@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("GeneratedElecInputs with " + this.getNumberSingleInputs() + " inputs in total\n");
Enumeration keys = this.myElecInputs.keys();
while (keys.hasMoreElements()) {
String input = (String) keys.nextElement();
ArrayList<SingleElectricalInput> singleInputList = myElecInputs.get(input);
StimulationSettings ss = project.elecInputInfo.getStim(input);
sb.append(
input + " (" + ss.toLongString() + ") has " + singleInputList.size() + " entries.\n");
for (int i = 0; (i < singleInputList.size() && i < 9); i++) {
SingleElectricalInput sei = singleInputList.get(i);
sb.append(" Input " + i + ": " + sei.toString());
if (sei.getInstanceProps() != null)
sb.append(" (" + sei.getInstanceProps().details(false) + ")");
sb.append("\n");
}
}
return sb.toString();
}
public int getNumberSingleInputs(String inputRef) {
if (!myElecInputs.containsKey(inputRef)) return 0;
ArrayList inputArrayList = myElecInputs.get(inputRef);
return inputArrayList.size();
}
public void addSingleInput(String inputReference, SingleElectricalInput oneInput) {
if (!myElecInputs.containsKey(inputReference)) {
ArrayList<SingleElectricalInput> newInputArrayList = new ArrayList<SingleElectricalInput>();
myElecInputs.put(inputReference, newInputArrayList);
}
ArrayList<SingleElectricalInput> inputVector = myElecInputs.get(inputReference);
inputVector.add(oneInput);
}
public int getNumberSingleInputs() {
int totalCount = 0;
Enumeration keys = this.myElecInputs.keys();
while (keys.hasMoreElements()) {
ArrayList inputArrayList = myElecInputs.get((String) keys.nextElement());
totalCount = totalCount + inputArrayList.size();
}
return totalCount;
}
public ArrayList<String> getNonEmptyInputRefs() {
ArrayList<String> neir = new ArrayList<String>();
Enumeration<String> keys = this.myElecInputs.keys();
while (keys.hasMoreElements()) {
String ref = keys.nextElement();
ArrayList inputArrayList = myElecInputs.get(ref);
if (inputArrayList.size() > 0) neir.add(ref);
}
return neir;
}
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 addSingleInput(
String inputReference,
String inputType,
String cellGroup,
int cellNumber,
int segmentId,
float fractionAlong,
InputInstanceProps ip) {
if (!myElecInputs.containsKey(inputReference)) {
ArrayList<SingleElectricalInput> newInputArrayList = new ArrayList<SingleElectricalInput>();
myElecInputs.put(inputReference, newInputArrayList);
}
ArrayList<SingleElectricalInput> inputVector = myElecInputs.get(inputReference);
inputVector.add(
new SingleElectricalInput(inputType, cellGroup, cellNumber, segmentId, fractionAlong, ip));
}
public String details(boolean html) {
StringBuilder sb = new StringBuilder();
sb.append(
"Network contains "
+ GeneralUtils.getBold(this.getNumberSingleInputs(), html)
+ " inputs in total"
+ GeneralUtils.getEndLine(html)
+ GeneralUtils.getEndLine(html));
Enumeration keys = this.myElecInputs.keys();
String indent = " ";
if (html) indent = " ";
while (keys.hasMoreElements()) {
String input = (String) keys.nextElement();
ArrayList<SingleElectricalInput> singleInputList = myElecInputs.get(input);
sb.append(
"Input: "
+ GeneralUtils.getBold(input, html)
+ " has "
+ GeneralUtils.getBold(singleInputList.size(), html)
+ " entries"
+ GeneralUtils.getEndLine(html));
for (int i = 0; (i < singleInputList.size()); i++) {
sb.append(
"Input "
+ i
+ ": "
+ singleInputList.get(i).details(html)
+ GeneralUtils.getEndLine(html));
if (singleInputList.get(i).getInstanceProps() != null) {
sb.append(
indent
+ "Input specific properties: "
+ singleInputList.get(i).getInstanceProps().details(html)
+ GeneralUtils.getEndLine(html));
}
}
sb.append(GeneralUtils.getEndLine(html));
}
return sb.toString();
}
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;
}