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NeuronWriter.java
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NeuronWriter.java
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package org.neuroml.export.neuron;
import static org.neuroml.export.neuron.ProcessManager.findNeuronHome;
import java.io.File;
import java.io.IOException;
import java.io.StringWriter;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Locale;
import java.util.Map;
import java.util.Set;
import org.apache.commons.lang.StringUtils;
import org.apache.velocity.VelocityContext;
import org.apache.velocity.app.VelocityEngine;
import org.lemsml.export.dlems.DLemsWriter;
import org.lemsml.jlems.core.expression.ParseError;
import org.lemsml.jlems.core.logging.E;
import org.lemsml.jlems.core.logging.MinimalMessageHandler;
import org.lemsml.jlems.core.sim.ContentError;
import org.lemsml.jlems.core.sim.LEMSException;
import org.lemsml.jlems.core.type.Component;
import org.lemsml.jlems.core.type.Dimension;
import org.lemsml.jlems.core.type.Exposure;
import org.lemsml.jlems.core.type.InstanceRequirement;
import org.lemsml.jlems.core.type.Lems;
import org.lemsml.jlems.core.type.LemsCollection;
import org.lemsml.jlems.core.type.ParamValue;
import org.lemsml.jlems.core.type.Property;
import org.lemsml.jlems.core.type.Requirement;
import org.lemsml.jlems.core.type.Target;
import org.lemsml.jlems.core.type.Meta;
import org.lemsml.jlems.core.type.dynamics.Case;
import org.lemsml.jlems.core.type.dynamics.ConditionalDerivedVariable;
import org.lemsml.jlems.core.type.dynamics.DerivedVariable;
import org.lemsml.jlems.core.type.dynamics.Dynamics;
import org.lemsml.jlems.core.type.dynamics.OnCondition;
import org.lemsml.jlems.core.type.dynamics.OnEntry;
import org.lemsml.jlems.core.type.dynamics.OnEvent;
import org.lemsml.jlems.core.type.dynamics.OnStart;
import org.lemsml.jlems.core.type.dynamics.Regime;
import org.lemsml.jlems.core.type.dynamics.StateAssignment;
import org.lemsml.jlems.core.type.dynamics.StateVariable;
import org.lemsml.jlems.core.type.dynamics.TimeDerivative;
import org.lemsml.jlems.core.type.dynamics.Transition;
import org.lemsml.jlems.core.type.simulation.EventWriter;
import org.lemsml.jlems.io.util.FileUtil;
import org.lemsml.jlems.io.IOUtil;
import org.neuroml.export.base.ANeuroMLBaseWriter;
import org.neuroml.export.exceptions.GenerationException;
import org.neuroml.export.exceptions.ModelFeatureSupportException;
import org.neuroml.export.utils.Format;
import org.neuroml.export.utils.LEMSQuantityPath;
import org.neuroml.export.utils.ProcessOutputWatcher;
import org.neuroml.export.utils.Utils;
import org.neuroml.export.utils.VelocityUtils;
import org.neuroml.export.utils.support.ModelFeature;
import org.neuroml.export.utils.support.SupportLevelInfo;
import org.neuroml.model.BiophysicalProperties;
import org.neuroml.model.BiophysicalProperties2CaPools;
import org.neuroml.model.Cell;
import org.neuroml.model.Cell2CaPools;
import org.neuroml.model.IntracellularProperties;
import org.neuroml.model.MembraneProperties;
import org.neuroml.model.MembraneProperties2CaPools;
import org.neuroml.model.Segment;
import org.neuroml.model.Species;
import org.neuroml.model.SpikeThresh;
import org.neuroml.model.util.CellUtils;
import org.neuroml.model.util.NeuroMLElements;
import org.neuroml.model.util.NeuroMLException;
@SuppressWarnings("StringConcatenationInsideStringBufferAppend")
public class NeuronWriter extends ANeuroMLBaseWriter
{
private final ArrayList<String> generatedModComponents = new ArrayList<String>();
private final List<File> outputFiles = new ArrayList<File>();
boolean nogui = false;
static boolean debug = false;
// if false, generate populations etc. in order found in LEMS/NeuroML (hopefully)
boolean generateAlphabetical = false;
public static final String NEURON_HOME_ENV_VAR = "NEURON_HOME";
public HashMap<String, String> modWritten = new HashMap<String, String>();
private final HashMap<String, Cell> compIdsVsCells = new HashMap<String, Cell>();
private final HashMap<String, String> popIdsVsCellIds = new HashMap<String, String>();
private final HashMap<String, Component> popIdsVsComps = new HashMap<String, Component>();
private final HashMap<String, IntracellularProperties> convertedCells = new HashMap<String, IntracellularProperties>();
private final String bIndent = " ";
private boolean parallelMode = false; // Some of the mod files etc. will have to be slightly different for Parallel NEURON
private static int MAX_LENGTH_LINE_MOD_FILE = 350;
private final HashMap<String, String> hocRefsVsInputs = new HashMap<String, String>();
