public void parseGroup(Group g) throws Hdf5Exception, EndOfSequenceException {
startGroup(g);
java.util.List members = g.getMemberList();
// NOTE: parsing contents twice to ensure subgroups are handled before datasets
// This is mainly because synapse_props groups will need to be parsed before dataset of
// connections
for (int j = 0; j < members.size(); j++) {
HObject obj = (HObject) members.get(j);
if (obj instanceof Group) {
Group subGroup = (Group) obj;
logger.logComment("--------- Found a sub group: " + subGroup.getName());
parseGroup(subGroup);
}
}
for (int j = 0; j < members.size(); j++) {
HObject obj = (HObject) members.get(j);
if (obj instanceof Dataset) {
Dataset ds = (Dataset) obj;
logger.logComment("Found a dataset: " + ds.getName());
dataSet(ds);
}
}
endGroup(g);
}
public void loadFromFile(File positionFile) throws java.io.IOException {
logger.logComment("Loading position records from file: " + positionFile.getAbsolutePath());
this.reset();
Reader in = new FileReader(positionFile);
LineNumberReader reader = new LineNumberReader(in);
String nextLine = null;
String currentInputRef = null;
while ((nextLine = reader.readLine()) != null) {
// logger.logComment("Parsing line: "+ nextLine);
if (nextLine.endsWith(":")) {
currentInputRef = nextLine.substring(0, nextLine.length() - 1);
logger.logComment("currentInputRef: " + currentInputRef);
} else {
SingleElectricalInput input = new SingleElectricalInput(nextLine);
this.addSingleInput(currentInputRef, input);
}
}
in.close();
logger.logComment("Finished loading cell info. Internal state: " + this.toString());
}
public static void main(String args[]) {
try {
logger.logComment("Sys prop: " + System.getProperty("java.library.path"), true);
// File projFile = new File("../copyNcModels/NewGranCellLayer/NewGranCellLayer.neuro.xml");
// File projFile = new File("../nC_projects/Bignet/Bignet.neuro.xml");
File projFile = new File("testProjects/TestNetworkML/TestNetworkML.neuro.xml");
// Project testProj = Project.loadProject(new File("projects/Parall/Parall.neuro.xml"),null);
// Project testProj = Project.loadProject(new
// File("examples/Ex5-Networks/Ex5-Networks.neuro.xml"),null);
Project testProj = Project.loadProject(projFile, null);
// File h5File = new File(projFile.getParentFile().getAbsolutePath()+
// "/savedNetworks/hhh.h5");
// File h5File = new File(projFile.getParentFile().getAbsolutePath()+
// "/savedNetworks/nnnn.h5");
File h5File = new File("testProjects/TestNetworkML/savedNetworks/small.h5");
// logger.logComment("Loading netml cell from "+ h5File.getAbsolutePath(), true);
NetworkMLReader nmlReader = new NetworkMLReader(testProj);
nmlReader.parse(h5File);
logger.logComment("Contents: " + testProj.generatedCellPositions);
logger.logComment("Net conns: " + testProj.generatedNetworkConnections);
logger.logComment("Inputs: " + testProj.generatedElecInputs.details(false));
} catch (Exception e) {
e.printStackTrace();
System.exit(0);
}
}
public void endGroup(Group g) throws Hdf5Exception {
logger.logComment("----- Going out of a group: " + g.getFullName());
if (g.getName().equals(NetworkMLConstants.POPULATIONS_ELEMENT)) {
inPopulations = false;
} else if (g.getName().equals(NetworkMLConstants.PROJECTIONS_ELEMENT)) {
inProjections = false;
} else if (g.getName().equals(NetworkMLConstants.INPUTS_ELEMENT)) {
inInputs = false;
} else if (g.getName().equals(NetworkMLConstants.INPUT_ELEMENT) && inInputs) {
currentInput = null;
} else if (g.getName().startsWith(NetworkMLConstants.POPULATION_ELEMENT) && inPopulations) {
currentCellGroup = null;
} else if (g.getName().startsWith(NetworkMLConstants.PROJECTION_ELEMENT) && inProjections) {
currentNetConn = null;
globConnProps = new ArrayList<ConnSpecificProps>();
} else if (g.getName().startsWith(NetworkMLConstants.CONNECTION_ELEMENT)) {
localConnProps = new ArrayList<ConnSpecificProps>();
localAPDelay = 0;
}
}
public void saveToFile(File inputsFile) throws java.io.IOException {
logger.logComment(
"Saving "
+ this.getNumberSingleInputs()
+ " inputs to file: "
+ inputsFile.getAbsolutePath());
// will create the parent dir if it doesn't exist.
