Examples of DataSource


Examples of org.vaadin.teemu.clara.binder.annotation.DataSource

    }

    public void bind(ComponentMapper mapper, Object controller) {
        Method[] methods = controller.getClass().getMethods();
        for (Method method : methods) {
            DataSource dataSource = method.getAnnotation(DataSource.class);
            if (dataSource != null) {
                bindDataSource(mapper, controller, method, dataSource);
            }

            EventHandler eventHandler = method
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Examples of primarydatamanager.mirrorupdater.data.DataSource

      System.exit(1);
    }
   
    String[] groupNames = groupNameList.split(":");
   
    DataSource source = new HttpDataSource("http://tvbrowser.dyndns.tv");
    DataTarget target = new FileDataTarget(new File("."));
   
    MirrorVisualizer visualizer = new HtmlMirrorVisualizer(source, target, groupNames);
    visualizer.visualize();
   
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Examples of railo.runtime.db.DataSource

     */
   
    ORMConfiguration ormConf = data.getORMConfiguration();
   
    // dialect
    DataSource ds = dc.getDatasource();
    String dialect=null;
    try  {
      if (Class.forName(ormConf.getDialect()) != null) {
        dialect = ormConf.getDialect();
      }
    }
    catch (Exception e) {
      // MZ: The dialect value could not be bound to a classname or instantiation causes an exception - ignore and use the default dialect entries
    }
    if (dialect == null) {
      dialect = Dialect.getDialect(ormConf.getDialect());
      if(Util.isEmpty(dialect)) dialect=Dialect.getDialect(ds);
    }
    if(Util.isEmpty(dialect))
      throw ExceptionUtil.createException(data,null,"A valid dialect definition inside the "+Constants.APP_CFC+"/"+Constants.CFAPP_NAME+" is missing. The dialect cannot be determinated automatically",null);
   
    // Cache Provider
    String cacheProvider = ormConf.getCacheProvider();
    Class<? extends RegionFactory> regionFactory=null;
   
    if(Util.isEmpty(cacheProvider) || "EHCache".equalsIgnoreCase(cacheProvider)) {
      regionFactory=net.sf.ehcache.hibernate.EhCacheRegionFactory.class;
      cacheProvider=regionFactory.getName();//"org.hibernate.cache.EhCacheProvider";
         
    }
    else if("JBossCache".equalsIgnoreCase(cacheProvider))   cacheProvider="org.hibernate.cache.TreeCacheProvider";
    else if("HashTable".equalsIgnoreCase(cacheProvider))   cacheProvider="org.hibernate.cache.HashtableCacheProvider";
    else if("SwarmCache".equalsIgnoreCase(cacheProvider))   cacheProvider="org.hibernate.cache.SwarmCacheProvider";
    else if("OSCache".equalsIgnoreCase(cacheProvider))     cacheProvider="org.hibernate.cache.OSCacheProvider";
 
    Resource cacheConfig = ormConf.getCacheConfig();
    Configuration configuration = new Configuration();
   
    // ormConfig
    Resource conf = ormConf.getOrmConfig();
    if(conf!=null){
      try {
        Document doc = CommonUtil.toDocument(conf);
        configuration.configure(doc);
      }
      catch (Throwable t) {
        ORMUtil.printError(t);
       
      }
    }
   
    try{
      configuration.addXML(mappings);
    }
    catch(MappingException me){
      throw ExceptionUtil.createException(data,null, me);
    }
   
    configuration
       
        // Database connection settings
        .setProperty("hibernate.connection.driver_class", ds.getClazz().getName())
      .setProperty("hibernate.connection.url", ds.getDsnTranslated())
      .setProperty("hibernate.connection.username", ds.getUsername())
      .setProperty("hibernate.connection.password", ds.getPassword())
      //.setProperty("hibernate.connection.release_mode", "after_transaction")
      .setProperty("hibernate.transaction.flush_before_completion", "false")
      .setProperty("hibernate.transaction.auto_close_session", "false")
     
      // JDBC connection pool (use the built-in)
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Examples of shocks.dawp.DataSource

