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NeuroML2Reader.java
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/**
* neuroConstruct
* Software for developing large scale 3D networks of biologically realistic neurons
*
* Copyright (c) 2009 Padraig Gleeson
* UCL Department of Neuroscience, Physiology and Pharmacology
*
* Development of this software was made possible with funding from the
* Medical Research Council and the Wellcome Trust
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
*/
package ucl.physiol.neuroconstruct.neuroml;
import java.io.*;
import java.nio.file.*;
import java.net.MalformedURLException;
import java.util.HashMap;
import java.util.TreeMap;
import java.util.LinkedHashMap;
import java.util.Vector;
import java.util.Random;
import java.awt.Color;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashSet;
import java.util.Properties;
import java.util.Set;
import org.neuroml.export.utils.Utils;
import org.neuroml.model.Connection;
import org.neuroml.model.ConnectionWD;
import org.neuroml.model.Input;
import org.neuroml.model.InputList;
import org.neuroml.model.Instance;
import org.neuroml.model.Location;
import org.neuroml.model.Network;
import org.neuroml.model.NeuroMLDocument;
import org.neuroml.model.Population;
import org.neuroml.model.Projection;
import org.neuroml.model.ElectricalProjection;
import org.neuroml.model.ElectricalConnectionInstance;
import org.neuroml.model.ElectricalConnection;
import org.neuroml.model.PulseGenerator;
import org.neuroml.model.PoissonFiringSynapse;
import org.neuroml.model.TransientPoissonFiringSynapse;
import org.neuroml.model.util.NeuroMLConverter;
import ucl.physiol.neuroconstruct.cell.Cell;
import ucl.physiol.neuroconstruct.cell.converters.NeuroML2CellReader;
import ucl.physiol.neuroconstruct.cell.utils.CellTopologyHelper;
import ucl.physiol.neuroconstruct.project.*;
import ucl.physiol.neuroconstruct.project.cellchoice.CellChooser;
import ucl.physiol.neuroconstruct.project.cellchoice.IndividualCells;
import ucl.physiol.neuroconstruct.project.PrePostAllowedLocs;
import ucl.physiol.neuroconstruct.project.stimulation.IClamp;
import ucl.physiol.neuroconstruct.utils.*;
import ucl.physiol.neuroconstruct.utils.units.UnitConverter;
import ucl.physiol.neuroconstruct.project.packing.CellPackingAdapter;
import ucl.physiol.neuroconstruct.project.packing.RandomCellPackingAdapter;
import ucl.physiol.neuroconstruct.project.packing.CellPackingException;
import ucl.physiol.neuroconstruct.project.packing.SinglePositionedCellPackingAdapter;
import ucl.physiol.neuroconstruct.project.segmentchoice.IndividualSegments;
import ucl.physiol.neuroconstruct.project.segmentchoice.SegmentLocationChooser;
import ucl.physiol.neuroconstruct.project.stimulation.RandomSpikeTrain;
import ucl.physiol.neuroconstruct.project.stimulation.RandomSpikeTrainExt;
import ucl.physiol.neuroconstruct.project.stimulation.ElectricalInput;
import ucl.physiol.neuroconstruct.simulation.IClampSettings;
import ucl.physiol.neuroconstruct.simulation.RandomSpikeTrainSettings;
import ucl.physiol.neuroconstruct.simulation.RandomSpikeTrainExtSettings;
import ucl.physiol.neuroconstruct.simulation.StimulationSettings;
import java.util.regex.*;
import ucl.physiol.neuroconstruct.cell.Section;
import ucl.physiol.neuroconstruct.cell.Segment;
import ucl.physiol.neuroconstruct.mechanisms.CellMechanism;
import ucl.physiol.neuroconstruct.mechanisms.CellMechanismHelper;
import ucl.physiol.neuroconstruct.mechanisms.NeuroML2Component;
import ucl.physiol.neuroconstruct.project.stimulation.IClampInstanceProps;
import ucl.physiol.neuroconstruct.project.stimulation.RandomSpikeTrainInstanceProps;