if (!inputsFile.exists()) {
logger.logComment("File: " + inputsFile + " doesn't exist.");
if (!inputsFile.getParentFile().exists()) {
logger.logComment("Parent dir: " + inputsFile.getParentFile() + " doesn't exist.");
// String parentDirName = inputsFile.getParentFile().getCanonicalPath();
File projectDir = inputsFile.getParentFile().getParentFile();
if (!projectDir.exists()) {
throw new FileNotFoundException(
"Project dir doesn't exist: " + projectDir.getAbsolutePath());
}
// logger.logComment("Going to create dir: "+ parentDirName +" in dir :"+ projectDir);
logger.logComment("Going to create dir: " + inputsFile.getParentFile());
inputsFile.getParentFile().mkdir();
logger.logComment("Success? " + inputsFile.getParentFile().exists());
}
}
FileWriter fw = new FileWriter(inputsFile);
Enumeration keys = this.myElecInputs.keys();
while (keys.hasMoreElements()) {
String input = (String) keys.nextElement();
ArrayList<SingleElectricalInput> inputs = getInputLocations(input);
fw.write(input + ":\n");
for (int i = 0; i < inputs.size(); i++) {
fw.write(inputs.get(i) + "\n");
}
}
logger.logComment("Finished saving data to file: " + inputsFile.getAbsolutePath());
fw.flush();
fw.close();
}
public void startGroup(Group g) throws Hdf5Exception {
logger.logComment("----- Going into a group: " + g.getFullName());
ArrayList<Attribute> attrs = Hdf5Utils.parseGroupForAttributes(g);
for (Attribute attribute : attrs) {
// attribute.
logger.logComment(
"Group: "
+ g.getName()
+ " has attribute: "
+ attribute.getName()
+ " = "
+ Hdf5Utils.getFirstStringValAttr(attrs, attribute.getName()));
}
if (g.getName().equals(NetworkMLConstants.ROOT_ELEMENT)) {
logger.logComment("Found the main group");
String simConfigName =
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.NC_SIM_CONFIG);
if (simConfigName != null) this.foundSimConfig = simConfigName;
String randomSeed =
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.NC_NETWORK_GEN_RAND_SEED);
if (randomSeed != null) this.foundRandomSeed = Long.parseLong(randomSeed);
} else if (g.getName().equals(NetworkMLConstants.POPULATIONS_ELEMENT)) {
logger.logComment("Found the pops group");
inPopulations = true;
} else if (g.getName().startsWith(NetworkMLConstants.POPULATION_ELEMENT) && inPopulations) {
String name = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.POP_NAME_ATTR);
logger.logComment("Found a population: " + name);
currentCellGroup = name;
} else if (g.getName().equals(NetworkMLConstants.PROJECTIONS_ELEMENT)) {
logger.logComment("Found the projections group");
inProjections = true;
String units = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.UNITS_ATTR);
projUnitSystem = UnitConverter.getUnitSystemIndex(units);
} else if (g.getName().startsWith(NetworkMLConstants.PROJECTION_ELEMENT) && inProjections) {
String name = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.PROJ_NAME_ATTR);
String source = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.SOURCE_ATTR);
String target = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.TARGET_ATTR);
logger.logComment("Found a projection: " + name + " from " + source + " to " + target);
if (!project.morphNetworkConnectionsInfo.isValidSimpleNetConn(name)
&& !project.volBasedConnsInfo.isValidVolBasedConn(name)) {
throw new Hdf5Exception(
"Error: there is a network connection with name: "
+ name
+ " specified in "
+ "that file, but no such NetConn exists in the project. Add one to allow import of this file");
}
/* TODO: Add checks on source & target!!
*/
if (project.morphNetworkConnectionsInfo.isValidSimpleNetConn(name)) {
// if (project.morphNetworkConnectionsInfo)
}
currentNetConn = name;
} else if (g.getName().startsWith(NetworkMLConstants.SYN_PROPS_ELEMENT + "_")
&& inProjections) {
String name = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.SYN_TYPE_ATTR);
ConnSpecificProps cp = new ConnSpecificProps(name);
String internalDelay =
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.INTERNAL_DELAY_ATTR);
if (internalDelay != null)
cp.internalDelay =
(float)
UnitConverter.getTime(
Float.parseFloat(internalDelay),
projUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
// Lump them in to the internal delay...