     *
     */
    if(initialized){
      // initialize and execute the incoming action.
      incoming.setFilterMetadata(metadata, "incoming");
      DataSource incomingFilterResult =
        incoming.execute(request, response, ctx);
     
      log.info(incomingFilterResult.toString());
      String str = (String) incomingFilterResult.getAttribute("result")
      log.info(str);
      if(str != null && str.equals("SUCCESS")) {
        try {
          // forward to next filter
          Result actionResult =
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Examples of simtools.data.DataSource

                color = dialog.getColor();
                bColor.setBackground(color);


                DataSource colorTempDataSource = dialog.getSource();
                colorMapper = dialog.getMapper();

                        if  (colorTempDataSource!=null){
                           
                            if (colorMapperSource!=null)
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Examples of simtools.data.DataSource

        // Same time reference for all created synchronous time stamped data
        TimeSource timeReference = null;
        AsynchronousMergeDataSource mds = null;
        for (int i = 0; i < dsc.size(); i++) {
            try {
                DataSource data = (DataSource) dsc.get(i);
                String id = DataInfo.getId(data);
                if (!(id.equals(DataInfo.getId(timeRef)))) {    // Do not add the time reference to merge collection
                    if (get(id) != null) {
                        id += "_" + DataInfo.getLabel(dsc); // if data source name already exists then add collection name as a suffix
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Examples of slash.navigation.datasources.DataSource

            }
        });
    }

    public void sendChecksums(final Download download) {
        final DataSource dataSource = RouteConverter.getInstance().getDataSourceManager().
                getDataSourceService().getDataSourceByUrlPrefix(download.getUrl());
        if (dataSource == null)
            return;

        final Map<FileAndChecksum, List<FileAndChecksum>> fileAndChecksums = new HashMap<>();
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Examples of spark.api.DataSource

      logger.debug("metadata = {}", ((ProtocolResult)r).getMetadata());
    }
  }
 
  public static void testQuery() throws Exception {
    DataSource myDS = new ProtocolDataSource("http://DBpedia.org/sparql");
    Connection conn = myDS.getConnection(NoCredentials.INSTANCE);
    Command query = conn.createCommand("SELECT ?p ?o WHERE { <http://dbpedia.org/resource/Terry_Gilliam> ?p ?o }");   
    Solutions solutions = query.executeQuery();
   
    showMetadata(solutions);
    logger.debug("vars = {}", solutions.getVariables());
    int row = 0;
    while(solutions.next()) {
      logger.debug("Row {}: {}", ++row, solutions.getResult());
    }
    solutions.close();
    query.close();
    conn.close();
    myDS.close();
  }
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Examples of uk.ac.starlink.util.DataSource

    private void votableLoad(URI sender, String votText, String id)
            throws IOException {
        Navigator navigator = _imageFrame.getNavigator();
        if (navigator != null) {
            final byte[] votBytes = votText.getBytes("UTF-8");
            DataSource datsrc = new DataSource() {
                public InputStream getRawInputStream() {
                    return new ByteArrayInputStream(votBytes);
                }
            };
            String name = sender.toString() + "-" + id;
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Examples of weka.core.converters.ConverterUtils.DataSource

    boolean noOutput = false,
    trainStatistics = true,
    printMargins = false, printComplexityStatistics = false,
    printGraph = false, classStatistics = false, printSource = false;
    StringBuffer text = new StringBuffer();
    DataSource trainSource = null, testSource = null;
    ObjectInputStream objectInputStream = null;
    BufferedInputStream xmlInputStream = null;
    CostMatrix costMatrix = null;
    StringBuffer schemeOptionsText = null;
    long trainTimeStart = 0, trainTimeElapsed = 0,
    testTimeStart = 0, testTimeElapsed = 0;
    String xml = "";
    String[] optionsTmp = null;
    Classifier classifierBackup;
    Classifier classifierClassifications = null;
    int actualClassIndex = -1// 0-based class index
    String splitPercentageString = "";
    double splitPercentage = -1;
    boolean preserveOrder = false;
    boolean trainSetPresent = false;
    boolean testSetPresent = false;
    String thresholdFile;
    String thresholdLabel;
    StringBuffer predsBuff = null; // predictions from cross-validation
    AbstractOutput classificationOutput = null;

