Effectue le calcul de la p-value de Student pour deux vecteurs
- independants supposes de variances differentes.
- """
- [aire, Q, reponse, message] = IndependantVectorsDifferentVariance(
- vector1 = V1,
- vector2 = V2,
- tolerance = self.parameters["tolerance"],
- )
- logging.info( message )
- answerStudentTest = False
- if (aire < (100.*self.parameters["tolerance"])) :
- answerStudentTest = False
- else:
- answerStudentTest = True
- return answerStudentTest
-
- def calculate(self, vector1 = None, vector2 = None, step = None):
- """
- Active la formule de calcul
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Student value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size, or the vector types are incompatible")
- value = self.formula( V1, V2 )
- self.store( value = value, step = step)
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- print " Test d'égalite des moyennes au sens de Student pour deux vecteurs"
- print " indépendants supposés de variances différentes."
- print
- #
- # Initialisation des inputs et appel du diagnostic
- # --------------------------------------------------------------------
- tolerance = 0.05
- D = ElementaryDiagnostic("IndependantVectorsDifferentVariance", parameters = {
- "tolerance":tolerance,
- })
- #
- # Tirage de l'echantillon aleatoire
- # --------------------------------------------------------------------
- x1 = numpy.array(([-0.23262176, 1.36065207, 0.32988102, 0.24400551, -0.66765848, -0.19088483, -0.31082575, 0.56849814, 1.21453443, 0.99657516]))
- x2 = numpy.array(([-0.23, 1.36, 0.32, 0.24, -0.66, -0.19, -0.31, 0.56, 1.21, 0.99]))
- #
- # Calcul
- # --------------------------------------------------------------------
- D.calculate(x1, x2)
-#
- if D.valueserie(0) :
- print " L'hypothèse d'égalité des moyennes est valide."
- print
- else :
- raise ValueError("The egality of the means is NOT valid")
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic qui effectue le test d egalite des moyennes de 2 vecteurs
- independants supposes de variances egales au sens du test de Student.
- En input : la tolerance
- En output : le resultat du diagnostic est une reponse booleenne au test :
- True si les moyennes sont egales au sens du Test de Student
- False dans le cas contraire.
-"""
-__author__ = "Sophie RICCI - Octobre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from ComputeStudent import IndependantVectorsEqualVariance
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- """
- Diagnostic qui effectue le test d egalite des moyennes de 2 vecteurs independants supposes de variances egales au sens du test de Student.
- En input : la tolerance
- En output : le resultat du diagnostic est une reponse booleenne au test :
- True si les moyennes sont egales au sens du Test de Student
- False dans le cas contraire.
- """
- def __init__(self, name="", unit="", basetype = None, parameters = {} ):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
- if not self.parameters.has_key("tolerance"):
- raise ValueError("A parameter named \"tolerance\" is required.")
-
- def formula(self, V1, V2):
- """
- Effectue le calcul de la p-value de Student pour deux vecteurs
- independants supposes de variances egales.
- """
- [aire, Q, reponse, message] = IndependantVectorsEqualVariance(
- vector1 = V1,
- vector2 = V2,
- tolerance = self.parameters["tolerance"],
- )
- logging.info( message )
- answerStudentTest = False
- if (aire < (100.*self.parameters["tolerance"])) :
- answerStudentTest = False
- else:
- answerStudentTest = True
- return answerStudentTest
-
- def calculate(self, vector1 = None, vector2 = None, step = None):
- """
- Active la formule de calcul
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Student value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size, or the vector types are incompatible")
- value = self.formula( V1, V2 )
- self.store( value = value, step = step)
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- print " Test d'égalite des moyennes au sens de Student pour deux vecteurs"
- print " indépendants supposés de variances égales"
- print
- #
- # Initialisation des inputs et appel du diagnostic
- # --------------------------------------------------------------------
- tolerance = 0.05
- D = ElementaryDiagnostic("ComputeMeanStudent_IndepVect_EgalVar", parameters = {
- "tolerance":tolerance,
- })
- #
- # Tirage de l'echantillon aleatoire
- # --------------------------------------------------------------------
- x1 = numpy.array(([-0.23262176, 1.36065207, 0.32988102, 0.24400551, -0.66765848, -0.19088483, -0.31082575, 0.56849814, 1.21453443, 0.99657516]))
- x2 = numpy.array(([-0.23, 1.36, 0.32, 0.24, -0.66, -0.19, -0.31, 0.56, 1.21, 0.99]))
- #
- # Calcul
- # --------------------------------------------------------------------
- D.calculate(x1, x2)
- #
- if D.valueserie(0) :
- print " L'hypothèse d'égalité des moyennes est valide."
- print
- else :
- raise ValueError("The egality of the means is NOT valid")
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic qui compare les variances de 2 vecteurs au sens de Fisher à
- l'aide du calcul de la p-value pour le test de Fisher.
- - entrée : la tolérance (tolerance) sous forme de paramètres dans le
- dictionnaire Par, et les deux vecteurs d'échantillons.
- - sortie : le résultat du diagnostic est une réponse booléenne au test :
- True si l'égalite des variances est valide au sens du test de Fisher,
- False dans le cas contraire
-"""
-__author__ = "Sophie RICCI - Juillet 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from ComputeFisher import ComputeFisher
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- """
- Diagnostic qui compare les variances de 2 vecteurs au sens de Fisher à
- l'aide du calcul de la p-value pour le test de Fisher.
- - entrée : la tolérance (tolerance) sous forme de paramètres dans le
- dictionnaire Par, et les deux vecteurs d'échantillons.
- - sortie : le résultat du diagnostic est une réponse booléenne au test :
- True si l'égalite des variances est valide au sens du test de Fisher,
- False dans le cas contraire
- """
- def __init__(self, name="", unit="", basetype = None, parameters = {} ):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
- if not self.parameters.has_key("tolerance"):
- raise ValueError("A parameter named \"tolerance\" is required.")
-
- def formula(self, V1, V2):
- """
- Effectue le test de Fisher avec la p-value pour 2 vecteurs
- """
- [aire, f, reponse, message] = ComputeFisher(
- vector1 = V1,
- vector2 = V2,
- tolerance = self.parameters["tolerance"],
- )
- answerKhisquareTest = False
- if (aire < (100.*self.parameters["tolerance"])) :
- answerKhisquareTest = False
- else:
- answerKhisquareTest = True
- logging.info( message )
- #
- return answerKhisquareTest
-
- def calculate(self, vector1 = None, vector2 = None, step = None):
- """
- Active la formule de calcul
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Fisher p-value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size, or the vector types are incompatible")
- #
- value = self.formula( V1, V2 )
- #
- self.store( value = value, step = step)
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- print " Test d'égalite des variances pour deux vecteurs de taille 10"
- print
- #
- # Initialisation des inputs et appel du diagnostic
- # --------------------------------------------------------------------
- tolerance = 0.05
- D = ElementaryDiagnostic("CompareVarianceFisher", parameters = {
- "tolerance":tolerance,
- })
- #
- # Tirage de l'echantillon aleatoire
- # --------------------------------------------------------------------
- x1 = numpy.array(([-0.23262176, 1.36065207, 0.32988102, 0.24400551, -0.66765848, -0.19088483, -0.31082575, 0.56849814, 1.21453443, 0.99657516]))
- x2 = numpy.array(([-0.23, 1.36, 0.32, 0.24, -0.66, -0.19, -0.31, 0.56, 1.21, 0.99]))
- #
- # Calcul
- # --------------------------------------------------------------------
- D.calculate(x1, x2)
- #
- if D.valueserie(0) :
- print " L'hypothèse d'égalité des deux variances est correcte."
- print
- else :
- raise ValueError("L'hypothèse d'égalité des deux variances est fausse.")
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Calcul du biais (i.e. la moyenne) à chaque pas. Ce diagnostic très simple
- est présent pour rappeller à l'utilisateur de l'assimilation qu'il faut
- qu'il vérifie le biais de ses erreurs en particulier.
-"""
-__author__ = "Sophie RICCI - Aout 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from AssimilationStudy import AssimilationStudy
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = float )
-
- def _formula(self, V):
- """
- Calcul du biais, qui est simplement la moyenne du vecteur
- """
- biais = V.mean()
- #
- return biais
-
- def calculate(self, vector = None, step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- """
- if vector is None:
- raise ValueError("One vector must be given to compute biais")
- V = numpy.array(vector)
- if V.size < 1:
- raise ValueError("The given vector must not be empty")
- #
- value = self._formula( V)
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- #
- # Instanciation de l'objet diagnostic
- # -----------------------------------
- D = ElementaryDiagnostic("Mon ComputeBiais")
- #
- # Tirage d un vecteur choisi
- # --------------------------
- x = numpy.matrix(([3., 4., 5.]))
- print " Le vecteur de type 'matrix' choisi est..:", x
- print " Le biais attendu de ce vecteur est......:", x.mean()
- #
- D.calculate( vector = x)
- print " Le biais obtenu de ce vecteur est.......:", D.valueserie(0)
- print
- #
- # Tirage d un vecteur choisi
- # --------------------------
- x = numpy.array(range(11))
- print " Le vecteur de type 'array' choisi est...:", x
- print " Le biais attendu de ce vecteur est......:", x.mean()
- #
- D.calculate( vector = x)
- print " Le biais obtenu de ce vecteur est.......:", D.valueserie(1)
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Calcul de la fonction coût
-"""
-__author__ = "Sophie RICCI - Octobre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from AssimilationStudy import AssimilationStudy
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name)
- Persistence.OneScalar.__init__( self, name, unit, basetype = float)
-
- def _formula(self, X, HX, Xb, Y, R, B):
- """
- Calcul de la fonction cout
- """
- Jb = 1./2. * (X - Xb).T * B.I * (X - Xb)
- logging.info( "Partial cost function : Jb = %s"%Jb )
- #
- Jo = 1./2. * (Y - HX).T * R.I * (Y - HX)
- logging.info( "Partial cost function : Jo = %s"%Jo )
- #
- J = Jb + Jo
- logging.info( "Total cost function : J = Jo + Jb = %s"%J )
- return J
-
- def calculate(self, x = None, Hx = None, xb = None, yo = None, R = None, B = None , step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- """
- if (x is None) or (xb is None) or (yo is None) :
- raise ValueError("Vectors x, xb and yo must be given to compute J")
-# if (type(x) is not float) and (type(x) is not numpy.float64) :
-# if (x.size < 1) or (xb.size < 1) or (yo.size < 1):
-# raise ValueError("Vectors x, xb and yo must not be empty")
- if hasattr(numpy.matrix(x),'A1') :
- X = numpy.matrix(x).A1
- if hasattr(numpy.matrix(xb),'A1') :
- Xb = numpy.matrix(xb).A1
- if hasattr(numpy.matrix(yo),'A1') :
- Y = numpy.matrix(yo).A1
- B = numpy.matrix(B)
- R = numpy.matrix(R)
- if (Hx is None ) :
- raise ValueError("The given vector must be given")
-# if (Hx.size < 1) :
-# raise ValueError("The given vector must not be empty")
- HX = Hx.A1
- if (B is None ) or (R is None ):
- raise ValueError("The matrices B and R must be given")
-# if (B.size < 1) or (R.size < 1) :
-# raise ValueError("The matrices B and R must not be empty")
- #
- value = self._formula(X, HX, Xb, Y, R, B)
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print "\nAUTOTEST\n"
- #
- D = ElementaryDiagnostic("Ma fonction cout")
- #
- # Vecteur de type array
- # ---------------------
- x = numpy.array([1., 2.])
- xb = numpy.array([2., 2.])
- yo = numpy.array([5., 6.])
- H = numpy.matrix(numpy.identity(2))
- Hx = H*x
- Hx = Hx.T
- B = numpy.matrix(numpy.identity(2))
- R = numpy.matrix(numpy.identity(2))
- #
- D.calculate( x = x, Hx = Hx, xb = xb, yo = yo, R = R, B = B)
- print "Le vecteur x choisi est...:", x
- print "L ebauche xb choisie est...:", xb
- print "Le vecteur d observation est...:", yo
- print "B = ", B
- print "R = ", R
- print "La fonction cout J vaut ...: %.2e"%D.valueserie(0)
- print "La fonction cout J vaut ...: ",D.valueserie(0)
-
- if (abs(D.valueserie(0) - 16.5) > 1.e-6) :
- raise ValueError("The computation of the cost function is NOT correct")
- else :
- print "The computation of the cost function is OK"
- print
- #
- # float simple
- # ------------
- x = 1.
- print type(x)
- xb = 2.
- yo = 5.
- H = numpy.matrix(numpy.identity(1))
- Hx = numpy.dot(H,x)
- Hx = Hx.T
- B = 1.
- R = 1.
- #
- D.calculate( x = x, Hx = Hx, xb = xb, yo = yo, R = R, B = B)
- print "Le vecteur x choisi est...:", x
- print "L ebauche xb choisie est...:", xb
- print "Le vecteur d observation est...:", yo
- print "B = ", B
- print "R = ", R
- print "La fonction cout J vaut ...: %.2e"%D.valueserie(1)
- if (abs(D.valueserie(1) - 8.5) > 1.e-6) :
- raise ValueError("The computation of the cost function is NOT correct")
- else :
- print "The computation of the cost function is OK"
- print
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Calcul de la fonction coût avec Hlin
- HX = Hxb + Hlin dx
-"""
-__author__ = "Sophie RICCI - Octobre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from AssimilationStudy import AssimilationStudy
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name)
- Persistence.OneScalar.__init__( self, name, unit, basetype = float)
- self.__name = str( name )
-
- def _formula(self, X = None, dX = None, Hlin = None, Xb=None, HXb = None, Y=None, R=None, B=None):
-
- """
- Calcul de la fonction cout
- """
- HX = HXb + Hlin.T * dX
- if hasattr(HX, 'A1') :
- HX = HX.A1
- #
- Jb = 1./2. * (X - Xb).T * B.I * (X - Xb)
- logging.info( "Partial cost function : Jb = %s"%Jb )
- #
- Jo = 1./2. * (Y - HX).T * R.I * (Y - HX)
- logging.info( "Partial cost function : Jo = %s"%Jo )
- #
- J = Jb + Jo
- logging.info( "Total cost function : J = Jo + Jb = %s"%J )
- return J
-
- def calculate(self, x = None, dx = None, Hlin = None, xb = None, Hxb = None, yo = None, R = None, B = None , step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- """
- if (x is None) or (xb is None) or (yo is None) or (dx is None):
- raise ValueError("Vectors x, dx, xb and yo must be given to compute J")
- dX = dx
- if hasattr(numpy.matrix(x), 'A1') :
- X = numpy.matrix(x).A1
- if hasattr(numpy.matrix(xb), 'A1') :
- Xb = numpy.matrix(xb).A1
- if hasattr(numpy.matrix(yo), 'A1') :
- Y = numpy.matrix(yo).A1
- B = numpy.matrix(B)
- R = numpy.matrix(R)
- if (Hlin is None ) :
- raise ValueError("HlinT vector must be given")
- if (Hxb is None ) :
- raise ValueError("The given vector must be given")
- HXb = Hxb
- if (B is None ) or (R is None ):
- raise ValueError("The matrices B and R must be given")
- #
- value = self._formula(X, dX, Hlin, Xb, HXb, Y, R, B)
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print "\nAUTOTEST\n"
- #
- D = ElementaryDiagnostic("Ma fonction cout")
- #
- # Vecteur de type array
- # ---------------------
- x = numpy.array([1., 2.])