public enum ChannelConductanceOption
{
FIXED_REVERSAL_POTENTIAL, USE_NERNST, USE_GHK, USE_GHK2;
float erev;
};
public NeuronWriter(Lems lems) throws ModelFeatureSupportException, LEMSException, NeuroMLException
{
super(lems, Format.NEURON);
E.info("Creating NeuronWriter");
}
public NeuronWriter(Lems lems, File outputFolder, String outputFileName) throws ModelFeatureSupportException, NeuroMLException, LEMSException
{
super(lems, Format.NEURON, outputFolder, outputFileName);
E.info("Creating NeuronWriter to output files to "+outputFolder.getAbsolutePath());
}
@Override
public void setSupportedFeatures()
{
sli.addSupportInfo(format, ModelFeature.ABSTRACT_CELL_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.COND_BASED_CELL_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.EXT_MORPH_BIOPHYS_CELL_MODEL, SupportLevelInfo.Level.HIGH);
sli.addSupportInfo(format, ModelFeature.SINGLE_COMP_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.NETWORK_MODEL, SupportLevelInfo.Level.LOW);
sli.addSupportInfo(format, ModelFeature.MULTI_CELL_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.MULTI_POPULATION_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.NETWORK_WITH_INPUTS_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.NETWORK_WITH_GAP_JUNCTIONS_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.NETWORK_WITH_ANALOG_CONNS_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.NETWORK_WITH_PROJECTIONS_MODEL, SupportLevelInfo.Level.LOW);
sli.addSupportInfo(format, ModelFeature.MULTICOMPARTMENTAL_CELL_MODEL, SupportLevelInfo.Level.LOW);
sli.addSupportInfo(format, ModelFeature.HH_CHANNEL_MODEL, SupportLevelInfo.Level.MEDIUM);
sli.addSupportInfo(format, ModelFeature.KS_CHANNEL_MODEL, SupportLevelInfo.Level.LOW);
}
public List<File> generateAndRun(boolean nogui, boolean compileMods, boolean run) throws LEMSException, GenerationException, NeuroMLException, IOException, ModelFeatureSupportException
{
return generateAndRun(nogui, compileMods, run, true);
}
public List<File> generateAndRun(boolean nogui, boolean compileMods, boolean run, boolean useNrnivForNoGui) throws LEMSException, GenerationException, NeuroMLException, IOException, ModelFeatureSupportException
{
this.nogui = nogui;
List<File> files = generateMainScriptAndMods();
if(compileMods || run)
{
if (compileMods)
{
E.info("Trying to compile all the mods in: " + this.getOutputFolder());
boolean complied = ProcessManager.compileFileWithNeuron(this.getOutputFolder(), false);
E.info("Success in compiling mods: " + complied);
if (!complied)
{
String mods = "";
for (File f: files )
{
if (f.getName().endsWith("mod"))
mods+=f.getAbsolutePath()+"; ";
}
throw new NeuroMLException("Error compiling mod files: "+mods);
}
}
if (run)
{
File neuronHome = findNeuronHome();
String nrncmd = nogui ? "nrniv" : "nrngui";
String fullPath = new File(this.getOutputFolder(), this.getOutputFileName()).getCanonicalPath();
List<String> commandToExecute = new ArrayList<String>();
commandToExecute.add(neuronHome.getCanonicalPath() + System.getProperty("file.separator") + "bin" + System.getProperty("file.separator") + nrncmd);
commandToExecute.add("-python");
commandToExecute.add(fullPath);
if (nogui && !useNrnivForNoGui)
{
commandToExecute.add("python");
commandToExecute.add(fullPath);
}
Runtime rt = Runtime.getRuntime();
//Process currentProcess = rt.exec(commandToExecute, null, this.getOutputFolder());
String[] commandToExecuteArr = new String[ commandToExecute.size() ];
commandToExecute.toArray( commandToExecuteArr );
Process currentProcess = rt.exec(commandToExecuteArr, null, this.getOutputFolder());
ProcessOutputWatcher procOutputMain = new ProcessOutputWatcher(currentProcess.getInputStream(), "NRN Output >>");
procOutputMain.start();
ProcessOutputWatcher procOutputError = new ProcessOutputWatcher(currentProcess.getErrorStream(), "NRN Error >>");
procOutputError.start();
E.info("Have successfully executed NEURON command: " + commandToExecute);
try
{
currentProcess.waitFor();
E.info("Exit value for running NEURON: " + currentProcess.exitValue());
String err = "Error, exit value from running this command: ["+commandToExecute
+"] in NEURON: "+currentProcess.exitValue()+"\n";
err += Utils.sysEnvInfo(" ");
if (currentProcess.exitValue()!=0)
throw new NeuroMLException(err);
}
catch(InterruptedException e)
{
E.error("Problem executing Neuron " + e);
E.error("with: " + Utils.sysEnvInfo(" "));
}
}
}
return files;
}
@Override
protected void addComment(StringBuilder sb, String comment)
{
addComment(sb, comment, "");
}
