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SymbolicJacobian.mo
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SymbolicJacobian.mo
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/*
* This file is part of OpenModelica.
*
* Copyright (c) 1998-2014, Open Source Modelica Consortium (OSMC),
* c/o Linköpings universitet, Department of Computer and Information Science,
* SE-58183 Linköping, Sweden.
*
* All rights reserved.
*
* THIS PROGRAM IS PROVIDED UNDER THE TERMS OF GPL VERSION 3 LICENSE OR
* THIS OSMC PUBLIC LICENSE (OSMC-PL) VERSION 1.2.
* ANY USE, REPRODUCTION OR DISTRIBUTION OF THIS PROGRAM CONSTITUTES
* RECIPIENT'S ACCEPTANCE OF THE OSMC PUBLIC LICENSE OR THE GPL VERSION 3,
* ACCORDING TO RECIPIENTS CHOICE.
*
* The OpenModelica software and the Open Source Modelica
* Consortium (OSMC) Public License (OSMC-PL) are obtained
* from OSMC, either from the above address,
* from the URLs: http://www.ida.liu.se/projects/OpenModelica or
* http://www.openmodelica.org, and in the OpenModelica distribution.
* GNU version 3 is obtained from: http://www.gnu.org/copyleft/gpl.html.
*
* This program is distributed WITHOUT ANY WARRANTY; without
* even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE, EXCEPT AS EXPRESSLY SET FORTH
* IN THE BY RECIPIENT SELECTED SUBSIDIARY LICENSE CONDITIONS OF OSMC-PL.
*
* See the full OSMC Public License conditions for more details.
*
*/
encapsulated package SymbolicJacobian
" file: SymbolicJacobian.mo
package: SymbolicJacobian
description: This package contains stuff that is related to symbolic jacobian or sparsity structure."
public import Absyn;
public import BackendDAE;
public import DAE;
public import FCore;
public import FGraph;
protected
import Array;
import BackendDAEOptimize;
import BackendDAETransform;
import BackendDAEUtil;
import BackendDump;
import BackendEquation;
import BackendVariable;
import BackendVarTransform;
import BaseHashSet;
import Ceval;
import ClockIndexes;
import Config;
import ComponentReference;
import Debug;
import Differentiate;
import DynamicOptimization;
import ElementSource;
import ExecStat.execStat;
import ExpandableArray;
import Expression;
import ExpressionDump;
import ExpressionSimplify;
import Error;
import Flags;
import FlagsUtil;
import GCExt;
import Global;
import Graph;
import HashSet;
import IndexReduction;
import List;
import StringUtil;
import System;
import UnorderedMap;
import Util;
import Values;
import ValuesUtil;
// =============================================================================
// section for postOptModule >>symbolicJacobian<<
//
// Detects the sparse pattern of the ODE system and calculates also the symbolic
// Jacobian if flag "--generateSymbolicJacobian" is enabled.
// =============================================================================
// From User Documentation for ida v5.4.0 equation (2.5) aka Alpha
// is the scalar in the system Jacobian, proportional to the inverse of the step size
// used for DAE_Mode symbolic jacobians
public constant String DAE_CJ = "$DAE_CJ";
public function symbolicJacobian "author: lochel
Detects the sparse pattern of the ODE system and calculates also the symbolic
Jacobian if flag '--generateSymbolicJacobian' is enabled."
input BackendDAE.BackendDAE inDAE;
output BackendDAE.BackendDAE outDAE;
algorithm
if Flags.getConfigBool(Flags.GENERATE_SYMBOLIC_JACOBIAN) then
outDAE := generateSymbolicJacobianPast(inDAE);
else
outDAE := detectSparsePatternODE(inDAE);
end if;
end symbolicJacobian;
// =============================================================================
// section for postOptModule >>calculateStateSetsJacobians<<
//
// =============================================================================
public function calculateStateSetsJacobians "author: wbraun
Calculates the Jacobian matrix with directional derivative method for dynamic
state selection."
input BackendDAE.BackendDAE inDAE;
output BackendDAE.BackendDAE outDAE;
algorithm
outDAE := BackendDAEUtil.mapEqSystem(inDAE, calculateEqSystemStateSetsJacobians);
end calculateStateSetsJacobians;
// =============================================================================
// section for postOptModule >>calculateStrongComponentJacobians<<
//
// Module for to calculate strong component Jacobian matrices
// =============================================================================
public function calculateStrongComponentJacobians "author: wbraun
Calculates Jacobian matrix with directional derivative method for each SCC."
input BackendDAE.BackendDAE inDAE;
output BackendDAE.BackendDAE outDAE;
algorithm
try
outDAE := BackendDAEUtil.mapEqSystem(inDAE, calculateEqSystemJacobians);
else
outDAE := inDAE;
end try;
end calculateStrongComponentJacobians;
// =============================================================================
// section for postOptModule >>constantLinearSystem<<
//
// constant Jacobian matrices. Linear system of equations (A x = b) where
// A and b are constant.