import ucl.physiol.neuroconstruct.project.stimulation.RandomSpikeTrainExtInstanceProps;
/**
* NeuroML 2 file Reader. Importer of NeuroML 2 files to neuroConstruct
*
* @author Padraig Gleeson
*
* @author Rokas Stanislovas
*/
public class NeuroML2Reader implements NetworkMLnCInfo
{
public static ClassLogger logger = new ClassLogger("NeuroML2Reader");
private long foundRandomSeed = Long.MIN_VALUE;
private String foundSimConfig = null;
private GeneratedCellPositions cellPos = null;
private GeneratedNetworkConnections netConns = null;
private ArrayList<ConnSpecificProps> localConnProps = new ArrayList<ConnSpecificProps>();
private GeneratedElecInputs elecInputs = null;
private Project project = null;
private String currentPopulation = null;
private String preCellType = null;
private String postCellType = null;
private Cell preCellnC;
private Cell postCellnC;
private int prePopSize = 0;
private int postPopSize = 0;
private String groupCellType = "";
private RectangularBox region = new RectangularBox(0, 0, 0, 100, 100, 100);
private Integer priority = 0;
private String currentElecInput = null;
private String currentInputType = null;
private String currentInputCellGroup = null;
private String currentInputName = null;
private ElectricalInput currentElectricalInput = null;
private SingleElectricalInput currentSingleInput = null;
private IClampInstanceProps iip = null;
private RandomSpikeTrainInstanceProps rp = null;
private RandomSpikeTrainExtInstanceProps trp = null;
private int inputUnitSystem = -1;
private SimConfig simConfigToUse = new SimConfig();
public boolean testMode = false;
private final static String PERSISTENT_POISSON = "persistentPoisson";
private final static String TRANSIENT_POISSON = "transientPoisson";
private final static float PICO_TO_MICRO_SCALING = (float) 0.000001;
private final static float PICO_TO_ONE_SCALING = (float) 0.000000000001;
private final static float PER_S_TO_PER_MS_SCALING = (float) 0.001;
private final boolean inferConnectivityConditions;
public NeuroML2Reader(Project project, boolean inferConnConditions)
{
this.cellPos = project.generatedCellPositions;
this.netConns = project.generatedNetworkConnections;
this.elecInputs = project.generatedElecInputs;
this.project = project;
this.testMode = false;
this.inferConnectivityConditions = inferConnConditions;
}
public void setTestMode(boolean test)
{
this.testMode = test;
}
public String getSimConfig()
{
return this.foundSimConfig;
}
public long getRandomSeed()
{
return this.foundRandomSeed;
}
public HashMap getValueAndUnits(String delay) throws NeuroMLException
{
HashMap<String, String> delay_dict = new HashMap<String, String>();
Pattern unit_string = Pattern.compile("[a-zA-Z&&[^E-]]+");
Matcher match_delay = unit_string.matcher(delay);
if (match_delay.find())
{
int letter_index = match_delay.start();
String sep_delay = new StringBuffer(delay).insert(letter_index, "_").toString();
String[] split_delay = sep_delay.split("_", 2);
delay_dict.put("value", split_delay[0].trim());
delay_dict.put("units", split_delay[1].trim());
}
else
{
throw new NeuroMLException("neuroConstruct cannot find units inside parameter string");
}
return delay_dict;
}
public void addNeuroML2Cell(org.neuroml.model.Cell NML2Cell, String CellIdPrefix, File NML2CellFile) throws NeuroMLException, org.neuroml.model.util.NeuroMLException
{
String newCellId = CellIdPrefix + NML2Cell.getId();
for (String cellTypeId : project.cellManager.getAllCellTypeNames())
{
if (NML2Cell.getId().equals(cellTypeId))
{
// newly found cellType will overide the existent cell type in the project
project.cellManager.deleteCellType(project.cellManager.getCell(cellTypeId));
