List of usage examples for java.io BufferedWriter newLine
public void newLine() throws IOException
From source file:org.apache.druid.query.aggregation.datasketches.quantiles.GenerateTestData.java
public static void main(String[] args) throws Exception { Path buildPath = FileSystems.getDefault().getPath("doubles_build_data.tsv"); Path sketchPath = FileSystems.getDefault().getPath("doubles_sketch_data.tsv"); BufferedWriter buildData = Files.newBufferedWriter(buildPath, StandardCharsets.UTF_8); BufferedWriter sketchData = Files.newBufferedWriter(sketchPath, StandardCharsets.UTF_8); Random rand = ThreadLocalRandom.current(); int sequenceNumber = 0; for (int i = 0; i < 20; i++) { int product = rand.nextInt(10); UpdateDoublesSketch sketch = UpdateDoublesSketch.builder().build(); for (int j = 0; j < 20; j++) { double value = rand.nextDouble(); buildData.write("2016010101"); buildData.write('\t'); buildData.write(Integer.toString(sequenceNumber)); // dimension with unique numbers for ingesting raw data buildData.write('\t'); buildData.write(Integer.toString(product)); // product dimension buildData.write('\t'); buildData.write(Double.toString(value)); buildData.newLine(); sketch.update(value);//from w ww .j av a 2 s.com sequenceNumber++; } sketchData.write("2016010101"); sketchData.write('\t'); sketchData.write(Integer.toString(product)); // product dimension sketchData.write('\t'); sketchData.write(Base64.encodeBase64String(sketch.toByteArray(true))); sketchData.newLine(); } buildData.close(); sketchData.close(); }
From source file:di.uniba.it.tee2.wiki.Wikidump2Text.java
/** * @param args the command line arguments *///from www . j a v a2 s. c o m public static void main(String[] args) { try { CommandLine cmd = cmdParser.parse(options, args); if (cmd.hasOption("l") && cmd.hasOption("d") && cmd.hasOption("o")) { encoding = cmd.getOptionValue("e", "UTF-8"); int counter = 0; try { BufferedWriter writer = new BufferedWriter(new OutputStreamWriter( new GZIPOutputStream(new FileOutputStream(cmd.getOptionValue("o"))), "UTF-8")); WikipediaDumpIterator it = new WikipediaDumpIterator(new File(cmd.getOptionValue("d")), encoding); PageCleaner cleaner = PageCleanerWrapper.getInstance(cmd.getOptionValue("l")); while (it.hasNext()) { WikiPage wikiPage = it.next(); ParsedPage parsedPage = wikiPage.getParsedPage(); if (parsedPage != null) { String title = wikiPage.getTitle(); if (!title.matches(notValidTitle)) { if (parsedPage.getText() != null) { writer.append(cleaner.clean(parsedPage.getText())); writer.newLine(); writer.newLine(); counter++; if (counter % 10000 == 0) { System.out.println(counter); writer.flush(); } } } } } writer.flush(); writer.close(); } catch (Exception ex) { Logger.getLogger(Wikidump2Text.class.getName()).log(Level.SEVERE, null, ex); } System.out.println("Indexed pages: " + counter); } else { HelpFormatter helpFormatter = new HelpFormatter(); helpFormatter.printHelp("Wikipedia dump to text", options, true); } } catch (ParseException ex) { Logger.getLogger(Wikidump2Text.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:edu.cmu.lti.oaqa.knn4qa.apps.QueryGenNMSLIB.java
public static void main(String[] args) { Options options = new Options(); options.addOption(CommonParams.QUERY_FILE_PARAM, null, true, CommonParams.QUERY_FILE_DESC); options.addOption(CommonParams.MEMINDEX_PARAM, null, true, CommonParams.MEMINDEX_DESC); options.addOption(CommonParams.KNN_QUERIES_PARAM, null, true, CommonParams.KNN_QUERIES_DESC); options.addOption(CommonParams.NMSLIB_FIELDS_PARAM, null, true, CommonParams.NMSLIB_FIELDS_DESC); options.addOption(CommonParams.MAX_NUM_QUERY_PARAM, null, true, CommonParams.MAX_NUM_QUERY_DESC); options.addOption(CommonParams.SEL_PROB_PARAM, null, true, CommonParams.SEL_PROB_DESC); CommandLineParser parser = new org.apache.commons.cli.GnuParser(); BufferedWriter knnQueries = null; int maxNumQuery = Integer.MAX_VALUE; Float selProb = null;//from ww w . ja v a 2s. com try { CommandLine cmd = parser.parse(options, args); String queryFile = null; if (cmd.hasOption(CommonParams.QUERY_FILE_PARAM)) { queryFile = cmd.getOptionValue(CommonParams.QUERY_FILE_PARAM); } else { Usage("Specify 'query file'", options); } String knnQueriesFile = cmd.getOptionValue(CommonParams.KNN_QUERIES_PARAM); if (null == knnQueriesFile) Usage("Specify '" + CommonParams.KNN_QUERIES_DESC + "'", options); String tmpn = cmd.getOptionValue(CommonParams.MAX_NUM_QUERY_PARAM); if (tmpn != null) { try { maxNumQuery = Integer.parseInt(tmpn); } catch (NumberFormatException e) { Usage("Maximum number of queries isn't integer: '" + tmpn + "'", options); } } String tmps = cmd.getOptionValue(CommonParams.NMSLIB_FIELDS_PARAM); if (null == tmps) Usage("Specify '" + CommonParams.NMSLIB_FIELDS_DESC + "'", options); String nmslibFieldList[] = tmps.split(","); knnQueries = new BufferedWriter(new FileWriter(knnQueriesFile)); knnQueries.write("isQueryFile=1"); knnQueries.newLine(); knnQueries.newLine(); String memIndexPref = cmd.getOptionValue(CommonParams.MEMINDEX_PARAM); if (null == memIndexPref) { Usage("Specify '" + CommonParams.MEMINDEX_DESC + "'", options); } String tmpf = cmd.getOptionValue(CommonParams.SEL_PROB_PARAM); if (tmpf != null) { try { selProb = Float.parseFloat(tmpf); } catch (NumberFormatException e) { Usage("A selection probability isn't a number in the range (0,1)'" + tmpf + "'", options); } if (selProb < Float.MIN_NORMAL || selProb + Float.MIN_NORMAL >= 1) Usage("A selection probability isn't a number in the range (0,1)'" + tmpf + "'", options); } BufferedReader inpText = new BufferedReader( new InputStreamReader(CompressUtils.createInputStream(queryFile))); String docText = XmlHelper.readNextXMLIndexEntry(inpText); NmslibQueryGenerator queryGen = new NmslibQueryGenerator(nmslibFieldList, memIndexPref); Random rnd = new Random(); for (int docNum = 1; docNum <= maxNumQuery && docText != null; ++docNum, docText = XmlHelper.readNextXMLIndexEntry(inpText)) { if (selProb != null) { if (rnd.nextFloat() > selProb) continue; } Map<String, String> docFields = null; try { docFields = XmlHelper.parseXMLIndexEntry(docText); String queryObjStr = queryGen.getStrObjForKNNService(docFields); knnQueries.append(queryObjStr); knnQueries.newLine(); } catch (SAXException e) { System.err.println("Parsing error, offending DOC:" + NL + docText + " doc # " + docNum); throw new Exception("Parsing error."); } } knnQueries.close(); } catch (ParseException e) { Usage("Cannot parse arguments", options); if (null != knnQueries) try { knnQueries.close(); } catch (IOException e1) { e1.printStackTrace(); } } catch (Exception e) { System.err.println("Terminating due to an exception: " + e); try { if (knnQueries != null) knnQueries.close(); } catch (IOException e1) { e1.printStackTrace(); } System.exit(1); } System.out.println("Terminated successfully!"); }
From source file:edu.cmu.lti.oaqa.knn4qa.apps.BuildRetrofitLexicons.java
public static void main(String[] args) { Options options = new Options(); options.addOption(CommonParams.GIZA_ROOT_DIR_PARAM, null, true, CommonParams.GIZA_ROOT_DIR_DESC); options.addOption(CommonParams.GIZA_ITER_QTY_PARAM, null, true, CommonParams.GIZA_ITER_QTY_DESC); options.addOption(CommonParams.MEMINDEX_PARAM, null, true, CommonParams.MEMINDEX_DESC); options.addOption(OUT_FILE_PARAM, null, true, OUT_FILE_DESC); options.addOption(MIN_PROB_PARAM, null, true, MIN_PROB_DESC); options.addOption(FORMAT_PARAM, null, true, FORMAT_DESC); CommandLineParser parser = new org.apache.commons.cli.GnuParser(); try {/* ww w . jav a2 s.c o m*/ CommandLine cmd = parser.parse(options, args); String gizaRootDir = cmd.getOptionValue(CommonParams.GIZA_ROOT_DIR_PARAM); int gizaIterQty = -1; if (cmd.hasOption(CommonParams.GIZA_ITER_QTY_PARAM)) { gizaIterQty = Integer.parseInt(cmd.getOptionValue(CommonParams.GIZA_ITER_QTY_PARAM)); } else { Usage("Specify: " + CommonParams.GIZA_ITER_QTY_PARAM, options); } String outFileName = cmd.getOptionValue(OUT_FILE_PARAM); if (null == outFileName) { Usage("Specify: " + OUT_FILE_PARAM, options); } String indexDir = cmd.getOptionValue(CommonParams.MEMINDEX_PARAM); if (null == indexDir) { Usage("Specify: " + CommonParams.MEMINDEX_DESC, options); } FormatType outType = FormatType.kOrig; String outTypeStr = cmd.getOptionValue(FORMAT_PARAM); if (null != outTypeStr) { if (outTypeStr.equals(ORIG_TYPE)) { outType = FormatType.kOrig; } else if (outTypeStr.equals(WEIGHTED_TYPE)) { outType = FormatType.kWeighted; } else if (outTypeStr.equals(UNWEIGHTED_TYPE)) { outType = FormatType.kUnweighted; } else { Usage("Unknown format type: " + outTypeStr, options); } } float minProb = 0; if (cmd.hasOption(MIN_PROB_PARAM)) { minProb = Float.parseFloat(cmd.getOptionValue(MIN_PROB_PARAM)); } else { Usage("Specify: " + MIN_PROB_PARAM, options); } System.out.println(String.format( "Saving lexicon to '%s' (output format '%s'), keep only entries with translation probability >= %f", outFileName, outType.toString(), minProb)); // We use unlemmatized text here, because lemmatized dictionary is going to be mostly subset of the unlemmatized one. InMemForwardIndex textIndex = new InMemForwardIndex(FeatureExtractor.indexFileName(indexDir, FeatureExtractor.mFieldNames[FeatureExtractor.TEXT_UNLEMM_FIELD_ID])); InMemForwardIndexFilterAndRecoder filterAndRecoder = new InMemForwardIndexFilterAndRecoder(textIndex); String prefix = gizaRootDir + "/" + FeatureExtractor.mFieldNames[FeatureExtractor.TEXT_UNLEMM_FIELD_ID] + "/"; GizaVocabularyReader answVoc = new GizaVocabularyReader(prefix + "source.vcb", filterAndRecoder); GizaVocabularyReader questVoc = new GizaVocabularyReader(prefix + "target.vcb", filterAndRecoder); GizaTranTableReaderAndRecoder gizaTable = new GizaTranTableReaderAndRecoder(false, // we don't need to flip the table for the purpose prefix + "/output.t1." + gizaIterQty, filterAndRecoder, answVoc, questVoc, (float) FeatureExtractor.DEFAULT_PROB_SELF_TRAN, minProb); BufferedWriter outFile = new BufferedWriter(new