Increase the robustness of the sympy infrastructure. Add bounded min, max functions.

models/aeif_cond_alpha.nestml

@@ -38,7 +38,7 @@ neuron aeif_cond_alpha_neuron:
   end
 
   equations:
-    V_bounded mV = min(V_m, V_peak) # prevent exponential divergence
+    V_bounded mV = bounded_min(V_m, V_peak) # prevent exponential divergence
     shape g_in = (e/tau_syn_in) * t * exp(-1/tau_syn_in*t)
     shape g_ex = (e/tau_syn_ex) * t * exp(-1/tau_syn_ex*t)
 

models/aeif_cond_alpha_implicit.nestml

@@ -42,12 +42,12 @@ neuron aeif_cond_alpha_implicit:
   equations:
     V_bounded mV = min(V_m, V_peak) # prevent exponential divergence
     # alpha function for the g_in
-    g_in'' = -g_in'/tau_syn_in
-    g_in' = g_in' - g_in/tau_syn_in
+    g_in'' = (-2/tau_syn_in) * g_in'-(1/tau_syn_in**2) * g_in
+    g_in' = g_in'
 
     # alpha function for the g_ex
-    g_ex'' = -g_ex'/tau_syn_ex
-    g_ex' = g_ex' - g_ex/tau_syn_ex
+    g_ex'' = (-2/tau_syn_ex) * g_ex'-(1/tau_syn_ex**2) * g_ex
+    g_ex' = g_ex'
 
     # Add aliases to simplify the equation definition of V_m
     exp_arg real = (V_bounded-V_th)/Delta_T

models/aeif_cond_exp.nestml

@@ -41,7 +41,7 @@ neuron aeif_cond_exp_neuron:
   end
 
   equations:
-    V_bounded mV = min(V_m, V_peak) # prevent exponential divergence
+    V_bounded mV = bounded_min(V_m, V_peak) # prevent exponential divergence
     shape g_in = exp(-1/tau_syn_in*t)
     shape g_ex = exp(-1/tau_syn_ex*t)
 

models/hh_cond_exp_traub.nestml

@@ -36,7 +36,7 @@ neuron hh_cond_exp_traub_neuron:
   state:
     V_m mV = E_L #  Membrane potential
 
-    # TODO: it should be possible, to define these variables in the internal block
+    # equilibrium values for (in)activation variables
     alias alpha_n_init real = 0.032 * ( 15. - V_m ) / ( exp( ( 15. - V_m ) / 5. ) - 1. )
     alias beta_n_init real = 0.5 * exp( ( 10. - V_m ) / 40. )
     alias alpha_m_init real = 0.32 * ( 13. - V_m ) / ( exp( ( 13. - V_m ) / 4. ) - 1. )
@@ -50,19 +50,22 @@ neuron hh_cond_exp_traub_neuron:
   end
 
   equations:
+    shape g_in = exp(-1/tau_syn_in*t)
+    shape g_ex = exp(-1/tau_syn_ex*t)
+
     # Add aliases to simplify the equation definition of V_m
+    # ionic currents
     I_Na  pA = g_Na * Act_m * Act_m * Act_m * Act_h * ( V_m - E_Na )
     I_K   pA  = g_K * Inact_n * Inact_n * Inact_n * Inact_n * ( V_m - E_K )
     I_L   pA = g_L * ( V_m - E_L )
+
     I_syn_exc pA = cond_sum(g_ex, spikeExc) * ( V_m - E_ex )
     I_syn_inh pA = cond_sum(g_in, spikeInh) * ( V_m - E_in )
 
-    shape g_in = exp(-1/tau_syn_in*t)
-    shape g_ex = exp(-1/tau_syn_ex*t)
-
+    # membrane potential
     V_m' =( -I_Na - I_K - I_L - I_syn_exc - I_syn_inh + I_stim + I_e ) / C_m
 
