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 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 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; }