String preDelay = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.PRE_DELAY_ATTR);
if (preDelay != null)
cp.internalDelay =
cp.internalDelay
+ (float)
UnitConverter.getTime(
Float.parseFloat(preDelay),
projUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
String postDelay = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.POST_DELAY_ATTR);
if (postDelay != null)
cp.internalDelay =
cp.internalDelay
+ (float)
UnitConverter.getTime(
Float.parseFloat(postDelay),
projUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
cp.weight =
Float.parseFloat(Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.WEIGHT_ATTR));
String propDelay = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.PROP_DELAY_ATTR);
if (propDelay != null)
globAPDelay =
(float)
UnitConverter.getTime(
Float.parseFloat(propDelay),
projUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
logger.logComment("Found: " + cp);
globConnProps.add(cp);
} else if (g.getName().equals(NetworkMLConstants.INPUTS_ELEMENT)) {
logger.logComment("Found the Inputs group");
inInputs = true;
String units = Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.UNITS_ATTR);
inputUnitSystem = UnitConverter.getUnitSystemIndex(units);
} else if (g.getName().startsWith(NetworkMLConstants.INPUT_ELEMENT) && inInputs) {
// The table of input sites is within the input group so get sites from here
String inputName = g.getName().substring(6);
// String inputName = Hdf5Utils.getFirstStringValAttr(attrs,
// NetworkMLConstants.INPUT_ELEMENT);
logger.logComment("Found an Input: " + inputName);
// inInput = true;
if (project.elecInputInfo.getStim(inputName) == null) {
throw new Hdf5Exception(
"Error: there is an electrical input with name: "
+ inputName
+ " specified in "
+ "that file, but no such electrical input exists in the project. Add one to allow import of this file");
}
// Get the atributes of the Input and compare them with the attributes within the project
// Test to find out what type of input this is
} else if (g.getName().startsWith("IClamp") && inInputs) {
String inputName = g.getParent().getName().substring(6);
// Get the input sites from the table
String cellGroup =
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.INPUT_TARGET_POPULATION_ATTR);
if (cellGroup == null) {
cellGroup =
Hdf5Utils.getFirstStringValAttr(
attrs, NetworkMLConstants.INPUT_TARGET_CELLGROUP_OLD_ATTR); // check old name
}
float readDelay =
(float)
UnitConverter.getTime(
Float.parseFloat(
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.INPUT_DELAY_ATTR)),
inputUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
float readDuration =
(float)
UnitConverter.getTime(
Float.parseFloat(
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.INPUT_DUR_ATTR)),
inputUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
float readAmp =
(float)
UnitConverter.getCurrent(
Float.parseFloat(
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.INPUT_AMP_ATTR)),
inputUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
StimulationSettings nextStim = project.elecInputInfo.getStim(inputName);
ElectricalInput myElectricalInput = nextStim.getElectricalInput();
IClamp ic = (IClamp) myElectricalInput;
logger.logComment("Found an IClamp Input");
float currDelay = -1, currDur = -1, currAmp = -1;
/*
try
{
ic.getDelay().reset();
currDelay = ic.getDelay().getNumber();
ic.getDuration().reset();
currDur = ic.getDuration().getNumber();
ic.getAmplitude().reset();
currAmp = ic.getAmplitude().getNumber();
}
catch (Exception ex)
{
logger.logError("Legacy error getting iclamp params!!");
}*/
currDelay = ic.getDel().getNominalNumber();
currDur = ic.getDur().getNominalNumber();
currAmp = ic.getAmp().getNominalNumber();
if ((!project.elecInputInfo.getStim(inputName).getCellGroup().equals(cellGroup))
|| (readDelay != currDelay)
|| (readDuration != currDur)
|| (readAmp != currAmp)) {
throw new Hdf5Exception(
"Error: the input properties of the file do not match those in the project for input "
+ inputName
+ ""
+ "\nreadDelay: "
+ readDelay
+ ", currDelay: "
+ currDelay
+ "\nreadDuration: "
+ readDuration
+ ", currDur: "
+ currDur
+ "\nreadAmp: "
+ readAmp
+ ", currAmp: "
+ currAmp
+ ", str: "
+ Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.INPUT_AMP_ATTR));
}
currentInput = inputName;
} else if (g.getName().startsWith("RandomSpikeTrain") && inInputs) {
String inputName = g.getParent().getName().substring(6);
// Get the input sites from the table
String cellGroup =