-    # equilibrium values for (in)activation variables
+    # channel dynamics
     V_rel mV = V_m - V_T
     alpha_n real = 0.032 * ( 15. - V_rel ) / ( exp( ( 15. - V_rel ) / 5. ) - 1. )
     beta_n real = 0.5 * exp( ( 10. - V_rel ) / 40. )

models/hh_psc_alpha.nestml

@@ -63,15 +63,15 @@ neuron hh_psc_alpha_neuron:
 
   equations:
     # synapses: alpha functions
-    shape I_in = 1 pA * (e/tau_syn_in) * t * exp(-1/tau_syn_in*t)
-    shape I_ex = 1 pA * (e/tau_syn_ex) * t * exp(-1/tau_syn_ex*t)
+    shape I_in = (e/tau_syn_in) * t * exp(-1/tau_syn_in*t)
+    shape I_ex = (e/tau_syn_ex) * t * exp(-1/tau_syn_ex*t)
 
     I_syn_exc pA = curr_sum(I_ex, spikeExc)
     I_syn_inh pA = curr_sum(I_in, spikeInh)
     I_Na  pA = g_Na * Act_m * Act_m * Act_m * Act_h * ( V_m - E_Na )
     I_K   pA  = g_K * Inact_n * Inact_n * Inact_n * Inact_n * ( V_m - E_K )
     I_L   pA = g_L * ( V_m - E_L )
-    V_m' =( -( I_Na + I_K + I_L ) + I_stim + I_e + I_syn_inh + I_syn_exc ) / C_m
+    V_m' =( -( I_Na + I_K + I_L ) + currents + I_e + I_syn_inh + I_syn_exc ) / C_m
 
     # Inact_n
     alpha_n real = ( 0.01 * ( V_m + 55. ) ) / ( 1. - exp( -( V_m + 55. ) / 10. ) )
@@ -83,7 +83,7 @@ neuron hh_psc_alpha_neuron:
     beta_m  real = 4. * exp( -( V_m + 65. ) / 18. )
     Act_m' = alpha_m * ( 1 - Act_m ) - beta_m * Act_m # m-variable
 
-    # Act_h'
+    # Act_h
     alpha_h real = 0.07 * exp( -( V_m + 65. ) / 20. )
     beta_h  real = 1. / ( 1. + exp( -( V_m + 35. ) / 10. ) )
     Act_h' = alpha_h * ( 1 - Act_h ) - beta_h * Act_h # h-variable
@@ -115,11 +115,6 @@ neuron hh_psc_alpha_neuron:
 
     RefractoryCounts integer = steps(t_ref) # refractory time in steps
     r integer # number of steps in the current refractory phase
-
-   # Input current injected by CurrentEvent.
-   # This variable is used to transport the current applied into the
-   # _dynamics function computing the derivative of the state vector.
-   I_stim pA = 0pA
   end
 
   input:
@@ -136,12 +131,11 @@ neuron hh_psc_alpha_neuron:
     # sending spikes: crossing 0 mV, pseudo-refractoriness and local maximum...
     if r > 0: # is refractory?
       r -= 1
-    elif V_m > 0 and U_old > V_m: # threshold && maximum
+    elif V_m > 0mV and U_old > V_m: # threshold && maximum
       r = RefractoryCounts
       emit_spike()
     end
 
-    I_stim = currents.get_sum()
   end
 
 end

models/iaf_cond_alpha_implicit.nestml

@@ -48,8 +48,8 @@ neuron iaf_cond_alpha_implicit:
     g_ex'' = -g_ex'/tau_syn_ex
     g_ex' = g_ex'  -g_ex/tau_syn_ex
 
-    I_syn_exc pA = cond_sum(g_ex, spikeExc)  * ( V_m - E_ex )
-    I_syn_inh pA = cond_sum(g_in, spikeInh)  * ( V_m - E_in )
+    I_syn_exc pA = cond_sum(g_ex, spikeExc) * ( V_m - E_ex )
+    I_syn_inh pA = cond_sum(g_in, spikeInh) * ( V_m - E_in )
     I_leak pA = g_L * ( V_m - E_L )
 
     V_m' = ( -I_leak - I_syn_exc - I_syn_inh + I_stim + I_e ) / C_m
@@ -106,8 +106,8 @@ neuron iaf_cond_alpha_implicit:
     end
 
     # add incoming spikes
-    g_ex' += spikeExc.get_sum() * PSConInit_E
-    g_in' += spikeInh.get_sum() * PSConInit_I
+    g_ex' += spikeExc * PSConInit_E
+    g_in' += spikeInh * PSConInit_I
     # set new input current
     I_stim = currents.get_sum()
   end

src/main/java/org/nest/codegeneration/NestCodeGenerator.java

@@ -5,13 +5,17 @@
  */
 package org.nest.codegeneration;
 