- dx = numpy.array([0.1, 0.2])
- xb = numpy.array([2., 2.])
- yo = numpy.array([5., 6.])
- Hlin = numpy.matrix(numpy.identity(2))
- Hxb = Hlin *xb
- Hxb = Hxb.T
- Hxb = Hxb.A1
- B = numpy.matrix(numpy.identity(2))
- R = numpy.matrix(numpy.identity(2))
- #
- D.calculate( x = x, dx = dx, Hlin = Hlin, xb = xb, Hxb = Hxb, yo = yo, R = R, B = B)
- print "Le vecteur x choisi est...:", x
- print "L ebauche xb choisie est...:", xb
- print "Le vecteur d observation est...:", yo
- print "B = ", B
- print "R = ", R
- print "La fonction cout J vaut ...: %.2e"%D.valueserie(0)
- #
- if (abs(D.valueserie(0) - 11.925) > 1.e-6) :
- raise ValueError("The computation of the cost function is NOT correct")
- else :
- print "The computation of the cost function is OK"
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Calcul de la variance d'un vecteur à chaque pas. Ce diagnostic très simple
- est présent pour rappeller à l'utilisateur de l'assimilation qu'il faut
- qu'il vérifie les variances de ses écarts en particulier.
-"""
-__author__ = "Jean-Philippe ARGAUD - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from AssimilationStudy import AssimilationStudy
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = float)
-
- def _formula(self, V):
- """
- Calcul de la variance du vecteur en argument. Elle est faite avec une
- division par la taille du vecteur.
- """
- variance = V.var()
- #
- return variance
-
- def calculate(self, vector = None, step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- """
- if vector is None:
- raise ValueError("One vector must be given to compute biais")
- V = numpy.array(vector)
- if V.size < 1:
- raise ValueError("The given vector must not be empty")
- #
- value = self._formula( V)
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- #
- D = ElementaryDiagnostic("Ma variance")
- #
- # Vecteur de type matrix
- # ----------------------
- x = numpy.matrix(([3., 4., 5.]))
- print " Le vecteur de type 'matrix' choisi est..:", x
- print " Le moyenne de ce vecteur est............:", x.mean()
- print " La variance attendue de ce vecteur est..:", x.var()
- #
- D.calculate( vector = x)
- print " La variance obtenue de ce vecteur est...:", D.valueserie(0)
- print
- #
- # Vecteur de type array
- # ---------------------
- x = numpy.array(range(11))
- print " Le vecteur de type 'array' choisi est...:", x
- print " Le moyenne de ce vecteur est............:", x.mean()
- print " La variance attendue de ce vecteur est..:", x.var()
- #
- D.calculate( vector = x)
- print " La variance obtenue de ce vecteur est...:", D.valueserie(1)
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic qui effectue le test du Khi2 pour juger de l'adéquation entre
- la distribution d'un échantillon et une distribution gaussienne dont la
- moyenne et l'écart-type sont calculés sur l'échantillon.
- En input : la tolerance(tolerance) et le nombre de classes(nbclasse)
- En output : Le resultat du diagnostic est une reponse booleenne au test :
- True si l adequation a une distribution gaussienne est valide
- au sens du test du Khi2,
- False dans le cas contraire.
-"""
-__author__ = "Sophie RICCI - Juillet 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-from numpy import random
-import Persistence
-from BasicObjects import Diagnostic
-from ComputeKhi2 import ComputeKhi2_Gauss
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- """
- """
- def __init__(self, name="", unit="", basetype = None, parameters = {} ):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
- for key in ["tolerance", "dxclasse", "nbclasses"]:
- if not self.parameters.has_key(key):
- raise ValueError("A parameter named \"%s\" is required."%key)
-
- def formula(self, V):
- """
- Effectue le calcul de la p-value pour un vecteur et une distribution
- gaussienne et un nombre de classes donne en parametre du diagnostic.
- """
-
- [vectclasse, eftho, efobs, valeurKhi2, areaKhi2, message] = ComputeKhi2_Gauss(
- vectorV = V,
- dx = self.parameters["dxclasse"],
- nbclasses = self.parameters["nbclasses"],
- SuppressEmptyClasses = True)
-
-
- logging.info( message )
- logging.info( "(si <%.2f %s on refuse effectivement l'adéquation)"%(100.*self.parameters["tolerance"],"%") )
- logging.info("vecteur des classes=%s"%numpy.size(vectclasse) )
- logging.info("valeurKhi2=%s"%valeurKhi2)
- logging.info("areaKhi2=%s"%areaKhi2)
- logging.info("tolerance=%s"%self.parameters["tolerance"])
-
- if (areaKhi2 < (100.*self.parameters["tolerance"])) :
- answerKhisquareTest = False
- else:
- answerKhisquareTest = True
- logging.info( "La réponse au test est donc est %s"%answerKhisquareTest )
- return answerKhisquareTest
-
- def calculate(self, vector = None, step = None):
- """
- Active la formule de calcul
- """
- if vector is None:
- raise ValueError("One vector must be given to calculate the Khi2 test")
- V = numpy.array(vector)
- if V.size < 1:
- raise ValueError("The given vector must not be empty")
- #
- value = self.formula( V )
- #
- self.store( value = value, step = step)
-
-# ==============================================================================
-if __name__ == "__main__":
- print "\n AUTODIAGNOSTIC \n"
-
- print " Test d adequation du khi-2 a une gaussienne pour un vecteur x"
- print " connu de taille 1000, issu d'une distribution gaussienne normale"
- print " en fixant la largeur des classes"
- print
- #
- # Initialisation des inputs et appel du diagnostic
- # ------------------------------------------------
- tolerance = 0.05
- dxclasse = 0.1
- D = ElementaryDiagnostic("AdequationGaussKhi2", parameters = {
- "tolerance":tolerance,
- "dxclasse":dxclasse,
- "nbclasses":None,
- })
- #
- # Tirage de l'echantillon aleatoire
- # ---------------------------------
- numpy.random.seed(2490)
- x = random.normal(50.,1.5,1000)
- #
- # Calcul
- # ------
- D.calculate(x)
- #
- if D.valueserie(0) :
- print " L'adequation a une distribution gaussienne est valide."
- print
- else :
- raise ValueError("L'adéquation a une distribution gaussienne n'est pas valide.")
-
-
- print " Test d adequation du khi-2 a une gaussienne pour u:n vecteur x"
- print " connu de taille 1000, issu d'une distribution gaussienne normale"
- print " en fixant le nombre de classes"
- print
- #
- # Initialisation des inputs et appel du diagnostic
- # ------------------------------------------------
- tolerance = 0.05
- nbclasses = 70.
- D = ElementaryDiagnostic("AdequationGaussKhi2", parameters = {
- "tolerance":tolerance,
- "dxclasse":None,
- "nbclasses":nbclasses
- })
- #
- # Tirage de l'echantillon aleatoire
- # ---------------------------------
- numpy.random.seed(2490)
- x = random.normal(50.,1.5,1000)
- #
- # Calcul
- # ------
- D.calculate(x)
- #
- if D.valueserie(0) :
- print " L'adequation a une distribution gaussienne est valide."
- print
- else :
- raise ValueError("L'adequation a une distribution gaussienne n'est pas valide.")
-
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnotic de test sur la validité de l'hypothèse de linéarité de l'opérateur
- H entre xp et xm
-
- Pour calculer Hlin on utilise un schéma différences finies centrées 2
- points. On définit un dxparam tel que :
- xp = xb + dxparam
- et
- xm = xb - dxparam
- On calcule Hxp et Hxm pour obtenir Hlin. Hlin est utilise dans le Blue pour
- caler un paramêtre. La question importante est de choisir un dxparam pas
- trop grand.
-
- On veut vérifier ici que l'hypothèse de linéarite du modèle par rapport au
- paramêtre est valide sur l'intervalle du paramêtre [xm, xp]. Pour cela on
- s'assure que l'on peut retrouver la valeur Hxb par les développemenents de
- Taylor en xp et xm. Ainsi on calcule 2 estimations de Hxb, l'une à partir de
- Hxp (notee Hx1) et l'autre à partir de Hxm (notee Hx2), que l'on compare à
- la valeur calculée de Hxb. On s'intèresse ensuite a la distance entre Hxb et
- ses estimés Hx1 et Hx2. Si la distance est inférieure a un seuil de
- tolerance, l hypothese est valide.
-"""
-__author__ = "Sophie RICCI - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from RMS import ElementaryDiagnostic as RMS
-from AssimilationStudy import AssimilationStudy
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name="", unit="", basetype = None, parameters = {} ):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
- if not self.parameters.has_key("tolerance"):
- raise ValueError("A parameter named \"tolerance\" is required.")
-
- def formula(self, H, dxparam, Hxp, Hxm, Hx):
- """
- Test sur la validite de l hypothese de linearite de H entre xp et xm
- """
- dimension = numpy.size(Hx)
- #
- # Reconstruit les valeurs Hx1 et Hx2 de Hx a partir de Hxm et Hxp
- # ---------------------------------------------------------------
- Hx1 = Hxm + H.T * dxparam
- Hx2 = Hxp - H.T * dxparam
- #
- # Calcul de l'ecart entre Hx1 et Hx et entre Hx2 et Hx
- # ----------------------------------------------------
- ADD = AssimilationStudy()
- ADD.setDiagnostic("RMS",
- name = "Calcul de la RMS entre Hx1 et Hx et entre Hx2 et Hx")
- RMS = ADD.get("Calcul de la RMS entre Hx1 et Hx et entre Hx2 et Hx")
- RMS.calculate(Hx1,Hx)
- std1 = RMS.valueserie(0)
- RMS.calculate(Hx2,Hx)
- std2 = RMS.valueserie(1)
- #
- # Normalisation des écarts par Hx pour comparer a un pourcentage
- # --------------------------------------------------------------
- RMS.calculate(Hx,Hx-Hx)
- std = RMS.valueserie(2)
- err1=std1/std
- err2=std2/std
- #
- # Comparaison
- # -----------
- if ( (err1 < self.parameters["tolerance"]) and (err2 < self.parameters["tolerance"]) ):
- reponse = True
- else:
- reponse = False
- return reponse
-
- def calculate(self, Hlin = None, deltaparam = None, Hxp = None, Hxm = None, Hx = None, step = None):
- """
- Arguments :
- - Hlin : Operateur d obsevation lineaire
- - deltaparam : pas sur le parametre param
- - Hxp : calcul en xp = xb + deltaparam
- - Hxm : calcul en xm = xb - deltaparam
- - Hx : calcul en x (generalement xb)
- """
- value = self.formula( Hlin, deltaparam, Hxp, Hxm, Hx )
- #
- self.store( value = value, step = step)
-
-#===============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- print " Diagnotic de test sur la validité de l'hypothèse de linéarité de"
- print " l'opérateur H entre xp et xm."
- print
- #
- dimension = 3
- #
- # Définition des données
- # ----------------------
- Hx = numpy.array(([ 2., 4., 6.]))
- Hxp = numpy.array(([ 3., 5., 7.]))
- Hxm = numpy.array(([ 1., 3., 5.]))
- H = (Hxp - Hxm)/(2.)
- dxparam = 1.
- #
- # Instanciation de l'objet diagnostic
- # -----------------------------------
- D = ElementaryDiagnostic("Mon TestHlin", parameters = {"tolerance": 0.1})
- #
- # Calcul
- # ------
- D.calculate( Hlin = H, deltaparam = dxparam, Hxp = Hxp, Hxm = Hxm, Hx = Hx)
-
- # Validation du calcul
- # --------------------
- if not D.valueserie(0) :
- raise ValueError("La linearisation de H autour de x entre xm et xp est fausse pour ce cas test lineaire")
- else :
- print " La linéarisation de H autour de x entre xm et xp est valide pour ce cas-test linéaire."
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic qui effectue le test du Khi2 pour juger de l'homogénéite entre
- les distributions de 2 vecteurs quelconques.
- - entrée : la tolerance (tolerance) et le nombre de classes (nbclasse),
- sous forme de paramètres dans le dictionnaire Par
- - sortie : le resultat du diagnostic est une reponse booleenne au test :
- True si l homogeneite est valide au sens du test du Khi2,
- False dans le cas contraire.
-"""
-__author__ = "Sophie RICCI - Juillet 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-from numpy import random
-
-import Persistence
-from BasicObjects import Diagnostic
-from ComputeKhi2 import ComputeKhi2_Homogen
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name="", unit="", basetype = None, parameters = {} ):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool )
- for key in ["tolerance", "dxclasse", "nbclasses"]:
- if not self.parameters.has_key(key):
- raise ValueError("A parameter named \"%s\" is required."%key)
-
- def _formula(self, V1, V2):
- """
- Effectue le calcul de la p-value pour deux vecteurs et un nombre de
- classes donne en parametre du diagnostic.