// =============================================================================
public function constantLinearSystem
input BackendDAE.BackendDAE inDAE;
output BackendDAE.BackendDAE outDAE;
algorithm
(outDAE, _) := BackendDAEUtil.mapEqSystemAndFold(inDAE, constantLinearSystem0, (false,1));
end constantLinearSystem;
// =============================================================================
// section for postOptModule >>detectSparsePatternODE<<
//
// Generate sparse pattern
// =============================================================================
protected function detectSparsePatternODE
input BackendDAE.BackendDAE inBackendDAE;
output BackendDAE.BackendDAE outBackendDAE;
protected
BackendDAE.BackendDAE DAE;
BackendDAE.EqSystems eqs;
BackendDAE.Shared shared;
BackendDAE.SparseColoring coloredCols;
BackendDAE.SparsePattern sparsePattern;
list<BackendDAE.Var> states;
BackendDAE.Var dummyVar;
BackendDAE.Variables v;
constant Boolean debug = false;
algorithm
// lochel: This module fails for some models (e.g. #3543)
try
if debug then execStat("detectSparsePatternODE -> start "); end if;
BackendDAE.DAE(eqs = eqs) := inBackendDAE;
// prepare a DAE
DAE := BackendDAEUtil.copyBackendDAE(inBackendDAE);
if debug then execStat("detectSparsePatternODE -> copy dae "); end if;
DAE := BackendDAEOptimize.collapseIndependentBlocks(DAE);
if debug then execStat("detectSparsePatternODE -> collapse blocks "); end if;
DAE := BackendDAEUtil.transformBackendDAE(DAE, SOME((BackendDAE.NO_INDEX_REDUCTION(), BackendDAE.EXACT())), NONE(), NONE());
if debug then execStat("detectSparsePatternODE -> transform backend dae "); end if;
// get states for DAE
BackendDAE.DAE(eqs = {BackendDAE.EQSYSTEM(orderedVars = v)}, shared=shared) := DAE;
states := BackendVariable.getAllStateVarFromVariables(v);
if debug then execStat("detectSparsePatternODE -> get all vars "); end if;
// generate sparse pattern
(sparsePattern, coloredCols) := generateSparsePattern(DAE, states, states);
if debug then execStat("detectSparsePatternODE -> generateSparsePattern "); end if;
shared := addBackendDAESharedJacobianSparsePattern(sparsePattern, coloredCols, BackendDAE.SymbolicJacobianAIndex, shared);
if debug then execStat("detectSparsePatternODE -> addBackendDAESharedJacobianSparsePattern "); end if;
outBackendDAE := BackendDAE.DAE(eqs, shared);
else
// skip this optimization module
Error.addCompilerWarning("The optimization module detectJacobianSparsePattern failed. This module will be skipped and the transformation process continued.");
outBackendDAE := inBackendDAE;
end try;
end detectSparsePatternODE;
// =============================================================================
// section for postOptModule >>symbolicJacobianDAE<<
//
// Generate symbolic jacobian for DAEMode
// =============================================================================
public function symbolicJacobianDAE
input BackendDAE.BackendDAE inBackendDAE;
output BackendDAE.BackendDAE outBackendDAE;
protected
BackendDAE.BackendDAE DAE;
BackendDAE.EqSystems eqs;
BackendDAE.Shared shared;
BackendDAE.SparseColoring coloredCols;
BackendDAE.SparsePattern sparsePattern;
BackendDAE.NonlinearPattern nonlinearPattern;
list<BackendDAE.Var> inDepVars;
list<BackendDAE.Var> depVars;
BackendDAE.Var dummyVar;
BackendDAE.Variables v, resVars;
BackendDAE.Variables emptyVars = BackendVariable.emptyVars();
Option<BackendDAE.SymbolicJacobian> symjac;
DAE.FunctionTree funcs;
constant Boolean debug = false;
algorithm
try
if debug then execStat(getInstanceName() + "-> start "); end if;
BackendDAE.DAE(eqs = eqs) := inBackendDAE;
// prepare a DAE
DAE := BackendDAEUtil.copyBackendDAE(inBackendDAE);
if debug then execStat(getInstanceName() + "-> copy dae "); end if;
DAE := BackendDAEOptimize.collapseIndependentBlocks(DAE);
if debug then execStat(getInstanceName() + "-> collapse blocks "); end if;
DAE := BackendDAEUtil.transformBackendDAE(DAE, SOME((BackendDAE.NO_INDEX_REDUCTION(), BackendDAE.EXACT())), NONE(), NONE());
if debug then execStat(getInstanceName() + "-> transform backend dae "); end if;
// get states for DAE
BackendDAE.DAE(eqs = {BackendDAE.EQSYSTEM(orderedVars = v)}, shared=shared) := DAE;
((_, resVars)) := BackendVariable.traverseBackendDAEVars(v, BackendVariable.collectVarKindVarinVariables, (BackendVariable.isDAEmodeResVar, emptyVars));
depVars := BackendVariable.varList(resVars);
inDepVars := listAppend(shared.daeModeData.stateVars, shared.daeModeData.algStateVars);
if debug then execStat(getInstanceName() + "-> get all vars "); end if;
if Flags.getConfigBool(Flags.GENERATE_SYMBOLIC_JACOBIAN) then
// generate symbolic jacobian and sparsity pattern
(symjac, funcs, sparsePattern, coloredCols, nonlinearPattern) := generateGenericJacobian(
inBackendDAE = DAE,
inDiffVars = inDepVars,
inStateVars = BackendVariable.emptyVars(),
inInputVars = BackendVariable.emptyVars(),
inParameterVars = shared.globalKnownVars,
inDifferentiatedVars = resVars,
inVars = BackendVariable.varList(v),
inName = "A",
onlySparsePattern = false,
daeMode = true);
if debug then execStat(getInstanceName() + "-> generateGenericJacobian "); end if;
shared.symjacs := List.set(shared.symjacs, BackendDAE.SymbolicJacobianAIndex, (symjac, sparsePattern, coloredCols, nonlinearPattern));
shared.functionTree := funcs;
if debug then BackendDump.dumpJacobianString(BackendDAE.GENERIC_JACOBIAN(symjac, sparsePattern, coloredCols, nonlinearPattern)); end if;
else
// only generate sparsity pattern
(sparsePattern, coloredCols) := generateSparsePattern(DAE, inDepVars, depVars);