+import com.google.common.collect.Lists;
+import com.google.common.io.Files;
 import de.monticore.generating.GeneratorEngine;
 import de.monticore.generating.GeneratorSetup;
 import de.monticore.generating.templateengine.GlobalExtensionManagement;
+import de.se_rwth.commons.logging.Log;
 import org.nest.codegeneration.converters.*;
 import org.nest.codegeneration.helpers.*;
 import org.nest.codegeneration.sympy.ODETransformer;
 import org.nest.codegeneration.sympy.OdeProcessor;
+import org.nest.codegeneration.sympy.TransformerBase;
 import org.nest.nestml._ast.ASTBody;
 import org.nest.nestml._ast.ASTNESTMLCompilationUnit;
 import org.nest.nestml._ast.ASTNeuron;
@@ -23,8 +27,11 @@
 import org.nest.utils.AstUtils;
 
 import java.io.File;
+import java.io.IOException;
+import java.nio.charset.Charset;
 import java.nio.file.Path;
 import java.nio.file.Paths;
+import java.nio.file.StandardOpenOption;
 import java.util.List;
 import java.util.Optional;
 
@@ -84,6 +91,7 @@ private ASTNeuron solveODESInNeuron(
     if (odesBlock.isPresent()) {
       if (odesBlock.get().getShapes().size() == 0) {
         info("The model will be solved numerically with GSL solver.", LOG_NAME);
+        markNumericSolver(astNeuron.getName(), outputBase);
         return astNeuron;
       }
       else {
@@ -98,6 +106,17 @@ private ASTNeuron solveODESInNeuron(
 
   }
 
+  private void markNumericSolver(final String neuronName, final Path outputBase) {
+    try {
+      Files.write("numeric",
+                  Paths.get(outputBase.toString(), neuronName + "." + TransformerBase.SOLVER_TYPE).toFile(),
+                  Charset.defaultCharset());
+    }
+    catch (IOException e) {
+      Log.error("Cannot write status file. Check you permissions.", e);
+    }
+  }
+
   private void generateNestCode(
       final ASTNeuron astNeuron,
       final Path outputBase) {

src/main/java/org/nest/codegeneration/SolverType.java

@@ -9,6 +9,7 @@
 
 import java.io.IOException;
 import java.nio.file.Files;
+import java.nio.file.NoSuchFileException;
 import java.nio.file.Path;
 import java.util.Arrays;
 import java.util.List;

src/main/java/org/nest/codegeneration/converters/GslReferenceConverter.java

@@ -81,10 +81,10 @@ public String convertFunctionCall(final ASTFunctionCall astFunctionCall) {
     if (PredefinedFunctions.POW.equals(functionName)) {
       return "pow(%s)";
     }
-    if (PredefinedFunctions.MAX.equals(functionName)) {
+    if (PredefinedFunctions.MAX.equals(functionName) || PredefinedFunctions.BOUNDED_MAX.equals(functionName)) {
       return "std::max(%s)";
     }
-    if (PredefinedFunctions.MIN.equals(functionName)) {
+    if (PredefinedFunctions.MIN.equals(functionName)|| PredefinedFunctions.BOUNDED_MIN.equals(functionName)) {
       return "std::min(%s)";
     }
     if (functionName.contains(PredefinedFunctions.EMIT_SPIKE)) {

src/main/java/org/nest/codegeneration/converters/NESTReferenceConverter.java

@@ -72,10 +72,10 @@ public String convertFunctionCall(final ASTFunctionCall astFunctionCall) {
     if (PredefinedFunctions.POW.equals(functionName)) {
       return "std::pow(%s)";
     }
-    if (PredefinedFunctions.MAX.equals(functionName)) {
+    if (PredefinedFunctions.MAX.equals(functionName) || PredefinedFunctions.BOUNDED_MAX.equals(functionName)) {
       return "std::max(%s)";
     }
-    if (PredefinedFunctions.MIN.equals(functionName)) {
+    if (PredefinedFunctions.MIN.equals(functionName) || PredefinedFunctions.BOUNDED_MIN.equals(functionName) ) {
       return "std::min(%s)";
     }
 

src/main/java/org/nest/codegeneration/sympy/DeltaSolutionTransformer.java

@@ -32,7 +32,6 @@
  */
 class DeltaSolutionTransformer extends TransformerBase {
   final static String PROPAGATOR_STEP = "propagator.step.tmp";
-  final static String ODE_TYPE = "solverType.tmp";
   final static String P30_FILE = "P30.tmp";
 