    // help requested?
    if (Utils.getFlag("h", options) || Utils.getFlag("help", options)) {

      // global info requested as well?
      boolean globalInfo = Utils.getFlag("synopsis", options) ||
        Utils.getFlag("info", options);

      throw new Exception("\nHelp requested."
          + makeOptionString(classifier, globalInfo));
    }

    try {
      // do we get the input from XML instead of normal parameters?
      xml = Utils.getOption("xml", options);
      if (!xml.equals(""))
        options = new XMLOptions(xml).toArray();

      // is the input model only the XML-Options, i.e. w/o built model?
      optionsTmp = new String[options.length];
      for (int i = 0; i < options.length; i++)
        optionsTmp[i] = options[i];

      String tmpO = Utils.getOption('l', optionsTmp);
      //if (Utils.getOption('l', optionsTmp).toLowerCase().endsWith(".xml")) {
      if (tmpO.endsWith(".xml")) {
        // try to load file as PMML first
        boolean success = false;
        try {
          PMMLModel pmmlModel = PMMLFactory.getPMMLModel(tmpO);
          if (pmmlModel instanceof PMMLClassifier) {
            classifier = ((PMMLClassifier)pmmlModel);
            success = true;
          }
        } catch (IllegalArgumentException ex) {
          success = false;
        }
        if (!success) {
          // load options from serialized data  ('-l' is automatically erased!)
          XMLClassifier xmlserial = new XMLClassifier();
          OptionHandler cl = (OptionHandler) xmlserial.read(Utils.getOption('l', options));

          // merge options
          optionsTmp = new String[options.length + cl.getOptions().length];
          System.arraycopy(cl.getOptions(), 0, optionsTmp, 0, cl.getOptions().length);
          System.arraycopy(options, 0, optionsTmp, cl.getOptions().length, options.length);
          options = optionsTmp;
        }
      }