if debug then execStat(getInstanceName() + "-> generateSparsePattern "); end if;
shared := addBackendDAESharedJacobianSparsePattern(sparsePattern, coloredCols, BackendDAE.SymbolicJacobianAIndex, shared);
if debug then execStat(getInstanceName() + "-> addBackendDAESharedJacobianSparsePattern "); end if;
end if;
outBackendDAE := BackendDAE.DAE(eqs, shared);
else
// skip this optimization module
Error.addCompilerWarning("The optimization module " + getInstanceName() + " failed. This module will be skipped and the transformation process continued.");
outBackendDAE := inBackendDAE;
end try;
end symbolicJacobianDAE;
// =============================================================================
// section for postOptModule >>generateSymbolicJacobianPast<<
//
// Symbolic Jacobian subsection
// =============================================================================
protected function generateSymbolicJacobianPast
input BackendDAE.BackendDAE inBackendDAE;
output BackendDAE.BackendDAE outBackendDAE;
protected
BackendDAE.EqSystems eqs;
BackendDAE.Shared shared;
Option<BackendDAE.SymbolicJacobian> symJacA;
BackendDAE.SparsePattern sparsePattern;
BackendDAE.SparseColoring sparseColoring;
BackendDAE.NonlinearPattern nonlinearPattern;
DAE.FunctionTree funcs, functionTree;
algorithm
System.realtimeTick(ClockIndexes.RT_CLOCK_EXECSTAT_JACOBIANS);
BackendDAE.DAE(eqs=eqs,shared=shared) := inBackendDAE;
(symJacA, funcs, sparsePattern, sparseColoring, nonlinearPattern) := createSymbolicJacobianforStates(inBackendDAE);
shared := addBackendDAESharedJacobian(symJacA, sparsePattern, sparseColoring, nonlinearPattern, shared);
functionTree := BackendDAEUtil.getFunctions(shared);
functionTree := DAE.AvlTreePathFunction.join(functionTree, funcs);
shared := BackendDAEUtil.setSharedFunctionTree(shared, functionTree);
outBackendDAE := BackendDAE.DAE(eqs,shared);
System.realtimeTock(ClockIndexes.RT_CLOCK_EXECSTAT_JACOBIANS);
end generateSymbolicJacobianPast;
protected function createSymbolicJacobianforStates "author: wbraun
all functionODE equation are differentiated with respect to the states."
input BackendDAE.BackendDAE inBackendDAE;
output Option<BackendDAE.SymbolicJacobian> outJacobian;
output DAE.FunctionTree outFunctionTree;
output BackendDAE.SparsePattern outSparsePattern;
output BackendDAE.SparseColoring outSparseColoring;
output BackendDAE.NonlinearPattern outNonlinearPattern;
protected
BackendDAE.BackendDAE backendDAE2;
list<BackendDAE.Var> varlst, knvarlst, states, inputvars, paramvars;
BackendDAE.Variables v, globalKnownVars;
algorithm
if Flags.isSet(Flags.JAC_DUMP2) then
print("analytical Jacobians -> start generate system for matrix A time : " + realString(clock()) + "\n");
end if;
backendDAE2 := BackendDAEUtil.copyBackendDAE(inBackendDAE);
backendDAE2 := BackendDAEOptimize.collapseIndependentBlocks(backendDAE2);
backendDAE2 := BackendDAEUtil.transformBackendDAE(backendDAE2,SOME((BackendDAE.NO_INDEX_REDUCTION(),BackendDAE.EXACT())),NONE(),NONE());
BackendDAE.DAE({BackendDAE.EQSYSTEM(orderedVars = v)},BackendDAE.SHARED(globalKnownVars = globalKnownVars)) := backendDAE2;
// Prepare all needed variables
varlst := BackendVariable.varList(v);
knvarlst := BackendVariable.varList(globalKnownVars);
states := BackendVariable.getAllStateVarFromVariables(v);
inputvars := List.select(knvarlst,BackendVariable.isInput);
paramvars := List.select(knvarlst, BackendVariable.isParam);
if Flags.isSet(Flags.JAC_DUMP2) then
print("analytical Jacobians -> prepared vars for symbolic matrix A time: " + realString(clock()) + "\n");
end if;
if Flags.isSet(Flags.JAC_DUMP2) then
BackendDump.bltdump("System to create symbolic jacobian of: ",backendDAE2);
end if;
(outJacobian, outFunctionTree, outSparsePattern, outSparseColoring, outNonlinearPattern) := generateGenericJacobian(backendDAE2,states,BackendVariable.listVar1(states),BackendVariable.listVar1(inputvars),BackendVariable.listVar1(paramvars),BackendVariable.listVar1(states),varlst,"A",false);
end createSymbolicJacobianforStates;
// =============================================================================
// section for postOptModule >>generateSymbolicSensitivities<<
//
// That function generates symbolic sentivities for parameters
// by differentiatiating the states with respect to the parameters
// =============================================================================
public function generateSymbolicSensitivities
input BackendDAE.BackendDAE inBackendDAE;
output BackendDAE.BackendDAE outBackendDAE;
protected
BackendDAE.EqSystems eqs;
BackendDAE.Shared shared;
Option<BackendDAE.SymbolicJacobian> symJacS;
BackendDAE.SparsePattern sparsePattern;
BackendDAE.SparseColoring sparseColoring;
BackendDAE.NonlinearPattern nonlinearPattern;
DAE.FunctionTree funcs, functionTree;
algorithm
System.realtimeTick(ClockIndexes.RT_CLOCK_EXECSTAT_JACOBIANS);
BackendDAE.DAE(eqs=eqs,shared=shared) := inBackendDAE;
(symJacS, funcs, sparsePattern, sparseColoring, nonlinearPattern) := createSymbolicJacobianforParameters(inBackendDAE);
shared := addBackendDAESharedJacobian(symJacS, sparsePattern, sparseColoring, nonlinearPattern, shared);
functionTree := BackendDAEUtil.getFunctions(shared);
functionTree := DAE.AvlTreePathFunction.join(functionTree, funcs);
shared := BackendDAEUtil.setSharedFunctionTree(shared, functionTree);
outBackendDAE := BackendDAE.DAE(eqs,shared);
System.realtimeTock(ClockIndexes.RT_CLOCK_EXECSTAT_JACOBIANS);
end generateSymbolicSensitivities;
protected function createSymbolicJacobianforParameters
"author: wbraun
all functionODE equation are differentiated with respect to the parameters."