- """
- [classes, eftheo, efobs, valeurKhi2, areaKhi2, message] = ComputeKhi2_Homogen(
- vectorV1 = V1,
- vectorV2 = V2,
- dx = self.parameters["dxclasse"],
- nbclasses = self.parameters["nbclasses"],
- SuppressEmptyClasses = True)
- #
- logging.info( message )
- logging.info( "(si <%.2f %s on refuse effectivement l'homogeneite)"%(100.*self.parameters["tolerance"],"%") )
- #
- answerKhisquareTest = False
- if (areaKhi2 < (100.*self.parameters["tolerance"])) :
- answerKhisquareTest = False
- else:
- answerKhisquareTest = True
- #
- return answerKhisquareTest
-
- def calculate(self, vector1 = None, vector2 = None, step = None):
- """
- Active la formule de calcul
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Khi2 value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size")
- #
- value = self._formula( V1, V2 )
- #
- self.store( value = value, step = step )
-
-# ==============================================================================
-if __name__ == "__main__":
- print "\n AUTODIAGNOSTIC \n"
-
- print " Test d'homogeneite du Khi-2 pour deux vecteurs de taille 10,"
- print " issus d'une distribution gaussienne normale"
- print
- #
- # Initialisation des inputs et appel du diagnostic
- # --------------------------------------------------------------------
- tolerance = 0.05
- dxclasse = 0.5
- D = ElementaryDiagnostic("HomogeneiteKhi2", parameters = {
- "tolerance":tolerance,
- "dxclasse":dxclasse,
- "nbclasses":None,
- })
- #
- # Tirage de l'echantillon aleatoire
- # --------------------------------------------------------------------
- numpy.random.seed(4000)
- x1 = random.normal(50.,1.5,10000)
- numpy.random.seed(2490)
- x2 = random.normal(50.,1.5,10000)
- #
- # Calcul
- # --------------------------------------------------------------------
- D.calculate(x1, x2)
- #
- print " La reponse du test est \"%s\" pour une tolerance de %.2e et une largeur de classe de %.2e "%(D.valueserie(0), tolerance, dxclasse)
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Classe pour tracer simplement un vecteur à chaque pas
-"""
-__author__ = "Jean-Philippe ARGAUD - Juillet 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import os.path
-import numpy
-from BasicObjects import Diagnostic
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- try:
- import Gnuplot
- self.__gnuplot = Gnuplot
- except:
- raise ImportError("The Gnuplot module is required to plot the vector")
-
- def _formula(self,
- Vector, Steps,
- title, xlabel, ylabel, ltitle,
- geometry,
- filename,
- persist,
- pause ):
- """
- Trace en gnuplot le vecteur Vector, avec une légende générale, en X et
- en Y
- """
- if persist:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -persist -geometry '+geometry
- else:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -geometry '+geometry
- #
- self.__g = self.__gnuplot.Gnuplot() # persist=1
- self.__g('set terminal '+self.__gnuplot.GnuplotOpts.default_term)
- self.__g('set style data lines')
- self.__g('set grid')
- self.__g('set autoscale')
- self.__g('set title "'+title +'"')
- self.__g('set xlabel "'+xlabel+'"')
- self.__g('set ylabel "'+ylabel+'"')
- self.__g.plot( self.__gnuplot.Data( Steps, Vector, title=ltitle ) )
- if filename != "":
- self.__g.hardcopy(filename=filename, color=1)
- if pause:
- raw_input('Please press return to continue...\n')
- #
- return 1
-
- def calculate(self, vector = None, steps = None,
- title = "", xlabel = "", ylabel = "", ltitle = None,
- geometry = "600x400",
- filename = "",
- persist = False,
- pause = True ):
- """
- Arguments :
- - vector : le vecteur à tracer, en liste ou en numpy.array
- - steps : liste unique des pas de l'axe des X, ou None si c'est
- la numérotation par défaut
- - title : titre général du dessin
- - xlabel : label de l'axe des X
- - ylabel : label de l'axe des Y
- - ltitle : titre associé au vecteur tracé
- - geometry : taille en pixels de la fenêtre et position du coin haut
- gauche, au format X11 : LxH+X+Y (défaut : 600x400)
- - filename : nom de fichier Postscript pour une sauvegarde à 1 pas
- Attention, il faut changer le nom à l'appel pour
- plusieurs pas de sauvegarde
- - persist : booléen indiquant que la fenêtre affichée sera
- conservée lors du passage au dessin suivant
- Par défaut, persist = False
- - pause : booléen indiquant une pause après chaque tracé, et
- attendant un Return
- Par défaut, pause = True
- """
- if vector is None:
- raise ValueError("One vector must be given to plot it.")
- if ltitle is None:
- ltitle = ""
- Vector = numpy.array(vector)
- if Vector.size < 1:
- raise ValueError("The given vector must not be empty")
- if steps is None:
- Steps = range(len( vector ))
- elif not ( type(steps) is type([]) or type(steps) is not type(numpy.array([])) ):
- raise ValueError("The steps must be given as a list/tuple.")
- else:
- Steps = list(steps)
- if os.path.isfile(filename):
- raise ValueError("Error: a file with this name \"%s\" already exists."%filename)
- #
- value = self._formula(
- Vector = Vector,
- Steps = Steps,
- title = str(title).encode('ascii','replace'),
- xlabel = str(xlabel).encode('ascii','replace'),
- ylabel = str(ylabel).encode('ascii','replace'),
- ltitle = str(ltitle),
- geometry = str(geometry),
- filename = str(filename),
- persist = bool(persist),
- pause = bool(pause) )
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- D = ElementaryDiagnostic("Mon Plot")
-
- vect = [1, 2, 1, 2, 1]
- D.calculate(vect, title = "Vecteur 1", xlabel = "Axe X", ylabel = "Axe Y" )
- vect = [1, 3, 1, 3, 1]
- D.calculate(vect, title = "Vecteur 2", filename = "vecteur.ps")
- vect = [1, 1, 1, 1, 1]
- D.calculate(vect, title = "Vecteur 3")
- vect = [0.29, 0.97, 0.73, 0.01, 0.20]
- D.calculate(vect, title = "Vecteur 4")
- vect = [-0.23262176, 1.36065207, 0.32988102, 0.24400551, -0.66765848, -0.19088483, -0.31082575, 0.56849814, 1.21453443, 0.99657516]
- D.calculate(vect, title = "Vecteur 5")
- vect = [0.29, 0.97, 0.73, 0.01, 0.20]
- D.calculate(vect, title = "Vecteur 6 affiche avec une autre geometrie et position", geometry="800x200+50+50")
- vect = 100*[0.29, 0.97, 0.73, 0.01, 0.20]
- D.calculate(vect, title = "Vecteur 7 : long construit par repetition")
- vect = [0.29, 0.97, 0.73, 0.01, 0.20]
- D.calculate(vect, title = "Vecteur 8", ltitle = "Vecteur 8")
- temps = [0.1,0.2,0.3,0.4,0.5]
- D.calculate(vect, temps, title = "Vecteur 8 avec axe du temps modifie")
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Classe pour tracer simplement une liste de vecteurs à chaque pas
-"""
-__author__ = "Jean-Philippe ARGAUD - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import os.path
-import numpy
-from BasicObjects import Diagnostic
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- try:
- import Gnuplot
- self.__gnuplot = Gnuplot
- except:
- raise ImportError("The Gnuplot module is required to plot the vector")
-
- def _formula(self,
- Vector, Steps,
- title, xlabel, ylabel, ltitle,
- geometry,
- filename,
- persist,
- pause ):
- """
- Trace en gnuplot chaque vecteur de la liste Vector, avec une légende
- générale, en X et en Y
- """
- if persist:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -persist -geometry '+geometry
- else:
- self.__gnuplot.GnuplotOpts.gnuplot_command = 'gnuplot -geometry '+geometry
- #
- self.__g = self.__gnuplot.Gnuplot() # persist=1
- self.__g('set terminal '+self.__gnuplot.GnuplotOpts.default_term)
- self.__g('set style data lines')
- self.__g('set grid')
- self.__g('set autoscale')
- self.__g('set title "'+title +'"')
- self.__g('set xlabel "'+xlabel+'"')
- self.__g('set ylabel "'+ylabel+'"')
- self.__g.plot( self.__gnuplot.Data( Steps, Vector.pop(0), title=ltitle.pop(0) ) )
- for vector in Vector:
- self.__g.replot( self.__gnuplot.Data( Steps, vector, title=ltitle.pop(0) ) )
- if filename != "":
- self.__g.hardcopy(filename=filename, color=1)
- if pause:
- raw_input('Please press return to continue...\n')
- #
- return 1
-
- def calculate(self, vector = None, steps = None,
- title = "", xlabel = "", ylabel = "", ltitle = None,
- geometry = "600x400",
- filename = "",
- persist = False,
- pause = True ):
- """
- Arguments :
- - vector : liste des vecteurs à tracer, chacun étant en liste ou
- en numpy.array
- - steps : liste unique des pas, ou None si c'est la numérotation
- par défaut
- - title : titre général du dessin
- - xlabel : label de l'axe des X
- - ylabel : label de l'axe des Y
- - ltitle : liste des titres associés à chaque vecteur, dans le
- même ordre que les vecteurs eux-mêmes
- - geometry : taille en pixels de la fenêtre et position du coin haut
- gauche, au format X11 : LxH+X+Y (défaut : 600x400)
- - filename : nom de fichier Postscript pour une sauvegarde à 1 pas
- Attention, il faut changer le nom à l'appel pour
- plusieurs pas de sauvegarde
- - persist : booléen indiquant que la fenêtre affichée sera
- conservée lors du passage au dessin suivant
- Par défaut, persist = False
- - pause : booléen indiquant une pause après chaque tracé, et
- attendant un Return
- Par défaut, pause = True
- """
- if vector is None:
- raise ValueError("One vector must be given to plot it.")
- if type(vector) is not type([]) and type(vector) is not type(()):
- raise ValueError("The vector(s) must be given as a list/tuple.")
- if ltitle is None or len(ltitle) != len(vector):
- ltitle = ["" for i in range(len(vector))]
- VectorList = []
- for onevector in vector:
- VectorList.append( numpy.array( onevector ) )
- if VectorList[-1].size < 1:
- raise ValueError("Each given vector must not be empty.")
- if steps is None:
- Steps = range(len(vector[0]))
- elif not ( type(steps) is type([]) or type(steps) is not type(numpy.array([])) ):
- raise ValueError("The steps must be given as a list/tuple.")
- else:
- Steps = list(steps)
- if os.path.isfile(filename):
- raise ValueError("Error: a file with this name \"%s\" already exists."%filename)
- #
- value = self._formula(
- Vector = VectorList,
- Steps = Steps,
- title = str(title).encode('ascii','replace'),
- xlabel = str(xlabel).encode('ascii','replace'),
- ylabel = str(ylabel).encode('ascii','replace'),
- ltitle = [str(lt) for lt in ltitle],
- geometry = str(geometry),
- filename = str(filename),
- persist = bool(persist),
- pause = bool(pause),
- )
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- D = ElementaryDiagnostic("Mon Plot")
-
- vect1 = [1, 2, 1, 2, 1]
- D.calculate([vect1,], title = "Vecteur 1", xlabel = "Axe X", ylabel = "Axe Y" )
- vect2 = [1, 3, 1, 3, 1]
- D.calculate([vect1,vect2], title = "Vecteurs 1 et 2", filename = "liste_de_vecteurs.ps")
- vect3 = [-1, 1, -1, 1, -1]
- D.calculate((vect1,vect2,vect3), title = "Vecteurs 1 a 3")
- vect4 = 100*[0.29, 0.97, 0.73, 0.01, 0.20]
- D.calculate([vect4,], title = "Vecteur 4 : long construit par repetition")
- D.calculate(
- (vect1,vect2,vect3),
- [0.1,0.2,0.3,0.4,0.5],
- title = "Vecteurs 1 a 3, temps modifie",
- ltitle = ["Vecteur 1","Vecteur 2","Vecteur 3"])
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Calcul d'une RMS
-"""
-__author__ = "Jean-Philippe ARGAUD - Juillet 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import math
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = float)
-
- def _formula(self, V1, V2):
- """
- Fait un écart RMS entre deux vecteurs V1 et V2
- """
- rms = math.sqrt( ((V2 - V1)**2).sum() / float(V1.size) )
- #
- return rms
-
- def calculate(self, vector1 = None, vector2 = None, step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- """
- if vector1 is None or vector2 is None:
- raise ValueError("Two vectors must be given to calculate their RMS")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if V1.size < 1 or V2.size < 1:
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size")
- #
- value = self._formula( V1, V2 )
- #
- self.store( value = value, step = step )
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
-
- D = ElementaryDiagnostic("Ma RMS")
-
- vect1 = [1, 2, 1, 2, 1]
- vect2 = [2, 1, 2, 1, 2]
- D.calculate(vect1,vect2)
- vect1 = [1, 3, 1, 3, 1]
- vect2 = [2, 2, 2, 2, 2]
- D.calculate(vect1,vect2)
- vect1 = [1, 1, 1, 1, 1]
- vect2 = [2, 2, 2, 2, 2]
- D.calculate(vect1,vect2)
- vect1 = [1, 1, 1, 1, 1]
- vect2 = [4, -2, 4, -2, -2]
- D.calculate(vect1,vect2)
- vect1 = [0.29, 0.97, 0.73, 0.01, 0.20]
- vect2 = [0.92, 0.86, 0.11, 0.72, 0.54]
- D.calculate(vect1,vect2)
- vect1 = [-0.23262176, 1.36065207, 0.32988102, 0.24400551, -0.66765848, -0.19088483, -0.31082575, 0.56849814, 1.21453443, 0.99657516]
- vect2 = [0,0,0,0,0,0,0,0,0,0]
- D.calculate(vect1,vect2)
- print " Les valeurs de RMS attendues sont les suivantes : [1.0, 1.0, 1.0, 3.0, 0.53162016515553656, 0.73784217096601323]"
- print " Les RMS obtenues................................:", D.valueserie()
- print " La moyenne......................................:", D.stepmean()
- print
-
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic sur la reduction du biais lors de l'analyse
-"""
-__author__ = "Sophie RICCI - Aout 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from AssimilationStudy import AssimilationStudy
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
-
- def _formula(self, V1, V2):
- """
- Vérification de la reduction du biais entre OMB et OMA lors de l'analyse
- """
- biaisOMB = V1.mean()
- biaisOMA = V2.mean()
- #
- if biaisOMA > biaisOMB:
- reducebiais = False
- else :
- reducebiais = True
- #
- return reducebiais
-
- def calculate(self, vectorOMB = None, vectorOMA = None, step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- Arguments :
- - vectorOMB : vecteur d'écart entre les observations et l'ébauche
- - vectorOMA : vecteur d'écart entre les observations et l'analyse
- """
- if ( (vectorOMB is None) or (vectorOMA is None) ):
- raise ValueError("Two vectors must be given to test the reduction of the biais after analysis")
- V1 = numpy.array(vectorOMB)
- V2 = numpy.array(vectorOMA)
- if V1.size < 1 or V2.size < 1:
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size")
- #
- value = self._formula( V1, V2 )
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- #
- # Instanciation de l'objet diagnostic
- # -----------------------------------
- D = ElementaryDiagnostic("Mon ReduceBiais")
- #
- # Tirage des 2 vecteurs choisis
- # -------------------------------
- x1 = numpy.matrix(([3. , 4., 5. ]))
- x2 = numpy.matrix(([1.5, 2., 2.5]))
- print " L'écart entre les observations et l'ébauche est OMB :", x1
- print " La moyenne de OMB (i.e. le biais) est de............:", x1.mean()
- print " L'écart entre les observations et l'analyse est OMA :", x2
- print " La moyenne de OMA (i.e. le biais) est de............:", x2.mean()
- #
- D.calculate( vectorOMB = x1, vectorOMA = x2)
- if not D.valueserie(0) :
- print " Résultat : l'analyse NE RÉDUIT PAS le biais"
- else :
- print " Résultat : l'analyse RÉDUIT le biais"
- print
- #
- # Tirage des 2 vecteurs choisis
- # -------------------------------
- x1 = numpy.matrix(range(-5,6))
- x2 = numpy.array(range(11))
- print " L'écart entre les observations et l'ébauche est OMB :", x1
- print " La moyenne de OMB (i.e. le biais) est de............:", x1.mean()
- print " L'écart entre les observations et l'analyse est OMA :", x2
- print " La moyenne de OMA (i.e. le biais) est de............:", x2.mean()
- #
- D.calculate( vectorOMB = x1, vectorOMA = x2)
- if not D.valueserie(1) :
- print " Résultat : l'analyse NE RÉDUIT PAS le biais"
- else :
- print " Résultat : l'analyse RÉDUIT le biais"
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic sur la reduction de la variance lors de l'analyse
-"""
-__author__ = "Jean-Philippe ARGAUD - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from AssimilationStudy import AssimilationStudy
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool )
-
- def _formula(self, V1, V2):
- """
- Vérification de la reduction de variance sur les écarts entre OMB et OMA
- lors de l'analyse
- """
- varianceOMB = V1.var()
- varianceOMA = V2.var()
- #
- if varianceOMA > varianceOMB:
- reducevariance = False
- else :
- reducevariance = True
- #
- return reducevariance
-
- def calculate(self, vectorOMB = None, vectorOMA = None, step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- Arguments :
- - vectorOMB : vecteur d'écart entre les observations et l'ébauche
- - vectorOMA : vecteur d'écart entre les observations et l'analyse
- """
- if ( (vectorOMB is None) or (vectorOMA is None) ):
- raise ValueError("Two vectors must be given to test the reduction of the variance after analysis")
- V1 = numpy.array(vectorOMB)
- V2 = numpy.array(vectorOMA)
- if V1.size < 1 or V2.size < 1:
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size")
- #
- value = self._formula( V1, V2 )
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- #
- # Instanciation de l'objet diagnostic
- # -----------------------------------
- D = ElementaryDiagnostic("Mon ReduceVariance")
- #
- # Vecteur de type matrix
- # ----------------------
- x1 = numpy.matrix(([3. , 4., 5. ]))
- x2 = numpy.matrix(([1.5, 2., 2.5]))
- print " L'écart entre les observations et l'ébauche est OMB :", x1
- print " La moyenne de OMB (i.e. le biais) est de............:", x1.mean()
- print " La variance de OMB est de...........................:", x1.var()
- print " L'écart entre les observations et l'analyse est OMA :", x2
- print " La moyenne de OMA (i.e. le biais) est de............:", x2.mean()
- print " La variance de OMA est de...........................:", x2.var()
- #
- D.calculate( vectorOMB = x1, vectorOMA = x2)
- if not D.valueserie(0) :
- print " Résultat : l'analyse NE RÉDUIT PAS la variance"
- else :
- print " Résultat : l'analyse RÉDUIT la variance"
- print
- #
- # Vecteur de type array
- # ---------------------
- x1 = numpy.array(range(11))
- x2 = numpy.matrix(range(-10,12,2))
- print " L'écart entre les observations et l'ébauche est OMB :", x1
- print " La moyenne de OMB (i.e. le biais) est de............:", x1.mean()
- print " La variance de OMB est de...........................:", x1.var()
- print " L'écart entre les observations et l'analyse est OMA :", x2
- print " La moyenne de OMA (i.e. le biais) est de............:", x2.mean()
- print " La variance de OMA est de...........................:", x2.var()
- #
- D.calculate( vectorOMB = x1, vectorOMA = x2)
- if not D.valueserie(1) :
- print " Résultat : l'analyse NE RÉDUIT PAS la variance"
- else :
- print " Résultat : l'analyse RÉDUIT la variance"
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic sur l'arrêt (ou le ralentissement) de la réduction de la variance
- au fil des pas (ou itérations) de l'analyse.