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.INPUT_TARGET_POPULATION_ATTR);
if (cellGroup == null) {
cellGroup =
Hdf5Utils.getFirstStringValAttr(
attrs, NetworkMLConstants.INPUT_TARGET_CELLGROUP_OLD_ATTR); // check old name
}
float frequency =
(float)
UnitConverter.getRate(
Float.parseFloat(
Hdf5Utils.getFirstStringValAttr(
attrs, NetworkMLConstants.RND_STIM_FREQ_ATTR)),
inputUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
String mechanism =
Hdf5Utils.getFirstStringValAttr(attrs, NetworkMLConstants.RND_STIM_MECH_ATTR);
StimulationSettings nextStim = project.elecInputInfo.getStim(inputName);
ElectricalInput myElectricalInput = nextStim.getElectricalInput();
RandomSpikeTrain rs = (RandomSpikeTrain) myElectricalInput;
logger.logComment("Found an Random Spike Train Input");
if ((!project.elecInputInfo.getStim(inputName).getCellGroup().equals(cellGroup))
|| frequency != rs.getRate().getFixedNum()
|| !rs.getSynapseType().equals(mechanism)) {
throw new Hdf5Exception(
"Error: the input properties of the file do not match those in the project for input "
+ inputName);
}
currentInput = inputName;
}
}
public void dataSet(Dataset d) throws Hdf5Exception {
logger.logComment("----- Looking through dataset: " + d);
ArrayList<Attribute> attrs = Hdf5Utils.parseDatasetForAttributes(d);
for (Attribute attribute : attrs) {
logger.logComment(
"Dataset: "
+ d.getName()
+ " has attribute: "
+ attribute.getName()
+ " = "
+ Hdf5Utils.getFirstStringValAttr(attrs, attribute.getName()));
}
float[][] data = Hdf5Utils.parse2Ddataset(d);
logger.logComment("Data has size: (" + data.length + ", " + data[0].length + ")");
if (inPopulations && currentCellGroup != null) {
for (int i = 0; i < data.length; i++) {
int id = (int) data[i][0];
float x = data[i][1];
float y = data[i][2];
float z = data[i][3];
PositionRecord posRec = new PositionRecord(id, x, y, z);
if (data[0].length == 5) {
posRec.setNodeId((int) data[i][4]);
}
this.project.generatedCellPositions.addPosition(currentCellGroup, posRec);
}
}
if (inProjections && currentNetConn != null) {
logger.logComment("Adding info for NetConn: " + currentNetConn);
int id_col = -1;
int pre_cell_id_col = -1;
int pre_segment_id_col = -1;
int pre_fraction_along_col = -1;
int post_cell_id_col = -1;
int post_segment_id_col = -1;
int post_fraction_along_col = -1;
int prop_delay_col = -1;
for (Attribute attribute : attrs) {
String storedInColumn = Hdf5Utils.getFirstStringValAttr(attrs, attribute.getName());
if (storedInColumn.equals(NetworkMLConstants.CONNECTION_ID_ATTR)) {
id_col = Integer.parseInt(attribute.getName().substring("column_".length()));
logger.logComment("id col: " + id_col);
} else if (storedInColumn.equals(NetworkMLConstants.PRE_CELL_ID_ATTR)) {
pre_cell_id_col = Integer.parseInt(attribute.getName().substring("column_".length()));
} else if (storedInColumn.equals(NetworkMLConstants.PRE_SEGMENT_ID_ATTR)) {
pre_segment_id_col = Integer.parseInt(attribute.getName().substring("column_".length()));
logger.logComment("pre_segment_id_col: " + pre_segment_id_col);
} else if (storedInColumn.equals(NetworkMLConstants.PRE_FRACT_ALONG_ATTR)) {
pre_fraction_along_col =
Integer.parseInt(attribute.getName().substring("column_".length()));
logger.logComment("pre_fraction_along_col: " + pre_fraction_along_col);
} else if (storedInColumn.equals(NetworkMLConstants.POST_CELL_ID_ATTR)) {
post_cell_id_col = Integer.parseInt(attribute.getName().substring("column_".length()));
} else if (storedInColumn.equals(NetworkMLConstants.POST_SEGMENT_ID_ATTR)) {
post_segment_id_col = Integer.parseInt(attribute.getName().substring("column_".length()));
} else if (storedInColumn.equals(NetworkMLConstants.POST_FRACT_ALONG_ATTR)) {
post_fraction_along_col =
Integer.parseInt(attribute.getName().substring("column_".length()));
} else if (storedInColumn.startsWith(NetworkMLConstants.PROP_DELAY_ATTR)) {
prop_delay_col = Integer.parseInt(attribute.getName().substring("column_".length()));
}
for (String synType : getConnectionSynTypes()) {
if (storedInColumn.endsWith(synType)) {
ConnSpecificProps cp = null;
for (ConnSpecificProps currCp : localConnProps) {
if (currCp.synapseType.equals(synType)) cp = currCp;
}
if (cp == null) {
cp = new ConnSpecificProps(synType);
cp.internalDelay = -1;
cp.weight = -1;
localConnProps.add(cp);
}
if (storedInColumn.startsWith(NetworkMLConstants.INTERNAL_DELAY_ATTR)) {
cp.internalDelay =
Integer.parseInt(
attribute
.getName()
.substring("column_".length())); // store the col num temporarily..