protected void addComment(StringBuilder sb, String comment, String indent)
{
if(!comment.contains("\n"))
{
sb.append(indent+NRNUtils.comm + comment + "\n");
}
else
{
sb.append(indent+NRNUtils.commPre + "\n" + indent+ comment.replaceAll("\n", "\n"+indent+"") + "\n" + indent + NRNUtils.commPost + "\n");
}
}
private void reset()
{
outputFiles.clear();
}
public void setParallelMode(boolean parallel)
{
parallelMode = parallel;
}
public void setNoGui(boolean nogui)
{
this.nogui = nogui;
}
public boolean isNoGui()
{
return nogui;
}
public void setGenerateAlphabetical(boolean generateAlphabetical)
{
this.generateAlphabetical = generateAlphabetical;
}
public List<File> generateMainScriptAndMods() throws LEMSException, GenerationException, NeuroMLException
{
File mainFile = new File(this.getOutputFolder(), this.getOutputFileName());
try
{
String main = getMainScript();
FileUtil.writeStringToFile(main, mainFile);
outputFiles.add(mainFile);
}
catch(IOException ex)
{
throw new GenerationException("Error writing to file: " + mainFile.getAbsolutePath(), ex);
}
return outputFiles;
}
public String getMainScript() throws GenerationException, NeuroMLException
{
this.setParallelMode(false);
try
{
reset();
StringBuilder main = new StringBuilder();
addComment(main, "Neuron simulator export for:\n\n" + lems.textSummary(false, false) + "\n\n" + Utils.getHeaderComment(format) + "\n");
main.append("\nimport neuron\n");
main.append("\nimport time");
main.append("\nimport datetime");
main.append("\nimport sys\n");
main.append("\nimport hashlib\n");
main.append("h = neuron.h\n");
if(nogui)
{
main.append("h.load_file(\"stdlib.hoc\")\n\n");
main.append("h.load_file(\"stdgui.hoc\")\n\n");
}
else
{
main.append("h.load_file(\"nrngui.hoc\")\n\n");
}
main.append("h(\"objref p\")\n");
main.append("h(\"p = new PythonObject()\")\n\n");
Target target = lems.getTarget();
Component simCpt = target.getComponent();
String len = simCpt.getStringValue("length");
len = len.replaceAll("ms", "");
if(len.indexOf("s") > 0)
{
len = len.replaceAll("s", "").trim();
len = "" + Float.parseFloat(len) * 1000;
}
/* cvode usage:
* https://nrn.readthedocs.io/en/latest/hoc/simctrl/cvode.html
* - we do not currently support the local variable time step method
*/
boolean nrn_cvode = false;
String dt = "0.01";
/* defaults from NEURON */
String abs_tol = "None";
String rel_tol = "None";
LemsCollection<Meta> metas = simCpt.metas;
for(Meta m : metas)
{
HashMap<String, String> attributes = m.getAttributes();
if (attributes.getOrDefault("for", "").equals("neuron"))
{
if (attributes.getOrDefault("method", "").equals("cvode"))
{
nrn_cvode = true;
abs_tol = attributes.getOrDefault("abs_tolerance", abs_tol);
rel_tol = attributes.getOrDefault("rel_tolerance", rel_tol);
E.info("CVode with abs_tol="+abs_tol+" , rel_tol="+rel_tol+" selected for NEURON simulation");
}
}
}
if (nrn_cvode == false)
{
dt = simCpt.getStringValue("step");
dt = dt.replaceAll("ms", "").trim();
if(dt.indexOf("s") > 0)
{
dt = dt.replaceAll("s", "").trim();
dt = "" + Float.parseFloat(dt) * 1000;
}
}
main.append("class NeuronSimulation():\n\n");
int seed = DLemsWriter.DEFAULT_SEED;
if (simCpt.hasStringValue("seed"))
seed = Integer.parseInt(simCpt.getStringValue("seed"));
main.append(" def __init__(self, tstop, dt=None, seed="+seed+", abs_tol=None, rel_tol=None):\n\n");
Component targetComp = simCpt.getRefComponents().get("target");
main.append(bIndent+"print(\"\\n Starting simulation in NEURON of %sms generated from NeuroML2 model...\\n\"%tstop)\n\n");
main.append(bIndent+"self.setup_start = time.time()\n");
main.append(bIndent+"self.seed = seed\n");
main.append(bIndent+"self.abs_tol = abs_tol\n");
main.append(bIndent+"self.rel_tol = rel_tol\n");
if (target.reportFile!=null)
{
main.append(bIndent+"import socket\n");
String reportFile = IOUtil.getCompleteReportFileName(target.reportFile, "NEURON", null);
main.append(bIndent+"self.report_file = open('"+reportFile+"','w')\n");
main.append(bIndent+"print('Simulator version: %s'%h.nrnversion())\n");
main.append(bIndent+"self.report_file.write('# Report of running simulation with %s\\n'%h.nrnversion())\n");
main.append(bIndent+"self.report_file.write('Simulator=NEURON\\n')\n");
main.append(bIndent+"self.report_file.write('SimulatorVersion=%s\\n'%h.nrnversion())\n\n");
main.append(bIndent+"self.report_file.write('SimulationFile=%s\\n'%__file__)\n");
main.append(bIndent+"self.report_file.write('PythonVersion=%s\\n'%sys.version.replace('\\n',' '))\n");
main.append(bIndent+"print('Python version: %s'%sys.version.replace('\\n',' '))\n");