- Ce diagnostic s'applique typiquement au vecteur de différence entre la
- variance de OMB et la variance de OMA au fil du temps ou des itérations:
- V[i] = vecteur des VAR(OMB)[i] - VAR(OMA)[i] au temps ou itération i.
-"""
-__author__ = "Sophie Ricci - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from AssimilationStudy import AssimilationStudy
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = int )
-
- def _formula(self, V, CutOffSlope, MultiSlope0):
- """
- Recherche du pas de temps ou iteration pour laquelle la reduction
- de la variance est
- - inferieure a la valeur seuil CutOffSlope
- (si une valeure est donnee a CutOffSlope)
- - inferieure a MultiSlope0 * la pente a la premiere iteration
- (si une valeure est donnee a MultiSlope0)
- V[i] = vecteur des VAR(OMB)[i] - VAR(OMA)[i] au temps ou iteration i.
- """
- N = V.size
- pente = numpy.matrix(numpy.zeros((N,))).T
- iterstopreduction = 0.
- for i in range (1, N) :
- pente[i] = V[i]- V[i-1]
- if pente[i] > 0.0 :
- raise ValueError("The analysis is INCREASING the variance a l iteration ", i)
- if CutOffSlope is not None:
- if numpy.abs(pente[i]) < CutOffSlope :
- iterstopreduction = i
- break
- if MultiSlope0 is not None:
- if numpy.abs(pente[i]) < MultiSlope0 * numpy.abs(pente[1]) :
- iterstopreduction = i
- break
- #
- return iterstopreduction
-
- def calculate(self, vector = None, CutOffSlope = None, MultiSlope0 = None, step = None) :
- """
- Teste les arguments, active la formule de calcul et stocke le resultat
- Arguments :
- - vector : vecteur des VAR(OMB) - VAR(OMA) au fil des iterations
- - CutOffSlope : valeur minimale de la pente
- - MultiSlope0 : Facteur multiplicatif de la pente initiale pour comparaison
- """
- if (vector is None) :
- raise ValueError("One vector must be given to test the convergence of the variance after analysis")
- V = numpy.array(vector)
- if V.size < 1 :
- raise ValueError("The given vector must not be empty")
- if (MultiSlope0 is None) and (CutOffSlope is None) :
- raise ValueError("You must set the value of ONE of the CutOffSlope of MultiSlope0 key word")
- #
- value = self._formula( V, CutOffSlope, MultiSlope0 )
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print "\n AUTODIAGNOSTIC \n"
-
- # Instanciation de l'objet diagnostic
- # ------------------------------------------------
- D = ElementaryDiagnostic("Mon StopReductionVariance")
-
- # Vecteur de reduction VAR(OMB)-VAR(OMA)
- # ------------------------------------------------
- x = numpy.array(([0.60898111, 0.30449056, 0.15224528, 0.07612264, 0.03806132, 0.01903066, 0.00951533, 0.00475766, 0.00237883, 0.00118942]))
- print " Le vecteur choisi est :", x
- print " Sur ce vecteur, la reduction a l iteration N = 7 est inferieure a 0.005"
- print " Sur ce vecteur, la reduction a l iteration N = 8 est inferieure a 0.01 * la reduction a l iteration 1"
-
- # Comparaison a la valeur seuil de la reduction
- # ------------------------------------------------
- D.calculate( vector = x, CutOffSlope = 0.005, MultiSlope0 = None)
- if (D.valueserie(0) - 7.) < 1.e-15 :
- print " Test : La comparaison a la valeur seuil de la reduction est juste"
- else :
- print " Test : La comparaison a la valeur seuil de la reduction est fausse"
-
- # Comparaison a alpha* la reduction a la premiere iteration
- # ------------------------------------------------
- D.calculate( vector = x, CutOffSlope = None, MultiSlope0 = 0.01)
- if (D.valueserie(1) - 8.) < 1.e-15 :
- print " Test : La comparaison a la reduction a la premiere iteration est juste"
- else :
- print " Test : La comparaison a la reduction a la premiere iteration est fausse"
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Diagnostic sur les variances dans B et R par rapport à l'ébauche Xb et aux
- observations Y. On teste si on a les conditions :
- 1%*xb < sigma_b < 10%*xb
- et
- 1%*yo < sigma_o < 10%*yo
- Le diagnostic renvoie True si les deux conditions sont simultanément
- vérifiées, False dans les autres cas.
-"""
-__author__ = "Sophie RICCI, Jean-Philippe ARGAUD - Septembre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-import Persistence
-from BasicObjects import Diagnostic
-from scipy.linalg import eig
-import logging
-
-# ==============================================================================
-class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
- def __init__(self, name = "", unit = "", basetype = None, parameters = {}):
- Diagnostic.__init__(self, name, parameters)
- Persistence.OneScalar.__init__( self, name, unit, basetype = bool )
-
- def _formula(self, xb, B, yo, R):
- """
- Comparaison des variables et de leur variance relative
- """
- valpB = eig(B, left = False, right = False)
- valpR = eig(R, left = False, right = False)
- logging.info(" Si l on souhaite 1%s*xb < sigma_b < 10%s*xb, les valeurs propres de B doivent etre comprises dans l intervalle [%.3e,%.3e]"%("%","%",1.e-4*xb.mean()*xb.mean(),1.e-2*xb.mean()*xb.mean()))
- logging.info(" Si l on souhaite 1%s*yo < sigma_o < 10%s*yo, les valeurs propres de R doivent etre comprises dans l intervalle [%.3e,%.3e]"%("%","%",1.e-4*yo.mean()*yo.mean(),1.e-2*yo.mean()*yo.mean()))
- #
- limite_inf_valp = 1.e-4*xb.mean()*xb.mean()
- limite_sup_valp = 1.e-2*xb.mean()*xb.mean()
- variancexb = (valpB >= limite_inf_valp).all() and (valpB <= limite_sup_valp).all()
- logging.info(" La condition empirique sur la variance de Xb est....: %s"%variancexb)
- #
- limite_inf_valp = 1.e-4*yo.mean()*yo.mean()
- limite_sup_valp = 1.e-2*yo.mean()*yo.mean()
- varianceyo = (valpR >= limite_inf_valp).all() and (valpR <= limite_sup_valp).all()
- logging.info(" La condition empirique sur la variance de Y est.....: %s",varianceyo)
- #
- variance = variancexb and varianceyo
- logging.info(" La condition empirique sur la variance globale est..: %s"%variance)
- #
- return variance
-
- def calculate(self, Xb = None, B = None, Y = None, R = None, step = None):
- """
- Teste les arguments, active la formule de calcul et stocke le résultat
- Arguments :
- - Xb : valeur d'ébauche du paramêtre
- - B : matrice de covariances d'erreur d'ébauche
- - yo : vecteur d'observation
- - R : matrice de covariances d'erreur d'observation
- """
- if (Xb is None) or (B is None) or (Y is None) or (R is None):
- raise ValueError("You must specify Xb, B, Y, R")
- yo = numpy.array(Y)
- BB = numpy.matrix(B)
- xb = numpy.array(Xb)
- RR = numpy.matrix(R)
- if (RR.size < 1 ) or (BB.size < 1) :
- raise ValueError("The background and the observation covariance matrices must not be empty")
- if ( yo.size < 1 ) or ( xb.size < 1 ):
- raise ValueError("The Xb background and the Y observation vectors must not be empty")
- if xb.size*xb.size != BB.size:
- raise ValueError("Xb background vector and B covariance matrix sizes are not consistent")
- if yo.size*yo.size != RR.size:
- raise ValueError("Y observation vector and R covariance matrix sizes are not consistent")
- if yo.all() == 0. or xb.all() == 0. :
- raise ValueError("The diagnostic can not be applied to zero vectors")
- #
- value = self._formula( xb, BB, yo, RR)
- #
- self.store( value = value, step = step )
-
-#===============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- #
- # Instanciation de l'objet diagnostic
- # -----------------------------------
- D = ElementaryDiagnostic("Mon OrdreVariance")
- #
- # Vecteur de type matrix
- # ----------------------
- xb = numpy.array([11000.])