}
if (storedInColumn.startsWith(NetworkMLConstants.WEIGHT_ATTR)) {
cp.weight =
Integer.parseInt(
attribute
.getName()
.substring("column_".length())); // store the col num temporarily..
}
}
}
}
for (int i = 0; i < data.length; i++) {
int pre_seg_id = 0;
float pre_fract_along = 0.5f;
int post_seg_id = 0;
float post_fract_along = 0.5f;
int id = (int) data[i][id_col];
int pre_cell_id = (int) data[i][pre_cell_id_col];
int post_cell_id = (int) data[i][post_cell_id_col];
float prop_delay = 0;
if (pre_segment_id_col >= 0) pre_seg_id = (int) data[i][pre_segment_id_col];
if (pre_fraction_along_col >= 0) pre_fract_along = data[i][pre_fraction_along_col];
if (post_segment_id_col >= 0) post_seg_id = (int) data[i][post_segment_id_col];
if (post_fraction_along_col >= 0) post_fract_along = data[i][post_fraction_along_col];
// (float)UnitConverter.getTime(XXXXXXXXX, UnitConverter.NEUROCONSTRUCT_UNITS,
// unitSystem)+"";
if (prop_delay_col >= 0)
prop_delay =
(float)
UnitConverter.getTime(
data[i][prop_delay_col], projUnitSystem, UnitConverter.NEUROCONSTRUCT_UNITS);
ArrayList<ConnSpecificProps> props = new ArrayList<ConnSpecificProps>();
if (localConnProps.size() > 0) {
for (ConnSpecificProps currCp : localConnProps) {
logger.logComment("Pre cp: " + currCp);
ConnSpecificProps cp2 = new ConnSpecificProps(currCp.synapseType);
if (currCp.internalDelay > 0) // index was stored in this val...
cp2.internalDelay =
(float)
UnitConverter.getTime(
data[i][(int) currCp.internalDelay],
projUnitSystem,
UnitConverter.NEUROCONSTRUCT_UNITS);
if (currCp.weight > 0) // index was stored in this val...
cp2.weight = data[i][(int) currCp.weight];
logger.logComment("Filled cp: " + cp2);
props.add(cp2);
}
}
this.project.generatedNetworkConnections.addSynapticConnection(
currentNetConn,
GeneratedNetworkConnections.MORPH_NETWORK_CONNECTION,
pre_cell_id,
pre_seg_id,
pre_fract_along,
post_cell_id,
post_seg_id,
post_fract_along,
prop_delay,
props);
}
}
if (inInputs && currentInput != null) {
logger.logComment("Adding info for: " + currentInput);
StimulationSettings nextStim = project.elecInputInfo.getStim(currentInput);
ElectricalInput myElectricalInput = nextStim.getElectricalInput();
String electricalInputType = myElectricalInput.getType();
String cellGroup = nextStim.getCellGroup();
for (int i = 0; i < data.length; i++) {
Float fileCellId = data[i][0];
Float fileSegmentId = data[i][1];
Float fractionAlong = data[i][2];
int cellId = fileCellId.intValue();
int segmentId = fileSegmentId.intValue();
SingleElectricalInput singleElectricalInputFromFile =
new SingleElectricalInput(
electricalInputType, cellGroup, cellId, segmentId, fractionAlong, null);
this.project.generatedElecInputs.addSingleInput(
currentInput, singleElectricalInputFromFile);
}
}
}
public ArrayList<SimpleXMLEntity> getNetworkMLEntities(
int unitSystem, NeuroMLConstants.NeuroMLVersion version, SimpleXMLElement topLevelCompElement)
throws NeuroMLException {
ArrayList<SimpleXMLEntity> entities = new ArrayList<SimpleXMLEntity>();
Units timeUnits = UnitConverter.timeUnits[unitSystem];
Units currentUnits = UnitConverter.currentUnits[unitSystem];
SimpleXMLElement inputsElement = null;
try {
logger.logComment(
"Going to save file in NeuroML format: "
+ this.getNumberSingleInputs()
+ " inputs in total");
if (getNumberSingleInputs() == 0) {
SimpleXMLComment comm =
new SimpleXMLComment("There are no electrical inputs present in the network");
entities.add(comm);
return entities;
}
boolean nml2 = version.isVersion2();
boolean nml2alpha = version.isVersion2alpha();
if (!nml2) {
inputsElement = new SimpleXMLElement(NetworkMLConstants.INPUTS_ELEMENT);
entities.add(inputsElement);
if (unitSystem == UnitConverter.GENESIS_PHYSIOLOGICAL_UNITS) {
inputsElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.UNITS_ATTR, NetworkMLConstants.UNITS_PHYSIOLOGICAL));
} else if (unitSystem == UnitConverter.GENESIS_SI_UNITS) {
inputsElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.UNITS_ATTR, NetworkMLConstants.UNITS_SI));
}
}