main.append(bIndent+"self.report_file.write('NeuroMLExportVersion="+Utils.ORG_NEUROML_EXPORT_VERSION+"\\n')\n");
main.append(bIndent+"self.report_file.write('SimulationSeed=%s\\n'%self.seed)\n");
main.append(bIndent+"self.report_file.write('Hostname=%s\\n'%socket.gethostname())\n");
}
main.append(bIndent+"self.randoms = []\n");
main.append(bIndent+"self.next_global_id = 0 # Used in Random123 classes for elements using random(), etc. \n\n");
main.append(bIndent+"self.next_spiking_input_id = 0 # Used in Random123 classes for elements using random(), etc. \n\n");
String info = "Adding simulation " + simCpt + " of network/component: " + targetComp.summary();
E.info(info);
addComment(main, info+"\n", bIndent);
ArrayList<Component> popsOrComponents = targetComp.getChildrenAL("populations", generateAlphabetical);
//E.info("popsOrComponents: " + popsOrComponents);
HashMap<String, Integer> compMechsCreated = new HashMap<String, Integer>();
HashMap<String, String> compMechNamesHoc = new HashMap<String, String>();
if(popsOrComponents.isEmpty())
{
popsOrComponents.add(targetComp);
}
else
{
if(targetComp.getComponentType().getName().equals("networkWithTemperature") || (targetComp.hasAttribute("type") && targetComp.getStringValue("type").equals("networkWithTemperature")))
{
String temp = targetComp.getStringValue("temperature");
float tempSI = Utils.getMagnitudeInSI(temp);
main.append("\n # Temperature used for network: " + tempSI + " K\n");
main.append(bIndent+"h.celsius = " + tempSI + " - 273.15\n\n");
}
}
for(Component popsOrComponent : popsOrComponents)
{
String compReference;
String popName;
int number;
Component popComp;
HashMap<Integer,String> locations = new HashMap<Integer,String>();
HashMap<Integer,String> locationStrs = new HashMap<Integer,String>();
if(popsOrComponent.getComponentType().getName().equals(NeuroMLElements.POPULATION))
{
number = Integer.parseInt(popsOrComponent.getStringValue(NeuroMLElements.POPULATION_SIZE));
popComp = popsOrComponent.getRefComponents().get("component");
popName = popsOrComponent.getID();
popIdsVsCellIds.put(popName, popComp.getID());
popIdsVsComps.put(popName, popComp);
}
else if(popsOrComponent.getComponentType().getName().equals(NeuroMLElements.POPULATION_LIST))
{
compReference = popsOrComponent.getStringValue(NeuroMLElements.POPULATION_COMPONENT);
popComp = popsOrComponent.getRefComponents().get("component");
number = 0;
for(Component instance : popsOrComponent.getAllChildren())
{
if(instance.getComponentType().getName().equals(NeuroMLElements.INSTANCE))
{
number++;
Component loc = instance.getChild(NeuroMLElements.LOCATION);
String location = "("+Float.parseFloat(loc.getAttributeValue(NeuroMLElements.LOCATION_X))
+", "+Float.parseFloat(loc.getAttributeValue(NeuroMLElements.LOCATION_Y))
+", "+Float.parseFloat(loc.getAttributeValue(NeuroMLElements.LOCATION_Z))+")";
String locationStr = "("+Float.parseFloat(loc.getAttributeValue(NeuroMLElements.LOCATION_X))
+", "+Float.parseFloat(loc.getAttributeValue(NeuroMLElements.LOCATION_Y))
+" + HALF_LENGTH, "+Float.parseFloat(loc.getAttributeValue(NeuroMLElements.LOCATION_Z))+", DIAMETER)";
locations.put(Integer.parseInt(instance.getID()), location);
locationStrs.put(Integer.parseInt(instance.getID()), locationStr);
}
}
popComp.getAllChildren().size();
popName = popsOrComponent.getID();
popIdsVsCellIds.put(popName, compReference);
popIdsVsComps.put(popName, popComp);
}
else
{
// compReference = popsOrComponent.getComponentType().getName();
number = 1;
popComp = popsOrComponent;
popName = LEMSQuantityPath.DUMMY_POPULATION_PREFIX + popComp.getName();
}
addComment(main, "###################### Population: "+popName," ");
E.info("Adding population: "+popName);
String compTypeName = popComp.getComponentType().getName();
main.append(bIndent+"print(\"Population " + popName + " contains " + number + " instance(s) of component: " + popComp.getID() + " of type: " + popComp.getComponentType().getName() + "\")\n\n");
if(popComp.getComponentType().isOrExtends(NeuroMLElements.CELL_COMP_TYPE))
{
Cell cell = getCellFromComponent(popComp);
IntracellularProperties ip;
if (convertedCells.containsKey(popComp.id))
{
ip = convertedCells.get(popComp.id);
}
else
{
ip = convertCellWithMorphology(popComp);
convertedCells.put(popComp.id, ip);
}
NamingHelper nh = new NamingHelper(cell);
String cellName = popComp.getID();
String fileName = cellName + ".hoc";
for (Species species: ip.getSpecies()) {
float internal = NRNUtils.convertToNeuronUnits(Utils.getMagnitudeInSI(species.getInitialConcentration()), "concentration");