      noCrossValidation = Utils.getFlag("no-cv", options);
      // Get basic options (options the same for all schemes)
      classIndexString = Utils.getOption('c', options);
      if (classIndexString.length() != 0) {
        if (classIndexString.equals("first"))
          classIndex = 1;
        else if (classIndexString.equals("last"))
          classIndex = -1;
        else
          classIndex = Integer.parseInt(classIndexString);
      }
      trainFileName = Utils.getOption('t', options);
      objectInputFileName = Utils.getOption('l', options);
      objectOutputFileName = Utils.getOption('d', options);
      testFileName = Utils.getOption('T', options);
      foldsString = Utils.getOption('x', options);
      if (foldsString.length() != 0) {
        folds = Integer.parseInt(foldsString);
      }
      seedString = Utils.getOption('s', options);
      if (seedString.length() != 0) {
        seed = Integer.parseInt(seedString);
      }
      if (trainFileName.length() == 0) {
        if (objectInputFileName.length() == 0) {
          throw new Exception("No training file and no object input file given.");
        }
        if (testFileName.length() == 0) {
          throw new Exception("No training file and no test file given.");
        }
      } else if ((objectInputFileName.length() != 0) &&
          ((!(classifier instanceof UpdateableClassifier)) ||
           (testFileName.length() == 0))) {
        throw new Exception("Classifier not incremental, or no " +
            "test file provided: can't "+
            "use both train and model file.");
      }
      try {
        if (trainFileName.length() != 0) {
          trainSetPresent = true;
          trainSource = new DataSource(trainFileName);
        }
        if (testFileName.length() != 0) {
          testSetPresent = true;
          testSource = new DataSource(testFileName);
        }
        if (objectInputFileName.length() != 0) {
          if (objectInputFileName.endsWith(".xml")) {
            // if this is the case then it means that a PMML classifier was
            // successfully loaded earlier in the code
            objectInputStream = null;
            xmlInputStream = null;
          } else {
            InputStream is = new FileInputStream(objectInputFileName);
            if (objectInputFileName.endsWith(".gz")) {
              is = new GZIPInputStream(is);
            }
            // load from KOML?
            if (!(objectInputFileName.endsWith(".koml") && KOML.isPresent()) ) {
              objectInputStream = new ObjectInputStream(is);
              xmlInputStream    = null;
            }
            else {
              objectInputStream = null;
              xmlInputStream    = new BufferedInputStream(is);
            }
          }
        }
      } catch (Exception e) {
        throw new Exception("Can't open file " + e.getMessage() + '.');
      }
      if (testSetPresent) {
        template = test = testSource.getStructure();
        if (classIndex != -1) {
          test.setClassIndex(classIndex - 1);
        } else {
          if ( (test.classIndex() == -1) || (classIndexString.length() != 0) )
            test.setClassIndex(test.numAttributes() - 1);
        }
        actualClassIndex = test.classIndex();
      }
      else {
        // percentage split
        splitPercentageString = Utils.getOption("split-percentage", options);
        if (splitPercentageString.length() != 0) {
          if (foldsString.length() != 0)
            throw new Exception(
                "Percentage split cannot be used in conjunction with "
                + "cross-validation ('-x').");
          splitPercentage = Double.parseDouble(splitPercentageString);
          if ((splitPercentage <= 0) || (splitPercentage >= 100))
            throw new Exception("Percentage split value needs be >0 and <100.");
        }
        else {
          splitPercentage = -1;
        }
        preserveOrder = Utils.getFlag("preserve-order", options);
        if (preserveOrder) {
          if (splitPercentage == -1)
            throw new Exception("Percentage split ('-percentage-split') is missing.");
        }
        // create new train/test sources
        if (splitPercentage > 0) {
          testSetPresent = true;
          Instances tmpInst = trainSource.getDataSet(actualClassIndex);
          if (!preserveOrder)
            tmpInst.randomize(new Random(seed));
          int trainSize =
            (int) Math.round(tmpInst.numInstances() * splitPercentage / 100);
          int testSize  = tmpInst.numInstances() - trainSize;
          Instances trainInst = new Instances(tmpInst, 0, trainSize);
          Instances testInst  = new Instances(tmpInst, trainSize, testSize);
          trainSource = new DataSource(trainInst);
          testSource  = new DataSource(testInst);
          template = test = testSource.getStructure();
          if (classIndex != -1) {
            test.setClassIndex(classIndex - 1);
          } else {
            if ( (test.classIndex() == -1) || (classIndexString.length() != 0) )
              test.setClassIndex(test.numAttributes() - 1);
          }
          actualClassIndex = test.classIndex();
        }
      }
      if (trainSetPresent) {
        template = train = trainSource.getStructure();
        if (classIndex != -1) {
          train.setClassIndex(classIndex - 1);
        } else {
          if ( (train.classIndex() == -1) || (classIndexString.length() != 0) )
            train.setClassIndex(train.numAttributes() - 1);
        }
        actualClassIndex = train.classIndex();
        if (!(classifier instanceof weka.classifiers.misc.InputMappedClassifier)) {
          if ((testSetPresent) && !test.equalHeaders(train)) {
            throw new IllegalArgumentException("Train and test file not compatible!\n" + test.equalHeadersMsg(train));
          }
        }
      }
      if (template == null) {
        throw new Exception("No actual dataset provided to use as template");
      }
      costMatrix = handleCostOption(
          Utils.getOption('m', options), template.numClasses());

      classStatistics = Utils.getFlag('i', options);
      noOutput = Utils.getFlag('o', options);
      trainStatistics = !Utils.getFlag('v', options);
      printComplexityStatistics = Utils.getFlag('k', options);
      printMargins = Utils.getFlag('r', options);
      printGraph = Utils.getFlag('g', options);
      sourceClass = Utils.getOption('z', options);
      printSource = (sourceClass.length() != 0);
      thresholdFile = Utils.getOption("threshold-file", options);
      thresholdLabel = Utils.getOption("threshold-label", options);