FileWriter(outFileName)); for (int srcWordId = 0; srcWordId <= textIndex.getMaxWordId(); ++srcWordId) { GizaOneWordTranRecs tranRecs = gizaTable.getTranProbs(srcWordId); if (null != tranRecs) { String wordSrc = textIndex.getWord(srcWordId); StringBuffer sb = new StringBuffer(); sb.append(wordSrc); for (int k = 0; k < tranRecs.mDstIds.length; ++k) { float prob = tranRecs.mProbs[k]; if (prob >= minProb) { int dstWordId = tranRecs.mDstIds[k]; if (dstWordId == srcWordId && outType != FormatType.kWeighted) continue; // Don't duplicate the word, unless it's probability weighted sb.append(' '); String dstWord = textIndex.getWord(dstWordId); if (null == dstWord) { throw new Exception( "Bug or inconsistent data: Couldn't retriev a word for wordId = " + dstWordId); } if (dstWord.indexOf(':') >= 0) throw new Exception( "Illegal dictionary word '" + dstWord + "' b/c it contains ':'"); sb.append(dstWord); if (outType != FormatType.kOrig) { sb.append(':'); sb.append(outType == FormatType.kWeighted ? prob : 1); } } } outFile.write(sb.toString()); outFile.newLine(); } } outFile.close(); } catch (ParseException e) { e.printStackTrace(); Usage("Cannot parse arguments", options); } catch (Exception e) { e.printStackTrace(); System.err.println("Terminating due to an exception: " + e); System.exit(1); } System.out.println("Terminated successfully!"); }
From source file:edu.cmu.lti.oaqa.knn4qa.apps.FilterTranTable.java
public static void main(String[] args) { Options options = new Options(); options.addOption(INPUT_PARAM, null, true, INPUT_DESC); options.addOption(OUTPUT_PARAM, null, true, OUTPUT_DESC); options.addOption(CommonParams.MEM_FWD_INDEX_PARAM, null, true, CommonParams.MEM_FWD_INDEX_DESC); options.addOption(CommonParams.GIZA_ITER_QTY_PARAM, null, true, CommonParams.GIZA_ITER_QTY_PARAM); options.addOption(CommonParams.GIZA_ROOT_DIR_PARAM, null, true, CommonParams.GIZA_ROOT_DIR_PARAM); options.addOption(CommonParams.MIN_PROB_PARAM, null, true, CommonParams.MIN_PROB_DESC); options.addOption(CommonParams.MAX_WORD_QTY_PARAM, null, true, CommonParams.MAX_WORD_QTY_PARAM); CommandLineParser parser = new org.apache.commons.cli.GnuParser(); try {/*www . java2 s . c o m*/ CommandLine cmd = parser.parse(options, args); String outputFile = null; outputFile = cmd.getOptionValue(OUTPUT_PARAM); if (null == outputFile) { Usage("Specify 'A name of the output file'", options); } String gizaRootDir = cmd.getOptionValue(CommonParams.GIZA_ROOT_DIR_PARAM); if (null == gizaRootDir) { Usage("Specify '" + CommonParams.GIZA_ROOT_DIR_DESC + "'", options); } String gizaIterQty = cmd.getOptionValue(CommonParams.GIZA_ITER_QTY_PARAM); if (null == gizaIterQty) { Usage("Specify '" + CommonParams.GIZA_ITER_QTY_DESC + "'", options); } float minProb = 0; String tmpf = cmd.getOptionValue(CommonParams.MIN_PROB_PARAM); if (tmpf != null) { minProb = Float.parseFloat(tmpf); } int maxWordQty = Integer.MAX_VALUE; String tmpi = cmd.getOptionValue(CommonParams.MAX_WORD_QTY_PARAM); if (null != tmpi) { maxWordQty = Integer.parseInt(tmpi); } String memFwdIndxName = cmd.getOptionValue(CommonParams.MEM_FWD_INDEX_PARAM); if (null == memFwdIndxName) { Usage("Specify '" + CommonParams.MEM_FWD_INDEX_DESC + "'", options); } System.out.println("Filtering index: " + memFwdIndxName + " max # of frequent words: " + maxWordQty + " min. probability:" + minProb); VocabularyFilterAndRecoder filter = new FrequentIndexWordFilterAndRecoder(memFwdIndxName, maxWordQty); String srcVocFile = CompressUtils.findFileVariant(gizaRootDir + "/source.vcb"); System.out.println("Source vocabulary file: " + srcVocFile); GizaVocabularyReader srcVoc = new GizaVocabularyReader(srcVocFile, filter); String dstVocFile = CompressUtils.findFileVariant(gizaRootDir + "/target.vcb"); System.out.println("Target vocabulary file: " + dstVocFile); GizaVocabularyReader dstVoc = new GizaVocabularyReader(CompressUtils.findFileVariant(dstVocFile), filter); String inputFile = CompressUtils.findFileVariant(gizaRootDir + "/output.t1." + gizaIterQty); BufferedReader finp = new BufferedReader( new InputStreamReader(CompressUtils.createInputStream(inputFile))); BufferedWriter fout = new BufferedWriter( new OutputStreamWriter(CompressUtils.createOutputStream(outputFile))); try { String line; int prevSrcId = -1; int wordQty = 0; long addedQty = 0; long totalQty = 0; boolean isNotFiltered = false; for (totalQty = 0; (line = finp.readLine()) != null;) { ++totalQty; // Skip empty lines line = line.trim(); if (line.isEmpty()) continue; GizaTranRec rec = new GizaTranRec(line); if (rec.mSrcId != prevSrcId) { ++wordQty; } if (totalQty % REPORT_INTERVAL_QTY == 0) { System.out.println(String.format( "Processed %d lines (%d source word entries) from '%s', added %d lines", totalQty, wordQty, inputFile, addedQty)); } // isNotFiltered should be set after procOneWord if (rec.mSrcId != prevSrcId) { if (rec.mSrcId == 0) isNotFiltered = true; else { String wordSrc = srcVoc.getWord(rec.mSrcId); isNotFiltered = filter == null || (wordSrc != null && filter.checkWord(wordSrc)); } } prevSrcId = rec.mSrcId; if (rec.mProb >= minProb && isNotFiltered) { String wordDst = dstVoc.getWord(rec.mDstId); if (filter == null || (wordDst != null && filter.checkWord(wordDst))) { fout.write(String.format(rec.mSrcId + " " + rec.mDstId + " " + rec.mProb)); fout.newLine(); addedQty++; } } } System.out.println( String.format("Processed %d lines (%d source word entries) from '%s', added %d lines", totalQty, wordQty, inputFile, addedQty)); } finally { finp.close(); fout.close(); } } catch (ParseException e) { Usage("Cannot parse arguments", options); } catch (Exception e) { e.printStackTrace(); System.err.println("Terminating due to an exception: " + e); System.exit(1); } }