- yo = numpy.array([1.e12 , 2.e12, 3.e12 ])
- B = 1.e06 * numpy.matrix(numpy.identity(1))
- R = 1.e22 * numpy.matrix(numpy.identity(3))
- #
- D.calculate( Xb = xb, B = B, Y = yo, R = R)
- print " L'ébauche est.......................................:",xb
- print " Les observations sont...............................:",yo
- print " La valeur moyenne des observations est..............: %.2e"%yo.mean()
- print " La valeur moyenne de l'ebauche est..................: %.2e"%xb.mean()
- print " La variance d'ébauche specifiée est.................: %.2e"%1.e6
- print " La variance d'observation spécifiée est.............: %.2e"%1.e22
- #
- if D.valueserie(0) :
- print " Les variances specifiées sont de l'ordre de 1% a 10% de l'ébauche et des observations"
- else :
- print " Les variances specifiées ne sont pas de l'ordre de 1% a 10% de l'ébauche et des observations"
- print
-
-
+++ /dev/null
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
+++ /dev/null
-<?xml version='1.0' encoding='iso-8859-1' ?>
-<proc>
- <property name="DefaultStudyID" value="1"/>
- <type name="bool" kind="bool"/>
- <sequence name="boolvec" content="bool"/>
- <type name="double" kind="double"/>
- <sequence name="dblevec" content="double"/>
- <objref name="file" id="file"/>
- <type name="int" kind="int"/>
- <sequence name="intvec" content="int"/>
- <objref name="pyobj" id="python:obj:1.0"/>
- <sequence name="seqdblevec" content="dblevec"/>
- <type name="string" kind="string"/>
- <sequence name="stringvec" content="string"/>
- <container name="DefaultContainer">
- <property name="container_name" value="FactoryServer"/>
- <property name="hostname" value="localhost"/>
- </container>
- <inline name="Building_B">
- <script><code><![CDATA[# Construction de la matrice de covariances d'erreurs d'ebauche
-import numpy
-
-dimension = len( parametres )
-
-B = numpy.matrix(numpy.core.identity(dimension))
-B = B.A1
-]]></code></script>
- <inport name="parametres" type="pyobj"/>
- <outport name="B" type="dblevec"/>
- </inline>
- <inline name="Building_R">
- <script><code><![CDATA[# Construction de la matrice de covariances d'erreurs de mesure
-import numpy
-
-dimension = len( experiences ) * len( experiences[0] )
-
-R = numpy.matrix(numpy.core.identity(dimension))
-R = R.A1
-]]></code></script>
- <inport name="experiences" type="pyobj"/>
- <outport name="R" type="dblevec"/>
- </inline>
- <inline name="Building_Xb">
- <script><code><![CDATA[# Script pour extraire l'ebauche comme une liste
-# et une liste de bornes [min,max]
-
-dx = 1.e-2 # Increment en pourcent de Xb
-
-Xb = []
-dXb = []
-Bornes = []
-
-for parametre in parametres:
- Xb.append( parametre[1] )
- dXb.append( dx * parametre[1] )
- Bornes.append( parametre[2:4] )
-]]></code></script>
- <inport name="parametres" type="pyobj"/>
- <outport name="Xb" type="dblevec"/>
- <outport name="Bornes" type="seqdblevec"/>
- <outport name="dXb" type="dblevec"/>
- </inline>
- <inline name="Building_Yo">
- <script><code><![CDATA[# Script pour extraire l'es mesures/observations et leur nom
-# comme une liste
-
-if len(calcul) != len(experiences):
- raise ValueError("Les nombres de variables calculees et observees doient etre les memes")
-
-if len(experiences) >=1:
- nb_observations_par_experience = len( experiences[0] )
-
-Yo = []
-Nom_Yo = []
-
-for i in range( len(experiences) ):
- Nom_Yo.append( calcul[i][2] )
- if len( experiences[i] ) != nb_observations_par_experience:
- raise ValueError("Le nombre de mesures par serie experimentale doit etre constant egal a %i mais la serie %i est longue de %i"%(nb_observations_par_experience,i,len(experiences[i])))
- for observation in experiences[i]:
- Yo.append( observation[1] )
-]]></code></script>
- <inport name="calcul" type="pyobj"/>
- <inport name="experiences" type="pyobj"/>
- <outport name="Yo" type="dblevec"/>
- <outport name="Nom_Yo" type="stringvec"/>
- </inline>
- <inline name="Entrees du calcul AD">
- <script><code><![CDATA[import numpy
-
-print
-print "### ============================================================="
-print "### Verification des arguments fabriques pour passer d'Aster a AD"
-print
-print " Tailles et types :"
-print " de Xb",len(Xb),type(Xb)
-print " de Yo",len(Yo),type(Yo)
-print " de B ",len(B),type(B)
-print " de R ",len(R),type(R)
-print " de H ",len(H),type(H)
-print
-print " Verification de remise a l'echelle :"
-dimensionXb = len( Xb )
-dimensionYo = len( Yo )
-print " de B"
-numpyB = numpy.matrix( B, numpy.float ).reshape((dimensionXb,dimensionXb))
-print " de R"
-numpyR = numpy.matrix( R, numpy.float ).reshape((dimensionYo,dimensionYo))
-print " de H"
-numpyH = numpy.matrix( H, numpy.float ).reshape((dimensionYo,dimensionXb))
-print "### ============================================================="
-print
-]]></code></script>
- <inport name="Xb" type="dblevec"/>
- <inport name="Yo" type="dblevec"/>
- <inport name="B" type="dblevec"/>
- <inport name="R" type="dblevec"/>
- <inport name="H" type="dblevec"/>
- <outport name="Xb" type="dblevec"/>
- <outport name="Yo" type="dblevec"/>
- <outport name="B" type="dblevec"/>
- <outport name="R" type="dblevec"/>
- <outport name="H" type="dblevec"/>
- </inline>
- <inline name="Sorties du calcul AD">
- <script><code><![CDATA[import numpy
-print "Diagnostics de sortie de test :"
-print " Remise en numpy des resultats"
-Xa = numpy.array(xa)
-Xb = numpy.array(xb)
-dimensionXb = len( Xb )
-dimensionYo = len( Yo )
-print "Verification de remise a l'echelle de B, R et H"
-B = numpy.matrix( B, numpy.float ).reshape((dimensionXb,dimensionXb))
-R = numpy.matrix( R, numpy.float ).reshape((dimensionYo,dimensionYo))
-H = numpy.matrix( H, numpy.float ).reshape((dimensionYo,dimensionXb))
-I = numpy.matrix(numpy.core.identity(dimensionYo))
-deltaB = Xa - Xb
-deltaA = (Yo - numpy.dot(H,Xa)).A1
-deltaI = (Yo - numpy.dot(H,Xb)).A1
-print "Calcul de LS"
-print "deltaA",deltaA
-LS = float(numpy.dot(deltaA, deltaA))
-print "Calcul de LSI"
-LSI = float(numpy.dot(deltaI, deltaI))
-print "Calcul de J"
-J = float( numpy.dot(deltaB, numpy.dot(B,deltaB).A1) + numpy.dot(deltaA, numpy.dot(R,deltaA).A1) )
-print "Calcul de JI"
-JI = float( numpy.dot(deltaI, numpy.dot(R,deltaI).A1) )
-
-print
-print "Sortie du test :"
-print " Xb =",Xb
-print " Xa =",Xa
-print "Difference Xa-Xb :"
-print " Xa-Xb =",deltaB
-print " max(Xa-Xb) =",max(deltaB)
-print " min(Xa-Xb) =",min(deltaB)
-print "Fonctionnelles d'ecarts :"
-print " Initialement : J =",JI
-print " LS =",LSI
-print " Analyse : J =",J,"(Baise de %i%s)"%(100.*(JI-J)/J,"%")
-print " LS =",LS,"(Baise de %i%s)"%(100.*(LSI-LS)/LSI,"%")
-print "Autres informations :"
-print " d =",Innovation
-print " A =",A
-print
-]]></code></script>
- <inport name="xa" type="dblevec"/>
- <inport name="A" type="dblevec"/>
- <inport name="Innovation" type="dblevec"/>
- <inport name="xb" type="dblevec"/>
- <inport name="Yo" type="dblevec"/>
- <inport name="B" type="dblevec"/>
- <inport name="R" type="dblevec"/>
- <inport name="H" type="dblevec"/>
- </inline>
- <parameter>
- <tonode>Building_B</tonode><toport>parametres</toport>
- <value><objref>(lp1
-.</objref></value>
- </parameter>
- <parameter>
- <tonode>Building_Xb</tonode><toport>parametres</toport>
- <value><objref>(lp1
-.</objref></value>
- </parameter>
- <parameter>
- <tonode>Building_Yo</tonode><toport>calcul</toport>
- <value><objref>(lp1
-.</objref></value>
- </parameter>
- <parameter>
- <tonode>Building_Yo</tonode><toport>experiences</toport>
- <value><objref>(lp1
-.</objref></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>xa</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>A</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>Innovation</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>xb</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>Yo</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>B</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>R</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Sorties du calcul AD</tonode><toport>H</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Building_R</tonode><toport>experiences</toport>
- <value><objref>(lp1
-.</objref></value>
- </parameter>
- <parameter>
- <tonode>Entrees du calcul AD</tonode><toport>Xb</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Entrees du calcul AD</tonode><toport>Yo</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Entrees du calcul AD</tonode><toport>B</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Entrees du calcul AD</tonode><toport>R</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>Entrees du calcul AD</tonode><toport>H</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <presentation name="Building_B" x="9" y="81.5" width="227.5" height="110"/>
- <presentation name="Building_Xb" x="9" y="233" width="227.5" height="168"/>
- <presentation name="Building_Yo" x="249" y="231" width="227.5" height="139"/>
- <presentation name="Sorties du calcul AD" x="246" y="432" width="223" height="313"/>
- <presentation name="Building_R" x="250" y="84.5" width="227.5" height="110"/>
- <presentation name="Entrees du calcul AD" x="9" y="429" width="227.5" height="226"/>
- <presentation name="__ROOT__" x="0" y="0" width="487" height="754"/>
-</proc>
+++ /dev/null
-<?xml version='1.0' encoding='iso-8859-1' ?>
-<proc>
- <property name="DefaultStudyID" value="1"/>
- <type name="bool" kind="bool"/>
- <sequence name="boolvec" content="bool"/>
- <type name="double" kind="double"/>
- <sequence name="dblevec" content="double"/>
- <objref name="file" id="file"/>
- <type name="int" kind="int"/>
- <sequence name="intvec" content="int"/>
- <objref name="pyobj" id="python:obj:1.0"/>
- <sequence name="seqdblevec" content="dblevec"/>
- <sequence name="seqint" content="int"/>
- <sequence name="seqintvec" content="intvec"/>
- <type name="string" kind="string"/>
- <sequence name="stringvec" content="string"/>
- <container name="DefaultContainer">
- <property name="container_name" value="FactoryServer"/>
- <property name="hostname" value="localhost"/>
- </container>
- <bloc name="H_linearization">
- <foreach name="Finite_differences_derivation" type="int">
- <bloc name="Elementary_calculation">
- <inline name="Perturbated_point_X">
- <script><code><![CDATA[print iter
-print seq_X[iter]
-X = seq_X[iter]
-]]></code></script>
- <inport name="iter" type="int"/>
- <inport name="seq_X" type="seqdblevec"/>
- <outport name="X" type="dblevec"/>
- <outport name="iter" type="int"/>
- </inline>
- <inline name="ASTER">
- <script><code><![CDATA[print 'Debut ASTER_recal'
-
-import os
-execfile( os.path.join(SOURCES_ROOT, 'N_Code_Aster.py') )
-
-print 'RESU_CALC (%s): %s ' % (iter, RESU_CALC)
-print 'DIAG (%s): %s' % (iter, DIAG)
-]]></code></script>
- <inport name="X" type="dblevec"/>
- <inport name="iter" type="int"/>
- <inport name="ASTER_ROOT" type="string"/>
- <inport name="rcdir" type="string"/>
- <inport name="debug" type="bool"/>
- <inport name="DISPLAY" type="string"/>
- <inport name="SOURCES_ROOT" type="string"/>
- <inport name="export" type="string"/>
- <inport name="parametres" type="pyobj"/>
- <inport name="calcul" type="pyobj"/>
- <inport name="experience" type="pyobj"/>
- <inport name="fileparameters" type="string"/>
- <outport name="FX" type="dblevec"/>
- <outport name="FY" type="dblevec"/>
- <outport name="DIMS" type="intvec"/>
- <outport name="DIAG" type="string"/>
- <outport name="iter" type="int"/>
- </inline>
- <control> <fromnode>Perturbated_point_X</fromnode> <tonode>ASTER</tonode> </control>
- <datalink control="false">
- <fromnode>Perturbated_point_X</fromnode> <fromport>X</fromport>
- <tonode>ASTER</tonode> <toport>X</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Perturbated_point_X</fromnode> <fromport>iter</fromport>
- <tonode>ASTER</tonode> <toport>iter</toport>
- </datalink>
- </bloc>
- </foreach>
- <inline name="Gradient">
- <script><code><![CDATA[
-print "seq_FX=", seq_FX
-print "seq_FY=", seq_FY
-print "seq_DIMS=", seq_DIMS
-print "lst_DIAG=", lst_DIAG
-print "lst_iter=", lst_iter
-print "dX=", dX
-
-import os
-execfile( os.path.join(SOURCES_ROOT, 'N_Gradient.py') )
-
-print "fonctionnelle=", fonctionnelle
-print "gradient=", gradient
-]]></code></script>
- <inport name="seq_FX" type="seqdblevec"/>
- <inport name="seq_FY" type="seqdblevec"/>
- <inport name="seq_DIMS" type="seqintvec"/>
- <inport name="lst_DIAG" type="stringvec"/>
- <inport name="lst_iter" type="intvec"/>
- <inport name="dX" type="dblevec"/>
- <inport name="SOURCES_ROOT" type="string"/>
- <outport name="fonctionnelle" type="dblevec"/>
- <outport name="gradient" type="seqdblevec"/>
- </inline>
- <inline name="Input">
- <function name="inputctrl">
- <code><![CDATA[# debug : nb permet de limiter le nb de directions pour les differences finies
-nb = None # on calcule pour tous les parametres
-#nb = 1
-
-print
-print "XXXXXXXXXXXXX======== Demarrage de la procedure"
-print
-import copy
-
-def inputctrl( X, dX ):
-
- if len( X ) != len( dX ):
- raise ValueError("X and dX has to present the same lenght")
-
- nbparam = len(X)
- if nb: nbparam = min(nb, nbparam)
- seq_X = [ X ]
- for i in range(nbparam):
- Xplus = copy.copy(X)
- Xplus[i] = X[i] + dX[i]
- seq_X.append( Xplus )
-
- nb_core = 2
- itervect = range( len(seq_X) )
- nbBranches = min(nb_core, len( itervect ))
-
- print " Taille du vecteur :", nbparam
- print "seq_X:", seq_X
- print "nbBranches:", nbBranches
- print "itervect:", itervect
-
- # debug
- #nbBranches = 1
- #if nbBranches==1:
- # itervect = [0]
- # seq_X = [ X ]
-
- return nbBranches, itervect, seq_X, dX
-]]></code>
- </function>
- <inport name="X" type="dblevec"/>
- <inport name="dX" type="dblevec"/>
- <outport name="nbBranches" type="int"/>
- <outport name="itervect" type="intvec"/>
- <outport name="seq_X" type="seqdblevec"/>
- <outport name="dX" type="dblevec"/>
- </inline>
- <inline name="Temporary_Parameters">
- <script><code><![CDATA[# Boitier pour une interface simple de variables temporaires]]></code></script>
- <inport name="ASTER_ROOT" type="string"/>
- <inport name="rcdir" type="string"/>
- <inport name="debug" type="bool"/>
- <inport name="DISPLAY" type="string"/>
- <inport name="SOURCES_ROOT" type="string"/>
- <inport name="export" type="string"/>
- <inport name="parametres" type="pyobj"/>
- <inport name="calcul" type="pyobj"/>
- <inport name="experience" type="pyobj"/>
- <inport name="fileparameters" type="string"/>
- <outport name="ASTER_ROOT" type="string"/>
- <outport name="rcdir" type="string"/>
- <outport name="debug" type="bool"/>
- <outport name="DISPLAY" type="string"/>
- <outport name="SOURCES_ROOT" type="string"/>
- <outport name="export" type="string"/>
- <outport name="parametres" type="pyobj"/>
- <outport name="calcul" type="pyobj"/>
- <outport name="experience" type="pyobj"/>
- <outport name="fileparameters" type="string"/>
- </inline>
- <control> <fromnode>Finite_differences_derivation</fromnode> <tonode>Gradient</tonode> </control>
- <control> <fromnode>Input</fromnode> <tonode>Finite_differences_derivation</tonode> </control>
- <control> <fromnode>Input</fromnode> <tonode>Gradient</tonode> </control>
- <control> <fromnode>Temporary_Parameters</fromnode> <tonode>Finite_differences_derivation</tonode> </control>
- <control> <fromnode>Temporary_Parameters</fromnode> <tonode>Gradient</tonode> </control>
- <datalink control="false">
- <fromnode>Finite_differences_derivation</fromnode> <fromport>SmplPrt</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.Perturbated_point_X</tonode> <toport>iter</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Input</fromnode> <fromport>nbBranches</fromport>
- <tonode>Finite_differences_derivation</tonode> <toport>nbBranches</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Input</fromnode> <fromport>itervect</fromport>