}
}
NeuroML2CellReader cellReader = new NeuroML2CellReader(NML2Cell, newCellId);
cellReader.parse();
Cell imported_cell = cellReader.getBuiltCell();
try
{
project.cellManager.addCellType(imported_cell);
project.markProjectAsEdited();
logger.logComment(imported_cell.getInstanceName() + " added to the project");
}
catch (Exception ex)
{
GuiUtils.showErrorMessage(logger, "Problem importing NeuroML2 file " + NML2CellFile.getAbsolutePath(), ex, null);
}
logger.logComment("Read in NeuroML 2 cell: " + CellTopologyHelper.printDetails(imported_cell, project), true);
}
public void addNeuroML2Cell2CaPools(org.neuroml.model.Cell2CaPools NML2Cell, String CellIdPrefix, File NML2CellFile) throws NeuroMLException, org.neuroml.model.util.NeuroMLException
{
String newCellId = CellIdPrefix + NML2Cell.getId();
for (String cellTypeId : project.cellManager.getAllCellTypeNames())
{
if (NML2Cell.getId().equals(cellTypeId))
{
// newly found cellType will overide the existent cell type in the project
project.cellManager.deleteCellType(project.cellManager.getCell(cellTypeId));
}
}
NeuroML2CellReader cellReader = new NeuroML2CellReader(NML2Cell, newCellId);
cellReader.parse();
Cell imported_cell = cellReader.getBuiltCell();
try
{
project.cellManager.addCellType(imported_cell);
project.markProjectAsEdited();
logger.logComment(imported_cell.getInstanceName() + " added to the project");
}
catch (Exception ex)
{
GuiUtils.showErrorMessage(logger, "Problem importing NeuroML2 file " + NML2CellFile.getAbsolutePath(), ex, null);
}
logger.logComment("Read in NeuroML 2 cell: " + CellTopologyHelper.printDetails(imported_cell, project), true);
}
public void addNeuroML2CellMechanism(String cellMechanismId, String CellMechanismType, File NML2CellMechanismFile, String CellMechanismNotes)
{
String PathToNML2CellMechanism = NML2CellMechanismFile.getPath();
//System.out.println("Full path to NeuroML2 cell mechanism: "+PathToNML2CellMechanism);
File CMLDir = ProjectStructure.getCellMechanismDir(project.getProjectMainDirectory());
//System.out.println("Cell Mechanism dir: "+CMLDir.getPath());
File newChannelDir = new File(CMLDir, cellMechanismId);
//System.out.println("Cell mechanism will be copied to: "+newChannelDir.getPath());
newChannelDir.mkdirs();
File ChannelFile = new File(newChannelDir, cellMechanismId + ".xml");
FileWriter fw;
try
{
fw = new FileWriter(ChannelFile);
fw.close();
}
catch (IOException ex)
{
logger.logComment("Problem creating a new file: " + ChannelFile);
}
Path CopyIonChannel = FileSystems.getDefault().getPath(PathToNML2CellMechanism);
String FullSavedPath = ChannelFile.getPath();
Path CopyIonChannelTo = FileSystems.getDefault().getPath(FullSavedPath);
//System.out.println("A full path of a saved channel:"+FullSavedPath);
try
{
Files.copy(CopyIonChannel, CopyIonChannelTo, StandardCopyOption.REPLACE_EXISTING);
}
catch (IOException ex)
{
GuiUtils.showErrorMessage(logger, "Problem copying a cell mechanism "
+ cellMechanismId + " from " + PathToNML2CellMechanism + " to " + FullSavedPath, ex, null);
}
// only testing; might require another class for NeuroML2
NeuroML2Component cmlMech = new NeuroML2Component();
cmlMech.setInstanceName(cellMechanismId);
cmlMech.setMechanismType(CellMechanismType);
cmlMech.setDescription(CellMechanismNotes);
cmlMech.setMechanismModel("NeuroML 2 cell mechanism imported from a network");
cmlMech.setXMLFile(ChannelFile.getName());
File propsFile = new File(newChannelDir, CellMechanismHelper.PROPERTIES_FILENAME);
Properties cellMechProps = new Properties();
cellMechProps.setProperty(CellMechanismHelper.PROP_CELL_MECH_MODEL, cmlMech.getMechanismModel());