Enumeration keys = myElecInputs.keys();
while (keys.hasMoreElements()) {
String inputReference = (String) keys.nextElement();
ArrayList<SingleElectricalInput> inputsHere = getInputLocations(inputReference);
logger.logComment("Adding " + inputsHere.size() + " inputs");
StimulationSettings nextStim = project.elecInputInfo.getStim(inputReference);
ElectricalInput myElectricalInput = nextStim.getElectricalInput();
SimpleXMLElement inputElement = new SimpleXMLElement(NetworkMLConstants.INPUT_ELEMENT);
inputElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.INPUT_NAME_ATTR, inputReference));
if (myElectricalInput instanceof IClamp) {
IClamp ic = (IClamp) myElectricalInput;
float delay = ic.getDel().getNominalNumber();
float duration = ic.getDur().getNominalNumber();
float amplitude = ic.getAmp().getNominalNumber();
SimpleXMLElement inputTypeElement =
new SimpleXMLElement(NetworkMLConstants.PULSEINPUT_ELEMENT);
float del =
(float) UnitConverter.getTime(delay, UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem);
float dur =
(float)
UnitConverter.getTime(duration, UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem);
float amp =
(float)
UnitConverter.getCurrent(
amplitude, UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem);
inputTypeElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.INPUT_DELAY_ATTR, del + ""));
inputTypeElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.INPUT_DUR_ATTR, dur + ""));
inputTypeElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.INPUT_AMP_ATTR, amp + ""));
inputElement.addChildElement(inputTypeElement);
inputElement.addContent("\n ");
if (nml2) {
SimpleXMLElement pulseGenElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_PULSE_GEN_ELEMENT);
pulseGenElement.addAttribute(NeuroMLConstants.NEUROML_ID_V2, inputReference);
pulseGenElement.addAttribute(
NetworkMLConstants.INPUT_DELAY_ATTR, del + timeUnits.getNeuroML2Symbol());
pulseGenElement.addAttribute(
NetworkMLConstants.INPUT_DUR_ATTR, dur + timeUnits.getNeuroML2Symbol());
pulseGenElement.addAttribute(
NetworkMLConstants.INPUT_AMP_ATTR, amp + currentUnits.getNeuroML2Symbol());
topLevelCompElement.addContent("\n\n ");
topLevelCompElement.addChildElement(pulseGenElement);
topLevelCompElement.addContent("\n\n ");
}
} else if (myElectricalInput instanceof RandomSpikeTrain) {
RandomSpikeTrain rst = (RandomSpikeTrain) myElectricalInput;
float stimFreq = rst.getRate().getNominalNumber();
String stimMech = rst.getSynapseType();
SimpleXMLElement inputTypeElement =
new SimpleXMLElement(NetworkMLConstants.RANDOMSTIM_ELEMENT);
float rate =
(float)
UnitConverter.getRate(stimFreq, UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem);
inputTypeElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.RND_STIM_FREQ_ATTR,
(float)
UnitConverter.getRate(
stimFreq, UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem)
+ ""));
inputTypeElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.RND_STIM_MECH_ATTR, stimMech));
inputElement.addChildElement(inputTypeElement);
inputElement.addContent("\n ");
if (nml2 && !nml2alpha) {
SimpleXMLElement spikeGenElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_SPIKE_GEN_POISSON_ELEMENT);
spikeGenElement.addAttribute(NeuroMLConstants.NEUROML_ID_V2, inputReference);
spikeGenElement.addAttribute(
NetworkMLConstants.NEUROML2_SPIKE_GEN_POISSON_RATE_ATTR,
rate
+ " "
+ UnitConverter.rateUnits[UnitConverter.NEUROCONSTRUCT_UNITS]
.getNeuroML2Symbol()
+ "");
topLevelCompElement.addContent("\n\n ");
topLevelCompElement.addChildElement(spikeGenElement);
topLevelCompElement.addContent("\n\n ");
}
} else {
throw new NeuroMLException(
"Error trying to save input "
+ inputReference
+ ". Cannot save in NeuroML an input of type: "
+ myElectricalInput.getType());
}
SimpleXMLElement inputTargetElement =
new SimpleXMLElement(NetworkMLConstants.INPUT_TARGET_ELEMENT);
inputTargetElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.INPUT_TARGET_POPULATION_ATTR, nextStim.getCellGroup()));
inputElement.addChildElement(inputTargetElement);
