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;
}