- <tonode>Finite_differences_derivation</tonode> <toport>SmplsCollection</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Input</fromnode> <fromport>seq_X</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.Perturbated_point_X</tonode> <toport>seq_X</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Input</fromnode> <fromport>dX</fromport>
- <tonode>Gradient</tonode> <toport>dX</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>ASTER_ROOT</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>ASTER_ROOT</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>rcdir</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>rcdir</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>debug</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>debug</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>DISPLAY</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>DISPLAY</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>SOURCES_ROOT</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>SOURCES_ROOT</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>SOURCES_ROOT</fromport>
- <tonode>Gradient</tonode> <toport>SOURCES_ROOT</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>export</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>export</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>parametres</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>parametres</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>calcul</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>calcul</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>experience</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>experience</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Temporary_Parameters</fromnode> <fromport>fileparameters</fromport>
- <tonode>Finite_differences_derivation.Elementary_calculation.ASTER</tonode> <toport>fileparameters</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Finite_differences_derivation.Elementary_calculation.ASTER</fromnode> <fromport>FX</fromport>
- <tonode>Gradient</tonode> <toport>seq_FX</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Finite_differences_derivation.Elementary_calculation.ASTER</fromnode> <fromport>FY</fromport>
- <tonode>Gradient</tonode> <toport>seq_FY</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Finite_differences_derivation.Elementary_calculation.ASTER</fromnode> <fromport>DIMS</fromport>
- <tonode>Gradient</tonode> <toport>seq_DIMS</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Finite_differences_derivation.Elementary_calculation.ASTER</fromnode> <fromport>DIAG</fromport>
- <tonode>Gradient</tonode> <toport>lst_DIAG</toport>
- </datalink>
- <datalink control="false">
- <fromnode>Finite_differences_derivation.Elementary_calculation.ASTER</fromnode> <fromport>iter</fromport>
- <tonode>Gradient</tonode> <toport>lst_iter</toport>
- </datalink>
- </bloc>
- <parameter>
- <tonode>H_linearization.Finite_differences_derivation.Elementary_calculation.ASTER</tonode><toport>X</toport>
- <value><array><data>
-<value><double>80000</double></value>
-<value><double>1000</double></value>
-<value><double>30</double></value>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>ASTER_ROOT</toport>
- <value><string>''</string></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>rcdir</toport>
- <value><string>''</string></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>debug</toport>
- <value><boolean>0</boolean></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>DISPLAY</toport>
- <value><string>:0.0</string></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>SOURCES_ROOT</toport>
- <value><string>.</string></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>export</toport>
- <value><string>''</string></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>parametres</toport>
- <value><objref><![CDATA[(lp1
-.]]></objref></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>calcul</toport>
- <value><objref><![CDATA[(lp1
-.]]></objref></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>experience</toport>
- <value><objref><![CDATA[(lp1
-.]]></objref></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Temporary_Parameters</tonode><toport>fileparameters</toport>
- <value><string>[]</string></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Input</tonode><toport>X</toport>
- <value><array><data>
-<value><double>80000</double></value>
-<value><double>1000</double></value>
-<value><double>30</double></value>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>H_linearization.Input</tonode><toport>dX</toport>
- <value><array><data>
-<value><double>0.001</double></value>
-<value><double>0.001</double></value>
-<value><double>0.0001</double></value>
-</data></array></value>
- </parameter>
- <presentation name="H_linearization.Finite_differences_derivation.Elementary_calculation.ASTER" x="396" y="80.5" width="227.5" height="429"/>
- <presentation name="H_linearization.Finite_differences_derivation" x="368.856" y="80.5" width="651.5" height="657.5"/>
- <presentation name="H_linearization.Finite_differences_derivation.Elementary_calculation.Perturbated_point_X" x="9" y="81" width="227.5" height="139"/>
- <presentation name="H_linearization.Gradient" x="1064.38" y="338.546" width="227.5" height="284"/>
- <presentation name="H_linearization.Finite_differences_derivation.Elementary_calculation" x="9" y="130" width="633" height="518.5"/>
- <presentation name="H_linearization.Temporary_Parameters" x="10.3499" y="364.775" width="258.5" height="371"/>
- <presentation name="H_linearization" x="10.06" y="80.75" width="1301.38" height="747"/>
- <presentation name="H_linearization.Input" x="9" y="83.5" width="227.5" height="197"/>
- <presentation name="__ROOT__" x="0" y="0" width="1320.44" height="836.75"/>
-</proc>
+++ /dev/null
-<?xml version='1.0' encoding='iso-8859-1' ?>
-<proc>
- <property name="DefaultStudyID" value="1"/>
- <type name="bool" kind="bool"/>
- <sequence name="boolvec" content="bool"/>
- <type name="double" kind="double"/>
- <sequence name="dblevec" content="double"/>
- <objref name="file" id="file"/>
- <type name="int" kind="int"/>
- <sequence name="intvec" content="int"/>
- <objref name="pyobj" id="python:obj:1.0"/>
- <type name="string" kind="string"/>
- <sequence name="stringvec" content="string"/>
- <container name="DefaultContainer">
- <property name="container_name" value="FactoryServer"/>
- <property name="hostname" value="localhost"/>
- </container>
- <inline name="BLUE par matrices">
- <function name="algorithm">
- <code><![CDATA[import sys, os
-sys.path.insert(0, "../../Sources/daCore")
-sys.path.insert(0, "../../ComposantAD/daCore")
-#sys.path.insert(0, os.path.join(os.environ["HOME"],"SALOME5/supplements_JPA/ComposantAD/daCore"))
-
-import numpy
-from AssimilationStudy import AssimilationStudy
-
-def algorithm(Yo, B, R, H, Xb):
- #
- # Remise en place des matrices
- # -------------------
- dimensionXb = len( Xb )
- dimensionYo = len( Yo )
- B = numpy.matrix( B, numpy.float ).reshape((dimensionXb,dimensionXb))
- R = numpy.matrix( R, numpy.float ).reshape((dimensionYo,dimensionYo))
- H = numpy.matrix( H, numpy.float ).reshape((dimensionYo,dimensionXb))
- #
- # Analyse
- # -------
- ADD = AssimilationStudy()
- ADD.setBackground (asVector = Xb )
- ADD.setBackgroundError (asCovariance = B )
- ADD.setObservation (asVector = Yo )
- ADD.setObservationError (asCovariance = R )
- ADD.setObservationOperator(asMatrix = H )
- #
- ADD.setAlgorithm(choice="Blue")
- #
- ADD.analyze()
- #
- Xa = ADD.get("Analysis").valueserie(0)
- Innovation = ADD.get("Innovation").valueserie(0)
- A = []
- #
- return Xa, A, Innovation
-]]></code>
- </function>
- <inport name="Yo" type="dblevec"/>
- <inport name="B" type="dblevec"/>
- <inport name="R" type="dblevec"/>
- <inport name="H" type="dblevec"/>
- <inport name="Xb" type="dblevec"/>
- <outport name="Xa" type="dblevec"/>
- <outport name="A" type="dblevec"/>
- <outport name="Innovation" type="dblevec"/>
- </inline>
- <inline name="3D-VAR par matrices">
- <function name="algorithm">
- <code><![CDATA[import sys, os
-sys.path.insert(0, "../../Sources/daCore")
-sys.path.insert(0, "../../ComposantAD/daCore")
-#sys.path.insert(0, os.path.join(os.environ["HOME"],"SALOME5/supplements_JPA/ComposantAD/daCore"))
-
-import numpy
-from AssimilationStudy import AssimilationStudy
-
-def algorithm(Yo, B, R, H, Xb):
- #
- # Remise en place des matrices
- # -------------------
- dimensionXb = len( Xb )
- dimensionYo = len( Yo )
- B = numpy.matrix( B, numpy.float ).reshape((dimensionXb,dimensionXb))
- R = numpy.matrix( R, numpy.float ).reshape((dimensionYo,dimensionYo))
- H = numpy.matrix( H, numpy.float ).reshape((dimensionYo,dimensionXb))
- #
- # Analyse
- # -------
- ADD = AssimilationStudy()
- ADD.setBackground (asVector = Xb )
- ADD.setBackgroundError (asCovariance = B )
- ADD.setObservation (asVector = Yo )
- ADD.setObservationError (asCovariance = R )
- ADD.setObservationOperator(asMatrix = H )
- #
- ADD.setAlgorithm(choice="3DVAR")
- #
- ADD.analyze()
- #
- Xa = ADD.get("Analysis").valueserie(0)
- Innovation = ADD.get("Innovation").valueserie(0)
- A = []
- #
- return Xa, A, Innovation
-]]></code>
- </function>
- <inport name="Yo" type="dblevec"/>
- <inport name="B" type="dblevec"/>
- <inport name="R" type="dblevec"/>
- <inport name="H" type="dblevec"/>
- <inport name="Xb" type="dblevec"/>
- <outport name="Xa" type="dblevec"/>
- <outport name="A" type="dblevec"/>
- <outport name="Innovation" type="dblevec"/>
- </inline>
- <inline name="3D-VAR par fonctions">
- <function name="algorithm">
- <code><![CDATA[import sys, os
-sys.path.insert(0, "../../Sources/daCore")
-sys.path.insert(0, "../../ComposantAD/daCore")
-#sys.path.insert(0, os.path.join(os.environ["HOME"],"SALOME5/supplements_JPA/ComposantAD/daCore"))
-
-import numpy
-from AssimilationStudy import AssimilationStudy
-
-def algorithm( Yo, B, R, FunctionH, TangentH, AdjointH, Xb, Bounds ):
- #
- # Remise en place des matrices
- # -------------------
- dimensionXb = len( Xb )
- dimensionYo = len( Yo )
- B = numpy.matrix( B, numpy.float ).reshape((dimensionXb,dimensionXb))
- R = numpy.matrix( R, numpy.float ).reshape((dimensionYo,dimensionYo))
- #
- # Analyse
- # -------
- ADD = AssimilationStudy()
- ADD.setBackground (asVector = Xb )
- ADD.setBackgroundError (asCovariance = B )
- ADD.setObservation (asVector = Yo )
- ADD.setObservationError (asCovariance = R )
- ADD.setObservationOperator(asFunction = {"Direct":FunctionH,
- "Tangent":TangentH,
- "Adjoint":AdjointH} )
- #
- ADD.setAlgorithm(choice="3DVAR")
- ADD.setAlgorithmParameters(asDico={
- "Minimizer":"LBFGSB",
- "Bounds" :Bounds,
- })
- #
- ADD.analyze()
- #
- Xa = ADD.get("Analysis").valueserie(0)
- Innovation = ADD.get("Innovation").valueserie(0)
- A = []
- #
- return Xa, A, Innovation
-]]></code>
- </function>
- <inport name="Yo" type="dblevec"/>
- <inport name="B" type="dblevec"/>
- <inport name="R" type="dblevec"/>
- <inport name="FunctionH" type="pyobj"/>
- <inport name="TangentH" type="pyobj"/>
- <inport name="AdjointH" type="pyobj"/>
- <inport name="Xb" type="dblevec"/>
- <inport name="Bounds" type="pyobj"/>
- <outport name="Xa" type="dblevec"/>
- <outport name="A" type="dblevec"/>
- <outport name="Innovation" type="dblevec"/>
- </inline>
- <parameter>
- <tonode>BLUE par matrices</tonode><toport>Yo</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>BLUE par matrices</tonode><toport>B</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>BLUE par matrices</tonode><toport>R</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>BLUE par matrices</tonode><toport>H</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>BLUE par matrices</tonode><toport>Xb</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par matrices</tonode><toport>Yo</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par matrices</tonode><toport>B</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par matrices</tonode><toport>R</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par matrices</tonode><toport>H</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par matrices</tonode><toport>Xb</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par fonctions</tonode><toport>Yo</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par fonctions</tonode><toport>B</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par fonctions</tonode><toport>R</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par fonctions</tonode><toport>Xb</toport>
- <value><array><data>
-</data></array></value>
- </parameter>
- <parameter>
- <tonode>3D-VAR par fonctions</tonode><toport>Bounds</toport>
- <value><objref>(lp1
-.</objref></value>
- </parameter>
- <presentation name="BLUE par matrices" x="9" y="80.5" width="227.5" height="226"/>
- <presentation name="3D-VAR par matrices" x="237.5" y="80.5" width="227.5" height="226"/>
- <presentation name="3D-VAR par fonctions" x="465.5" y="80.5" width="227.5" height="313"/>
- <presentation name="__ROOT__" x="0" y="0" width="702" height="402.5"/>
-</proc>
+++ /dev/null
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
+++ /dev/null
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Outil numérique de calcul de la variable de Fisher pour comparer les
- variances de 2 échantillons
-
- Ce calcul nécessite :
- - en input :
- - les deux vecteurs (comme liste, array ou matrix) d'échantillons
- dont on veut comparer la variance,
- - la tolérance
- - en output :
- - la p-value,
- - la valeur de la variable aléatoire,
- - la réponse au test ainsi que
- - le message qui interprete la reponse du test.
-"""
-__author__ = "Sophie RICCI - Juillet 2008"
-
-import numpy
-from scipy.stats import betai
-
-# ==============================================================================
-def ComputeFisher(vector1 = None, vector2 = None, tolerance = 0.05 ):
- """
- Outil numérique de calcul de la variable de Fisher pour comparer les
- variances de 2 échantillons
-
- Ce calcul nécessite :
- - en input : les deux vecteurs (comme liste, array ou matrix)
- d'échantillons dont on veut comparer la variance, la
- tolérance
- - en output : la p-value, la valeur de la variable aléatoire,
- la réponse au test ainsi que le message qui interprete
- la reponse du test.
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Fisher value value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- #
- # Calcul des variances des echantillons
- # -------------------------------------
- # où var est calculee comme : var = somme (xi -xmean)**2 /(n-1)
- n1 = V1.size
- n2 = V2.size
- var1 = V1.std() * V1.std()
- var2 = V2.std() * V2.std()
- if (var1 > var2):
- f = var1/var2
- df1 = n1-1
- df2 = n2-1
- else:
- f= var2/var1
- df1 = n2-1
- df2 = n1-1
- prob1= betai(0.5*df2,0.5*df1,float(df2)/float(df2+df1*f))
- prob2= (1. - betai(0.5*df1, 0.5*df2, float(df1)/float(df1+df2/f)))
- prob = prob1 + prob2
- #
- # Calcul de la p-value
- # --------------------
- areafisher = 100 * prob
- #
- # Test
- # ----
- message = "Il y a %.2f%s de chance de se tromper en refusant l'hypothèse d'égalité des variances des 2 échantillons (si <%.2f%s, on refuse effectivement l'égalité)"%(areafisher,"%",100.*tolerance,"%")
- if (areafisher < (100.*tolerance)) :
- answerTestFisher = False
- else:
- answerTestFisher = True
- # print "La reponse au test est", answerTestFisher
-
- return areafisher, f, answerTestFisher, message
-
-# ==============================================================================
-if __name__ == "__main__":
- print "\nAUTOTEST\n"
- #
- # Echantillons
- # ------------
- x1 = [-1., 0., 4., 2., -1., 3.]
- x2 = [-1., 0., 4., 2., -1., 3.]