   ASTNeuron addExactSolution(

src/main/java/org/nest/codegeneration/sympy/NESTMLASTCreator.java

@@ -38,7 +38,7 @@ public class NESTMLASTCreator {
   }
 
   static List<ASTAliasDecl> createAliases(final Path declarationFile) {
-    checkArgument(Files.exists(declarationFile));
+    checkArgument(Files.exists(declarationFile), declarationFile.toString());
 
     try {
       return Files.lines(declarationFile)

src/main/java/org/nest/codegeneration/sympy/ODESolverGenerator.java

@@ -161,6 +161,7 @@ private static Path generateSympyScript(
 
     glex.setGlobalValue("variables", variables);
     glex.setGlobalValue("aliases", aliases);
+    glex.setGlobalValue("neuronName", neuron.getName());
 
     final ExpressionsPrettyPrinter expressionsPrinter  = new ExpressionsPrettyPrinter();
     glex.setGlobalValue("printer", expressionsPrinter);

src/main/java/org/nest/codegeneration/sympy/ODETransformer.java

@@ -1,6 +1,7 @@
 package org.nest.codegeneration.sympy;
 
 import com.google.common.base.Preconditions;
+import com.google.common.collect.Lists;
 import de.monticore.ast.ASTNode;
 import org.nest.commons._ast.ASTExpr;
 import org.nest.commons._ast.ASTFunctionCall;
@@ -19,23 +20,49 @@
  * @author plotnikov
  */
 public class ODETransformer {
+  private static final List<String> functions = Lists.newArrayList(
+      PredefinedFunctions.CURR_SUM,
+      PredefinedFunctions.COND_SUM,
+      PredefinedFunctions.BOUNDED_MIN,
+      PredefinedFunctions.BOUNDED_MAX);
+
+  private static final List<String> sumFunctions = Lists.newArrayList(
+      PredefinedFunctions.CURR_SUM,
+      PredefinedFunctions.COND_SUM);
+
+
+  // this function is used in freemarker templates und must be public
+  public static <T extends ASTNode> T replaceFunctions(final T astOde) {
+    // since the transformation replaces the call inplace, make a copy to preserve the information for further steps
+    final List<ASTFunctionCall> functionsCalls = getFunctionCalls(astOde, functions);
+
+    final T workingCopy = (T) astOde.deepClone(); // IT is OK, since the deepClone returns T
+    functionsCalls.forEach(functionCall -> replaceFunctionCallThroughFirstArgument(astOde, functionCall)); // TODO deepClone
+    return astOde;
+  }
+
   // this function is used in freemarker templates und must be public
   public static <T extends ASTNode> T replaceSumCalls(final T astOde) {
     // since the transformation replaces the call inplace, make a copy to preserve the information for further steps
-    final List<ASTFunctionCall> functions = get_sumFunctionCalls(astOde);
+    final List<ASTFunctionCall> functionsCalls = get_sumFunctionCalls(astOde);
 
     final T workingCopy = (T) astOde.deepClone(); // IT is OK, since the deepClone returns T
-    functions.forEach(node -> replaceFunctionCallThroughFirstArgument(astOde, node)); // TODO deepClone
+    functionsCalls.forEach(functionCall -> replaceFunctionCallThroughFirstArgument(astOde, functionCall)); // TODO deepClone
     return astOde;
   }
 
