@Override
public void actionPerformed(ActionEvent udalost) {
try {
Evidence evidence = new Evidence(localPath);
TableModel modelVipFirmy = new ModelStudenti(evidence);
TableModel modelNeplaticiFirmy = new ModelStatickeStranky(evidence);
TableModel modelVsechnyFirmy = new ModelOdevzdaneUkoly(evidence);
TableModel modelPlaticiFirmy = new ModelPublikace(evidence);
TableModel modelVipPlatFirmy = new ModelUkoly(evidence);
PraceSDaty archiv = evidence.getArchiv();
Gui gui =
new Gui(
modelVipFirmy,
modelNeplaticiFirmy,
modelVsechnyFirmy,
modelPlaticiFirmy,
modelVipPlatFirmy,
archiv,
evidence,
ftpClient,
nacitani);
} catch (Exception ex) {
Logger.getLogger(Nacitani.class.getName()).log(Level.SEVERE, null, ex);
}
}
/**
* Método utilizado para testar o encontro de uma evidência pelas autoridades. Método de teste
* apenas
*/
public void testEvidenceInvestigation() {
if (!evidences.isEmpty()) {
if (successTest(opRisk, 0)) {
Evidence evi = evidences.get(0);
evi.getCause().getPlayer().setHeat(true, evi.getHeatInc());
evidences.remove(evi);
}
}
}
/** @return */
public String toXML() {
OptetPlugin optetPluginJAXB = new OptetPlugin();
for (Iterator iterator = categoriesList.iterator(); iterator.hasNext(); ) {
Categories cates = (Categories) iterator.next();
com.thalesgroup.optet.plugin.template.jaxb.OptetPlugin.Categories categoriesJAXB =
new com.thalesgroup.optet.plugin.template.jaxb.OptetPlugin.Categories();
categoriesJAXB.setName(cates.getName());
System.out.println("JAXB " + cates.getName());
Collection<Category> listcategory = cates.getCategories().values();
for (Iterator iterator2 = listcategory.iterator(); iterator2.hasNext(); ) {
Category cat = (Category) iterator2.next();
com.thalesgroup.optet.plugin.template.jaxb.OptetPlugin.Categories.Category catJAXB =
new com.thalesgroup.optet.plugin.template.jaxb.OptetPlugin.Categories.Category();
catJAXB.setName(cat.getName());
System.out.println("JAXB " + cat.getName());
Collection<Evidence> listevidence = cat.getMetrics().values();
for (Iterator iterator3 = listevidence.iterator(); iterator3.hasNext(); ) {
Evidence evidence = (Evidence) iterator3.next();
com.thalesgroup.optet.plugin.template.jaxb.OptetPlugin.Categories.Category.Evidence
evidenceJAXB =
new com.thalesgroup.optet.plugin.template.jaxb.OptetPlugin.Categories.Category
.Evidence();
evidenceJAXB.setName(evidence.getSummary());
System.out.println("JAXB " + evidence.getSummary());
catJAXB.getEvidence().add(evidenceJAXB);
}
categoriesJAXB.getCategory().add(catJAXB);
}
optetPluginJAXB.getCategories().add(categoriesJAXB);
}
JAXBContext jaxbContext;
StringWriter stringWriter = new StringWriter();
try {
jaxbContext = JAXBContext.newInstance(OptetPlugin.class);
Marshaller jaxbMarshaller = jaxbContext.createMarshaller();
// output pretty printed
jaxbMarshaller.setProperty(Marshaller.JAXB_FORMATTED_OUTPUT, true);
jaxbMarshaller.marshal(optetPluginJAXB, stringWriter);
System.out.println("res " + stringWriter);
} catch (JAXBException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
// TODO Auto-generated method stub
return stringWriter.toString();
}
/**
* * Método a ser usado no update region para decrementar o timer de todas as evidencias na região
* e remove-las caso o timer zere.