float external = NRNUtils.convertToNeuronUnits(Utils.getMagnitudeInSI(species.getInitialExtConcentration()), "concentration");
main.append(bIndent+"print(\"Setting the default initial concentrations for " + species.getIon() + " (used in "+cellName
+") to "+internal+" mM (internal), "+external+" mM (external)\")\n");
main.append(bIndent+"h(\"" + species.getIon() + "i0_" + species.getIon() + "_ion = " + internal + "\")\n");
main.append(bIndent+"h(\"" + species.getIon() + "o0_" + species.getIon() + "_ion = " + external + "\")\n\n");
}
StringBuilder popInfo = new StringBuilder();
popInfo.append(bIndent+"h.load_file(\"" + fileName + "\")\n");
popInfo.append(" a_" + popName + " = []\n");
popInfo.append(bIndent+"h(\"{ n_" + popName + " = " + number + " }\")\n");
popInfo.append(bIndent+"h(\"objectvar a_" + popName + "[n_" + popName + "]\")\n");
popInfo.append(" for i in range(int(h.n_" + popName + ")):\n");
// main.append(" cell = h."+cellName+"()\n");
popInfo.append(" h(\"a_" + popName + "[%i] = new " + cellName + "()\"%i)\n");
// main.append(" cell."+getNrnSectionName(cell.getMorphology().getSegment().get(0))+".push()\n");
popInfo.append(" h(\"access a_" + popName + "[%i]." + nh.getNrnSectionName(cell.getMorphology().getSegment().get(0)) + "\"%i)\n\n");
popInfo.append(" self.next_global_id+=1\n\n");
for (Integer cell_id: locations.keySet()) {
popInfo.append(bIndent+"h(\"{ a_" + popName + "["+cell_id+"].position"+locations.get(cell_id)+" }\")\n");
}
popInfo.append(String.format("\n h(\"proc initialiseV_%s() { for i = 0, n_%s-1 { a_%s[i].set_initial_v() } }\")\n", popName, popName, popName));
popInfo.append(String.format(bIndent+"h(\"objref fih_%s\")\n", popName));
popInfo.append(String.format(bIndent+"h(\'{fih_%s = new FInitializeHandler(0, \"initialiseV_%s()\")}\')\n\n", popName, popName));
popInfo.append(String.format(bIndent+"h(\"proc initialiseIons_%s() { for i = 0, n_%s-1 { a_%s[i].set_initial_ion_properties() } }\")\n", popName, popName, popName));
popInfo.append(String.format(bIndent+"h(\"objref fih_ion_%s\")\n", popName));
popInfo.append(String.format(bIndent+"h(\'{fih_ion_%s = new FInitializeHandler(1, \"initialiseIons_%s()\")}\')\n\n", popName, popName));
main.append(popInfo);
}
else
{
generateModForComp(popComp);
main.append(bIndent+"h(\" {n_" + popName + " = " + number + "} \")\n");
String mechName = NRNUtils.getMechanismName(popComp, popName);
addComment(main, "Population " + popName + " contains instances of " + popComp + "\n" +
"whose dynamics will be implemented as a mechanism (" + popComp.getID() + ") in a mod file"," ");
main.append(bIndent+"h(\" create " + popName + "[" + number + "]\")\n");
main.append(bIndent+"h(\" objectvar " + mechName + "[" + number + "] \")\n\n");
main.append(bIndent+"for i in range(int(h.n_" + popName + ")):\n");
String instName = popName + "[i]";
// main.append(instName + " = h.Section()\n");
double defaultRadius = 5;
double length = defaultRadius * 2;
double diameter = defaultRadius * 2;
if(popComp.getComponentType().isOrExtends(NeuroMLElements.BASE_CELL_CAP_COMP_TYPE) ||
popComp.getComponentType().isOrExtends(NeuroMLElements.BASE_PYNN_CELL))
{
double capTotSI = -1;
if (popComp.getComponentType().isOrExtends(NeuroMLElements.BASE_CELL_CAP_COMP_TYPE))
{
//main.append(bIndent+" #...\n");
if (popComp.hasParam("length"))
{
length = popComp.getParamValue("length").getDoubleValue() * 1e6;
}
if (popComp.hasParam("diameter"))
{
diameter = popComp.getParamValue("diameter").getDoubleValue() * 1e6;
}
if (popComp.hasParam("refract") && popComp.getParamValue("refract").getDoubleValue()==0)
{
throw new NeuroMLException("Unfortunately the NEURON export for IaF cells cannot *YET* handle "
+ "cases when refract = 0 (as in cell "+popComp.getID()+")");
}
capTotSI = popComp.getParamValue("C").getDoubleValue();
}
else if (popComp.getComponentType().isOrExtends(NeuroMLElements.BASE_PYNN_CELL))
{
if (popComp.hasParam("tau_refrac") && popComp.getParamValue("tau_refrac").getDoubleValue()==0)
{
throw new NeuroMLException("Unfortunately the NEURON export for PyNN cells cannot *YET* handle "
+ "cases when tau_refrac = 0 (as in cell "+popComp.getID()+")");
}
capTotSI = popComp.getParamValue("cm").getDoubleValue() * 1e-9;
}
double area = Math.PI * length * diameter;
double specCapNeu = 10e13 * capTotSI / area;
main.append(bIndent+" h." + instName + "(0.5).cm = " + specCapNeu + " # Computed from area "+area+"\n");
}
else
{
// See https://github.com/NeuroML/org.neuroml.export/issues/60
main.append(bIndent+" h." + instName + "(0.5).cm = 318.31\n");
}
main.append(bIndent+" h." + instName + ".L = " + length + " # length to use for section in Neuron\n");
