From: André Date: Wed, 21 Apr 2010 12:42:25 +0000 (+0200) Subject: On recommence .. X-Git-Tag: V6_4_0rc3~178 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=32639a917dd03456e1f5ed5e629f4710d50e6e85;p=modules%2Fadao.git On recommence .. --- diff --git a/AUTHORS b/AUTHORS deleted file mode 100644 index e69de29..0000000 diff --git a/COPYING b/COPYING deleted file mode 100644 index b1e3f5a..0000000 --- a/COPYING +++ /dev/null @@ -1,504 +0,0 @@ - GNU LESSER GENERAL PUBLIC LICENSE - Version 2.1, February 1999 - - Copyright (C) 1991, 1999 Free Software Foundation, Inc. - 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - -[This is the first released version of the Lesser GPL. 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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` - diff --git a/NEWS b/NEWS deleted file mode 100644 index e69de29..0000000 diff --git a/README b/README deleted file mode 100644 index e69de29..0000000 diff --git a/adm_local/Makefile.am b/adm_local/Makefile.am deleted file mode 100644 index f35273f..0000000 --- a/adm_local/Makefile.am +++ /dev/null @@ -1,24 +0,0 @@ -# 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 diff --git a/adm_local/unix/make_common_starter.am b/adm_local/unix/make_common_starter.am deleted file mode 100644 index cd85151..0000000 --- a/adm_local/unix/make_common_starter.am +++ /dev/null @@ -1,96 +0,0 @@ -# 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; diff --git a/build_configure b/build_configure deleted file mode 100755 index 299c6f7..0000000 --- a/build_configure +++ /dev/null @@ -1,104 +0,0 @@ -#!/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() 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() 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 diff --git a/clean_configure b/clean_configure deleted file mode 100755 index f57f7b3..0000000 --- a/clean_configure +++ /dev/null @@ -1,35 +0,0 @@ -#!/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 diff --git a/configure.ac b/configure.ac deleted file mode 100644 index fdfb80f..0000000 --- a/configure.ac +++ /dev/null @@ -1,184 +0,0 @@ -# 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 .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 \ -]) diff --git a/doc/ComposantAD.pdf b/doc/ComposantAD.pdf deleted file mode 100644 index 19a5fe8..0000000 Binary files a/doc/ComposantAD.pdf and /dev/null differ diff --git a/resources/DATASSIMCatalog.xml b/resources/DATASSIMCatalog.xml deleted file mode 100644 index e69de29..0000000 diff --git a/resources/Makefile.am b/resources/Makefile.am deleted file mode 100644 index 987593a..0000000 --- a/resources/Makefile.am +++ /dev/null @@ -1,21 +0,0 @@ -# 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 diff --git a/src/Makefile.am b/src/Makefile.am deleted file mode 100644 index 77d5101..0000000 --- a/src/Makefile.am +++ /dev/null @@ -1,21 +0,0 @@ -# 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 diff --git a/src/daComposant/Makefile.am b/src/daComposant/Makefile.am deleted file mode 100644 index 522eda7..0000000 --- a/src/daComposant/Makefile.am +++ /dev/null @@ -1,24 +0,0 @@ -# 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 - diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py deleted file mode 100644 index d1ad427..0000000 --- a/src/daComposant/daAlgorithms/3DVAR.py +++ /dev/null @@ -1,216 +0,0 @@ -#-*-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' diff --git a/src/daComposant/daAlgorithms/Blue.py b/src/daComposant/daAlgorithms/Blue.py deleted file mode 100644 index 3e5704d..0000000 --- a/src/daComposant/daAlgorithms/Blue.py +++ /dev/null @@ -1,83 +0,0 @@ -#-*-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' diff --git a/src/daComposant/daAlgorithms/EnsembleBlue.py