*/
private void decrementEvidenceTimer() {
for (Iterator<Evidence> it = evidences.iterator(); it.hasNext(); ) {
Evidence e = it.next();
if (!e.decrementTimer()) {
e.setReg(null);
it.remove();
System.out.println("Timer zerado, removendo evidencia");
}
}
}
public double[] calculatePriorMarginals(int nodeIndex) {
Evidence evidence = getEvidence();
setEvidence(Evidence.tautology(evidence.getVariableSource()));
double[] marginals = new double[evidence.getNumCategories(nodeIndex)];
for (int i = 0; i < getBayesIm().getNumColumns(nodeIndex); i++) {
marginals[i] = getMarginal(nodeIndex, i);
}
setEvidence(evidence);
return marginals;
}
/** {@inheritDoc} */
@Override
public Evidence[] mapEdgeToEvidence(Graph graph, Edge edge) {
if (graph == null) throw new NullPointerException("graph cannot be null");
if (edge == null) throw new NullPointerException("edge cannot be null");
if (edge.metadata == null) return new Evidence[0];
Object evidences = edge.metadata.get("evidences");
if (evidences == null || !(evidences instanceof List)) return new Evidence[0];
List<Map> evidenceMap = typedList(((List) evidences), Map.class);
List<Evidence> evidenceList = new ArrayList<Evidence>();
for (Map item : evidenceMap) {
@SuppressWarnings("unchecked")
Map<String, Object> evMap = (Map<String, Object>) item;
Evidence ev = new Evidence();
ev.belStatement = getOrEmptyString("bel_statement", evMap).replace("\\\"", "\"");
ev.summaryText = getOrEmptyString("summary_text", evMap);
Citation citation = new Citation();
@SuppressWarnings("unchecked")
Map<String, Object> citationMap = (Map<String, Object>) evMap.get("citation");
if (citationMap != null) {
citation.id = getOrEmptyString("id", citationMap);
citation.type = getOrEmptyString("type", citationMap);
citation.name = getOrEmptyString("name", citationMap);
}
ev.citation = citation;
BiologicalContext context = new BiologicalContext();
@SuppressWarnings("unchecked")
Map<String, Object> contextMap = (Map<String, Object>) evMap.get("biological_context");
if (contextMap != null) {
context.speciesCommonName = getOrEmptyString("species_common_name", contextMap);
context.ncbiTaxId = getOrZero("ncbi_tax_id", contextMap);
Set<String> varying = new HashSet<String>(contextMap.keySet());
varying.removeAll(asList("species_common_name", "ncbi_tax_id"));
for (String key : varying) {
context.variedAnnotations.put(key, contextMap.get(key));
}
ev.biologicalContext = context;
}
evidenceList.add(ev);
}
return evidenceList.toArray(new Evidence[evidenceList.size()]);
}
public double[] calculateUpdatedMarginals(int nodeIndex) {
double[] marginals = new double[evidence.getNumCategories(nodeIndex)];
for (int i = 0; i < getBayesIm().getNumColumns(nodeIndex); i++) {
marginals[i] = getMarginal(nodeIndex, i);
}
return marginals;
}
/** {@inheritDoc} */
@Override
public Evidence[] mapFromTable(CyEdge edge, CyTable table) {
if (edge == null) throw new NullPointerException("edge cannot be null");
if (table == null) throw new NullPointerException("table cannot be null");
Set<String> nonAnnotationColumns =
new HashSet<String>(
asList(
CyNetwork.SUID,
NETWORK_SUID,
NETWORK_NAME,
EDGE_SUID,
BEL_STATEMENT,
SUMMARY_TEXT,
CITATION_ID,
CITATION_NAME,
CITATION_TYPE,
SPECIES));
Collection<CyRow> evidenceRows = table.getMatchingRows(EDGE_SUID, edge.getSUID());
List<Evidence> evidences = new ArrayList<Evidence>(evidenceRows.size());
if (!evidenceRows.isEmpty()) {
for (CyRow evRow : evidenceRows) {
Evidence e = new Evidence();
e.belStatement = evRow.get(BEL_STATEMENT, String.class);
e.summaryText = evRow.get(SUMMARY_TEXT, String.class);
e.citation = new Citation();
e.citation.id = evRow.get(CITATION_ID, String.class);
e.citation.name = evRow.get(CITATION_NAME, String.class);
e.citation.type = evRow.get(CITATION_TYPE, String.class);
e.biologicalContext = new BiologicalContext();
e.biologicalContext.speciesCommonName = evRow.get(SPECIES, String.class);
e.biologicalContext.variedAnnotations = new HashMap<String, Object>();
for (Entry<String, Object> columnValue : evRow.getAllValues().entrySet()) {
if (nonAnnotationColumns.contains(columnValue.getKey())) continue;