main.append(bIndent+" h." + instName + "(0.5).diam = " + diameter + " # diameter to use for section in Neuron\n");
main.append(bIndent+" h." + instName + ".push()\n");
main.append(bIndent+" h(\" " + instName.replaceAll("\\[i\\]", "[%i]") + " { " + mechName + "[%i] = new " + popComp.getID() + "(0.5) } \"%(i,i))\n\n");
if(!compMechsCreated.containsKey(compTypeName))
{
compMechsCreated.put(compTypeName, 0);
}
compMechsCreated.put(compTypeName, compMechsCreated.get(compTypeName) + 1);
String hocMechName = NRNUtils.getMechanismName(popComp, popName) + "[i]";
compMechNamesHoc.put(instName, hocMechName);
LemsCollection<ParamValue> pvs = popComp.getParamValues();
for(ParamValue pv : pvs)
{
main.append(bIndent+" h." + hocMechName + "." + pv.getName()
+ " = " + NRNUtils.convertToNeuronUnits((float) pv.getDoubleValue(), pv.getDimensionName())
+ " # NRN unit is: "+NRNUtils.getNeuronUnit(pv.getDimensionName())+"\n");
}
if (!popComp.getComponentType().isOrExtends(NeuroMLElements.SPIKE_ARRAY) &&
!popComp.getComponentType().isOrExtends(NeuroMLElements.SPIKE_GENERATOR) &&
(popComp.getComponentType().isOrExtends(NeuroMLElements.BASE_SPIKE_SOURCE_COMP_TYPE) ||
popComp.getComponentType().isOrExtends(NeuroMLElements.BASE_VOLT_DEP_CURR_SRC_SPIKING_COMP_TYPE)))
{
main.append(bIndent+" # Spiking element ("+popComp.getComponentType().getName()+"), will require seeding...\n");
main.append(bIndent+" rand = h.Random()\n");
main.append(bIndent+" self.randoms.append(rand)\n");
main.append(bIndent+" #print(\"Seeding random generator on "+hocMechName+" (i=%i) with stim seed %s\"%(i, self.seed))\n");
main.append(bIndent+" self._init_stim_randomizer(rand,\""+popName+"\",i, self.seed)\n");
main.append(bIndent+" rand.negexp(1)\n");
main.append(bIndent+" h."+hocMechName+".noiseFromRandom(rand)\n\n");
}
if(popComp.getComponentType().isOrExtends(NeuroMLElements.BASE_RATE_UNIT))
{
main.append(bIndent+" # use internal i_cap to gather info on incoming currents\n");
main.append(bIndent+" h(\"setpointer " + NRNUtils.getMechanismName(popComp, popName) + "[%i].isyn_in, "+popName+"[%i].i_cap(0.5)\"%(i,i))\n\n");
}
main.append(bIndent+" h.pop_section()\n\n");
main.append(bIndent+" self.next_global_id+=1\n\n");
for (Integer cell_id: locationStrs.keySet()) {
main.append(bIndent+"h(\" " + popName + "["+cell_id+"] { pt3dclear() } \")\n");
main.append(bIndent+"h(\" " + popName + "["+cell_id+"] { pt3dadd"+locationStrs.get(cell_id).replace("HALF_LENGTH","("+length/2.+")").replace("DIAMETER","("+diameter+")")+" } \")\n");
main.append(bIndent+"h(\" " + popName + "["+cell_id+"] { pt3dadd"+locationStrs.get(cell_id).replace("HALF_LENGTH","("+length/-2.+")").replace("DIAMETER","("+diameter+")")+" } \")\n");
}
main.append("\n");
}
}
// / Add projections/connections
E.info("Adding projections/connections...");
String synObjArrayName = null;
ArrayList<Component> projections = targetComp.getChildrenAL("projections", generateAlphabetical);
// TODO: change this to use arrays when vars are recorded too...
boolean synArrayFormat = !NRNUtils.isPlottingSavingSynVariables(simCpt, nogui);
HashMap<String, Integer> segmentSynapseCounts = new HashMap<String, Integer>();
for(Component projection : projections)
{
String projId = projection.getID();
String prePop = projection.getStringValue("presynapticPopulation");
String postPop = projection.getStringValue("postsynapticPopulation");
Cell preCell = compIdsVsCells.get(popIdsVsCellIds.get(prePop));
Cell postCell = compIdsVsCells.get(popIdsVsCellIds.get(postPop));
String synapse = projection.getStringValue("synapse");
int number = 0;
for(Component comp : projection.getAllChildren())
{
if(comp.getComponentType().isOrExtends(NeuroMLElements.CONNECTION))
{
number++;
}
}
addComment(main, "###################### Projection: "+projId," ");
info = String.format("Adding projection: %s, from %s to %s with synapse %s, %d connection(s)", projId, prePop, postPop, synapse, number);
main.append(bIndent+"print(\""+info+"\")\n\n");
Component synapseComp = projection.getRefComponents().get("synapse");
generateModForComp(synapseComp);
String netConnObjArrayName = null;
if (synArrayFormat)
{
synObjArrayName = String.format("syn_%s_%s", projId, synapse);
netConnObjArrayName = String.format("netConn_%s_%s", projId, synapse);
main.append(String.format(bIndent+"h(\"objectvar %s[%d]\")\n\n", synObjArrayName, number));
main.append(String.format(bIndent+"h(\"objectvar %s[%d]\")\n\n", netConnObjArrayName, number));
}
NamingHelper nhPre = new NamingHelper(preCell);
NamingHelper nhPost = new NamingHelper(postCell);
int connIndex = 0;