      String classifications = Utils.getOption("classifications", options);
      String classificationsOld = Utils.getOption("p", options);
      if (classifications.length() > 0) {
        noOutput = true;
        classificationOutput = AbstractOutput.fromCommandline(classifications);
        classificationOutput.setHeader(template);
      }
      // backwards compatible with old "-p range" and "-distribution" options
      else if (classificationsOld.length() > 0) {
        noOutput = true;
        classificationOutput = new PlainText();
        classificationOutput.setHeader(template);
        if (!classificationsOld.equals("0"))
          classificationOutput.setAttributes(classificationsOld);
        classificationOutput.setOutputDistribution(Utils.getFlag("distribution", options));
      }
      // -distribution flag needs -p option
      else {
        if (Utils.getFlag("distribution", options))
          throw new Exception("Cannot print distribution without '-p' option!");
      }

      // if no training file given, we don't have any priors
      if ( (!trainSetPresent) && (printComplexityStatistics) )
        throw new Exception("Cannot print complexity statistics ('-k') without training file ('-t')!");

      // If a model file is given, we can't process
      // scheme-specific options
      if (objectInputFileName.length() != 0) {
        Utils.checkForRemainingOptions(options);
      } else {

        // Set options for classifier
        if (classifier instanceof OptionHandler) {
          for (int i = 0; i < options.length; i++) {
            if (options[i].length() != 0) {
              if (schemeOptionsText == null) {
                schemeOptionsText = new StringBuffer();
              }
              if (options[i].indexOf(' ') != -1) {
                schemeOptionsText.append('"' + options[i] + "\" ");
              } else {
                schemeOptionsText.append(options[i] + " ");
              }
            }
          }
          ((OptionHandler)classifier).setOptions(options);
        }
      }

      Utils.checkForRemainingOptions(options);
    } catch (Exception e) {
      throw new Exception("\nWeka exception: " + e.getMessage()
          + makeOptionString(classifier, false));
    }

    if (objectInputFileName.length() != 0) {
      // Load classifier from file
      if (objectInputStream != null) {
        classifier = (Classifier) objectInputStream.readObject();
        // try and read a header (if present)
        Instances savedStructure = null;
        try {
          savedStructure = (Instances) objectInputStream.readObject();
        } catch (Exception ex) {
          // don't make a fuss
        }
        if (savedStructure != null) {
          // test for compatibility with template
          if (!template.equalHeaders(savedStructure)) {
            throw new Exception("training and test set are not compatible\n" + template.equalHeadersMsg(savedStructure));
          }
        }
        objectInputStream.close();
      }
      else if (xmlInputStream != null) {
        // whether KOML is available has already been checked (objectInputStream would null otherwise)!
        classifier = (Classifier) KOML.read(xmlInputStream);
        xmlInputStream.close();
      }
    }
   
    // Setup up evaluation objects
    Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
    Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix);
    if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
      Instances mappedClassifierHeader =
        ((weka.classifiers.misc.InputMappedClassifier)classifier).
          getModelHeader(new Instances(template, 0));
           
      trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
      testingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
    }

    // disable use of priors if no training file given
    if (!trainSetPresent)
      testingEvaluation.useNoPriors();

    // backup of fully setup classifier for cross-validation
    classifierBackup = AbstractClassifier.makeCopy(classifier);

    // Build the classifier if no object file provided
    if ((classifier instanceof UpdateableClassifier) &&
        (testSetPresent || noCrossValidation) &&
        (costMatrix == null) &&
        (trainSetPresent)) {
      // Build classifier incrementally
      trainingEvaluation.setPriors(train);
      testingEvaluation.setPriors(train);
      trainTimeStart = System.currentTimeMillis();
      if (objectInputFileName.length() == 0) {
        classifier.buildClassifier(train);
      }
      Instance trainInst;
      while (trainSource.hasMoreElements(train)) {
        trainInst = trainSource.nextElement(train);
        trainingEvaluation.updatePriors(trainInst);
        testingEvaluation.updatePriors(trainInst);
        ((UpdateableClassifier)classifier).updateClassifier(trainInst);
      }
      trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
    } else if (objectInputFileName.length() == 0) {
      // Build classifier in one go
      tempTrain = trainSource.getDataSet(actualClassIndex);
     