inputTargetElement.addContent("\n ");
SimpleXMLElement inputTargetSitesElement =
new SimpleXMLElement(NetworkMLConstants.INPUT_TARGET_SITES_ELEMENT);
inputTargetSitesElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.INPUT_SITES_SIZE_ATTR, inputsHere.size() + ""));
inputTargetElement.addChildElement(inputTargetSitesElement);
SimpleXMLElement stimProjElement = null;
if (version.isVersion2betaOrLater()) {
if (myElectricalInput instanceof IClamp) {
SimpleXMLElement inputListElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_INPUT_LIST_ELEMENT);
entities.add(inputListElement);
inputListElement.addAttribute(NeuroMLConstants.NEUROML_ID_V2, nextStim.getReference());
inputListElement.addAttribute(
NetworkMLConstants.NEUROML2_INPUT_COMPONENT, inputReference);
inputListElement.addAttribute(
NetworkMLConstants.NEUROML2_INPUT_POPULATION, nextStim.getCellGroup());
// inputElement.addContent("\n ");
inputTargetSitesElement = inputListElement;
} else if (myElectricalInput instanceof RandomSpikeTrain) {
SimpleXMLElement popElement =
new SimpleXMLElement(NetworkMLConstants.POPULATION_ELEMENT);
entities.add(0, popElement);
popElement.addAttribute(
NeuroMLConstants.NEUROML_ID_V2, nextStim.getReference() + "_population");
popElement.addAttribute(
NetworkMLConstants.NEUROML2_POPULATION_COMPONENT,
nextStim.getReference() + "_population");
popElement.addAttribute(
NetworkMLConstants.NEUROML2_POPULATION_SIZE, inputsHere.size() + "");
stimProjElement = new SimpleXMLElement(NetworkMLConstants.PROJECTION_ELEMENT);
stimProjElement.addAttribute(
NeuroMLConstants.NEUROML_ID_V2, nextStim.getReference() + "_projection");
entities.add(stimProjElement);
}
}
// Iterate around the list of sites
for (int i = 0; i < inputsHere.size(); i++) {
inputTargetSitesElement.addContent("\n ");
SingleElectricalInput sei = inputsHere.get(i);
SimpleXMLElement inputTargetSiteElement =
new SimpleXMLElement(NetworkMLConstants.INPUT_TARGET_SITE_ELEMENT);
inputTargetSiteElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.INPUT_SITE_CELLID_ATTR, sei.getCellNumber() + ""));
inputTargetSiteElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.INPUT_SITE_SEGID_ATTR, sei.getSegmentId() + ""));
inputTargetSiteElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.INPUT_SITE_FRAC_ATTR, sei.getFractionAlong() + ""));
if (!nml2) inputTargetSitesElement.addChildElement(inputTargetSiteElement);
if (nml2 && !nml2alpha) {
if (myElectricalInput instanceof RandomSpikeTrain) {
String connElName = NetworkMLConstants.CONNECTION_ELEMENT;
SimpleXMLElement connElement = new SimpleXMLElement(connElName);
connElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.CONNECTION_ID_ATTR, i + ""));
stimProjElement.addContent("\n ");
stimProjElement.addChildElement(connElement);
stimProjElement.addContent("\n ");
}
}
if (sei.getInstanceProps() != null) {
inputTargetSiteElement.addContent("\n ");
inputTargetSiteElement.addComment("Adding the site specific props");
if (sei.getInstanceProps() instanceof IClampInstanceProps) {
IClampInstanceProps ic = (IClampInstanceProps) sei.getInstanceProps();
float delay =
(float)
UnitConverter.getTime(
ic.getDelay(), UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem);
float duration =
(float)
UnitConverter.getTime(
ic.getDuration(), UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem);
float amp =
(float)
UnitConverter.getCurrent(
ic.getAmplitude(), UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem);
if (!nml2) {
SimpleXMLElement inputTypeElement =
new SimpleXMLElement(NetworkMLConstants.PULSEINPUT_INSTANCE_ELEMENT);
inputTypeElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.INPUT_DELAY_ATTR, delay + ""));
inputTypeElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.INPUT_DUR_ATTR, duration + ""));
// System.out.println("Converted "+amp+" to "+ a);
inputTypeElement.addAttribute(
new SimpleXMLAttribute(NetworkMLConstants.INPUT_AMP_ATTR, amp + ""));
inputTargetSiteElement.addContent(" ");
inputTargetSiteElement.addChildElement(inputTypeElement);