e.biologicalContext.variedAnnotations.put(columnValue.getKey(), columnValue.getValue());
}
evidences.add(e);
}
}
return evidences.toArray(new Evidence[evidences.size()]);
}
private void storeEvidence() throws ObjectStoreException {
for (Set<Evidence> annotationEvidence : goTermGeneToEvidence.values()) {
List<String> evidenceRefIds = new ArrayList<String>();
Integer goAnnotationRefId = null;
for (Evidence evidence : annotationEvidence) {
Item goevidence = createItem("DOEvidence");
goevidence.setReference("code", evidenceCodes.get(evidence.getEvidenceCode()));
List<String> publicationEvidence = evidence.getPublications();
if (!publicationEvidence.isEmpty()) {
goevidence.setCollection("publications", publicationEvidence);
}
store(goevidence);
evidenceRefIds.add(goevidence.getIdentifier());
goAnnotationRefId = evidence.getStoredAnnotationId();
}
ReferenceList refIds = new ReferenceList("evidence", new ArrayList<String>(evidenceRefIds));
store(refIds, goAnnotationRefId);
}
}
public void setEvidence(Evidence evidence) {
if (evidence == null) {
throw new NullPointerException();
}
if (evidence.getProposition().getVariableSource() != this) {
throw new IllegalArgumentException(
"Can only take evidence for " + "this particular object; please convert the evidence.");
}
this.evidence = evidence;
}
public List<String[]> getInspectable() {
List<String[]> list = new ArrayList<String[]>();
BioPAXNode.addNamesAndTypeAndID(list, cont);
for (Evidence ev : cont.getEvidence()) {
list.add(new String[] {"Evidence", ev.toString()});
}
if (!cont.getInteractionType().isEmpty()) {
String s = BioPAXNode.formatInString(cont.getInteractionType());
list.add(new String[] {"Interaction Type", s});
}
if (cont.getControlType() != null) {
list.add(new String[] {"Control Type", cont.getControlType().toString()});
}
BioPAXNode.addDataSourceAndXrefAndComments(list, cont);
return list;
}
public void init() {
categoriesList = new ArrayList<Categories>();
Categories categories = new Categories("static");
Category category = new Category("SoftwareComplexity", categories);
Evidence evidence = new Evidence("SoftwareComplexityMetric", category);
category.getMetrics().put(evidence.getSummary(), evidence);
categories.getCategories().put(category.getName(), category);
category = new Category("Maintainability", categories);
categories.getCategories().put(category.getName(), category);
evidence = new Evidence("MaintainabilityMetric", category);
category.getMetrics().put(evidence.getSummary(), evidence);
category = new Category("Reliability", categories);
categories.getCategories().put(category.getName(), category);
evidence = new Evidence("ReliabilityMetric", category);
category.getMetrics().put(evidence.getSummary(), evidence);
category = new Category("Security", categories);
categories.getCategories().put(category.getName(), category);
evidence = new Evidence("SecurityMetric", category);
category.getMetrics().put(evidence.getSummary(), evidence);
categoriesList.add(categories);
categories = new Categories("runtime");
category = new Category("UnitTest", categories);
categories.getCategories().put(category.getName(), category);
category = new Category("CodeCoverage", categories);
categories.getCategories().put(category.getName(), category);
categoriesList.add(categories);
}
@Override
public Void doInBackground() throws Exception {
setProgress(0);
try {
Thread.sleep(1000);
} catch (InterruptedException ignore) {
}
setProgress(10);
ftpClient.download();
setProgress(30);
apiClient.downloadFile("admin/api/students", "studenti.csv");
setProgress(40);
apiClient.downloadFile("admin/api/static/1", "menu.csv");
setProgress(48);
apiClient.downloadFile("admin/api/statickat", "kategoriemenu.csv");
setProgress(54);
apiClient.downloadFile("admin/api/publikace/1", "publikace.csv");
setProgress(61);
apiClient.downloadFile("admin/api/publikacekat", "publikacekategorie.csv");
setProgress(70);
apiClient.downloadFile("admin/api/skoly", "skoly.csv");
setProgress(75);
apiClient.downloadFile("admin/api/fakulty", "fakulty.csv");
setProgress(79);
