Merge pull request #258 from DimitriPlotnikov/master

Improve stabilit of the symboltable infrastructure and use the latest MC version.

models/iaf_psc_exp.nestml

@@ -1,78 +1,78 @@
 /*
-  Name: iaf_psc_exp_nestml - Leaky integrate-and-fire neuron model with exponential
-                     PSCs.
-
-  Description:
-  iaf_psc_exp_nestml is an implementation of a leaky integrate-and-fire model
-  with exponential shaped postsynaptic currents (PSCs) according to [1].
-  Thus, postsynaptic currents have an infinitely short rise time.
-
-  The threshold crossing is followed by an absolute refractory period (t_ref)
-  during which the membrane potential is clamped to the resting potential
-  and spiking is prohibited.
-
-  The linear subthresold dynamics is integrated by the Exact
-  Integration scheme [2]. The neuron dynamics is solved on the time
-  grid given by the computation step size. Incoming as well as emitted
-  spikes are forced to that grid.
-
-  An additional state variable and the corresponding differential
-  equation represents a piecewise constant external current.
-
-  The general framework for the consistent formulation of systems with
-  neuron like dynamics interacting by point events is described in
-  [2]. A flow chart can be found in [3].
-
-  Remarks:
-  The present implementation uses individual variables for the
-  components of the state vector and the non-zero matrix elements of
-  the propagator.  Because the propagator is a lower triangular matrix
-  no full matrix multiplication needs to be carried out and the
-  computation can be done "in place" i.e. no temporary state vector
-  object is required.
-
-  The template support of recent C++ compilers enables a more succinct
-  formulation without loss of runtime performance already at minimal
-  optimization levels. A future version of iaf_psc_exp_nestml will probably
-  address the problem of efficient usage of appropriate vector and
-  matrix objects.
-
-  Remarks: If tau_m is very close to tau_syn_ex or tau_syn_in, the model
-  will numerically behave as if tau_m is equal to tau_syn_ex or
-  tau_syn_in, respectively, to avoid numerical instabilities.
-  For details, please see IAF_Neruons_Singularity.ipynb in the
-  NEST source code (docs/model_details).
-
-  iaf_psc_exp_nestml can handle current input in two ways: Current input
-  through receptor_type 0 are handled as stepwise constant current
-  input as in other iaf models, i.e., this current directly enters
-  the membrane potential equation. Current input through
-  receptor_type 1, in contrast, is filtered through an exponential
-  kernel with the time constant of the excitatory synapse,
-  tau_syn_ex. For an example application, see [4].
-
-  References:
-  [1] Misha Tsodyks, Asher Uziel, and Henry Markram (2000) Synchrony Generation
-  in Recurrent Networks with Frequency-Dependent Synapses, The Journal of
-  Neuroscience, 2000, Vol. 20 RC50 p. 1-5
-  [2] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
-  systems with applications to neuronal modeling. Biologial Cybernetics
-  81:381-402.
-  [3] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001) State space
-  analysis of synchronous spiking in cortical neural networks.
-  Neurocomputing 38-40:565-571.
-  [4] Schuecker J, Diesmann M, Helias M (2015) Modulated escape from a
-  metastable state driven by colored noise.
-  Physical Review E 92:052119
-
-  Sends: SpikeEvent
-
-  Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
-
-  SeeAlso: iaf_psc_exp_ps
-
-  FirstVersion: March 2006
-  Author: Moritz Helias
+Name: iaf_psc_exp_nestml - Leaky integrate-and-fire neuron model with exponential
+                   PSCs.
+
+Description:
+iaf_psc_exp_nestml is an implementation of a leaky integrate-and-fire model
+with exponential shaped postsynaptic currents (PSCs) according to [1].
+Thus, postsynaptic currents have an infinitely short rise time.
+
+The threshold crossing is followed by an absolute refractory period (t_ref)
+during which the membrane potential is clamped to the resting potential
+and spiking is prohibited.
+
+The linear subthresold dynamics is integrated by the Exact
+Integration scheme [2]. The neuron dynamics is solved on the time
+grid given by the computation step size. Incoming as well as emitted
+spikes are forced to that grid.
+
+An additional state variable and the corresponding differential
+equation represents a piecewise constant external current.
+
+The general framework for the consistent formulation of systems with
+neuron like dynamics interacting by point events is described in
+[2]. A flow chart can be found in [3].
+
+Remarks:
+The present implementation uses individual variables for the
+components of the state vector and the non-zero matrix elements of
+the propagator.  Because the propagator is a lower triangular matrix
+no full matrix multiplication needs to be carried out and the
+computation can be done "in place" i.e. no temporary state vector
+object is required.
+
+The template support of recent C++ compilers enables a more succinct
+formulation without loss of runtime performance already at minimal
+optimization levels. A future version of iaf_psc_exp_nestml will probably
+address the problem of efficient usage of appropriate vector and
+matrix objects.
+
+Remarks: If tau_m is very close to tau_syn_ex or tau_syn_in, the model
+will numerically behave as if tau_m is equal to tau_syn_ex or
+tau_syn_in, respectively, to avoid numerical instabilities.
+For details, please see IAF_Neruons_Singularity.ipynb in the
+NEST source code (docs/model_details).
+
+iaf_psc_exp_nestml can handle current input in two ways: Current input
+through receptor_type 0 are handled as stepwise constant current
+input as in other iaf models, i.e., this current directly enters
+the membrane potential equation. Current input through
+receptor_type 1, in contrast, is filtered through an exponential
+kernel with the time constant of the excitatory synapse,
+tau_syn_ex. For an example application, see [4].
+
+References:
+[1] Misha Tsodyks, Asher Uziel, and Henry Markram (2000) Synchrony Generation
+in Recurrent Networks with Frequency-Dependent Synapses, The Journal of
+Neuroscience, 2000, Vol. 20 RC50 p. 1-5
+[2] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
+systems with applications to neuronal modeling. Biologial Cybernetics
+81:381-402.
+[3] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001) State space
+analysis of synchronous spiking in cortical neural networks.
+Neurocomputing 38-40:565-571.
+[4] Schuecker J, Diesmann M, Helias M (2015) Modulated escape from a
+metastable state driven by colored noise.
+Physical Review E 92:052119
+
+Sends: SpikeEvent
+
+Receives: SpikeEvent, CurrentEvent, DataLoggingRequest
+
+SeeAlso: iaf_psc_exp_ps
+
+FirstVersion: March 2006
+Author: Moritz Helias
 */
 neuron iaf_psc_exp_neuron:
 