From source file:com.twentyn.chemicalClassifier.Runner.java
public static void main(String[] args) throws Exception { BufferedReader reader = new BufferedReader(new FileReader(args[0])); BufferedWriter writer = new BufferedWriter(new FileWriter(args[1])); try {/*w ww . j a v a2 s . c om*/ Oscar oscar = new Oscar(); String line = null; /* NOTE: this is exactly the wrong way to write a TSV reader. Caveat emptor. * See http://tburette.github.io/blog/2014/05/25/so-you-want-to-write-your-own-CSV-code/ * and then use org.apache.commons.csv.CSVParser instead. */ while ((line = reader.readLine()) != null) { // TSV means split on tabs! Nothing else will do. List<String> fields = Arrays.asList(line.split("\t")); // Choke if our invariants aren't satisfied. We expect ever line to have a name and an InChI. if (fields.size() != 2) { throw new RuntimeException( String.format("Found malformed line (all lines must have two fields: %s", line)); } String name = fields.get(1); List<ResolvedNamedEntity> entities = oscar.findAndResolveNamedEntities(name); System.out.println("**********"); System.out.println("Name: " + name); List<String> outputFields = new ArrayList<>(fields.size() + 1); outputFields.addAll(fields); if (entities.size() == 0) { System.out.println("No match"); outputFields.add("noMatch"); } else if (entities.size() == 1) { ResolvedNamedEntity entity = entities.get(0); NamedEntity ne = entity.getNamedEntity(); if (ne.getStart() != 0 || ne.getEnd() != name.length()) { System.out.println("Partial match"); printEntity(entity); outputFields.add("partialMatch"); } else { System.out.println("Exact match"); printEntity(entity); outputFields.add("exactMatch"); List<ChemicalStructure> structures = entity.getChemicalStructures(FormatType.STD_INCHI); for (ChemicalStructure s : structures) { outputFields.add(s.getValue()); } } } else { // Multiple matches found! System.out.println("Multiple matches"); for (ResolvedNamedEntity e : entities) { printEntity(e); } outputFields.add("multipleMatches"); } writer.write(String.join("\t", outputFields)); writer.newLine(); } } finally { writer.flush(); writer.close(); } }
From source file:org.apache.airavata.messaging.client.RabbitMQListener.java
public static void main(String[] args) { File file = new File("/tmp/latency_client"); parseArguments(args);// w ww .java 2 s . c om try { FileOutputStream fos = new FileOutputStream(file, false); final BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(fos)); String brokerUrl = ServerSettings.getSetting(RABBITMQ_BROKER_URL); System.out.println("broker url " + brokerUrl); final String exchangeName = ServerSettings.getSetting(RABBITMQ_EXCHANGE_NAME); RabbitMQStatusConsumer consumer = new RabbitMQStatusConsumer(brokerUrl, exchangeName); consumer.listen(new MessageHandler() { @Override public Map<String, Object> getProperties() { Map<String, Object> props = new HashMap<String, Object>(); List<String> routingKeys = new ArrayList<String>(); if (allMessages) { routingKeys.add("*"); routingKeys.add("*.*"); routingKeys.add("*.*.*"); routingKeys.add("*.*.*.*"); routingKeys.add("*.*.*.*.*"); } else { if (gatewayLevelMessages) { routingKeys.add(gatewayId); routingKeys.add(gatewayId + ".*"); routingKeys.add(gatewayId + ".*.*"); routingKeys.add(gatewayId + ".*.*.*"); routingKeys.add(gatewayId + ".*.*.*.*"); } else if (experimentLevelMessages) { routingKeys.add(gatewayId); routingKeys.add(gatewayId + "." + experimentId); routingKeys.add(gatewayId + "." + experimentId + ".*"); routingKeys.add(gatewayId + "." + experimentId + ".*.*"); routingKeys.add(gatewayId + "." + experimentId + ".*.*.*"); } else if (jobLevelMessages) { routingKeys.add(gatewayId); routingKeys.add(gatewayId + "." + experimentId); routingKeys.add(gatewayId + "." + experimentId + ".*"); routingKeys.add(gatewayId + "." + experimentId + ".*.*"); routingKeys.add(gatewayId + "." + experimentId + ".