cellMechProps.setProperty(CellMechanismHelper.PROP_CELL_MECH_TYPE, cmlMech.getMechanismType());
cellMechProps.setProperty(CellMechanismHelper.PROP_CHANNELML_FILE, cmlMech.getXMLFile());
cellMechProps.setProperty(CellMechanismHelper.PROP_IMPL_METHOD, CellMechanism.NEUROML2_BASED_CELL_MECHANISM);
cellMechProps.setProperty(CellMechanismHelper.PROP_CELL_MECH_NAME, cmlMech.getInstanceName());
cellMechProps.setProperty(CellMechanismHelper.PROP_CELL_MECH_DESCRIPTION, cmlMech.getDescription());
try
{
FileOutputStream fos2 = new FileOutputStream(propsFile);
cellMechProps.storeToXML(fos2,
"\nProperties associated with the Cell Mechanism which allow it to be loaded into neuroConstruct.\n\n"
+ "Note the following: \n"
+ " The Cell Mechanism name should not contain spaces and should match the name of the directory it's in\n"
+ " The name and description here will be replaced by the corresponding values in a ChannelML file if found\n"
+ " The filenames for the mappings are relative to the cellMechanism/(cellMechInstanceName) directory\n"
+ " Mechanism Type should only have values: "
+ CellMechanism.CHANNEL_MECHANISM + ", " + CellMechanism.SYNAPTIC_MECHANISM
+ ", " + CellMechanism.ION_CONCENTRATION + ", " + CellMechanism.POINT_PROCESS + ", " + CellMechanism.GAP_JUNCTION + "\n\n");
fos2.close();
}
catch (Exception ex2)
{
GuiUtils.showErrorMessage(logger, "Error storing information on NeuroML2 Cell Mechanism: " + cmlMech.getInstanceName(), ex2, null);
}
try
{
cmlMech.initPropsFromPropsFile(propsFile);
}
catch (IOException ex4)
{
GuiUtils.showErrorMessage(logger, "Error initiating cell mechanism properties in: " + newChannelDir, ex4, null);
}
project.cellMechanismInfo.addCellMechanism(cmlMech);
}
public ConnectivityConditions determineConnectivityConditions(Projection projection,
int PrePopulationSize, int PostPopulationSize, String preCellComponentType, String postCellComponentType)
{
Set<Integer> preCellSet = new HashSet<Integer>();
Set<Integer> postCellSet = new HashSet<Integer>();
Set<Integer> postCellAppearance = new HashSet<Integer>();
Set<Integer> preCellAppearance = new HashSet<Integer>();
ArrayList<Integer> preCellList = new ArrayList<Integer>();
ArrayList<Integer> postCellList = new ArrayList<Integer>();
HashMap<Integer, Integer> presynapticDict = new HashMap<Integer, Integer>();
HashMap<Integer, Integer> postsynapticDict = new HashMap<Integer, Integer>();
TreeMap<String, Integer> preSegPerSecCounters = new TreeMap<String, Integer>();
TreeMap<String, Integer> postSegPerSecCounters = new TreeMap<String, Integer>();
LinkedHashMap<String, Integer> preSegPerSecCountersSorted = new LinkedHashMap<String, Integer>();
LinkedHashMap<String, Integer> postSegPerSecCountersSorted = new LinkedHashMap<String, Integer>();
preCellnC = project.cellManager.getCell(preCellComponentType);
postCellnC = project.cellManager.getCell(postCellComponentType);
Set<Integer> postCellSegIds = new HashSet<Integer>();
Set<Integer> preCellSegIds = new HashSet<Integer>();
ArrayList<Segment> preCellSegs = new ArrayList<Segment>();
ArrayList<Segment> postCellSegs = new ArrayList<Segment>();
ArrayList<String> potentialPreSegGroups = new ArrayList<String>();
ArrayList<String> potentialPostSegGroups = new ArrayList<String>();
boolean autapsesAllowed = false;
boolean preAxonGroupContainsAll = false;
boolean preSomaGroupContainsAll = false;
boolean preDendriteGroupContainsAll = false;
boolean postAxonGroupContainsAll = false;
boolean postSomaGroupContainsAll = false;
boolean postDendriteGroupContainsAll = false;
boolean minimalSetOfPreSegGroups = false;
boolean minimalSetOfPostSegGroups = false;
boolean preAxonAllowed = false;