pom.xml

@@ -16,7 +16,7 @@
 
         <!-- Monticore versions and dependencies -->
         <antlr.version>4.5</antlr.version>
-        <monticore.version>4.4.1</monticore.version>
+        <monticore.version>4.4.2</monticore.version>
 
         <!-- .. Plugins ....................................................... -->
         <compiler.plugin>3.5.1</compiler.plugin>

src/main/java/org/nest/nestml/_cocos/AssignmentToAlias.java

@@ -0,0 +1,41 @@
+/*
+ * Copyright (c) 2015 RWTH Aachen. All rights reserved.
+ *
+ * http://www.se-rwth.de/
+ */
+package org.nest.nestml._cocos;
+
+import de.monticore.symboltable.Scope;
+import org.nest.nestml._ast.ASTAliasDecl;
+import org.nest.spl._ast.ASTAssignment;
+import org.nest.spl._cocos.SPLASTAssignmentCoCo;
+import org.nest.symboltable.symbols.VariableSymbol;
+
+import static com.google.common.base.Preconditions.checkArgument;
+import static de.se_rwth.commons.logging.Log.error;
+
+/**
+ * Every alias declaration has exactly one variable
+ *
+ * @author (last commit) ippen, plotnikov
+ */
+public class AssignmentToAlias implements SPLASTAssignmentCoCo {
+
+  public static final String ERROR_CODE = "NESTML_Assignment";
+
+  @Override
+  public void check(final ASTAssignment astAssignment) {
+    checkArgument(astAssignment.getEnclosingScope().isPresent(), "Run symboltable creator.");
+
+    final Scope scope =  astAssignment.getEnclosingScope().get();
+    final String variableName = astAssignment.getLhsVarialbe().toString();
+    final VariableSymbol lhsVariable = VariableSymbol.resolve(variableName, scope);
+    if (lhsVariable.isAlias()) {
+      String msg = CocoErrorStrings.getInstance().getErrorMsg(this, variableName);
+
+      error(msg, astAssignment.get_SourcePositionStart());
+    }
+
+  }
+
+}