for(Component conn : projection.getAllChildren())
{
if(conn.getComponentType().isOrExtends(NeuroMLElements.CONNECTION))
{
int preCellId = Utils.parseCellRefStringForCellNum(conn.getStringValue("preCellId"));
int postCellId = Utils.parseCellRefStringForCellNum(conn.getStringValue("postCellId"));
int preSegmentId = conn.hasAttribute("preSegmentId") ? Integer.parseInt(conn.getAttributeValue("preSegmentId")) : 0;
int postSegmentId = conn.hasAttribute("postSegmentId") ? Integer.parseInt(conn.getAttributeValue("postSegmentId")) : 0;
float preFractionAlong0 = conn.hasAttribute("preFractionAlong") ? Float.parseFloat(conn.getAttributeValue("preFractionAlong")) : 0.5f;
float postFractionAlong0 = conn.hasAttribute("postFractionAlong") ? Float.parseFloat(conn.getAttributeValue("postFractionAlong")) : 0.5f;
String preSecName;
if(preCell != null)
{
preSecName = String.format("a_%s[%s].%s", prePop, preCellId, nhPre.getNrnSectionName(CellUtils.getSegmentWithId(preCell, preSegmentId)));
}
else
{
preSecName = prePop + "[" + preCellId + "]";
}
String postSecName;
if(postCell != null)
{
postSecName = String.format("a_%s[%s].%s", postPop, postCellId, nhPost.getNrnSectionName(CellUtils.getSegmentWithId(postCell, postSegmentId)));
}
else
{
postSecName = postPop + "[" + postCellId + "]";
}
String segSynRef = String.format("syn_%s_%s_%s_%s", postPop,postCellId, postSegmentId, synapse);
if (!segmentSynapseCounts.containsKey(segSynRef))
segmentSynapseCounts.put(segSynRef, 0);
int synIndex = segmentSynapseCounts.get(segSynRef);
String synObjName = null;
if (synArrayFormat)
{
synObjName = String.format("%s[%s]", synObjArrayName, connIndex);
}
else
{
synObjName = String.format("%s_%s", segSynRef, connIndex);
}
segmentSynapseCounts.put(segSynRef, synIndex+1);
float postFract = postFractionAlong0;
if(postCell != null)
{
postFract = !CellUtils.hasUnbranchedNonOverlappingInfo(postCell) ? postFractionAlong0 : (float) CellUtils.getFractionAlongSegGroupLength(postCell, postSecName.split("\\.")[1], postSegmentId, postFractionAlong0);
}
float preFract = preFractionAlong0;
if(preCell != null)
{
preFract = !CellUtils.hasUnbranchedNonOverlappingInfo(preCell) ? preFractionAlong0 : (float) CellUtils.getFractionAlongSegGroupLength(preCell, preSecName.split("\\.")[1], preSegmentId, preFractionAlong0);
}
String sourceVarToListenFor = "&v("+ preFract+")";
float weight = 1;
float delay = 0;
if (conn.getComponentType().isOrExtends(NeuroMLElements.CONNECTION_WEIGHT_DELAY))
{
weight = Float.parseFloat(conn.getAttributeValue("weight"));
delay = NRNUtils.convertToNeuronUnits(conn.getAttributeValue("delay"), lems);
}
String comment = String.format(Locale.US, "Connection %s: cell %d, seg %d (%s) [%s on %s] -> cell %d, seg %d (%s) [%s on %s], weight: %s, delay %s", conn.getID(), preCellId, preSegmentId, preFractionAlong0, preFract, preSecName, postCellId, postSegmentId, postFractionAlong0, postFract, postSecName, weight, delay);
//System.out.println("comment@: "+comment);
addComment(main, comment," ");
if (!synArrayFormat)
{
main.append(String.format(bIndent+"h(\"objectvar %s\")\n", synObjName));
}
main.append(String.format(Locale.US, bIndent+"h(\"%s %s = new %s(%s)\")\n", postSecName, synObjName, synapse, postFract));
if(preCell != null)
{
float threshold;
if (getMembraneProperties(preCell).getSpikeThresh().size()>0)
{
SpikeThresh st = getMembraneProperties(preCell).getSpikeThresh().get(0);
if (!st.getSegmentGroup().equals(NeuroMLElements.SEGMENT_GROUP_ALL))
{
throw new NeuroMLException("Cannot yet handle <spikeThresh> when it is not on segmentGroup all");
}
threshold = NRNUtils.convertToNeuronUnits(st.getValue(), lems);
}
else
{
threshold = 0;
}
if(postCell != null)
{
main.append(String.format(bIndent+"h(\"%s a_%s[%d].synlist.append(new NetCon(%s, %s, %s, %s, %s))\") # *->cell\n\n", preSecName, postPop, postCellId, sourceVarToListenFor, synObjName,
threshold, delay, weight));
}
else
{
main.append(String.format(bIndent+"h(\"%s %s[%d] = new NetCon(%s, %s, %g, %g, %g)\") # cell->abst\n\n", preSecName, netConnObjArrayName, connIndex, sourceVarToListenFor, synObjName, threshold, delay, weight));
}
}
else
{
Component preComp = popIdsVsComps.get(prePop);
float threshold = NRNUtils.getThreshold(preComp, lems);
if(preComp.getComponentType().isOrExtends(NeuroMLElements.BASE_SPIKE_SOURCE_COMP_TYPE))
{
String hocMechName = NRNUtils.getMechanismName(preComp, prePop) + "["+preCellId+"]";
sourceVarToListenFor = hocMechName;
}
if (synArrayFormat)
{
main.append(String.format(Locale.US, bIndent+"h(\"%s %s[%d] = new NetCon(%s, %s, %s, %s, %s)\") \n\n", preSecName, netConnObjArrayName, connIndex, sourceVarToListenFor, synObjName, threshold, delay, weight));