boolean preSomaAllowed = false;
boolean preDendriteAllowed = false;
boolean postAxonAllowed = false;
boolean postSomaAllowed = false;
boolean postDendriteAllowed = false;
PrePostAllowedLocs lociAllowed = new PrePostAllowedLocs();
boolean setSynLoci = false;
int totalNumOfSegs = 0;
ConnectivityConditions connConds = new ConnectivityConditions();
String synapse = projection.getSynapse();
int cellCounter;
if (!projection.getConnection().isEmpty())
{
for (Connection conn : projection.getConnection())
{
int preSeg = conn.getPreSegmentId();
int postSeg = conn.getPostSegmentId();
int preCellId = parseForCellNumber(conn.getPreCellId());
int postCellId = parseForCellNumber(conn.getPostCellId());
if ((preCellId == postCellId)
&& (projection.getPostsynapticPopulation().equals(projection.getPresynapticPopulation())))
{
autapsesAllowed = true;
}
preCellSet.add(preCellId);
preCellList.add(preCellId);
postCellSet.add(postCellId);
postCellList.add(postCellId);
preCellSegIds.add(preSeg);
postCellSegIds.add(postSeg);
}
}
else if (!projection.getConnectionWD().isEmpty())
{
for (ConnectionWD conn : projection.getConnectionWD())
{
int preSeg = conn.getPreSegmentId();
int postSeg = conn.getPostSegmentId();
int preCellId = parseForCellNumber(conn.getPreCellId());
int postCellId = parseForCellNumber(conn.getPostCellId());
if ((preCellId == postCellId)
&& (projection.getPostsynapticPopulation().equals(projection.getPresynapticPopulation())))
{
autapsesAllowed = true;
}
preCellSet.add(preCellId);
preCellList.add(preCellId);
postCellSet.add(postCellId);
postCellList.add(postCellId);
preCellSegIds.add(preSeg);
postCellSegIds.add(postSeg);
}
}
for (Integer segId : preCellSegIds)
{
Segment targetSeg = preCellnC.getSegmentWithId(segId);
preCellSegs.add(targetSeg);
}
for (Integer segId : postCellSegIds)
{
Segment targetSeg = postCellnC.getSegmentWithId(segId);
postCellSegs.add(targetSeg);
}
int numOfPreSegs = preCellSegIds.size();
//System.out.println("Number of unique pre seg ids: "+numOfPreSegs);
int numOfPostSegs = postCellSegIds.size();
//System.out.println("Number of unique post seg ids: "+numOfPostSegs);
for (String segmentGroup : preCellnC.getAllGroupNames())
{
int segCounter = 0;
ArrayList<Section> allSectionsPerGroup = preCellnC.getSectionsInGroup(segmentGroup);
for (Section section : allSectionsPerGroup)
{
for (Segment preSeg : preCellSegs)
{
String sectionName = preSeg.getSection().getSectionName();
if (section.getSectionName().equals(sectionName))
{
segCounter++;
}
}
}
if ((segCounter != 0) && (!segmentGroup.contains("ModelView")) && (!segmentGroup.equals("all")))
{
preSegPerSecCounters.put(segmentGroup, segCounter);
}
}
for (String segmentGroup : postCellnC.getAllGroupNames())
{
int segCounter = 0;
ArrayList<Section> allSectionsPerGroup = postCellnC.getSectionsInGroup(segmentGroup);
for (Section section : allSectionsPerGroup)
{
for (Segment postSeg : postCellSegs)
{
String sectionName = postSeg.getSection().getSectionName();
if (section.getSectionName().equals(sectionName))
{
segCounter++;
}
}
}
if ((segCounter != 0) && (!segmentGroup.contains("ModelView")) && (!segmentGroup.equals("all")))
{
postSegPerSecCounters.put(segmentGroup, segCounter);
}
}
int maxPreSet = Collections.max(preSegPerSecCounters.values());
int maxPostSet = Collections.max(postSegPerSecCounters.values());
while (!preSegPerSecCounters.isEmpty())
{
for (String segGroup : preSegPerSecCounters.keySet())
{
if (maxPreSet == preSegPerSecCounters.get(segGroup))
{
preSegPerSecCountersSorted.put(segGroup, preSegPerSecCounters.get(segGroup));
preSegPerSecCounters.remove(segGroup);
if (!preSegPerSecCounters.isEmpty())
{