src/main/java/org/nest/nestml/_cocos/BufferNotAssignable.java

@@ -28,7 +28,7 @@ public class BufferNotAssignable implements SPLASTAssignmentCoCo {
   public static final String ERROR_CODE = "NESTML_SPL_BUFFER_NOT_ASSIGNABLE";
 
   public void check(final ASTAssignment astAssignment) {
-    checkArgument(astAssignment.getEnclosingScope().isPresent(), "Run symboltable creator. ");
+    checkArgument(astAssignment.getEnclosingScope().isPresent(), "Run symboltable creator.");
     final Scope enclosingScope = astAssignment.getEnclosingScope().get();
     final String varName = astAssignment.getLhsVarialbe().toString();
 

src/main/java/org/nest/nestml/_cocos/CocoErrorStrings.java

@@ -228,4 +228,8 @@ public String getErrorMsg(final DerivativeOrderAtLeastOne coco, final String var
 
     return DerivativeOrderAtLeastOne.ERROR_CODE + ":" + "The variable on the righthandside of an equation must be derivative variable, e.g. " + variableName + "'";
   }
+
+  public String getErrorMsg(AssignmentToAlias assignmentToAlias, final String variableName) {
+    return AssignmentToAlias.ERROR_CODE + ":" + "You cannot assign a value to an alias: " + variableName;
+  }
 }

src/main/java/org/nest/nestml/_cocos/VariableDoesNotExist.java

@@ -23,12 +23,9 @@
  * @author  plotnikov
  */
 public class VariableDoesNotExist implements ODEASTOdeDeclarationCoCo {
-
   public static final String ERROR_CODE = "NESTML_VARIABLE_DOESNT_EXIST";
   private static final String ERROR_MSG_FORMAT = "The variable %s is not defined in %s.";
 
-
-
   @Override
   public void check(final ASTOdeDeclaration node) {
     node.getODEAliass().forEach(

src/main/java/org/nest/nestml/_symboltable/NESTMLCoCosManager.java

@@ -162,11 +162,14 @@ private void registerCocos() {
     final DerivativeOrderAtLeastOne derivativeOrderAtLeastOne = new DerivativeOrderAtLeastOne();
     nestmlCoCoChecker.addCoCo(derivativeOrderAtLeastOne);
 
+    final AssignmentToAlias assignmentToAlias = new AssignmentToAlias();
+    nestmlCoCoChecker.addCoCo(assignmentToAlias);
+
     final SPLCoCosManager splCoCosManager = new SPLCoCosManager();
     splCoCosManager.addSPLCocosToNESTMLChecker(nestmlCoCoChecker);
   }
 
-  public List<Finding> checkVariableUniqueness(final ASTNeuron astNeuron) {
+  List<Finding> checkVariableUniqueness(final ASTNeuron astNeuron) {
     Log.getFindings().clear();
 
     uniquenessChecker.addCoCo(new VariableDefinedMultipleTimes());

src/main/java/org/nest/nestml/_symboltable/NESTMLLanguage.java

@@ -36,17 +36,16 @@ public class NESTMLLanguage extends NESTMLLanguageTOP {
   public NESTMLLanguage() {
     super("NESTML Language", FILE_ENDING);
 
-    addResolver(CommonResolvingFilter.create(NeuronSymbol.class, NeuronSymbol.KIND));
+    addResolver(CommonResolvingFilter.create(NeuronSymbol.KIND));
 
-    addResolver(new PredefinedTypesFilter(TypeSymbol.KIND));
-
-    addResolver(new PredefinedMethodsFilter(MethodSymbol.KIND));
+    addResolver(new PredefinedTypesFilter());
+    addResolver(new PredefinedMethodsFilter());
     addResolver(CommonResolvingFilter.create(MethodSymbol.KIND));
 
-    addResolver(CommonResolvingFilter.create(VariableSymbol.class, VariableSymbol.KIND));
-    addResolver(new PredefinedVariablesFilter(VariableSymbol.class, VariableSymbol.KIND));
+    addResolver(CommonResolvingFilter.create(VariableSymbol.KIND));
+    addResolver(new PredefinedVariablesFilter());
 