*." + jobId); } } props.put(MessagingConstants.RABBIT_ROUTING_KEY, routingKeys); return props; } @Override public void onMessage(MessageContext message) { try { long latency = System.currentTimeMillis() - message.getUpdatedTime().getTime(); bw.write(message.getMessageId() + " :" + latency); bw.newLine(); bw.flush(); } catch (IOException e) { e.printStackTrace(); } if (message.getType().equals(MessageType.EXPERIMENT)) { try { ExperimentStatusChangeEvent event = new ExperimentStatusChangeEvent(); TBase messageEvent = message.getEvent(); byte[] bytes = ThriftUtils.serializeThriftObject(messageEvent); ThriftUtils.createThriftFromBytes(bytes, event); System.out.println(" Message Received with message id '" + message.getMessageId() + "' and with message type '" + message.getType() + "' and with state : '" + event.getState().toString() + " for Gateway " + event.getGatewayId()); } catch (TException e) { logger.error(e.getMessage(), e); } } else if (message.getType().equals(MessageType.PROCESS)) { /*try { WorkflowNodeStatusChangeEvent event = new WorkflowNodeStatusChangeEvent(); TBase messageEvent = message.getEvent(); byte[] bytes = ThriftUtils.serializeThriftObject(messageEvent); ThriftUtils.createThriftFromBytes(bytes, event); System.out.println(" Message Received with message id '" + message.getMessageId() + "' and with message type '" + message.getType() + "' and with state : '" + event.getState().toString() + " for Gateway " + event.getWorkflowNodeIdentity().getGatewayId()); } catch (TException e) { logger.error(e.getMessage(), e); }*/ } else if (message.getType().equals(MessageType.TASK)) { try { TaskStatusChangeEvent event = new TaskStatusChangeEvent(); TBase messageEvent = message.getEvent(); byte[] bytes = ThriftUtils.serializeThriftObject(messageEvent); ThriftUtils.createThriftFromBytes(bytes, event); System.out.println(" Message Received with message id '" + message.getMessageId() + "' and with message type '" + message.getType() + "' and with state : '" + event.getState().toString() + " for Gateway " + event.getTaskIdentity().getGatewayId()); } catch (TException e) { logger.error(e.getMessage(), e); } } else if (message.getType().equals(MessageType.JOB)) { try { JobStatusChangeEvent event = new JobStatusChangeEvent(); TBase messageEvent = message.getEvent(); byte[] bytes = ThriftUtils.serializeThriftObject(messageEvent); ThriftUtils.createThriftFromBytes(bytes, event); System.out.println(" Message Received with message id '" + message.getMessageId() + "' and with message type '" + message.getType() + "' and with state : '" + event.getState().toString() + " for Gateway " + event.getJobIdentity().getGatewayId()); } catch (TException e) { logger.error(e.getMessage(), e); } } } }); } catch (ApplicationSettingsException e) { logger.error("Error reading airavata server properties", e); } catch (Exception e) { logger.error(e.getMessage(), e); } }
From source file:diffhunter.DiffHunter.java
/** * @param args the command line arguments * @throws org.apache.commons.cli.ParseException * @throws java.io.IOException//from w w w . j a v a2 s .co m */ public static void main(String[] args) throws ParseException, IOException { //String test_ = Paths.get("J:\\VishalData\\additional\\", "Sasan" + "_BDB").toAbsolutePath().toString(); // TODO code application logic here /*args = new String[] { "-i", "-b", "J:\\VishalData\\additional\\Ptbp2_E18_5_cortex_CLIP_mm9_plus_strand_sorted.bed", "-r", "J:\\VishalData\\additional\\mouse_mm9.txt", "-o", "J:\\VishalData" };*/ /*args = new String[] { "-c", "-r", "J:\\VishalData\\additional\\mouse_mm9.txt", "-1", "J:\\VishalData\\Ptbp2_Adult_testis_CLIP_mm9_plus_strand_sorted_BDB", "-2", "J:\\VishalData\\Ptbp2_E18_5_cortex_CLIP_mm9_plus_strand_sorted_BDB", "-w", "200", "-s", "50", "-o", "J:\\VishalData" };*/ Options options = new Options(); // add t option options.addOption("i", "index", false, "Indexing BED files."); options.addOption("b", "bed", true, "bed file to be indexed"); options.addOption("o", "output", true, "Folder that the index/comparison file will be created."); options.addOption("r", "reference", true, "Reference annotation file to be used for indexing"); options.addOption("c", "compare", false, "Finding differences between two conditions"); options.addOption("1", "first", true, "First sample index location"); options.addOption("2", "second", true, "Second sample index location"); options.addOption("w", "window", true, "Length of window for identifying differences"); options.addOption("s", "sliding", true, "Length of sliding"); CommandLineParser parser = new BasicParser(); CommandLine cmd = parser.parse(options, args); boolean indexing = false; boolean comparing = false; //Indexing! if (cmd.hasOption("i")) { //if(cmd.hasOption("1")) //System.err.println("sasan"); //System.out.println("sasa"); indexing = true; } else if (cmd.hasOption("c")) { //System.err.println(""); comparing = true; } else { //System.err.println("Option is not deteced."); HelpFormatter formatter = new HelpFormatter(); formatter.printHelp("diffhunter", options); return; } //Indexing is selected // if (indexing == true) { //Since indexing is true. //User have to provide file for indexing. if (!