- #
- # Appel du calcul
- # ---------------
- [aire, f, reponse, message] = ComputeFisher(
- vector1 = x1,
- vector2 = x2,
- tolerance = 0.05 )
- #
- print " aire.....:", aire
- print " f........:", f
- print " reponse..:", reponse
- print " message..:", message
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Outil numerique de calcul de la variable Khi2
-
- On peut realiser deux types de test du Khi2 :
- - test d'adequation : comparer la distribution d'un echantillon a une
- distribution theorique,
- - test d'homogeneite : comparer les distributions de 2 vecteurs.
-
- Pour le test d'adequation, on travaille sur une gaussienne
- dont la moyenne et l'ecart type sont calcules sur
- l'echantillon, soit donnes.
-
- Ce fichier contient une classe "StatspourTests" de methodes qui realisent
- differentes etapes utiles aux calculs des tests du Khi2.
-
- Ce fichier contient de plus 3 methodes : ComputeKhi2_testGauss,
- ComputeKhi2_Gauss et ComputeKhi2_Homogen.
- - ComputeKhi2_testGauss : calcul la distance du Khi2 entre un vecteur
- aleatoire issu d un gaussienne et une distribution theorique gaussienne
- dont on specifie la moyenne et l ecart type
- - ComputeKhi2_Gauss : calcul la distance du Khi2 entre un vecteur donne et
- une distribution theorique gaussienne dont la moyenne et l ecart type sont
- calcules sur l echantillon
- - ComputeKhi2_Homogen : calcul la distance du Khi2 entre deux vecteurs donnes
-
- Ces methodes necessitent et fournissent :
- - en input :
- - le ou les vecteurs dont on etudie la distribution,
- - la distribution theorique et eventuellement la moyenne et ecart type,
- - la largeur des classes,
- - un booleen traduisant la suppression des classes vides
- - en output :
- - le vecteur des classes,
- - les pdf theorique et donnee,
- - la valeur du Khi2,
- - la p-value qui represent l'aire de la queue de la distribution du
- Khi2 et
- - le message qui interprete le test.
-"""
-__author__ = "Sophie RICCI - Mars 2010"
-
-import numpy
-from numpy import random
-from scipy import arange, asarray, stats
-from scipy.stats import histogram2, chisquare, chisqprob, norm
-import logging
-
-# ==============================================================================
-class StatspourTests :
- """
- Classe de methodes pour la preparation du test de Khi2
- """
- def __init__(self, cdftheo=None, meantheo = None, stdtheo = None, pdftest=None,obs=None,use_mean_std_exp=True, dxmin=0.01, obsHomogen = None, nbclasses = None) :
-
-
- if (pdftest is None and obs is None) :
- raise ValueError('Donner soit une pdf de test soit un vecteur obs')
- if not obs is None :
- if pdftest is None :
- self.__obs=asarray(obs)
- if not pdftest is None :
- if obs is None :
- if len(pdftest) == 3:
- niter=eval(pdftest[2])
- obs=[eval(" ".join(pdftest[:2])) for z in range(niter)]
- self.__obs=asarray(obs)
- else :
- self.__obs=asarray(eval(" ".join(pdftest[:2])))
- if not (obsHomogen is None) :
- self.__obsHomogen = asarray(obsHomogen)
- self.__testHomogen = True
- else :
- self.__testHomogen = False
-
-
- self.__mean_exp = self.__obs.mean()
- self.__std_exp = self.__obs.std()
-
- if cdftheo is None : raiseValueError(" ... Definir le parametre cdftheo ...")
- if use_mean_std_exp :
- self.__cdf=cdftheo( self.__mean_exp, self.__std_exp).cdf
- else :
- self.__cdf=cdftheo( meantheo, stdtheo).cdf
-
- self.__min=min(self.__obs)
- self.__max=max(self.__obs)
- self.__N=len(self.__obs)
- self.__use_mean_std_exp=use_mean_std_exp
- self.__dxmin=dxmin
- self.__nbclasses = nbclasses
- if not (dxmin is None) and not (nbclasses is None) :
- raise ValueError("... Specifier soit le nombre de classes, soit la largeur des classes")
- if (dxmin is None) and (nbclasses is None) :
- raise ValueError("... Specifier soit le nombre de classes, soit la largeur des classes")
- if not (nbclasses is None) and (dxmin is None) :
- self.__dxmin = (self.__max - self.__min ) / float(self.__nbclasses)
- return None
-
- def MakeClasses(self) :
- """
- Classification en classes
- """
- self.__subdiv=arange(self.__min,self.__max+self.__dxmin,self.__dxmin)
- self.__modalites=len(self.__subdiv)
- return None
-
- def ComputeObs(self):
- """
- Calcul de la probabilite observee de chaque classe
- """
- self.__kobs=histogram2(self.__obs,self.__subdiv)[1:]
- return self.__kobs
-
- def ComputeObsHomogen(self):
- """
- Calcul de la probabilite observee pour le test homogeneite de chaque classe
- """
- self.__kobsHomogen=histogram2(self.__obsHomogen,self.__subdiv)[1:]
- return self.__kobsHomogen
-
- def ComputeTheo(self):
- """
- Calcul de la probabilite theorique de chaque classe
- """
- self.__ktheo=[self.__cdf(self.__subdiv[i+1])-self.__cdf(self.__subdiv[i]) for i in range(self.__modalites-1)]
- self.__ktheo=asarray(self.__ktheo)
- self.__ktheo=(sum(self.__kobs)/sum(self.__ktheo))*self.__ktheo
-
- def Computepdfs(self) :
-
- self.__subdiv=self.__subdiv[1:]
- self.__pdfobs=[self.__kobs[i+1]/(self.__subdiv[i+1]-self.__subdiv[i]) for i in range(self.__modalites-2)]
-
- if self.__testHomogen :
- self.__pdftheo=[self.__kobsHomogen[i+1]/(self.__subdiv[i+1]-self.__subdiv[i]) for i in range(self.__modalites-2)]
- else :
- self.__pdftheo=[self.__ktheo[i+1]/(self.__subdiv[i+1]-self.__subdiv[i]) for i in range(self.__modalites-2)]
-
- return self.__subdiv, self.__pdftheo, self.__pdfobs
-
- def Computeddl(self):
- """
- Calcul du nombre de degres de liberte
- """
- self.__ddl = self.__modalites - 1.
- if self.__use_mean_std_exp :
- self.__ddl = self.__ddl - 2.
- if (self.__ddl < 1.):
- raise ValueError("The ddl is 0, you must increase the number of classes nbclasse ")
- logging.debug("Nombre de degres de liberte=%s"%self.__ddl)
-
- def ComputeValue(self) :
- """
- Calcul de la variable Q qui suit une loi Khi-2
- """
- if self.__testHomogen :
- kobs,ktheo=self.__kobs.tolist(),self.__kobsHomogen.tolist()
- else :
- kobs,ktheo=self.__kobs.tolist(),self.__ktheo.tolist()
-
- # on supprime les classes theoriques qui ont moins d'un element (sinon la distance khi2 tendrait vers l'infini)
- ko,kt=[],[]
- self.__count0 = 0.
- for k,val in enumerate(ktheo):
- if val > 1.0:
- kt.append(val)
- ko.append(kobs[k])
- else :
- self.__count0 = self.__count0 +1.
- logging.debug("WARNING : nombre de classes vides supprimees (effectif theorique inferieur a 1.) pour le calcul de la valeur du Khi2 = %s"%self.__count0)
- ef1,ef2=asarray(ko),asarray(kt)
- count = 0.
- for el in ef1.tolist() :
- if el < 5. :
- count = count +1.
- for el in ef2.tolist() :
- if el < 5. :
- count = count +1.
- pourcent_nbclasse_effecinf = count /(2.*len(ef1.tolist())) *100.
- if (pourcent_nbclasse_effecinf > 20.) :
- logging.debug("WARNING : nombre de classes dont l effectif est inferieur a 5 elements %s"%pourcent_nbclasse_effecinf)
- k,p = chisquare(ef1, ef2)
- k2, p2 = [k],[p]
- for shift in range(1,6):
- k,p=chisquare(ef1[shift:],ef2[:-shift])
- k2.append(k)
- p2.append(p)
- k,p=chisquare(ef1[:-shift],ef2[shift:])
- k2.append(k)
- p2.append(p)
- logging.debug("Liste des valeurs du Khi2 = %s"%k2)
- self.__khi2=min(k2)
- self.__Q=self.__khi2
-
- logging.debug("Valeur du Khi2=%s"%self.__Q)
- return self.__Q
-
- def ComputeArea(self):
- """
- Calcul de la p-value
- """
- self.__areakhi2 = 100 * chisqprob(self.__Q, self.__ddl)
- return self.__areakhi2
-
- def WriteMessage(self):
- """
- Interpretation du test
- """
- message = "Il y a %.2f%s de chance de se tromper en refusant l'adequation"%(self.__areakhi2,"%")
- return message
-
- def WriteMessageHomogen(self):
- """
- Interpretation du test
- """
- message = "Il y a %.2f%s de chance de se tromper en refusant l'homogeneite"%(self.__areakhi2,"%")
- return message
-
-# ==============================================================================
-def ComputeKhi2_testGauss(
- meantheo = 0.,
- stdtheo = 1.,
- nech = 10,
- dx = 0.1,
- nbclasses = None,
- SuppressEmptyClasses = True,
- ):
- """
- Test du Khi2 d adequation entre tirage aleatoire dans gaussienne et une gaussienne theo
- """
- essai = StatspourTests( cdftheo=norm, meantheo = meantheo, stdtheo = stdtheo, pdftest = ["random.normal","(%.3f,%.2f,%d)"%(meantheo,stdtheo,nech)], obs = None, use_mean_std_exp=False,dxmin=dx, obsHomogen = None, nbclasses = nbclasses)
- essai.MakeClasses()
- essai.ComputeObs()
- essai.ComputeTheo()
- classes,eftheo, efobs = essai.Computepdfs()
- essai.Computeddl()
- valeurKhi2= essai.ComputeValue()
- areaKhi2 = essai.ComputeArea()
- message = essai.WriteMessage()
- logging.debug("message %s"%message)
- return classes, eftheo, efobs, valeurKhi2, areaKhi2, message
-
-def ComputeKhi2_Gauss(
- vectorV = None,
- dx = 0.1,
- SuppressEmptyClasses = True,
- nbclasses = None
- ):
- """
- Test du Khi2 d adequation entre un vecteur donne et une gaussienne theo de mean et std celles du vecteur
- """
- essai = StatspourTests( cdftheo=norm, pdftest = None, obs = vectorV, use_mean_std_exp=True,dxmin=dx, obsHomogen = None, nbclasses = nbclasses)
- essai.MakeClasses()
- essai.ComputeObs()
- essai.ComputeTheo()
- classes,eftheo, efobs = essai.Computepdfs()
- essai.Computeddl()
- valeurKhi2= essai.ComputeValue()
- areaKhi2 = essai.ComputeArea()
- message = essai.WriteMessage()
- logging.debug("message %s"%message)
- return classes, eftheo, efobs, valeurKhi2, areaKhi2, message
-
-def ComputeKhi2_Homogen(
- vectorV1 = None,
- vectorV2 = None,
- dx = 0.1,
- SuppressEmptyClasses = True,
- nbclasses = None
- ):
- """
- Test du Khi2 d homogeniete entre 2 vecteurs
- """
- essai = StatspourTests( cdftheo=norm, pdftest = None, obs = vectorV1, use_mean_std_exp=True,dxmin=dx, obsHomogen = vectorV2, nbclasses = nbclasses)
- essai.MakeClasses()
- essai.ComputeObs()
- essai.ComputeObsHomogen()
- classes,eftheo, efobs = essai.Computepdfs()
- essai.Computeddl()
- valeurKhi2= essai.ComputeValue()
- areaKhi2 = essai.ComputeArea()
- message = essai.WriteMessageHomogen()
- logging.debug("message %s"%message)
- return classes, eftheo, efobs, valeurKhi2, areaKhi2, message
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- #
- numpy.random.seed(100)
-
- # Test de verification d adequation entre une gaussienne et un tirage gaussien
- print ''
- print 'Test de verification d adequation entre une gaussienne centree normale et un tirage gaussien'
- classes, eftheo, efobs, valeurKhi2, areaKhi2, message = ComputeKhi2_testGauss(meantheo = 0., stdtheo = 1., nech = 1000., dx = 0.1, SuppressEmptyClasses = True, nbclasses = None)
- print ' valeurKhi2=',valeurKhi2
- print ' areaKhi2=',areaKhi2
- print ' ',message
-
- if (numpy.abs(areaKhi2 - 99.91)< 1.e-2) :
- print "The computation of the khisquare value is OK"
- else :
- raise ValueError("The computation of the khisquare value is WRONG")
-
- numpy.random.seed(2490)
-
- # Test de verification d adequation entre une gaussienne et un vecteur donne
- print ''
- print 'Test de verification d adequation entre une gaussienne et un vecteur donne'
- V = random.normal(50.,1.5,1000)
- classes, eftheo, efobs, valeurKhi2, areaKhi2, message = ComputeKhi2_Gauss(dx = 0.1, vectorV = V, SuppressEmptyClasses = True, nbclasses = None)
- print ' valeurKhi2=',valeurKhi2
- print ' areaKhi2=',areaKhi2
- print ' ',message
-
- if (numpy.abs(areaKhi2 - 99.60)< 1.e-2) :
- print "The computation of the khisquare value is OK"
- else :
- raise ValueError("The computation of the khisquare value is WRONG")
-
- # Test de d homogeneite entre 2 vecteurs donnes
- print ''
- print 'Test d homogeneite entre 2 vecteurs donnes'
- V1 = random.normal(50.,1.5,10000)
- numpy.random.seed(2490)
- V2 = random.normal(50.,1.5,10000)
- classes, eftheo, efobs, valeurKhi2, areaKhi2, message = ComputeKhi2_Homogen(dx = 0.5, vectorV1 = V1, vectorV2 = V2, SuppressEmptyClasses = True, nbclasses = None)
- print ' valeurKhi2=',valeurKhi2
- print ' areaKhi2=',areaKhi2
- print ' ',message
-
- if (numpy.abs(areaKhi2 - 99.98)< 1.e-2) :
- print "The computation of the khisquare value is OK"
- else :
- raise ValueError("The computation of the khisquare value is WRONG")
-
- # Test de verification d adequation entre une gaussienne et un tirage gaussien en faisant varier le nombre de classes, echantillon de taille 10000
- print ''
- print 'Test de verification d adequation entre une gaussienne et un vecteur aleatoire gaussien de taille 10000'
-# file = 'ComputeKhi2_adequationGauss_fctnbclasses_nech10000.gnu'
-# fid = open(file, "w")
-# lines = '%s\n' % ('# dx , nbclasses, valeurKhi2, ProbKhi2' )
- numpy.random.seed(4000)
- V = random.normal(0., 1.,10000)
- aire = []
- for dx in arange(0.01, 1., 0.001) :
- classes, eftheo, efobs, valeurKhi2, areaKhi2, message = ComputeKhi2_Gauss(dx = dx, vectorV = V, SuppressEmptyClasses = True, nbclasses = None)
-# lines += '%f %f %f %f\n' % (dx, numpy.size(classes), valeurKhi2, areaKhi2)
- aire.append(areaKhi2)
- meanaire = numpy.asarray(aire)
-# fid.writelines(lines)
-
- print " En moyenne, il y a ", meanaire.mean(),"% de chance de se tromper en refusant l adequation a une loi gaussienne pour un echantillon de taille 10000"
- print
- if (numpy.abs( meanaire.mean() - 71.79)< 1.e-2) :
- print "The computation of the khisquare value is OK"
- else :
- raise ValueError("The computation of the khisquare value is WRONG")
-
- # Test de verification d adequation entre une gaussienne et un tirage gaussien en faisant varier le nombre de classes, echantillon de taille 1000
- print ''
- print 'Test de verification d adequation entre une gaussienne et un vecteur aleatoire gaussien de taille 1000'
-# file = 'ComputeKhi2_adequationGauss_fctnbclasses_nech1000.gnu'
-# fid = open(file, "w")
-# lines = '%s\n' % ('# dx , nbclasses, valeurKhi2, ProbKhi2' )
- numpy.random.seed(4000)
- V = random.normal(0., 1.,1000)
- aire = []
- for dx in arange(0.05, 1., 0.001) :
- classes, eftheo, efobs, valeurKhi2, areaKhi2, message = ComputeKhi2_Gauss(dx = dx, vectorV = V, SuppressEmptyClasses = True, nbclasses = None)
-# lines += '%f %f %f %f\n' % (dx, numpy.size(classes), valeurKhi2, areaKhi2)
- aire.append(areaKhi2)
- meanaire = numpy.asarray(aire)
-# fid.writelines(lines)
-
- print " En moyenne, il y a ", meanaire.mean(),"% de chance de se tromper en refusant l adequation a une loi gaussienne pour un echantillon de taille 1000"
- print
- if (numpy.abs( meanaire.mean() - 90.60)< 1.e-2) :
- print "The computation of the khisquare value is OK"
- else :
- raise ValueError("The computation of the khisquare value is WRONG")
-
- # Test de verification d adequation entre une gaussienne et un tirage gaussien en faisant varier le nombre de classes, echantillon de taille 100
- print ''
- print 'Test de verification d adequation entre une gaussienne et un vecteur aleatoire gaussien de taille 100'
-# file = 'ComputeKhi2_adequationGauss_fctnbclasses_nech100.gnu'
-# fid = open(file, "w")
-# lines = '%s\n' % ('# dx , nbclasses, valeurKhi2, ProbKhi2' )
- numpy.random.seed(4000)
- V = random.normal(0., 1.,100)
- aire = []
- for dx in arange(0.1, 1., 0.01) :
- classes, eftheo, efobs, valeurKhi2, areaKhi2, message = ComputeKhi2_Gauss(dx = dx, vectorV = V, SuppressEmptyClasses = True, nbclasses = None)
-# lines += '%f %f %f %f\n' % (dx, numpy.size(classes), valeurKhi2, areaKhi2)
- aire.append(areaKhi2)
- meanaire = numpy.asarray(aire)
-# fid.writelines(lines)
-
- print " En moyenne, il y a ", meanaire.mean(),"% de chance de se tromper en refusant l adequation a une loi gaussienne pour un echantillon de taille 100"
- print
+++ /dev/null
-#-*-coding:iso-8859-1-*-
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
-__doc__ = """
- Outil numérique de calcul des variables de Student pour 2 vecteurs
- dépendants ou indépendants, avec variances supposées égales ou différentes
-"""
-__author__ = "Sophie RICCI, Jean-Philippe ARGAUD - Octobre 2008"
-
-import sys ; sys.path.insert(0, "../daCore")
-
-import numpy
-from scipy.stats import ttest_rel, ttest_ind, betai
-import logging
-
-# ==============================================================================
-def DependantVectors(vector1 = None, vector2 = None, tolerance = 0.05 ):
- """
- Outil numérique de calcul de la variable de Student pour 2 vecteurs
- dépendants
- Ce calcul nécessite :
- - en input :
- - les deux vecteurs (comme liste, array ou matrix)
- d'échantillons dont on veut comparer la variance,
- - la tolérance
- - en output :
- - la p-value,
- - la valeur de la variable aléatoire,
- - la reponse au test pour une tolerance ainsi que
- - le message qui interprete la reponse du test.