input BackendDAE.BackendDAE inBackendDAE;
output Option<BackendDAE.SymbolicJacobian> outJacobian;
output DAE.FunctionTree outFunctionTree;
output BackendDAE.SparsePattern outSparsePattern;
output BackendDAE.SparseColoring outSparseColoring;
output BackendDAE.NonlinearPattern outNonlinearPattern;
protected
BackendDAE.BackendDAE backendDAE2;
list<BackendDAE.Var> varlst, knvarlst, states, inputvars, paramvars;
BackendDAE.Variables v, globalKnownVars;
algorithm
if Flags.isSet(Flags.JAC_DUMP2) then
print("analytical Jacobians -> start generate system for matrix S time : " + realString(clock()) + "\n");
end if;
backendDAE2 := BackendDAEUtil.copyBackendDAE(inBackendDAE);
backendDAE2 := BackendDAEOptimize.collapseIndependentBlocks(backendDAE2);
backendDAE2 := BackendDAEUtil.transformBackendDAE(backendDAE2,SOME((BackendDAE.NO_INDEX_REDUCTION(),BackendDAE.EXACT())),NONE(),NONE());
BackendDAE.DAE({BackendDAE.EQSYSTEM(orderedVars = v)},BackendDAE.SHARED(globalKnownVars = globalKnownVars)) := backendDAE2;
// Prepare all needed variables
varlst := BackendVariable.varList(v);
knvarlst := BackendVariable.varList(globalKnownVars);
states := BackendVariable.getAllStateVarFromVariables(v);
inputvars := List.select(knvarlst,BackendVariable.isInput);
paramvars := List.select(knvarlst, BackendVariable.isParam);
if Flags.isSet(Flags.JAC_DUMP2) then
print("analytical Jacobians -> prepared vars for symbolic matrix S time: " + realString(clock()) + "\n");
end if;
if Flags.isSet(Flags.JAC_DUMP2) then
BackendDump.bltdump("System to create symbolic jacobian of: ",backendDAE2);
end if;
(outJacobian, outFunctionTree, outSparsePattern, outSparseColoring, outNonlinearPattern) := generateGenericJacobian(backendDAE2,paramvars,BackendVariable.listVar1(states),BackendVariable.listVar1(inputvars),BackendVariable.listVar1(states),BackendVariable.listVar1(states),varlst,"S",false);
end createSymbolicJacobianforParameters;
// =============================================================================
// section for postOptModule >>generateSymbolicLinearizationPast<<
//
// =============================================================================
public function generateSymbolicLinearizationPast
input BackendDAE.BackendDAE inBackendDAE;
output BackendDAE.BackendDAE outBackendDAE;
algorithm
outBackendDAE := matchcontinue(inBackendDAE)
local
BackendDAE.EqSystems eqs;
BackendDAE.Shared shared;
BackendDAE.SymbolicJacobians linearModelMatrices;
DAE.FunctionTree funcs, functionTree;
list< .DAE.Constraint> constraints;
case(_) equation
true = Flags.getConfigBool(Flags.GENERATE_SYMBOLIC_LINEARIZATION);
BackendDAE.DAE(eqs=eqs,shared=shared) = inBackendDAE;
(linearModelMatrices, funcs) = createLinearModelMatrices(inBackendDAE, Config.acceptOptimicaGrammar());
shared = BackendDAEUtil.setSharedSymJacs(shared, linearModelMatrices);
functionTree = BackendDAEUtil.getFunctions(shared);
functionTree = DAE.AvlTreePathFunction.join(functionTree, funcs);
shared = BackendDAEUtil.setSharedFunctionTree(shared, functionTree);
outBackendDAE = BackendDAE.DAE(eqs,shared);
then outBackendDAE;
else inBackendDAE;
end matchcontinue;
end generateSymbolicLinearizationPast;
// =============================================================================
// section for postOptModule >>inputDerivativesUsed<<
//
// check for derivatives of inputs
// =============================================================================
public function inputDerivativesUsed "author: Frenkel TUD 2012-10
checks if der(input) is used and report a warning/error."
input BackendDAE.BackendDAE inDAE;
output BackendDAE.BackendDAE outDAE;
algorithm
(outDAE, _) := BackendDAEUtil.mapEqSystemAndFold(inDAE, inputDerivativesUsedWork, false);
end inputDerivativesUsed;
protected function inputDerivativesUsedWork "author: Frenkel TUD 2012-10"
input BackendDAE.EqSystem isyst;
input BackendDAE.Shared inShared;
input Boolean inChanged;
output BackendDAE.EqSystem osyst;
output BackendDAE.Shared outShared = inShared "unused";
output Boolean outChanged;
protected
Boolean hasFailed = false;
algorithm
(osyst, outChanged) := matchcontinue(isyst)
local
BackendDAE.EquationArray orderedEqs;
list<DAE.Exp> explst;
String s;
case BackendDAE.EQSYSTEM(orderedEqs=orderedEqs) equation
((_, explst as _::_)) = BackendDAEUtil.traverseBackendDAEExpsEqns(orderedEqs, traverserinputDerivativesUsed, (BackendVariable.daeGlobalKnownVars(inShared), {}));
s = stringDelimitList(List.map(explst, ExpressionDump.printExpStr), "\n");
Error.addMessage(Error.DERIVATIVE_INPUT, {s});
hasFailed = true;
then (BackendDAEUtil.setEqSystEqs(isyst, orderedEqs), true);
else (isyst, inChanged);
end matchcontinue;
// Fail after error is displayed.