maxPreSet = Collections.max(preSegPerSecCounters.values());
}
break;
}
}
}
while (!postSegPerSecCounters.isEmpty())
{
for (String segGroup : postSegPerSecCounters.keySet())
{
if (maxPostSet == postSegPerSecCounters.get(segGroup))
{
postSegPerSecCountersSorted.put(segGroup, postSegPerSecCounters.get(segGroup));
postSegPerSecCounters.remove(segGroup);
if (!postSegPerSecCounters.isEmpty())
{
maxPostSet = Collections.max(postSegPerSecCounters.values());
}
break;
}
}
}
for (String segGroup : preSegPerSecCountersSorted.keySet())
{
if ((!segGroup.equals("axon_group")) && (!segGroup.equals("soma_group"))
&& (!segGroup.equals("dendrite_group")))
{
potentialPreSegGroups.add(segGroup);
totalNumOfSegs = preSegPerSecCountersSorted.get(segGroup) + totalNumOfSegs;
if (segGroup.contains("dend"))
{
preDendriteAllowed = true;
}
if (segGroup.contains("soma"))
{
preSomaAllowed = true;
}
if (segGroup.contains("axon"))
{
preAxonAllowed = true;
}
// cases such as "apic_shaft"
if ((!segGroup.contains("dend")) && (!segGroup.contains("soma")) && (!segGroup.contains("axon")))
{
preDendriteAllowed = true;
}
}
if (preSegPerSecCountersSorted.get(segGroup) >= numOfPreSegs)
{
if (segGroup.equals("axon_group"))
{
preAxonGroupContainsAll = true;
}
if (segGroup.equals("soma_group"))
{
preSomaGroupContainsAll = true;
}
if (segGroup.equals("dendrite_group"))
{
preDendriteGroupContainsAll = true;
}
}
if (totalNumOfSegs >= numOfPreSegs)
{
minimalSetOfPreSegGroups = true;
break;
}
}
if (!minimalSetOfPreSegGroups)
{
preAxonAllowed = false;
preSomaAllowed = false;
preDendriteAllowed = false;
if (preAxonGroupContainsAll)
{
preCellnC.associateGroupWithSynapse("axon_group", synapse);
preAxonAllowed = true;
}
else if (preSomaGroupContainsAll)
{
preCellnC.associateGroupWithSynapse("soma_group", synapse);
preSomaAllowed = true;
}
else if (preDendriteGroupContainsAll)
{
preCellnC.associateGroupWithSynapse("dendrite_group", synapse);
preDendriteAllowed = true;
}
//// revert to the widest scope of the segment group specificty
else
{
preCellnC.associateGroupWithSynapse("axon_group", synapse);
preCellnC.associateGroupWithSynapse("soma_group", synapse);
preCellnC.associateGroupWithSynapse("dendrite_group", synapse);
preAxonAllowed = true;
preSomaAllowed = true;
preDendriteAllowed = true;
}
}
else
{
for (String preSegGroup : potentialPreSegGroups)
{
preCellnC.associateGroupWithSynapse(preSegGroup, synapse);
}
}
totalNumOfSegs = 0;
for (String segGroup : postSegPerSecCountersSorted.keySet())
{
if ((!segGroup.equals("axon_group")) && (!segGroup.equals("soma_group"))
&& (!segGroup.equals("dendrite_group")))
{
potentialPostSegGroups.add(segGroup);
totalNumOfSegs = postSegPerSecCountersSorted.get(segGroup) + totalNumOfSegs;
if (segGroup.contains("dend"))
{
postDendriteAllowed = true;
}
if (segGroup.contains("soma"))
{
postSomaAllowed = true;
}
if (segGroup.contains("axon"))
{
postAxonAllowed = true;
}
// cases such as "apic_shaft"
if ((!segGroup.contains("dend")) && (!segGroup.contains("soma")) && (!segGroup.contains("axon")))
{
postDendriteAllowed = true;
}
}
if (postSegPerSecCountersSorted.get(segGroup) >= numOfPostSegs)
{
if (segGroup.equals("axon_group"))
{
postAxonGroupContainsAll = true;
}
if (segGroup.equals("soma_group"))
{
postSomaGroupContainsAll = true;
}
if (segGroup.equals("dendrite_group"))
{
postDendriteGroupContainsAll = true;
}
}
if (totalNumOfSegs >= numOfPostSegs)
{
minimalSetOfPostSegGroups = true;
break;
}
}
if (!minimalSetOfPostSegGroups)
{
postAxonAllowed = false;
postSomaAllowed = false;
postDendriteAllowed = false;
if (postAxonGroupContainsAll)
{
postCellnC.associateGroupWithSynapse("axon_group", synapse);