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Student value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- if V1.size != V2.size:
- raise ValueError("The two given vectors must have the same size, or the vector types are incompatible")
- #
- # Calcul de la p-value du Test de Student
- # --------------------------------------------------------------------
- [t, prob] = ttest_rel(V1, V2)
- areastudent = 100. * prob
- #
- logging.debug("DEPENDANTVECTORS t = %.3f, areastudent = %.3f"%(t, areastudent))
- #
- # Tests
- # --------------------------------------------------------------------
- message = "DEPENDANTVECTORS Il y a %.2f %s de chance de se tromper en refusant l'hypothèse d'égalité des moyennes des 2 échantillons dépendants (si <%.2f %s on refuse effectivement l'égalité)"%(areastudent, "%", 100.*tolerance,"%")
- logging.debug(message)
- #
- if (areastudent < (100.*tolerance)) :
- answerTestStudent = False
- else:
- answerTestStudent = True
- #
- return areastudent, t, answerTestStudent, message
-
-# ==============================================================================
-def IndependantVectorsDifferentVariance(vector1 = None, vector2 = None, tolerance = 0.05 ):
- """
- Outil numerique de calcul de la variable de Student pour 2 vecteurs independants supposes de variances vraies differentes
- En input : la tolerance
- En output : la p-value, la valeur de la variable aleatoire, la reponse au test pour une tolerance ainsi que le message qui interprete la reponse du test.
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Student value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- #
- # Calcul de la p-value du Test de Student
- # --------------------------------------------------------------------
- # t = (m1 - m2)/ sqrt[ (var1/n1 + var2/n2) ]
- # ou var est calcule comme var = somme (xi -xmena)**2 /(n-1)
- n1 = V1.size
- n2 = V2.size
- mean1 = V1.mean()
- mean2 = V2.mean()
- var1 = numpy.sqrt(n1)/numpy.sqrt(n1-1) * V1.std() * numpy.sqrt(n1)/numpy.sqrt(n1-1) * V1.std()
- var2 = numpy.sqrt(n2)/numpy.sqrt(n2-1) * V2.std() * numpy.sqrt(n2)/numpy.sqrt(n2-1) * V2.std()
- t = (mean1 - mean2)/ numpy.sqrt( var1/n1 + var2/n2 )
- df = ( (var1/n1 + var2/n2) * (var1/n1 + var2/n2) ) / ( (var1/n1)*(var1/n1)/(n1-1) + (var2/n2)*(var2/n2)/(n2-1) )
- zerodivproblem = var1/n1 + var2/n2 == 0
- t = numpy.where(zerodivproblem, 1.0, t) # replace NaN t-values with 1.0
- prob = betai(0.5*df,0.5,float(df)/(df+t*t))
- areastudent = 100. * prob
- #
- logging.debug("IndependantVectorsDifferentVariance t = %.3f, areastudent = %.3f"%(t, areastudent))
- #
- # Tests
- # --------------------------------------------------------------------
- message = "IndependantVectorsDifferentVariance Il y a %.2f %s de chance de se tromper en refusant l'hypothèse d'égalité des moyennes des 2 échantillons indépendants supposés de variances différentes (si <%.2f %s on refuse effectivement l'égalité)"%(areastudent, "%", 100.* tolerance,"%")
- logging.debug(message)
- if (areastudent < (100.*tolerance)) :
- answerTestStudent = False
- else:
- answerTestStudent = True
- #
- return areastudent, t, answerTestStudent, message
-
-# ==============================================================================
-def IndependantVectorsEqualVariance(vector1 = None, vector2 = None, tolerance = 0.05 ):
- """
- Outil numerique de calcul de la variable de Student pour 2 vecteurs independants supposes de meme variance vraie
- En input : la tolerance
- En output : la p-value, la valeur de la variable aleatoire, la reponse au test pour une tolerance ainsi que le message qui interprete la reponse du test.
- """
- if (vector1 is None) or (vector2 is None) :
- raise ValueError("Two vectors must be given to calculate the Student value")
- V1 = numpy.array(vector1)
- V2 = numpy.array(vector2)
- if (V1.size < 1) or (V2.size < 1):
- raise ValueError("The given vectors must not be empty")
- #
- # Calcul de la p-value du Test de Student
- # --------------------------------------------------------------------
- # t = sqrt(n1+n2-2) * (m1 - m2)/ sqrt[ (1/n1 +1/n2) * ( (n1-1)var1 + (n2-1)var2 )]
- # ou var est calcule comme var = somme (xi -xmena)**2 /(n-1)
- [t, prob] = ttest_ind(V1, V2)
- areastudent = 100. * prob
- #
- logging.debug("IndependantVectorsEqualVariance t = %.3f, areastudent = %.3f"%(t, areastudent))
- # Tests
- # --------------------------------------------------------------------
- message = "IndependantVectorsEqualVariance Il y a %.2f %s de chance de se tromper en refusant l'hypothèse d'égalité des moyennes des 2 échantillons indépendants supposés de même variance (si <%.2f %s on refuse effectivement l'égalité)"%(areastudent, "%", 100.* tolerance,"%")
- logging.debug(message)
- if (areastudent < (100.*tolerance)) :
- answerTestStudent = False
- else:
- answerTestStudent = True
-
- return areastudent, t, answerTestStudent, message
-
-# ==============================================================================
-if __name__ == "__main__":
- print '\n AUTODIAGNOSTIC \n'
- # logging.getLogger().setLevel(logging.DEBUG)
-
- print
- print " Test de Student pour des vecteurs dépendants"
- print " --------------------------------------------"
- # Tirage de l'echantillon
- V1 = numpy.matrix(([-1., 0., 4.])).T
- V2 = numpy.matrix(([-2., 0., 8.])).T
- V1 = V1.A1
- V2 = V2.A1
- #
- # Appel de l outil DependantVectors et initialisation des inputs
- [aire, Q, reponse, message] = DependantVectors(
- vector1 = V1,
- vector2 = V2,
- tolerance = 0.05)
- #
- # Verification par les calculs sans les routines de scipy.stats
- # (ref numerical recipes)
- n = V1.size
- df= n -1
- # Les routines de scipy.stats utilisent une variance calculee avec n-1 et non n comme dans std
- # t = (m1 - m2)/ sqrt[(varx1 + varx2 - 2 cov(x1, x2))/n ]
- # ou var est calcule comme var = somme (xi -xmean)**2 /(n-1)
- var1 = numpy.sqrt(n)/numpy.sqrt(n-1)* V1.std() * numpy.sqrt(n)/numpy.sqrt(n-1) * V1.std()
- var2 = numpy.sqrt(n)/numpy.sqrt(n-1)* V2.std() * numpy.sqrt(n)/numpy.sqrt(n-1) * V2.std()
- m1 = V1.mean()
- m2 = V2.mean()
- cov = 0.
- for j in range(0, n) :
- cov = cov + (V1[j] - m1)*(V2[j] - m2)
- cov = cov /df
- sd = numpy.sqrt((var1 + var2 - 2. *cov) / n)
- tverif = (m1 -m2) /sd
- aireverif = 100. * betai(0.5*df,0.5,float(df)/(df+tverif*tverif))
- if (aireverif - aire < 1.e-5) :
- print " Le calcul est conforme à celui de l'algorithme du Numerical Recipes"
- else :
- raise ValueError("Le calcul n'est pas conforme à celui de l'algorithme Numerical Recipes")
-
- if (numpy.abs(aire - 57.99159)< 1.e-5) :
- print " Le calcul est JUSTE sur cet exemple."
- else :
- raise ValueError("Le calcul est FAUX sur cet exemple.")
-
- print
- print " Test de Student pour des vecteurs independants supposés de même variance"
- print " ------------------------------------------------------------------------"
- # Tirage de l'echantillon
- V1 = numpy.matrix(([-1., 0., 4.])).T
- V2 = numpy.matrix(([-2., 0., 8.])).T
- V1 = V1.A1
- V2 = V2.A1
- #
- # Appel de l outil IndependantVectorsDifferentVariance et initialisation des inputs
- [aire, Q, reponse, message] = IndependantVectorsDifferentVariance(
- vector1 = V1,
- vector2 = V2,
- tolerance = 0.05)
- #
- if (numpy.abs(aire - 78.91339)< 1.e-5) :
- print " Le calcul est JUSTE sur cet exemple."
- else :
- raise ValueError("Le calcul est FAUX sur cet exemple.")
-
- print
- print " Test de Student pour des vecteurs indépendants supposés de même variance"
- print " ------------------------------------------------------------------------"
- # Tirage de l'echantillon
- V1 = numpy.matrix(([-1., 0., 4.])).T
- V2 = numpy.matrix(([-2., 0., 8.])).T
- V1 = V1.A1
- V2 = V2.A1
- #
- # Appel de l outil IndependantVectorsEqualVariance et initialisation des inputs
- [aire, Q, reponse, message] = IndependantVectorsEqualVariance(
- vector1 = V1,
- vector2 = V2,
- tolerance = 0.05)
- #
- # Verification par les calculs sans les routines de scipy.stats (ref numerical recipes)
- n1 = V1.size
- n2 = V2.size
- df= n1 + n2 -2
- # Les routines de scipy.stats utilisent une variance calculee avec n-1 et non n comme dans std
- var1 = numpy.sqrt(n1)/numpy.sqrt(n1-1)* V1.std() * numpy.sqrt(n1)/numpy.sqrt(n1-1) * V1.std()
- var2 = numpy.sqrt(n2)/numpy.sqrt(n2-1)* V2.std() * numpy.sqrt(n2)/numpy.sqrt(n2-1) * V2.std()
- m1 = V1.mean()
- m2 = V2.mean()
- var = ((n1 -1.) *var1 + (n2 -1.) *var2 ) /df
- tverif = (m1 -m2) /numpy.sqrt(var*(1./n1 + 1./n2))
- aireverif = 100. * betai(0.5*df,0.5,float(df)/(df+tverif*tverif))
- #
- if (aireverif - aire < 1.e-5) :
- print " Le calcul est conforme à celui de l'algorithme du Numerical Recipes"
- else :
- raise ValueError("Le calcul n'est pas conforme à celui de l'algorithme Numerical Recipes")
-
- if (numpy.abs(aire - 78.42572)< 1.e-5) :
- print " Le calcul est JUSTE sur cet exemple."
- else :
- raise ValueError("Le calcul est FAUX sur cet exemple.")
-
- print
+++ /dev/null
-#
-# Copyright (C) 2008-2009 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
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-#
-# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
-#