// We do it this way, because I was to lazy to rewrite all of this function.
if hasFailed then fail(); end if;
end inputDerivativesUsedWork;
protected function traverserinputDerivativesUsed "author: Frenkel TUD 2012-10"
input DAE.Exp inExp;
input tuple<BackendDAE.Variables,list<DAE.Exp>> itpl;
output DAE.Exp e;
output tuple<BackendDAE.Variables,list<DAE.Exp>> tpl;
algorithm
(e,tpl) := Expression.traverseExpTopDown(inExp,traverserExpinputDerivativesUsed,itpl);
end traverserinputDerivativesUsed;
protected function traverserExpinputDerivativesUsed
input DAE.Exp inExp;
input tuple<BackendDAE.Variables,list<DAE.Exp>> tpl;
output DAE.Exp outExp;
output Boolean cont;
output tuple<BackendDAE.Variables,list<DAE.Exp>> outTpl;
algorithm
(outExp,cont,outTpl) := matchcontinue (inExp,tpl)
local
BackendDAE.Variables vars;
DAE.Type tp;
DAE.Exp e;
DAE.ComponentRef cr;
BackendDAE.Var var;
list<DAE.Exp> explst;
case (e as DAE.CALL(path=Absyn.IDENT(name = "der"),expLst={DAE.CALL(path=Absyn.IDENT(name = "der"),expLst={DAE.CREF(componentRef=cr)})}),(vars,explst))
equation
(var,_) = BackendVariable.getVarSingle(cr, vars);
true = BackendVariable.isVarOnTopLevelAndInput(var);
then (e,false,(vars,e::explst));
case (e as DAE.CALL(path=Absyn.IDENT(name = "der"),expLst={DAE.CREF(componentRef=cr)}),(vars,explst))
equation
(var,_) = BackendVariable.getVarSingle(cr, vars);
true = BackendVariable.isVarOnTopLevelAndInput(var);
then (e,false,(vars,e::explst));
else (inExp,true,tpl);
end matchcontinue;
end traverserExpinputDerivativesUsed;
// =============================================================================
// solve linear systems with constant jacobian and variable b-Vector
//
// =============================================================================
protected function jacobianIsConstant
input list<tuple<Integer, Integer, BackendDAE.Equation>> jac;
output Boolean isConst;
protected
list<BackendDAE.Equation> eqs;
list<DAE.Exp> exps;
algorithm
eqs := List.map(jac, Util.tuple33);
isConst := not List.exist(eqs, variableResidual);
end jacobianIsConstant;
protected function variableResidual
input BackendDAE.Equation eq;
output Boolean isNotConst;
algorithm
isNotConst := match(eq)
case BackendDAE.RESIDUAL_EQUATION(exp=DAE.RCONST(_))
then false;
else true;
end match;
end variableResidual;
protected function replaceStrongComponent "replaces the indexed component with compsNew and adds compsAdd at the end. the assignments will be updated"
input BackendDAE.EqSystem systIn;
input Integer idx;
input BackendDAE.StrongComponents compsNew;
input BackendDAE.StrongComponents compsAdd;
output BackendDAE.EqSystem systOut = systIn;
protected
BackendDAE.Variables orderedVars;
BackendDAE.EquationArray orderedEqs;
BackendDAE.Matching matching;
array<Integer> ass1, ass2, ass1add, ass2add;
BackendDAE.StrongComponents comps;
algorithm
BackendDAE.EQSYSTEM(matching=BackendDAE.MATCHING(ass1=ass1, ass2=ass2, comps=comps)) := systIn;
if not listEmpty(compsAdd) then
ass1add := arrayCreate(listLength(compsAdd), 0);
ass2add := arrayCreate(listLength(compsAdd), 0);
ass1 := arrayAppend(ass1, ass1add);
ass2 := arrayAppend(ass2, ass1add);
List.map2_0(compsAdd, updateAssignment, ass1, ass2);
end if;
List.map2_0(compsNew, updateAssignment, ass1, ass2);
comps := List.replaceAtWithList(compsNew, idx-1, comps);
systOut.matching := BackendDAE.MATCHING(ass1, ass2, listAppend(comps, compsAdd));
systOut := BackendDAEUtil.setEqSystMatrices(systOut);
end replaceStrongComponent;
protected function updateAssignment
input BackendDAE.StrongComponent comp;
input array<Integer> ass1;
input array<Integer> ass2;
algorithm
_ := matchcontinue(comp,ass1,ass2)
local
Integer eq,var;
case(BackendDAE.SINGLEEQUATION(eqn=eq,var=var),_,_)
equation
arrayUpdate(ass2,eq,var);
arrayUpdate(ass1,var,eq);
then ();
else
then ();
end matchcontinue;
end updateAssignment;
protected function solveConstJacLinearSystem
input BackendDAE.EqSystem syst;
input BackendDAE.Shared ishared;
input list<BackendDAE.Equation> eqn_lst;
input list<Integer> eqn_indxs;
input list<BackendDAE.Var> var_lst;
input list<Integer> var_indxs;
input list<tuple<Integer, Integer, BackendDAE.Equation>> jac;
input Integer sysIdxIn;
input Integer compIdxIn;
output list<BackendDAE.Equation> sysEqsOut;
output list<BackendDAE.Equation> bEqsOut;
output list<BackendDAE.Var> bVarsOut;
output array<Integer> orderOut;
output Integer sysIdxOut;
protected
BackendDAE.Variables vars,vars1,v;
BackendDAE.EquationArray eqns,eqns1, eqns2;
list<DAE.Exp> beqs;
list<DAE.ElementSource> sources;
list<list<Real>> jacVals;
BackendDAE.Matching matching;