inputTargetSiteElement.addContent("\n ");
} else {
SimpleXMLElement pulseGenElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_PULSE_GEN_ELEMENT);
pulseGenElement.addAttribute(
NeuroMLConstants.NEUROML_ID_V2, inputReference + "__" + i);
pulseGenElement.addAttribute(
NetworkMLConstants.INPUT_DELAY_ATTR, delay + timeUnits.getNeuroML2Symbol());
pulseGenElement.addAttribute(
NetworkMLConstants.INPUT_DUR_ATTR, duration + timeUnits.getNeuroML2Symbol());
pulseGenElement.addAttribute(
NetworkMLConstants.INPUT_AMP_ATTR, amp + currentUnits.getNeuroML2Symbol());
topLevelCompElement.addContent("\n\n ");
topLevelCompElement.addChildElement(pulseGenElement);
topLevelCompElement.addContent("\n\n ");
if (version.isVersion2alpha()) {
String target = nextStim.getCellGroup() + "[" + sei.getCellNumber() + "]";
SimpleXMLElement expInputElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_EXP_INPUT_ELEMENT);
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_EXP_INPUT_TARGET_ATTR, target);
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_EXP_INPUT_INPUT_ATTR, inputReference + "__" + i);
entities.add(expInputElement);
} else {
String target =
"../"
+ nextStim.getCellGroup()
+ "/"
+ sei.getCellNumber()
+ "/"
+ project.cellGroupsInfo.getCellType(nextStim.getCellGroup());
SimpleXMLElement expInputElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_INPUT_LIST_ELEMENT);
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_EXP_INPUT_TARGET_ATTR, target);
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_EXP_INPUT_INPUT_ATTR, inputReference + "__" + i);
entities.add(expInputElement);
}
}
} else if (sei.getInstanceProps() instanceof RandomSpikeTrainInstanceProps) {
RandomSpikeTrainInstanceProps rst =
(RandomSpikeTrainInstanceProps) sei.getInstanceProps();
float stimFreq = rst.getRate();
// String stimMech = rst.get;
SimpleXMLElement inputTypeElement =
new SimpleXMLElement(NetworkMLConstants.RANDOMSTIM_INSTANCE_ELEMENT);
inputTypeElement.addAttribute(
new SimpleXMLAttribute(
NetworkMLConstants.RND_STIM_FREQ_ATTR,
(float)
UnitConverter.getRate(
stimFreq, UnitConverter.NEUROCONSTRUCT_UNITS, unitSystem)
+ ""));
// inputTypeElement.addAttribute(new
// SimpleXMLAttribute(NetworkMLConstants.RND_STIM_MECH_ATTR, stimMech));
inputTargetSiteElement.addContent(" ");
inputTargetSiteElement.addChildElement(inputTypeElement);
inputTargetSiteElement.addContent("\n ");
} else {
throw new NeuroMLException(
"Error trying to save input "
+ inputReference
+ ". Cannot save in NeuroML an input of type: "
+ myElectricalInput.getType());
}
} else {
if (nml2) {
if (version.isVersion2alpha()) {
String target = nextStim.getCellGroup() + "[" + sei.getCellNumber() + "]";
SimpleXMLElement expInputElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_EXP_INPUT_ELEMENT);
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_EXP_INPUT_TARGET_ATTR, target);
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_EXP_INPUT_INPUT_ATTR, inputReference);
entities.add(expInputElement);
} else {
String target =
"../"
+ nextStim.getCellGroup()
+ "/"
+ sei.getCellNumber()
+ "/"
+ project.cellGroupsInfo.getCellType(nextStim.getCellGroup());
SimpleXMLElement expInputElement =
new SimpleXMLElement(NetworkMLConstants.NEUROML2_INPUT_ELEMENT);
expInputElement.addAttribute(NeuroMLConstants.NEUROML_ID_V2, i + "");
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_EXP_INPUT_TARGET_ATTR, target);
expInputElement.addAttribute(
NetworkMLConstants.NEUROML2_INPUT_DESTINATION,
NetworkMLConstants.NEUROML2_INPUT_DESTINATION_DEFAULT);
inputTargetSitesElement.addChildElement(expInputElement);
}
}
}
if (i == inputsHere.size() - 1) inputTargetSitesElement.addContent("\n ");
// Next Site
}
inputTargetElement.addContent("\n ");
if (!nml2) {
inputsElement.addChildElement(inputElement);
inputElement.addContent("\n ");
}
}
logger.logComment("Finished saving data to inputs element");
} catch (Exception ex) {
ex.printStackTrace();
throw new NeuroMLException("Problem creating inputs element file", ex);
}
return entities;
}