apiClient.downloadFile("admin/api/skupiny", "skupinysemestrpredmet.csv");
setProgress(85);
apiClient.downloadFile("admin/api/predmety", "predmet.csv");
setProgress(90);
apiClient.downloadFile("admin/api/ukoly/1", "ukoly.csv");
setProgress(95);
apiClient.downloadFile("admin/api/userukoly/1", "userukoly.csv");
setProgress(100);
Evidence evidence = new Evidence(localPath);
TableModel modelStudenti = new ModelStudenti(evidence);
TableModel modelStatickeStranky = new ModelStatickeStranky(evidence);
TableModel modelOdevzdaneUkoly = new ModelOdevzdaneUkoly(evidence);
TableModel modelPublikace = new ModelPublikace(evidence);
TableModel modelUkoly = new ModelUkoly(evidence);
PraceSDaty archiv = evidence.getArchiv();
Gui gui =
new Gui(
modelStudenti,
modelStatickeStranky,
modelOdevzdaneUkoly,
modelPublikace,
modelUkoly,
archiv,
evidence,
ftpClient,
nacitani);
return null;
}
/**
* This method takes an instantiated Bayes net (BayesIm) whose graph include all the variables
* (observed and latent) and computes estimated counts using the data in the DataSet mixedData.
* The counts that are estimated correspond to cells in the conditional probability tables of the
* Bayes net. The outermost loop (indexed by j) is over the set of variables. If the variable has
* no parents, each case in the dataset is examined and the count for the observed value of the
* variables is increased by 1.0; if the value of the variable is missing the marginal
* probabilities its values given the values of the variables that are available for that case are
* used to increment the corresponding estimated counts. If a variable has parents then there is a
* loop which steps through all possible sets of values of its parents. This loop is indexed by
* the variable "row". Each case in the dataset is examined. It the variable and all its parents
* have values in the case the corresponding estimated counts are incremented by 1.0. If the
* variable or any of its parents have missing values, the joint marginal is computed for the
* variable and the set of values of its parents corresponding to "row" and the corresponding
* estimated counts are incremented by the appropriate probability. The estimated counts are
* stored in the double[][][] array estimatedCounts. The count (possibly fractional) of the number
* of times each combination of parent values occurs is stored in the double[][] array
* estimatedCountsDenom. These two arrays are used to compute the estimated conditional
* probabilities of the output Bayes net.
*/
private BayesIm expectation(BayesIm inputBayesIm) {
// System.out.println("Entered method expectation.");
int numCases = mixedData.getNumRows();
// StoredCellEstCounts estCounts = new StoredCellEstCounts(variables);
int numVariables = allVariables.size();
RowSummingExactUpdater rseu = new RowSummingExactUpdater(inputBayesIm);
for (int j = 0; j < numVariables; j++) {
DiscreteVariable var = (DiscreteVariable) allVariables.get(j);
String varName = var.getName();
Node varNode = graph.getNode(varName);
int varIndex = inputBayesIm.getNodeIndex(varNode);
int[] parentVarIndices = inputBayesIm.getParents(varIndex);
// System.out.println("graph = " + graph);
// for(int col = 0; col < var.getNumSplits(); col++)
// System.out.println("Category " + col + " = " + var.getCategory(col));
// System.out.println("Updating estimated counts for node " + varName);
// This segment is for variables with no parents:
if (parentVarIndices.length == 0) {
// System.out.println("No parents");
for (int col = 0; col < var.getNumCategories(); col++) {
estimatedCounts[j][0][col] = 0.0;
}
for (int i = 0; i < numCases; i++) {
// System.out.println("Case " + i);
// If this case has a value for var
if (mixedData.getInt(i, j) != -99) {
estimatedCounts[j][0][mixedData.getInt(i, j)] += 1.0;
// System.out.println("Adding 1.0 to " + varName +
// " row 0 category " + mixedData[j][i]);
} else {
// find marginal probability, given obs data in this case, p(v=0)
Evidence evidenceThisCase = Evidence.tautology(inputBayesIm);
boolean existsEvidence = false;
// Define evidence for updating by using the values of the other vars.