DAE.FunctionTree funcs;
BackendDAE.Shared shared;
BackendDAE.StateSets stateSets;
BackendDAE.BaseClockPartitionKind partitionKind;
array<Real> A,b;
Real entry;
Integer row,col,n, systIdx;
array<Integer> order;
algorithm
BackendDAE.EQSYSTEM(orderedVars=vars,orderedEqs=eqns,matching=matching,stateSets=stateSets,partitionKind=partitionKind) := syst;
BackendDAE.SHARED(functionTree=funcs) := ishared;
eqns1 := BackendEquation.listEquation(eqn_lst);
v := BackendVariable.listVar1(var_lst);
n := listLength(var_lst);
(beqs,sources) := BackendDAEUtil.getEqnSysRhs(eqns1,v,SOME(funcs));
beqs := listReverse(beqs);
//print("bside: \n"+ExpressionDump.printExpListStr(beqs)+"\n");
jacVals := evaluateConstantJacobian(listLength(var_lst),jac);
//print("JacVals\n"+stringDelimitList(List.map(jacVals,rListStr),"\n")+"\n\n");
A := arrayCreate(n*n,0.0);
b := arrayCreate(n*n,0.0); // i.e. a matrix for the b-vars to get their coefficients independently [(b1,0,0);(0,b2,0),(0,0,b3)]
order := arrayCreate(n,0);
for row in 1:n loop
for col in 1:n loop
entry := listGet(listGet(jacVals,row),col);
arrayUpdate(A,((row-1)*n+col),entry);
end for;
arrayUpdate(b,(row-1)*n+row,1.0);
end for;
//print("b\n"+stringDelimitList(List.map(arrayList(b),realString),", ")+"\n\n");
//print("A\n"+stringDelimitList(List.map(arrayList(A),realString),", ")+"\n\n");
gauss(A,b,1,n,List.intRange(n),order);
//print("the order: "+stringDelimitList(List.map(arrayList(order),intString),",")+"\n");
(bVarsOut,bEqsOut) := createBVecVars(sysIdxIn,compIdxIn,n,DAE.T_REAL_DEFAULT,beqs);
sysEqsOut := createSysEquations(A,b,n,order,var_lst,bVarsOut);
GCExt.free(A);
GCExt.free(b);
sysIdxOut := sysIdxIn+1;
orderOut := order;
end solveConstJacLinearSystem;
protected function createSysEquations "creates new equations for a linear system with constant Jacobian matrix.
author: Waurich TUD 2015-03"
input array<Real> A;
input array<Real> b;
input Integer n;
input array<Integer> order;
input list<BackendDAE.Var> xVars;
input list<BackendDAE.Var> bVars;
output list<BackendDAE.Equation> sysEqs = {};
protected
Integer i;
Integer row;
DAE.Exp lhs, rhs;
list<DAE.Exp> exps, coeffExps, xExps, bExps, xProds, bProds;
list<Real> coeffs;
BackendDAE.Equation eq;
algorithm
xExps := List.map(xVars, BackendVariable.varExp2);
bExps := List.map(bVars, BackendVariable.varExp2);
for i in 1:n loop
row := arrayGet(order,i);
coeffs := Array.getRange((row-1)*n+1,(row*n),A);
coeffExps := List.map(coeffs,Expression.makeRealExp);
xProds := List.threadMap1(coeffExps,xExps,makeBinaryExp,DAE.MUL(DAE.T_REAL_DEFAULT));
lhs := List.fold1(xProds,Expression.makeBinaryExp,DAE.ADD(DAE.T_REAL_DEFAULT),DAE.RCONST(0.0));
(lhs,_) := ExpressionSimplify.simplify(lhs);
coeffs := Array.getRange((row-1)*n+1,(row*n),b);
coeffExps := List.map(coeffs,Expression.makeRealExp);
bProds := List.threadMap1(coeffExps,bExps,makeBinaryExp,DAE.MUL(DAE.T_REAL_DEFAULT));
rhs := List.fold1(bProds,Expression.makeBinaryExp,DAE.ADD(DAE.T_REAL_DEFAULT),DAE.RCONST(0.0));
(rhs,_) := ExpressionSimplify.simplify(rhs);
eq := BackendDAE.EQUATION(lhs,rhs,DAE.emptyElementSource,BackendDAE.EQ_ATTR_DEFAULT_DYNAMIC);
sysEqs := eq::sysEqs;
end for;
end createSysEquations;
public function makeBinaryExp
input DAE.Exp inLhs;
input DAE.Exp inRhs;
input DAE.Operator inOp;
output DAE.Exp outExp;
algorithm
outExp := DAE.BINARY(inLhs, inOp, inRhs);
end makeBinaryExp;
protected function createBVecVars "creates variables for the b-Vector of a linear system with constant Jacobian
author:Waurich TUD 2015-03"
input Integer sysIdx;
input Integer compIdx;
input Integer size;
input DAE.Type typ;
input list<DAE.Exp> bExps;
output list<BackendDAE.Var> varLst = {};
output list<BackendDAE.Equation> eqLst = {};
protected
String ident;
Integer i;
DAE.ComponentRef cref;
BackendDAE.Var var;
BackendDAE.Equation beq;
algorithm
for i in 1:size loop
ident := "$sys"+intString(sysIdx)+"_"+intString(compIdx)+"_b"+intString(i);
cref := ComponentReference.makeCrefIdent(ident,typ,{});
var := BackendVariable.makeVar(cref);
varLst := var::varLst;
beq := BackendDAE.EQUATION(listGet(bExps,i),Expression.crefExp(cref),DAE.emptyElementSource,BackendDAE.EQ_ATTR_DEFAULT_DYNAMIC);
eqLst := beq::eqLst;
end for;
end createBVecVars;
protected function gauss
input array<Real> A;
input array<Real> b;
input Integer indxIn;
input Integer n;
input list<Integer> rangeIn;
input array<Integer> permutation;
protected
Integer pivotIdx,pos, ir, ic, p_ir;// ir=rowIdx, ic=columnIdx, p_ir=permuted row idx
Real pivot, entry, pr_entry, b_entry, first;
list<Integer> range;
algorithm
_ := matchcontinue(A,b,indxIn,n,rangeIn,permutation)