(cmd.hasOption("o") || cmd.hasOption("r") || cmd.hasOption("b"))) { HelpFormatter formatter = new HelpFormatter(); formatter.printHelp("diffhunter", options); return; } String bedfile_ = cmd.getOptionValue("b"); String reference_file = cmd.getOptionValue("r"); String folder_loc = cmd.getOptionValue("o"); String sample_name = FilenameUtils.getBaseName(bedfile_); try (Database B2 = BerkeleyDB_Box.Get_BerkeleyDB( Paths.get(folder_loc, sample_name + "_BDB").toAbsolutePath().toString(), true, sample_name)) { Indexer indexing_ = new Indexer(reference_file); indexing_.Make_Index(B2, bedfile_, Paths.get(folder_loc, sample_name + "_BDB").toAbsolutePath().toString()); B2.close(); } } else if (comparing == true) { if (!(cmd.hasOption("o") || cmd.hasOption("w") || cmd.hasOption("s") || cmd.hasOption("1") || cmd.hasOption("2"))) { HelpFormatter formatter = new HelpFormatter(); formatter.printHelp("diffhunter", options); return; } String folder_loc = cmd.getOptionValue("o"); int window_ = Integer.parseInt(cmd.getOptionValue("w")); //int window_=600; int slide_ = Integer.parseInt(cmd.getOptionValue("s")); String first = cmd.getOptionValue("1").replace("_BDB", ""); String second = cmd.getOptionValue("2").replace("_BDB", ""); String reference_file = cmd.getOptionValue("r"); //String folder_loc=cmd.getOptionValue("o"); String sample_name_first = FilenameUtils.getBaseName(first); String sample_name_second = FilenameUtils.getBaseName(second); Database B1 = BerkeleyDB_Box.Get_BerkeleyDB(first + "_BDB", false, sample_name_first); Database B2 = BerkeleyDB_Box.Get_BerkeleyDB(second + "_BDB", false, sample_name_second); List<String> first_condition_genes = Files .lines(Paths.get(first + "_BDB", sample_name_first + ".txt").toAbsolutePath()) .collect(Collectors.toList()); List<String> second_condition_genes = Files .lines(Paths.get(second + "_BDB", sample_name_second + ".txt").toAbsolutePath()) .collect(Collectors.toList()); System.out.println("First and second condition are loaded!!! "); List<String> intersection_ = new ArrayList<>(first_condition_genes); intersection_.retainAll(second_condition_genes); BufferedWriter output = new BufferedWriter( new FileWriter(Paths.get(folder_loc, "differences_" + window_ + "_s" + slide_ + "_c" + ".txt") .toAbsolutePath().toString(), false)); List<Result_Window> final_results = Collections.synchronizedList(new ArrayList<>()); Worker_New worker_class = new Worker_New(); worker_class.Read_Reference(reference_file); while (!intersection_.isEmpty()) { List<String> selected_genes = new ArrayList<>(); //if (intersection_.size()<=10000){selected_genes.addAll(intersection_.subList(0, intersection_.size()));} //else selected_genes.addAll(intersection_.subList(0, 10000)); if (intersection_.size() <= intersection_.size()) { selected_genes.addAll(intersection_.subList(0, intersection_.size())); } else { selected_genes.addAll(intersection_.subList(0, intersection_.size())); } intersection_.removeAll(selected_genes); //System.out.println("Intersection count is:"+intersection_.size()); //final List<Result_Window> resultssss_=new ArrayList<>(); IntStream.range(0, selected_genes.size()).parallel().forEach(i -> { System.out.println(selected_genes.get(i) + "\tprocessing......"); String gene_of_interest = selected_genes.get(i);//"ENSG00000142657|PGD";//intersection_.get(6);////"ENSG00000163395|IGFN1";//"ENSG00000270066|SCARNA2"; int start = worker_class.dic_genes.get(gene_of_interest).start_loc; int end = worker_class.dic_genes.get(gene_of_interest).end_loc; Map<Integer, Integer> first_ = Collections.EMPTY_MAP; try { first_ = BerkeleyDB_Box.Get_Coord_Read(B1, gene_of_interest); } catch (IOException | ClassNotFoundException ex) { Logger.getLogger(DiffHunter.class.getName()).log(Level.SEVERE, null, ex); } Map<Integer, Integer> second_ = Collections.EMPTY_MAP; try { second_ = BerkeleyDB_Box.Get_Coord_Read(B2, gene_of_interest); } catch (IOException | ClassNotFoundException ex) { Logger.getLogger(DiffHunter.class.getName()).log(Level.SEVERE, null, ex); } List<Window> top_windows_first = worker_class.Get_Top_Windows(window_, first_, slide_); List<Window> top_windows_second = worker_class.Get_Top_Windows(window_, second_, slide_); //System.out.println("passed for window peak call for gene \t"+selected_genes.get(i)); // System.out.println("top_window_first_Count\t"+top_windows_first.size()); // System.out.println("top_window_second_Count\t"+top_windows_second.size()); if (top_windows_first.isEmpty() && top_windows_second.isEmpty()) { return; } List<Result_Window> res_temp = new Worker_New().Get_Significant_Windows(gene_of_interest, start, end, top_windows_first, top_windows_second, second_, first_, sample_name_first, sample_name_second, 0.01); if (!res_temp.isEmpty()) { final_results.addAll(res_temp);//final_results.addAll(worker_class.Get_Significant_Windows(gene_of_interest, start, end, top_windows_first, top_windows_second, second_, first_, first_condition, second_condition, 0.01)); } //System.out.println(selected_genes.get(i)+"\tprocessed."); }); /*selected_genes.parallelStream().forEach(i -> { });*/ List<Double> pvals = new ArrayList<>(); for (int i = 0; i < final_results.size(); i++) { pvals.add(final_results.get(i).p_value); } List<Double> qvals = MultipleTestCorrection.benjaminiHochberg(pvals); System.out.println("Writing to file..."); output.append("Gene_Symbol\tContributing_Sample\tStart\tEnd\tOddsRatio\tp_Value\tFDR"); output.newLine(); for (int i = 0; i < final_results.size(); i++) { Result_Window item = final_results.get(i); output.append(item.associated_gene_symbol + "\t" + item.contributing_windows + "\t" + item.start_loc + "\t" + item.end_loc + "\t" + item.oddsratio_ + "\t" + item.p_value + "\t" + qvals.get(i)); //+ "\t" + item.average_other_readcount_cotributing + "\t" + item.average_other_readcount_cotributing + "\t" + item.average_window_readcount_non + "\t" + item.average_other_readcount_non); output.newLine(); } /* for (Result_Window item : final_results) { output.append(item.associated_gene_symbol + "\t" + item.contributing_windows + "\t" + item.start_loc + "\t" + item.end_loc + "\t" + item.oddsratio_ + "\t" + item.p_value); //+ "\t" + item.average_other_readcount_cotributing + "\t" + item.average_other_readcount_cotributing + "\t" + item.average_window_readcount_non + "\t" + item.average_other_readcount_non); output.newLine(); } */ final_results.clear(); } output.close(); } System.out.println("Done."); }
From source file:com.yahoo.labs.yamall.local.Yamall.java
public static void main(String[] args) { String[] remainingArgs = null; String inputFile = null;/*from w ww . j av a 2 s . c o m*/ String predsFile = null; String saveModelFile = null; String initialModelFile = null; String lossName = null; String parserName = null; String linkName = null; String invertHashName = null; double learningRate = 1; String minPredictionString = null; String maxPredictionString = null; String fmNumberFactorsString = null; int bitsHash; int numberPasses; int holdoutPeriod = 10; boolean testOnly = false; boolean exponentialProgress; double progressInterval; options.addOption("h", "help", false, "displays this help"); options.addOption("t", false, "ignore label information and just test"); options.addOption(Option.builder().hasArg(false).required(false).longOpt("binary") .desc("reports loss as binary classification with -1,1 labels").build()); options.addOption( Option.builder().hasArg(false).required(false).longOpt("solo").desc("uses SOLO optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pcsolo") .desc("uses Per Coordinate SOLO optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pistol") .desc("uses PiSTOL optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("kt") .desc("(EXPERIMENTAL) uses KT optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pckt") .desc("(EXPERIMENTAL) uses Per Coordinate KT optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("pccocob") .desc("(EXPERIMENTAL) uses Per Coordinate COCOB optimizer").build()); options.addOption(Option.builder().hasArg(false).required(false).longOpt("cocob") .desc("(EXPERIMENTAL) uses COCOB optimizer").build()); options.addOption( Option.builder().hasArg(false).required(false).longOpt("fm").desc("Factorization Machine").build()); options.addOption(Option.builder("f").hasArg(true).required(false).desc("final regressor to save") .type(String.class).longOpt("final_regressor").build()); options.addOption(Option.builder("p").hasArg(true).required(false).desc("file to output predictions to") .longOpt("predictions").type(String.class).build()); options.addOption( Option.builder("i").hasArg(true).required(false).desc("initial regressor(s) to load into memory") .longOpt("initial_regressor").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc( "specify the loss function to be used. Currently available ones are: absolute, squared (default), hinge, logistic") .longOpt("loss_function").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc( "specify the link function used in the output of the predictions. Currently available ones are: identity (default), logistic") .longOpt("link").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("output human-readable final regressor with feature names").longOpt("invert_hash") .type(String.class).build()); options.addOption( Option.builder("l").hasArg(true).required(false).desc("set (initial) learning Rate, default = 1.0") .longOpt("learning_rate").type(String.class).build()); options.addOption(Option.builder("b").hasArg(true).required(false) .desc("number of bits in the feature table, default = 18").longOpt("bit_precision") .type(String.class).build()); options.addOption(Option.builder("P").hasArg(true).required(false) .desc("progress update frequency, integer: additive; float: multiplicative, default = 2.0") .longOpt("progress").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("smallest prediction to output, before the link function, default = -50") .longOpt("min_prediction").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("smallest prediction to output, before the link function, default = 50") .longOpt("max_prediction").