for (int k = 0; k < numVariables; k++) {
if (k == j) {
continue;
}
Node otherVar = allVariables.get(k);
if (mixedData.getInt(i, k) == -99) {
continue;
}
existsEvidence = true;
String otherVarName = otherVar.getName();
Node otherNode = graph.getNode(otherVarName);
int otherIndex = inputBayesIm.getNodeIndex(otherNode);
evidenceThisCase.getProposition().setCategory(otherIndex, mixedData.getInt(i, k));
}
if (!existsEvidence) {
continue; // No other variable contained useful data
}
rseu.setEvidence(evidenceThisCase);
for (int m = 0; m < var.getNumCategories(); m++) {
estimatedCounts[j][0][m] += rseu.getMarginal(varIndex, m);
// System.out.println("Adding " + p + " to " + varName +
// " row 0 category " + m);
// find marginal probability, given obs data in this case, p(v=1)
// estimatedCounts[j][0][1] += 0.5;
}
}
}
// Print estimated counts:
// System.out.println("Estimated counts: ");
// Print counts for each value of this variable with no parents.
// for(int m = 0; m < var.getNumSplits(); m++)
// System.out.print(" " + m + " " + estimatedCounts[j][0][m]);
// System.out.println();
} else { // For variables with parents:
int numRows = inputBayesIm.getNumRows(varIndex);
for (int row = 0; row < numRows; row++) {
int[] parValues = inputBayesIm.getParentValues(varIndex, row);
estimatedCountsDenom[varIndex][row] = 0.0;
for (int col = 0; col < var.getNumCategories(); col++) {
estimatedCounts[varIndex][row][col] = 0.0;
}
for (int i = 0; i < numCases; i++) {
// for a case where the parent values = parValues increment the estCount
boolean parentMatch = true;
for (int p = 0; p < parentVarIndices.length; p++) {
if (parValues[p] != mixedData.getInt(i, parentVarIndices[p])
&& mixedData.getInt(i, parentVarIndices[p]) != -99) {
parentMatch = false;
break;
}
}
if (!parentMatch) {
continue; // Not a matching case; go to next.
}
boolean parentMissing = false;
for (int parentVarIndice : parentVarIndices) {
if (mixedData.getInt(i, parentVarIndice) == -99) {
parentMissing = true;
break;
}
}
if (mixedData.getInt(i, j) != -99 && !parentMissing) {
estimatedCounts[j][row][mixedData.getInt(i, j)] += 1.0;
estimatedCountsDenom[j][row] += 1.0;
continue; // Next case
}
// for a case with missing data (either var or one of its parents)
// compute the joint marginal
// distribution for var & this combination of values of its parents
// and update the estCounts accordingly
// To compute marginals create the evidence
boolean existsEvidence = false;
Evidence evidenceThisCase = Evidence.tautology(inputBayesIm);
// "evidenceVars" not used.
// List<String> evidenceVars = new LinkedList<String>();
// for (int k = 0; k < numVariables; k++) {
// //if(k == j) continue;
// Variable otherVar = allVariables.get(k);
// if (mixedData.getInt(i, k) == -99) {
// continue;
// }
// existsEvidence = true;
// String otherVarName = otherVar.getName();
// Node otherNode = graph.getNode(otherVarName);
// int otherIndex = inputBayesIm.getNodeIndex(
// otherNode);
// evidenceThisCase.getProposition().setCategory(
// otherIndex, mixedData.getInt(i, k));
// evidenceVars.add(otherVarName);
// }
if (!existsEvidence) {
continue;
}
rseu.setEvidence(evidenceThisCase);
estimatedCountsDenom[j][row] += rseu.getJointMarginal(parentVarIndices, parValues);
int[] parPlusChildIndices = new int[parentVarIndices.length + 1];
int[] parPlusChildValues = new int[parentVarIndices.length + 1];
parPlusChildIndices[0] = varIndex;
for (int pc = 1; pc < parPlusChildIndices.length; pc++) {
parPlusChildIndices[pc] = parentVarIndices[pc - 1];
parPlusChildValues[pc] = parValues[pc - 1];
}
for (int m = 0; m < var.getNumCategories(); m++) {