}
else
{
String netConnName = String.format("nc_%s_%d", synObjName, connIndex);
main.append(String.format(bIndent+"h(\"objectvar %s\")\n", netConnName));
main.append(String.format(Locale.US, bIndent+"h(\"%s %s = new NetCon(%s, %s, %s, %s, %s)\") \n\n", preSecName, netConnName, sourceVarToListenFor, synObjName, threshold, delay, weight));
}
}
connIndex++;
}
}
}
/* <synapticConnection> and <synapticConnectionWD> elements */
ArrayList<Component> synapticConnections = targetComp.getChildrenAL("synapticConnections");
synapticConnections.addAll(targetComp.getChildrenAL("synapticConnectionWDs"));
/* First, group connections by synapse type */
HashMap<Component, ArrayList<Component>> connectionsByType = new HashMap<Component, ArrayList<Component>>();
for(Component connection : synapticConnections)
{
Component synType = connection.getRefComponents().get("synapse");
if(connectionsByType.containsKey(synType) == false)
{
connectionsByType.put(synType, new ArrayList<Component>());
}
connectionsByType.get(synType).add(connection);
}
for(Map.Entry<Component, ArrayList<Component>> entry : connectionsByType.entrySet())
{
Component synapseComp = entry.getKey();
ArrayList<Component> connections = entry.getValue();
generateModForComp(synapseComp);
/* Array of synapses of this type */
String info0 = String.format("Adding synapse %s used in %s connections", synapseComp.getID(), connections.size());
addComment(main, info0,bIndent);
//String synArrayName = String.format("synapses_%s", synapseComp.getID());
//main.append(String.format(bIndent+"h(\"objectvar %s[%d]\")\n\n", synArrayName, connections.size()));
for(int i = 0; i < connections.size(); i++)
{
Component connection = connections.get(i);
String fromRef = connection.getStringValue("from");
int preCellId = Utils.parseCellRefStringForCellNum(fromRef);
String prePop = Utils.parseCellRefStringForPopulation(fromRef);
Cell preCell = compIdsVsCells.get(popIdsVsCellIds.get(prePop));
String preSecName;
if(preCell != null)
{
NamingHelper nh0 = new NamingHelper(preCell);
preSecName = String.format("a_%s[%s].%s", prePop, preCellId, nh0.getNrnSectionName(preCell.getMorphology().getSegment().get(0)));
}
else
{
preSecName = prePop + "[" + preCellId + "]";
}
String postRef = connection.getStringValue("to");
int postCellId = Utils.parseCellRefStringForCellNum(postRef);
String postPop = Utils.parseCellRefStringForPopulation(postRef);
Cell postCell = compIdsVsCells.get(popIdsVsCellIds.get(postPop));
String toSecName;
if(postCell != null)
{
NamingHelper nh0 = new NamingHelper(postCell);
toSecName = String.format("a_%s[%s].%s", postPop, postCellId, nh0.getNrnSectionName(postCell.getMorphology().getSegment().get(0)));
}
else
{
toSecName = postPop + "[" + postCellId + "]";
}
String info1 = String.format("Adding connection from %s to %s", fromRef, postRef);
addComment(main, info1,bIndent);
String segSynRef = String.format("syn_%s_%s_%s_%s", postPop, postCellId, 0, synapseComp.getID());
if (!segmentSynapseCounts.containsKey(segSynRef))
segmentSynapseCounts.put(segSynRef, 0);
int synIndex = segmentSynapseCounts.get(segSynRef);
String synObjName = String.format("%s_%s", segSynRef, synIndex);
main.append(String.format(bIndent+"h(\"objectvar %s\")\n\n", synObjName));
segmentSynapseCounts.put(segSynRef, synIndex+1);
main.append(String.format(Locale.US, bIndent+"h(\"%s %s = new %s(%s)\")\n", toSecName, synObjName, synapseComp.getID(), 0.5));
float delay = connection.hasParam("delay") ? (float) connection.getParamValue("delay").getDoubleValue() * 1000 : 0.0f;
//this also accounts for dimensional weights, if we ever want to support that.
float weight = connection.hasParam("weight") ? (float) NRNUtils.convertToNeuronUnits(connection.getAttributeValue("weight") , lems) : 1.0f;
if(postCell != null)
{
Component fromComp = popIdsVsComps.get(prePop);
float fract = 0.5f;
String sourceVarToListenFor = "&v("+fract+")";
float threshold = NRNUtils.getThreshold(fromComp, lems);
if(fromComp.getComponentType().isOrExtends(NeuroMLElements.BASE_SPIKE_SOURCE_COMP_TYPE))
{
String hocMechName = NRNUtils.getMechanismName(fromComp, prePop) + "["+preCellId+"]";
sourceVarToListenFor = hocMechName;
}
main.append(String.format(bIndent+"h(\"%s a_%s[%d].synlist.append(new NetCon(%s, %s, %g, %g, %g))\") # ...\n\n", preSecName, postPop, postCellId, sourceVarToListenFor, synObjName, threshold, delay, weight));
}
else
{
Component fromComp = popIdsVsComps.get(prePop);
float threshold = NRNUtils.getThreshold(fromComp, lems);