+
+
   // this function is used in freemarker templates und must be public
   static List<ASTFunctionCall> get_sumFunctionCalls(final ASTNode workingCopy) {
+    return getFunctionCalls(workingCopy, sumFunctions);
+  }
+
+  // this function is used in freemarker templates und must be public
+  private static List<ASTFunctionCall> getFunctionCalls(final ASTNode workingCopy, final List<String> functionNames) {
     return AstUtils.getAll(workingCopy, ASTFunctionCall.class)
         .stream()
-        .filter(astFunctionCall ->
-            astFunctionCall.getCalleeName().equals(PredefinedFunctions.CURR_SUM) ||
-            astFunctionCall.getCalleeName().equals(PredefinedFunctions.COND_SUM))
+        .filter(astFunctionCall -> functionNames.contains(astFunctionCall.getCalleeName()))
         .collect(Collectors.toList());
   }
 

src/main/java/org/nest/codegeneration/sympy/OdeProcessor.java

@@ -100,7 +100,7 @@ private ASTNeuron handleDeltaShape(
 
       checkState(successfulExecution, "Error during solver script evaluation.");
 
-      final Path odeTypePath = Paths.get(outputBase.toString(), DeltaSolutionTransformer.ODE_TYPE);
+      final Path odeTypePath = Paths.get(outputBase.toString(), astNeuron.getName() + "." + DeltaSolutionTransformer.SOLVER_TYPE);
       final SolverType solutionType = SolverType.fromFile(odeTypePath);
 
       if (solutionType.equals(SolverType.EXACT)) {
@@ -109,8 +109,8 @@ private ASTNeuron handleDeltaShape(
             LOG_NAME);
         deltaSolutionTransformer.addExactSolution(
             astNeuron,
-            Paths.get(outputBase.toString(), DeltaSolutionTransformer.P30_FILE),
-            Paths.get(outputBase.toString(), DeltaSolutionTransformer.PROPAGATOR_STEP));
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + DeltaSolutionTransformer.P30_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + DeltaSolutionTransformer.PROPAGATOR_STEP));
       }
       else {
         Log.warn(astNeuron.getName() + " has a delta shape function with a non-linear ODE.");
@@ -137,29 +137,29 @@ protected ASTNeuron handleNeuronWithODE(
 
       checkState(successfulExecution, "Error during solver script evaluation.");
 
-      final Path odeTypePath = Paths.get(outputBase.toString(), TransformerBase.SOLVER_TYPE);
+      final Path odeTypePath = Paths.get(outputBase.toString(), astNeuron.getName() + "." + TransformerBase.SOLVER_TYPE);
       final SolverType solutionType = SolverType.fromFile(odeTypePath);
 
       if (solutionType.equals(SolverType.EXACT)) {
         info("ODE is solved exactly.", LOG_NAME);
 
         return linearSolutionTransformer.addExactSolution(
             astNeuron,
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.P30_FILE),
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.PSC_INITIAL_VALUE_FILE),
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.STATE_VARIABLES_FILE),
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.PROPAGATOR_MATRIX_FILE),
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.PROPAGATOR_STEP_FILE),
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.STATE_VECTOR_TMP_DECLARATIONS_FILE),
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.STATE_VECTOR_UPDATE_STEPS_FILE),
-            Paths.get(outputBase.toString(), LinearSolutionTransformer.STATE_VECTOR_TMP_BACK_ASSIGNMENTS_FILE));
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.P30_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.PSC_INITIAL_VALUE_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.STATE_VARIABLES_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.PROPAGATOR_MATRIX_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.PROPAGATOR_STEP_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.STATE_VECTOR_TMP_DECLARATIONS_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.STATE_VECTOR_UPDATE_STEPS_FILE),
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + LinearSolutionTransformer.STATE_VECTOR_TMP_BACK_ASSIGNMENTS_FILE));
       }
       else if (solutionType.equals(SolverType.NUMERIC)) {
         info("ODE is solved numerically.", LOG_NAME);
         return implicitFormTransformer.transformToImplicitForm(
             astNeuron,
-            Paths.get(outputBase.toString(),ImplicitFormTransformer.PSC_INITIAL_VALUE_FILE),
-            Paths.get(outputBase.toString(),ImplicitFormTransformer.EQUATIONS_FILE));
+            Paths.get(outputBase.toString(), astNeuron.getName() + "." + ImplicitFormTransformer.PSC_INITIAL_VALUE_FILE),
+            Paths.get(outputBase.toString(),astNeuron.getName() + "." + ImplicitFormTransformer.EQUATIONS_FILE));
       }
       else {
         warn(astNeuron.getName() + ": ODEs could not be solved. The model remains unchanged.");