parPlusChildValues[0] = m;
/*
if(varName.equals("X1") && i == 0 ) {
System.out.println("Calling getJointMarginal with parvalues");
for(int k = 0; k < parPlusChildIndices.length; k++) {
int pIndex = parPlusChildIndices[k];
Node pNode = inputBayesIm.getNode(pIndex);
String pName = pNode.getName();
System.out.println(pName + " " + parPlusChildValues[k]);
}
}
*/
/*
if(varName.equals("X1") && i == 0 ) {
System.out.println("Evidence = " + evidenceThisCase);
//int[] vars = {l1Index, x1Index};
Node nodex1 = inputBayesIm.getNode("X1");
int x1Index = inputBayesIm.getNodeIndex(nodex1);
Node nodel1 = inputBayesIm.getNode("L1");
int l1Index = inputBayesIm.getNodeIndex(nodel1);
int[] vars = {l1Index, x1Index};
int[] vals = {0, 0};
double ptest = rseu.getJointMarginal(vars, vals);
System.out.println("Joint marginal (X1=0, L1 = 0) = " + p);
}
*/
estimatedCounts[j][row][m] +=
rseu.getJointMarginal(parPlusChildIndices, parPlusChildValues);
// System.out.println("Case " + i + " parent values ");
// for (int pp = 0; pp < parentVarIndices.length; pp++) {
// Variable par = (Variable) allVariables.get(parentVarIndices[pp]);
// System.out.print(" " + par.getName() + " " + parValues[pp]);
// }
// System.out.println();
// System.out.println("Adding " + p + " to " + varName +
// " row " + row + " category " + m);
}
// }
}
// Print estimated counts:
// System.out.println("Estimated counts: ");
// System.out.println(" Parent values: ");
// for (int i = 0; i < parentVarIndices.length; i++) {
// Variable par = (Variable) allVariables.get(parentVarIndices[i]);
// System.out.print(" " + par.getName() + " " + parValues[i] + " ");
// }
// System.out.println();
// for(int m = 0; m < var.getNumSplits(); m++)
// System.out.print(" " + m + " " + estimatedCounts[j][row][m]);
// System.out.println();
}
} // else
} // j < numVariables
BayesIm outputBayesIm = new MlBayesIm(bayesPm);
for (int j = 0; j < nodes.length; j++) {
DiscreteVariable var = (DiscreteVariable) allVariables.get(j);
String varName = var.getName();
Node varNode = graph.getNode(varName);
int varIndex = inputBayesIm.getNodeIndex(varNode);
// int[] parentVarIndices = inputBayesIm.getParents(varIndex);
int numRows = inputBayesIm.getNumRows(j);
// System.out.println("Conditional probabilities for variable " + varName);
int numCols = inputBayesIm.getNumColumns(j);
if (numRows == 1) {
double sum = 0.0;
for (int m = 0; m < numCols; m++) {
sum += estimatedCounts[j][0][m];
}
for (int m = 0; m < numCols; m++) {
condProbs[j][0][m] = estimatedCounts[j][0][m] / sum;
// System.out.print(" " + condProbs[j][0][m]);
outputBayesIm.setProbability(varIndex, 0, m, condProbs[j][0][m]);
}
// System.out.println();
} else {
for (int row = 0; row < numRows; row++) {
// int[] parValues = inputBayesIm.getParentValues(varIndex,
// row);
// int numCols = inputBayesIm.getNumColumns(j);
// for (int p = 0; p < parentVarIndices.length; p++) {
// Variable par = (Variable) allVariables.get(parentVarIndices[p]);
// System.out.print(" " + par.getName() + " " + parValues[p]);
// }
// double sum = 0.0;
// for(int m = 0; m < numCols; m++)
// sum += estimatedCounts[j][row][m];
for (int m = 0; m < numCols; m++) {
if (estimatedCountsDenom[j][row] != 0.0) {
condProbs[j][row][m] = estimatedCounts[j][row][m] / estimatedCountsDenom[j][row];
} else {
condProbs[j][row][m] = Double.NaN;
}
// System.out.print(" " + condProbs[j][row][m]);
outputBayesIm.setProbability(varIndex, row, m, condProbs[j][row][m]);
}
// System.out.println();
}
}
}
return outputBayesIm;
}
/**
* Returns an evidence object containing the proposition in evidence, with null Bayes IM and null
* manipulation.