postAxonAllowed = true;
}
else if (postSomaGroupContainsAll)
{
postCellnC.associateGroupWithSynapse("soma_group", synapse);
postSomaAllowed = true;
}
else if (postDendriteGroupContainsAll)
{
postCellnC.associateGroupWithSynapse("dendrite_group", synapse);
postDendriteAllowed = true;
}
//// revert to the widest scope of the segment group specificty
else
{
postCellnC.associateGroupWithSynapse("axon_group", synapse);
postCellnC.associateGroupWithSynapse("soma_group", synapse);
postCellnC.associateGroupWithSynapse("dendrite_group", synapse);
postAxonAllowed = true;
postSomaAllowed = true;
postDendriteAllowed = true;
}
}
else
{
for (String postSegGroup : potentialPostSegGroups)
{
postCellnC.associateGroupWithSynapse(postSegGroup, synapse);
}
}
for (Integer preCellId : preCellSet)
{
cellCounter = 0;
for (Integer preCell : preCellList)
{
if (preCellId == preCell)
{
cellCounter++;
}
}
presynapticDict.put(preCellId, cellCounter);
}
for (Integer postCellId : postCellSet)
{
cellCounter = 0;
for (Integer postCell : postCellList)
{
if (postCellId == postCell)
{
cellCounter++;
}
}
postsynapticDict.put(postCellId, cellCounter);
}
for (Integer CellId : presynapticDict.keySet())
{
preCellAppearance.add(presynapticDict.get(CellId));
}
for (Integer CellId : postsynapticDict.keySet())
{
postCellAppearance.add(postsynapticDict.get(CellId));
}
if (postCellAppearance.size() == 1)
{
int NumPerPostCell = 0;
int NumPerPreCell = 0;
if (preCellAppearance.size() == 1)
{
for (Integer value : postCellAppearance)
{
NumPerPostCell = value;
}
for (Integer value : preCellAppearance)
{
NumPerPreCell = value;
}
if (NumPerPreCell > NumPerPostCell)
{
connConds.setGenerationDirection(1);
connConds.setNumConnsInitiatingCellGroup(new NumberGenerator(NumPerPostCell));
//System.out.println("This is a convergent projection with "+NumPerPostCell+" connections per postsynaptic target cell");
}
else if (NumPerPreCell < NumPerPostCell)
{
connConds.setGenerationDirection(0);
connConds.setNumConnsInitiatingCellGroup(new NumberGenerator(NumPerPreCell));
//System.out.println("This a divergent projection with "+NumPerPreCell+" connections per presynaptic source cell");
}
else if ((NumPerPreCell == 1) && (NumPerPostCell == 1))
{
connConds.setOnlyConnectToUniqueCells(true);
}
}
else
{
for (Integer value : postCellAppearance)
{
NumPerPostCell = value;
}
connConds.setGenerationDirection(1);
connConds.setNumConnsInitiatingCellGroup(new NumberGenerator(NumPerPostCell));
//System.out.println("This is a convergent projection with "+NumPerPostCell+" connections per postsynaptic target cell");
}
}
else if (preCellAppearance.size() == 1)
{
int NumPerPreCell = 0;
for (Integer value : preCellAppearance)
{
NumPerPreCell = value;
}
connConds.setGenerationDirection(0);
connConds.setNumConnsInitiatingCellGroup(new NumberGenerator(NumPerPreCell));
//System.out.println("This a divergent projection with "+NumPerPreCell+" connections per presynaptic source cell");
}
else
{
//TODO, for now stays to the default values in neuroConstruct, e.g. SOURCE_TO_TARGET only
}
if (postAxonAllowed || postDendriteAllowed || postSomaAllowed)
{
lociAllowed.setAxonsAllowedPost(postAxonAllowed);
lociAllowed.setDendritesAllowedPost(postDendriteAllowed);
lociAllowed.setSomaAllowedPost(postSomaAllowed);
setSynLoci = true;
}
if (preAxonAllowed || preDendriteAllowed || preSomaAllowed)
{
lociAllowed.setAxonsAllowedPre(preAxonAllowed);
lociAllowed.setDendritesAllowedPre(preDendriteAllowed);
lociAllowed.setSomaAllowedPre(preSomaAllowed);
setSynLoci = true;
}
if (setSynLoci)
{
connConds.setPrePostAllowedLoc(lociAllowed);
}
connConds.setAllowAutapses(autapsesAllowed);
return connConds;
}