b/src/daComposant/daAlgorithms/EnsembleBlue.py deleted file mode 100644 index 287e81a..0000000 --- a/src/daComposant/daAlgorithms/EnsembleBlue.py +++ /dev/null @@ -1,88 +0,0 @@ -#-*-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' - - diff --git a/src/daComposant/daAlgorithms/Kalman.py b/src/daComposant/daAlgorithms/Kalman.py deleted file mode 100644 index d4c817f..0000000 --- a/src/daComposant/daAlgorithms/Kalman.py +++ /dev/null @@ -1,96 +0,0 @@ -#-*-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' diff --git a/src/daComposant/daAlgorithms/LinearLeastSquares.py b/src/daComposant/daAlgorithms/LinearLeastSquares.py deleted file mode 100644 index 855d8a1..0000000 --- a/src/daComposant/daAlgorithms/LinearLeastSquares.py +++ /dev/null @@ -1,62 +0,0 @@ -#-*-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' - - diff --git a/src/daComposant/daAlgorithms/__init__.py b/src/daComposant/daAlgorithms/__init__.py deleted file mode 100644 index 6bcb582..0000000 --- a/src/daComposant/daAlgorithms/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# -# 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 -# diff --git a/src/daComposant/daCore/AssimilationStudy.py b/src/daComposant/daCore/AssimilationStudy.py deleted file mode 100644 index 83b4813..0000000 --- a/src/daComposant/daCore/AssimilationStudy.py +++ /dev/null @@ -1,598 +0,0 @@ -#-*-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 - diff --git a/src/daComposant/daCore/BasicObjects.py b/src/daComposant/daCore/BasicObjects.py deleted file mode 100644 index bdcae37..0000000 --- a/src/daComposant/daCore/BasicObjects.py +++ /dev/null @@ -1,213 +0,0 @@ -#-*-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' diff --git a/src/daComposant/daCore/Logging.py b/src/daComposant/daCore/Logging.py deleted file mode 100644 index b56f932..0000000 --- a/src/daComposant/daCore/Logging.py +++ /dev/null @@ -1,162 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daCore/Persistence.py b/src/daComposant/daCore/Persistence.py deleted file mode 100644 index 4f15a46..0000000 --- a/src/daComposant/daCore/Persistence.py +++ /dev/null @@ -1,663 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daCore/PlatformInfo.py b/src/daComposant/daCore/PlatformInfo.py deleted file mode 100644 index 6e50e12..0000000 --- a/src/daComposant/daCore/PlatformInfo.py +++ /dev/null @@ -1,255 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daCore/version.py b/src/daComposant/daCore/version.py deleted file mode 100644 index 7128d1a..0000000 --- a/src/daComposant/daCore/version.py +++ /dev/null @@ -1,23 +0,0 @@ -#-*-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)" diff --git a/src/daComposant/daDiagnostics/CompareMeanDependantVectors.py b/src/daComposant/daDiagnostics/CompareMeanDependantVectors.py deleted file mode 100644 index d2b0dc1..0000000 --- a/src/daComposant/daDiagnostics/CompareMeanDependantVectors.py +++ /dev/null @@ -1,118 +0,0 @@ -#-*-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") diff --git a/src/daComposant/daDiagnostics/CompareMeanIndependantVectorsDifferentVariance.py b/src/daComposant/daDiagnostics/CompareMeanIndependantVectorsDifferentVariance.py deleted file mode 100644 index 15e7865..0000000 --- a/src/daComposant/daDiagnostics/CompareMeanIndependantVectorsDifferentVariance.py +++ /dev/null @@ -1,117 +0,0 @@ -#-*-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") - diff --git a/src/daComposant/daDiagnostics/CompareMeanIndependantVectorsEqualVariance.py b/src/daComposant/daDiagnostics/CompareMeanIndependantVectorsEqualVariance.py deleted file mode 100644 index 927cba2..0000000 --- a/src/daComposant/daDiagnostics/CompareMeanIndependantVectorsEqualVariance.py +++ /dev/null @@ -1,116 +0,0 @@ -#-*-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") - diff --git a/src/daComposant/daDiagnostics/CompareVarianceFisher.py b/src/daComposant/daDiagnostics/CompareVarianceFisher.py deleted file mode 100644 index 0fc3a96..0000000 --- a/src/daComposant/daDiagnostics/CompareVarianceFisher.py +++ /dev/null @@ -1,119 +0,0 @@ -#-*-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.") diff --git a/src/daComposant/daDiagnostics/ComputeBiais.py b/src/daComposant/daDiagnostics/ComputeBiais.py deleted file mode 100644 index 9c05425..0000000 --- a/src/daComposant/daDiagnostics/ComputeBiais.py +++ /dev/null @@ -1,89 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/ComputeCostFunction.py b/src/daComposant/daDiagnostics/ComputeCostFunction.py deleted file mode 100644 index 9504abf..0000000 --- a/src/daComposant/daDiagnostics/ComputeCostFunction.py +++ /dev/null @@ -1,141 +0,0 @@ -#-*-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 - diff --git a/src/daComposant/daDiagnostics/ComputeCostFunctionLin.py b/src/daComposant/daDiagnostics/ComputeCostFunctionLin.py deleted file mode 100644 index 550be82..0000000 --- a/src/daComposant/daDiagnostics/ComputeCostFunctionLin.py +++ /dev/null @@ -1,119 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/ComputeVariance.py b/src/daComposant/daDiagnostics/ComputeVariance.py deleted file mode 100644 index 22ae8e6..0000000 --- a/src/daComposant/daDiagnostics/ComputeVariance.py +++ /dev/null @@ -1,90 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/GaussianAdequation.py b/src/daComposant/daDiagnostics/GaussianAdequation.py deleted file mode 100644 index c027659..0000000 --- a/src/daComposant/daDiagnostics/GaussianAdequation.py +++ /dev/null @@ -1,159 +0,0 @@ -#-*-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.") - - diff --git a/src/daComposant/daDiagnostics/HLinearity.py b/src/daComposant/daDiagnostics/HLinearity.py deleted file mode 100644 index 9509de6..0000000 --- a/src/daComposant/daDiagnostics/HLinearity.py +++ /dev/null @@ -1,143 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/HomogeneiteKhi2.py b/src/daComposant/daDiagnostics/HomogeneiteKhi2.py deleted file mode 100644 index acb2413..0000000 --- a/src/daComposant/daDiagnostics/HomogeneiteKhi2.py +++ /dev/null @@ -1,121 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/PlotVector.py b/src/daComposant/daDiagnostics/PlotVector.py deleted file mode 100644 index 97b3372..0000000 --- a/src/daComposant/daDiagnostics/PlotVector.py +++ /dev/null @@ -1,153 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/PlotVectors.py b/src/daComposant/daDiagnostics/PlotVectors.py deleted file mode 100644 index 219519e..0000000 --- a/src/daComposant/daDiagnostics/PlotVectors.py +++ /dev/null @@ -1,157 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/RMS.py b/src/daComposant/daDiagnostics/RMS.py deleted file mode 100644 index 9ca170f..0000000 --- a/src/daComposant/daDiagnostics/RMS.py +++ /dev/null @@ -1,92 +0,0 @@ -#-*-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 - diff --git a/src/daComposant/daDiagnostics/ReduceBiais.py b/src/daComposant/daDiagnostics/ReduceBiais.py deleted file mode 100644 index 31ebc18..0000000 --- a/src/daComposant/daDiagnostics/ReduceBiais.py +++ /dev/null @@ -1,111 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/ReduceVariance.py b/src/daComposant/daDiagnostics/ReduceVariance.py deleted file mode 100644 index 2272eac..0000000 --- a/src/daComposant/daDiagnostics/ReduceVariance.py +++ /dev/null @@ -1,116 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/StopReductionVariance.py b/src/daComposant/daDiagnostics/StopReductionVariance.py deleted file mode 100644 index 7ebe03a..0000000 --- a/src/daComposant/daDiagnostics/StopReductionVariance.py +++ /dev/null @@ -1,121 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daDiagnostics/VarianceOrder.py b/src/daComposant/daDiagnostics/VarianceOrder.py deleted file mode 100644 index 202838e..0000000 --- a/src/daComposant/daDiagnostics/VarianceOrder.py +++ /dev/null @@ -1,129 +0,0 @@ -#-*-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 - - diff --git a/src/daComposant/daDiagnostics/__init__.py b/src/daComposant/daDiagnostics/__init__.py deleted file mode 100644 index 6bcb582..0000000 --- a/src/daComposant/daDiagnostics/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# -# 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 -# diff --git a/src/daComposant/daExternals/ASTER/Building_AD_from_Aster.xml b/src/daComposant/daExternals/ASTER/Building_AD_from_Aster.xml deleted file mode 100644 index 51be879..0000000 --- a/src/daComposant/daExternals/ASTER/Building_AD_from_Aster.xml +++ /dev/null @@ -1,275 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Building_Bparametres - (lp1 -. - - - Building_Xbparametres - (lp1 -. - - - Building_Yocalcul - (lp1 -. - - - Building_Yoexperiences - (lp1 -. - - - Sorties du calcul ADxa - - - - - Sorties du calcul ADA - - - - - Sorties du calcul ADInnovation - - - - - Sorties du calcul ADxb - - - - - Sorties du calcul ADYo - - - - - Sorties du calcul ADB - - - - - Sorties du calcul ADR - - - - - Sorties du calcul ADH - - - - - Building_Rexperiences - (lp1 -. - - - Entrees du calcul ADXb - - - - - Entrees du calcul ADYo - - - - - Entrees du calcul ADB - - - - - Entrees du calcul ADR - - - - - Entrees du calcul ADH - - - - - - - - - - - diff --git a/src/daComposant/daExternals/ASTER/Building_H_linear.xml b/src/daComposant/daExternals/ASTER/Building_H_linear.xml deleted file mode 100644 index 9bc3e25..0000000 --- a/src/daComposant/daExternals/ASTER/Building_H_linear.xml +++ /dev/null @@ -1,335 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Perturbated_point_X ASTER - - Perturbated_point_X X - ASTER X - - - Perturbated_point_X iter - ASTER iter - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Finite_differences_derivation Gradient - Input Finite_differences_derivation - Input Gradient - Temporary_Parameters Finite_differences_derivation - Temporary_Parameters Gradient - - Finite_differences_derivation SmplPrt - Finite_differences_derivation.Elementary_calculation.Perturbated_point_X iter - - - Input nbBranches - Finite_differences_derivation nbBranches - - - Input itervect - Finite_differences_derivation SmplsCollection - - - Input seq_X - Finite_differences_derivation.Elementary_calculation.Perturbated_point_X seq_X - - - Input dX - Gradient dX - - - Temporary_Parameters ASTER_ROOT - Finite_differences_derivation.Elementary_calculation.ASTER ASTER_ROOT - - - Temporary_Parameters rcdir - Finite_differences_derivation.Elementary_calculation.ASTER rcdir - - - Temporary_Parameters debug - Finite_differences_derivation.Elementary_calculation.ASTER debug - - - Temporary_Parameters DISPLAY - Finite_differences_derivation.Elementary_calculation.ASTER DISPLAY - - - Temporary_Parameters SOURCES_ROOT - Finite_differences_derivation.Elementary_calculation.ASTER SOURCES_ROOT - - - Temporary_Parameters SOURCES_ROOT - Gradient SOURCES_ROOT - - - Temporary_Parameters export - Finite_differences_derivation.Elementary_calculation.ASTER export - - - Temporary_Parameters parametres - Finite_differences_derivation.Elementary_calculation.ASTER parametres - - - Temporary_Parameters calcul - Finite_differences_derivation.Elementary_calculation.ASTER calcul - - - Temporary_Parameters experience - Finite_differences_derivation.Elementary_calculation.ASTER experience - - - Temporary_Parameters fileparameters - Finite_differences_derivation.Elementary_calculation.ASTER fileparameters - - - Finite_differences_derivation.Elementary_calculation.ASTER FX - Gradient seq_FX - - - Finite_differences_derivation.Elementary_calculation.ASTER FY - Gradient seq_FY - - - Finite_differences_derivation.Elementary_calculation.ASTER DIMS - Gradient seq_DIMS - - - Finite_differences_derivation.Elementary_calculation.ASTER DIAG - Gradient lst_DIAG - - - Finite_differences_derivation.Elementary_calculation.ASTER iter - Gradient lst_iter - - - - H_linearization.Finite_differences_derivation.Elementary_calculation.ASTERX - -80000 -1000 -30 - - - - H_linearization.Temporary_ParametersASTER_ROOT - '' - - - H_linearization.Temporary_Parametersrcdir - '' - - - H_linearization.Temporary_Parametersdebug - 0 - - - H_linearization.Temporary_ParametersDISPLAY - :0.0 - - - H_linearization.Temporary_ParametersSOURCES_ROOT - . - - - H_linearization.Temporary_Parametersexport - '' - - - H_linearization.Temporary_Parametersparametres - - - - H_linearization.Temporary_Parameterscalcul - - - - H_linearization.Temporary_Parametersexperience - - - - H_linearization.Temporary_Parametersfileparameters - [] - - - H_linearization.InputX - -80000 -1000 -30 - - - - H_linearization.InputdX - -0.001 -0.001 -0.0001 - - - - - - - - - - - - diff --git a/src/daComposant/daExternals/YACS/Algorithmes_AD.xml b/src/daComposant/daExternals/YACS/Algorithmes_AD.xml deleted file mode 100644 index fd2ac9e..0000000 --- a/src/daComposant/daExternals/YACS/Algorithmes_AD.xml +++ /dev/null @@ -1,252 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - BLUE par matricesYo - - - - - BLUE par matricesB - - - - - BLUE par matricesR - - - - - BLUE par matricesH - - - - - BLUE par matricesXb - - - - - 3D-VAR par matricesYo - - - - - 3D-VAR par matricesB - - - - - 3D-VAR par matricesR - - - - - 3D-VAR par matricesH - - - - - 3D-VAR par matricesXb - - - - - 3D-VAR par fonctionsYo - - - - - 3D-VAR par fonctionsB - - - - - 3D-VAR par fonctionsR - - - - - 3D-VAR par fonctionsXb - - - - - 3D-VAR par fonctionsBounds - (lp1 -. - - - - - - diff --git a/src/daComposant/daExternals/__init__.py b/src/daComposant/daExternals/__init__.py deleted file mode 100644 index 6bcb582..0000000 --- a/src/daComposant/daExternals/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# -# 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 -# diff --git a/src/daComposant/daMatrices/__init__.py b/src/daComposant/daMatrices/__init__.py deleted file mode 100644 index 6bcb582..0000000 --- a/src/daComposant/daMatrices/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# -# 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 -# diff --git a/src/daComposant/daNumerics/ComputeFisher.py b/src/daComposant/daNumerics/ComputeFisher.py deleted file mode 100644 index d6c46f7..0000000 --- a/src/daComposant/daNumerics/ComputeFisher.py +++ /dev/null @@ -1,116 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daNumerics/ComputeKhi2.py b/src/daComposant/daNumerics/ComputeKhi2.py deleted file mode 100644 index 31e26d0..0000000 --- a/src/daComposant/daNumerics/ComputeKhi2.py +++ /dev/null @@ -1,420 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daNumerics/ComputeStudent.py b/src/daComposant/daNumerics/ComputeStudent.py deleted file mode 100644 index 3736490..0000000 --- a/src/daComposant/daNumerics/ComputeStudent.py +++ /dev/null @@ -1,260 +0,0 @@ -#-*-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 diff --git a/src/daComposant/daNumerics/__init__.py b/src/daComposant/daNumerics/__init__.py deleted file mode 100644 index 6bcb582..0000000 --- a/src/daComposant/daNumerics/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# -# 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 -#