*/
private Evidence getEvidence() {
return new Evidence(evidence.getProposition());
}
/** {@inheritDoc} */
public void process(Reader reader) throws ObjectStoreException, IOException {
initialiseMapsForFile();
BufferedReader br = new BufferedReader(reader);
String line = null;
// loop through entire file
while ((line = br.readLine()) != null) {
if (line.startsWith("!")) {
continue;
}
String[] array = line.split("\t", -1); // keep trailing empty Strings
if (array.length < 13) {
throw new IllegalArgumentException(
"Not enough elements (should be > 13 not " + array.length + ") in line: " + line);
}
String taxonId = parseTaxonId(array[12]);
Config config = configs.get(taxonId);
if (config == null) {
throw new NullPointerException(
"No entry for organism with taxonId = '" + taxonId + "' found in config file.");
}
int readColumn = config.readColumn();
String productId = array[readColumn];
String goId = array[4];
String qualifier = array[3];
String strEvidence = array[6];
String withText = array[7];
if (StringUtils.isNotEmpty(strEvidence)) {
storeEvidenceCode(strEvidence);
} else {
throw new IllegalArgumentException(
"Evidence is a required column but not "
+ "found for doterm "
+ goId
+ " and productId "
+ productId);
}
// type of gene product, we're not interested in at the moment
// String type = array[11];
String type = configs.get(taxonId).annotationType;
// Wormbase has some proteins with UniProt accessions and some with WB:WP ids,
// hack here to get just the UniProt ones.
// if (("protein".equalsIgnoreCase(type) && !array[0].startsWith("UniProt"))
// || (!"protein".equalsIgnoreCase(type) && array[0].startsWith("UniProt"))) {
// continue;
// }
// create unique key for go annotation
GoTermToGene key = new GoTermToGene(productId, goId, qualifier);
String dataSourceCode = array[14];
Item organism = newOrganism(taxonId);
String productIdentifier = newProduct(productId, type, organism, dataSourceCode, true, null);
// null if resolver could not resolve an identifier
if (productIdentifier != null) {
// null if no pub found
String pubRefId = newPublication(array[5]);
// get evidence codes for this goterm|gene pair
Set<Evidence> allEvidenceForAnnotation = goTermGeneToEvidence.get(key);
// new evidence
if (allEvidenceForAnnotation == null) {
String goTermIdentifier = newGoTerm(goId, dataSourceCode);
Evidence evidence = new Evidence(strEvidence, pubRefId);
allEvidenceForAnnotation = new LinkedHashSet<Evidence>();
allEvidenceForAnnotation.add(evidence);
goTermGeneToEvidence.put(key, allEvidenceForAnnotation);
Integer storedAnnotationId =
createGoAnnotation(
productIdentifier,
type,
goTermIdentifier,
organism,
qualifier,
withText,
dataSourceCode);
evidence.setStoredAnnotationId(storedAnnotationId);
} else {
boolean seenEvidenceCode = false;
Integer storedAnnotationId = null;
for (Evidence evidence : allEvidenceForAnnotation) {
String evidenceCode = evidence.getEvidenceCode();
// already have evidence code, just add pub
if (evidenceCode.equals(strEvidence)) {
evidence.addPublicationRefId(pubRefId);
seenEvidenceCode = true;
}
storedAnnotationId = evidence.storedAnnotationId;
}
if (!seenEvidenceCode) {
Evidence evidence = new Evidence(strEvidence, pubRefId);
evidence.storedAnnotationId = storedAnnotationId;
allEvidenceForAnnotation.add(evidence);
}
}
}
}
storeProductCollections();
storeEvidence();
}
/*! Query a value from this node
* \param[in] query the query to the CPF or Evidence
* \return the value or the sum of values or the evidence
*/
public Value query(int[] query) {
if (_Evidence != null) {
return _Evidence.getEvidenceValue(query[0]);
}
return getCPF().get(query);
}
/**
* * Método que retorna a evidência na região causada por uma determinada ação. Apenas deve
* existir uma evidência por causa em uma determinada região.
*
* @param act Ação que causou a evidência
* @return Retorna a evidência se ela existir, ou nulo se ela não existir.
*/
public Evidence getEvidenceByCause(Action act) {
for (Evidence e : evidences) {
if (e.getCauseType() == act.getClass()) return e;
}
return null;
}