case(_,_,_,_,_,_)
algorithm
true := intLe(indxIn,n);
(pivotIdx,pivot) := getPivotElement(A,rangeIn,indxIn,n);
//print("pivot: "+intString(pivotIdx)+" has value: "+realString(pivot)+"\n");
arrayUpdate(permutation,indxIn,pivotIdx);
range := List.deleteMember(rangeIn,pivotIdx);
// the pivot row in the A-matrix divided by the pivot element
for ic in indxIn:n loop
pos := (pivotIdx-1)*n+ic;
entry := arrayGet(A,pos);
entry := realDiv(entry,pivot); //divide column entry with pivot element
//print(" pos "+intString(pos)+" entry "+realString(arrayGet(A,pos))+"\n");
arrayUpdate(A,pos,entry);
end for;
// the complete pivot row of the b-vector divided by the pivot element
for ic in 1:n loop
pos := (pivotIdx-1)*n+ic;
b_entry := arrayGet(b,pos);
b_entry := realDiv(b_entry,pivot);
arrayUpdate(b,pos,b_entry);
end for;
// the remaining rows
for ir in range loop
first := arrayGet(A,(ir-1)*n+indxIn); //the first row element, that is going to be zero
//print("first "+realString(first)+"\n");
for ic in indxIn:n loop
pos := (ir-1)*n+ic;
entry := arrayGet(A,pos); // the current entry
pivot := arrayGet(A,(pivotIdx-1)*n+ic); // the element from the column in the pivot row
//print("pivot "+realString(pivot)+"\n");
//print("ir "+intString(ir)+" pos "+intString(pos)+" entry0 "+realString(entry)+" entry1 "+realString(realSub(entry,realDiv(first,pivot)))+"\n");
entry := realSub(entry,realMul(first,pivot));
arrayUpdate(A,pos,entry);
b_entry := arrayGet(b,pos);
pivot := arrayGet(b,(pivotIdx-1)*n+ic);
b_entry := b_entry - realMul(first,pivot);
arrayUpdate(b,pos,b_entry);
end for;
end for;
//print("A\n"+stringDelimitList(List.map(arrayList(A),realString),", ")+"\n\n");
//print("b\n"+stringDelimitList(List.map(arrayList(b),realString),", ")+"\n\n");
//print("new permutation: "+stringDelimitList(List.map(arrayList(permutation),intString),",")+"\n");
//print("JACB "+intString(indxIn)+" \n"+stringDelimitList(List.map(arrayList(jacB),rListStr),"\n ")+"\n\n");
gauss(A,b,indxIn+1,n,range,permutation);
then();
else ();
end matchcontinue;
end gauss;
protected function getPivotElement "gets the highest element in the startIdx'th to n'th rows and the startidx'th column"
input array<Real> A;
input list<Integer> rangeIn;
input Integer startIdx;
input Integer n;
output Integer pos = 0;
output Real value = 0.0;
protected
Integer i;
Real entry;
algorithm
for i in rangeIn loop
entry := arrayGet(A,(i-1)*n+startIdx);
//print("i "+intString(i)+" pi "+intString(p_i)+" entry "+realString(entry)+"\n");
if realAbs(entry) > value then
value := entry;
pos := i;
end if;
end for;
end getPivotElement;
protected function rListStr
input list<Real> l;
output String s;
algorithm
s := stringDelimitList(List.map(l,realString)," , ");
end rListStr;
// =============================================================================
// unsorted section
//
// =============================================================================
protected function constantLinearSystem0
input BackendDAE.EqSystem isyst;
input BackendDAE.Shared inShared;
input tuple<Boolean, Integer> iTpl "<inChanged,sysIdxIn>";
output BackendDAE.EqSystem osyst;
output BackendDAE.Shared outShared;
output tuple<Boolean,Integer> oTpl "<oChanged,sysIdxOut>";
protected
Boolean changed;
Integer sysIdx;
BackendDAE.StrongComponents comps;
algorithm
((changed,sysIdx)) := iTpl;
BackendDAE.EQSYSTEM(matching=BackendDAE.MATCHING(comps=comps)) := isyst;
(osyst, outShared, changed, sysIdx) := constantLinearSystem1(isyst, inShared, comps, changed, sysIdx, 1);
osyst := constantLinearSystem2(changed, osyst);
oTpl := (changed,sysIdx+1);
end constantLinearSystem0;
protected function constantLinearSystem2
input Boolean b;
input BackendDAE.EqSystem isyst;
output BackendDAE.EqSystem osyst;
algorithm
osyst := match(b,isyst)
local
BackendDAE.Variables vars;
BackendDAE.EquationArray eqns;
BackendDAE.StateSets stateSets;
BackendDAE.BaseClockPartitionKind partitionKind;
case (false,_) then isyst;
// case (true,BackendDAE.EQSYSTEM(orderedVars=vars,orderedEqs=eqns,matching=BackendDAE.NO_MATCHING()))
case (true,BackendDAE.EQSYSTEM(orderedVars=vars, orderedEqs=eqns, stateSets=stateSets, partitionKind=partitionKind))
equation
// remove empty entries from vars/eqns
vars = BackendVariable.listVar1(BackendVariable.varList(vars));
eqns = BackendEquation.listEquation(BackendEquation.equationList(eqns));
then
BackendDAEUtil.createEqSystem(vars, eqns, stateSets, partitionKind);
/* case (true,BackendDAE.EQSYSTEM(orderedVars=vars,orderedEqs=eqns,matching=BackendDAE.MATCHING(ass1=ass1,ass2=ass2,comps=comps)))
then