      if (classifier instanceof weka.classifiers.misc.InputMappedClassifier &&
          !trainingEvaluation.getHeader().equalHeaders(tempTrain)) {
        // we need to make a new dataset that maps the training instances to
        // the structure expected by the mapped classifier - this is only
        // to ensure that the structure and priors computed by the *testing*
        // evaluation object is correct with respect to the mapped classifier
        Instances mappedClassifierDataset =
          ((weka.classifiers.misc.InputMappedClassifier)classifier).
            getModelHeader(new Instances(template, 0));
        for (int zz = 0; zz < tempTrain.numInstances(); zz++) {
          Instance mapped = ((weka.classifiers.misc.InputMappedClassifier)classifier).
            constructMappedInstance(tempTrain.instance(zz));
          mappedClassifierDataset.add(mapped);
        }
        tempTrain = mappedClassifierDataset;
      }
     
      trainingEvaluation.setPriors(tempTrain);
      testingEvaluation.setPriors(tempTrain);
      trainTimeStart = System.currentTimeMillis();
      classifier.buildClassifier(tempTrain);
      trainTimeElapsed = System.currentTimeMillis() - trainTimeStart;
    }

    // backup of fully trained classifier for printing the classifications
    if (classificationOutput != null) {
      classifierClassifications = AbstractClassifier.makeCopy(classifier);
      if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) {
        classificationOutput.setHeader(trainingEvaluation.getHeader());
      }
    }

    // Save the classifier if an object output file is provided
    if (objectOutputFileName.length() != 0) {
      OutputStream os = new FileOutputStream(objectOutputFileName);
      // binary
      if (!(objectOutputFileName.endsWith(".xml") || (objectOutputFileName.endsWith(".koml") && KOML.isPresent()))) {
        if (objectOutputFileName.endsWith(".gz")) {
          os = new GZIPOutputStream(os);
        }
        ObjectOutputStream objectOutputStream = new ObjectOutputStream(os);
        objectOutputStream.writeObject(classifier);
        if (template != null) {
          objectOutputStream.writeObject(template);
        }
        objectOutputStream.flush();
        objectOutputStream.close();
      }
      // KOML/XML
      else {
        BufferedOutputStream xmlOutputStream = new BufferedOutputStream(os);
        if (objectOutputFileName.endsWith(".xml")) {
          XMLSerialization xmlSerial = new XMLClassifier();
          xmlSerial.write(xmlOutputStream, classifier);
        }
        else
          // whether KOML is present has already been checked
          // if not present -> ".koml" is interpreted as binary - see above
          if (objectOutputFileName.endsWith(".koml")) {
            KOML.write(xmlOutputStream, classifier);
          }
        xmlOutputStream.close();
      }
    }

    // If classifier is drawable output string describing graph
    if ((classifier instanceof Drawable) && (printGraph)){
      return ((Drawable)classifier).graph();
    }

    // Output the classifier as equivalent source
    if ((classifier instanceof Sourcable) && (printSource)){
      return wekaStaticWrapper((Sourcable) classifier, sourceClass);
    }

    // Output model
    if (!(noOutput || printMargins)) {
      if (classifier instanceof OptionHandler) {
        if (schemeOptionsText != null) {
          text.append("\nOptions: "+schemeOptionsText);
          text.append("\n");
        }
      }
      text.append("\n" + classifier.toString() + "\n");
    }

    if (!printMargins && (costMatrix != null)) {
      text.append("\n=== Evaluation Cost Matrix ===\n\n");
      text.append(costMatrix.toString());
    }

    // Output test instance predictions only
    if (classificationOutput != null) {
      DataSource source = testSource;
      predsBuff = new StringBuffer();
      classificationOutput.setBuffer(predsBuff);
      // no test set -> use train set
      if (source == null && noCrossValidation) {
        source = trainSource;
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