-    addResolver(CommonResolvingFilter.create(UsageSymbol.class, UsageSymbol.KIND));
+    addResolver(CommonResolvingFilter.create( UsageSymbol.KIND));
 
     setModelNameCalculator(new CommonModelNameCalculator() {
 

src/main/java/org/nest/nestml/_symboltable/PredefinedMethodsFilter.java

@@ -11,28 +11,28 @@
 import de.monticore.symboltable.resolving.ResolvingInfo;
 import org.nest.symboltable.symbols.MethodSymbol;
 
+import java.util.Collection;
 import java.util.List;
+import java.util.Map;
 import java.util.Optional;
 
 import static org.nest.symboltable.predefined.PredefinedFunctions.getMethodSymbolIfExists;
 
 /**
- * TODO
+ * Returns a predefined methods if one exists: e.g. pow, exp, ...
  *
  * @author plotnikov
  */
 public class PredefinedMethodsFilter extends CommonResolvingFilter<MethodSymbol> {
 
-  public PredefinedMethodsFilter(
-      final SymbolKind targetKind) {
-    super(targetKind);
+  public PredefinedMethodsFilter() {
+    super(MethodSymbol.KIND);
 
 
   }
 
   @Override
-  public Optional<Symbol> filter(ResolvingInfo resolvingInfo, String name,
-      List<Symbol> symbols) {
+  public Optional<Symbol> filter(ResolvingInfo resolvingInfo, String name, Map<String, Collection<Symbol>> symbols) {
     final Optional<MethodSymbol> foundPredefinedMethod = getMethodSymbolIfExists(name);
 
     if (foundPredefinedMethod.isPresent()) {

src/main/java/org/nest/nestml/_symboltable/PredefinedTypesFilter.java

@@ -12,33 +12,30 @@
 import org.nest.symboltable.predefined.PredefinedTypes;
 import org.nest.symboltable.symbols.TypeSymbol;
 
+import java.util.Collection;
 import java.util.List;
+import java.util.Map;
 import java.util.Optional;
 
 /**
- * TODO
+ * All types in NESTML are predefined. This filter matches the type by name and returns if one exists
  *
  * @author plotnikov
  */
 public class PredefinedTypesFilter extends CommonResolvingFilter<TypeSymbol> {
-  public PredefinedTypesFilter(
-      final SymbolKind targetKind) {
-    super(targetKind);
+  public PredefinedTypesFilter() {
+    super(TypeSymbol.KIND);
   }
 
   @Override
-  public Optional<Symbol> filter(
-      final ResolvingInfo resolvingInfo,
-      final String name,
-      final List<Symbol> symbols) {
+  public Optional<Symbol> filter(ResolvingInfo resolvingInfo, String name, Map<String, Collection<Symbol>> symbols) {
     final Optional<TypeSymbol> typeSymbol = PredefinedTypes.getTypeIfExists(name);
     if (typeSymbol.isPresent()) {
       return Optional.of(typeSymbol.get());
     }
     else {
       return Optional.empty();
     }
-
   }
 
 }

src/main/java/org/nest/nestml/_symboltable/PredefinedVariablesFilter.java

@@ -12,28 +12,23 @@
 import org.nest.symboltable.predefined.PredefinedVariables;
 import org.nest.symboltable.symbols.VariableSymbol;
 
+import java.util.Collection;
 import java.util.List;
+import java.util.Map;
 import java.util.Optional;
 
 /**
- * TODO
+ * Returns a predefined variable (e.g. 't') if one exists.
  *
- * @author (last commit) $$Author$$
- * @version $$Revision$$, $$Date$$
- * @since TODO
+ * @author plotnikov
  */
 public class PredefinedVariablesFilter extends CommonResolvingFilter<VariableSymbol> {
-  public PredefinedVariablesFilter(
-      final Class<VariableSymbol> symbolClass,
-      final SymbolKind targetKind) {
-    super(targetKind);
+  public PredefinedVariablesFilter() {
+    super(VariableSymbol.KIND);
   }
 
   @Override
-  public Optional<Symbol> filter(
-      final ResolvingInfo resolvingInfo,
-      final String name,
-      final List<Symbol> symbols) {
+  public Optional<Symbol> filter(ResolvingInfo resolvingInfo, String name, Map<String, Collection<Symbol>> symbols) {
     final Optional<VariableSymbol> predefinedVariable = PredefinedVariables.getVariableIfExists(name);
     if (predefinedVariable.isPresent()) {
       return Optional.of(predefinedVariable.get());