updateEquationSystemMatching(vars,eqns,ass1,ass2,comps);
*/ end match;
end constantLinearSystem2;
protected function constantLinearSystem1
input BackendDAE.EqSystem isyst;
input BackendDAE.Shared ishared;
input BackendDAE.StrongComponents inComps;
input Boolean inRunMatching;
input Integer sysIdxIn;
input Integer compIdxIn;
output BackendDAE.EqSystem osyst;
output BackendDAE.Shared oshared;
output Boolean runMatching;
output Integer sysIdxOut;
algorithm
(osyst, oshared, runMatching, sysIdxOut) := match (inComps)
local
BackendDAE.StrongComponents comps;
BackendDAE.StrongComponent comp;
Boolean b;
BackendDAE.EqSystem syst;
BackendDAE.Shared shared;
Integer sysIdx, compIdx;
case {}
then (isyst, ishared, inRunMatching, sysIdxIn);
case comp::comps equation
(syst, shared, b, sysIdx, compIdx) = constantLinearSystemWork(isyst, ishared, comp, sysIdxIn, compIdxIn);
(syst, shared, runMatching, sysIdx) = constantLinearSystem1(syst, shared, comps, b or inRunMatching, sysIdx, compIdx);
then (syst, shared, runMatching, sysIdx);
end match;
end constantLinearSystem1;
protected function constantLinearSystemWork
input BackendDAE.EqSystem isyst;
input BackendDAE.Shared ishared;
input BackendDAE.StrongComponent comp;
input Integer sysIdxIn;
input Integer compIdxIn;
output BackendDAE.EqSystem osyst;
output BackendDAE.Shared oshared;
output Boolean outRunMatching;
output Integer sysIdxOut;
output Integer compIdxOut;
algorithm
(osyst, oshared, outRunMatching, sysIdxOut, compIdxOut):=
matchcontinue (isyst, ishared, comp)
local
BackendDAE.Variables vars;
BackendDAE.EquationArray eqns;
BackendDAE.StrongComponents comps;
BackendDAE.StrongComponent comp1;
Boolean b,b1;
list<BackendDAE.Equation> eqn_lst;
list<BackendDAE.Var> var_lst;
list<Integer> eindex,vindx;
list<tuple<Integer, Integer, BackendDAE.Equation>> jac;
BackendDAE.EqSystem syst;
BackendDAE.Shared shared;
Integer sysIdx;
array<Integer> order;
list<Integer> bVarIdcs,bEqIdcs;
list<BackendDAE.Var> bVars;
list<BackendDAE.Equation> bEqs,sysEqs;
BackendDAE.StrongComponents bComps,sysComps;
BackendDAE.Matching matching;
BackendDAE.StateSets stateSets;
BackendDAE.BaseClockPartitionKind partitionKind;
case (syst, shared, (BackendDAE.EQUATIONSYSTEM( eqns=eindex, vars=vindx, jac=BackendDAE.FULL_JACOBIAN(SOME(jac)),
jacType=BackendDAE.JAC_CONSTANT() )))
equation
//the A-matrix and the b-Vector are constant
eqn_lst = BackendEquation.getList(eindex, syst.orderedEqs);
var_lst = List.map1r(vindx, BackendVariable.getVarAt, syst.orderedVars);
(syst,shared) = solveLinearSystem(syst, shared, eqn_lst, eindex, var_lst, vindx, jac);
then (syst,shared,true,sysIdxIn,compIdxIn+1);
case ( syst as BackendDAE.EQSYSTEM(orderedVars=vars, orderedEqs=eqns), shared,
BackendDAE.EQUATIONSYSTEM( eqns=eindex, vars=vindx, jac=BackendDAE.FULL_JACOBIAN(SOME(jac)),
jacType=BackendDAE.JAC_LINEAR() ) )
equation
true = BackendDAEUtil.isSimulationDAE(ishared);
//only the A-matrix is constant, apply Gaussian Elimination
eqn_lst = BackendEquation.getList(eindex, eqns);
var_lst = List.map1r(vindx, BackendVariable.getVarAt, vars);
true = jacobianIsConstant(jac);
true = Flags.isSet(Flags.CONSTJAC);
//true = intEq(compIdxIn,37) and intEq(sysIdxIn,1);
//print("ITS CONSTANT\n");
//print("THE COMPIDX: "+intString(compIdxIn)+" THE SYSIDX"+intString(sysIdxIn)+"\n");
//BackendDump.dumpEqnsSolved2({comp},eqns,vars);
eqn_lst = BackendEquation.getList(eindex,eqns);
var_lst = List.map1r(vindx, BackendVariable.getVarAt, vars);
(sysEqs, bEqs, bVars, order, sysIdx) =
solveConstJacLinearSystem(syst, shared, eqn_lst, eindex, listReverse(var_lst), vindx, jac, sysIdxIn, compIdxIn);
//print("the b-vector stuff \n");
//BackendDump.printEquationList(bEqs);
//BackendDump.printVarList(bVars);
//print("the sysEqs stuff \n");
//BackendDump.printEquationList(sysEqs);
//build comps
//print("size"+intString(BackendEquation.equationArraySize(eqns))+"\n");
//print("numberOfElement"+intString(BackendEquation.getNumberOfEquations(eqns))+"\n");
//print("arrSize"+intString(BackendDAEUtil.equationArraySize2(eqns))+"\n");
//print("length"+intString(listLength(BackendEquation.equationList(eqns)))+"\n");
bVarIdcs = List.intRange2(BackendVariable.varsSize(vars)+1, BackendVariable.varsSize(vars)+listLength(bVars));
bEqIdcs = List.intRange2(BackendEquation.getNumberOfEquations(eqns)+1, BackendEquation.getNumberOfEquations(eqns)+listLength(bEqs));
bComps = List.threadMap(bEqIdcs, bVarIdcs, BackendDAEUtil.makeSingleEquationComp);
sysComps = List.threadMap( List.map1(arrayList(order), List.getIndexFirst, eindex), listReverse(vindx),
BackendDAEUtil.makeSingleEquationComp );