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("ignore namespaces beginning with the characters in <arg>").longOpt("ignore") .type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc("number of training passes") .longOpt("passes").type(String.class).build()); options.addOption( Option.builder().hasArg(true).required(false).desc("holdout period for test only, default = 10") .longOpt("holdout_period").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("number of factors for Factorization Machines default = 8").longOpt("fmNumberFactors") .type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false) .desc("specify the parser to use. Currently available ones are: vw (default), libsvm, tsv") .longOpt("parser").type(String.class).build()); options.addOption(Option.builder().hasArg(true).required(false).desc("schema file for the TSV input") .longOpt("schema").type(String.class).build()); CommandLineParser parser = new DefaultParser(); CommandLine cmd = null; try { cmd = parser.parse(options, args); } catch (ParseException e) { System.out.println("Unrecognized option"); help(); } if (cmd.hasOption("h")) help(); if (cmd.hasOption("t")) testOnly = true; if (cmd.hasOption("binary")) { binary = true; System.out.println("Reporting binary loss"); } initialModelFile = cmd.getOptionValue("i"); predsFile = cmd.getOptionValue("p"); lossName = cmd.getOptionValue("loss_function", "squared"); linkName = cmd.getOptionValue("link", "identity"); saveModelFile = cmd.getOptionValue("f"); learningRate = Double.parseDouble(cmd.getOptionValue("l", "1.0")); bitsHash = Integer.parseInt(cmd.getOptionValue("b", "18")); invertHashName = cmd.getOptionValue("invert_hash"); minPredictionString = cmd.getOptionValue("min_prediction", "-50"); maxPredictionString = cmd.getOptionValue("max_prediction", "50"); fmNumberFactorsString = cmd.getOptionValue("fmNumberFactors", "8"); parserName = cmd.getOptionValue("parser", "vw"); numberPasses = Integer.parseInt(cmd.getOptionValue("passes", "1")); System.out.println("Number of passes = " + numberPasses); if (numberPasses > 1) { holdoutPeriod = Integer.parseInt(cmd.getOptionValue("holdout_period", "10")); System.out.println("Holdout period = " + holdoutPeriod); } remainingArgs = cmd.getArgs(); if (remainingArgs.length == 1) inputFile = remainingArgs[0]; InstanceParser instanceParser = null; if (parserName.equals("vw")) instanceParser = new VWParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null)); else if (parserName.equals("libsvm")) instanceParser = new LIBSVMParser(bitsHash, (invertHashName != null)); else if (parserName.equals("tsv")) { String schema = cmd.getOptionValue("schema"); if (schema == null) { System.out.println("TSV parser requires a schema file."); System.exit(0); } else { String spec = null; try { spec = new String(Files.readAllBytes(Paths.get(schema))); } catch (IOException e) { System.out.println("Error reading the TSV schema file."); e.printStackTrace(); System.exit(0); } instanceParser = new TSVParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null), spec); } } else { System.out.println("Unknown parser."); System.exit(0); } System.out.println("Num weight bits = " + bitsHash); // setup progress String progress = cmd.getOptionValue("P", "2.0"); if (progress.indexOf('.') >= 0) { exponentialProgress = true; progressInterval = (double) Double.parseDouble(progress); } else { exponentialProgress = false; progressInterval = (double) Integer.parseInt(progress); } // min and max predictions minPrediction = (double) Double.parseDouble(minPredictionString); maxPrediction = (double) Double.parseDouble(maxPredictionString); // number of factors for Factorization Machines fmNumberFactors = (int) Integer.parseInt(fmNumberFactorsString); // configure the learner Loss lossFnc = null; LinkFunction link = null; if (initialModelFile == null) { if (cmd.hasOption("kt")) { learner = new KT(bitsHash); } else if (cmd.hasOption("pckt")) { learner = new PerCoordinateKT(bitsHash); } else if (cmd.hasOption("pcsolo")) { learner = new PerCoordinateSOLO(bitsHash); } else if (cmd.hasOption("solo")) { learner = new SOLO(bitsHash); } else if (cmd.hasOption("pccocob")) { learner = new PerCoordinateCOCOB(bitsHash); } else if (cmd.hasOption("cocob")) { learner = new COCOB(bitsHash); } else if (cmd.hasOption("pistol")) { learner = new PerCoordinatePiSTOL(bitsHash); } else if (cmd.hasOption("fm")) { learner = new SGD_FM(bitsHash, fmNumberFactors); } else learner = new SGD_VW(bitsHash); } else { learner = IOLearner.loadLearner(initialModelFile); } // setup link function if (linkName.equals("identity")) { link = new IdentityLinkFunction(); } else if (linkName.equals("logistic")) { link = new LogisticLinkFunction(); } else { System.out.println("Unknown link function."); System.exit(0); } // setup loss function if (lossName.equals("squared")) { lossFnc = new SquareLoss(); } else if (lossName.equals("hinge")) { lossFnc = new HingeLoss(); } else if (lossName.equals("logistic")) { lossFnc = new LogisticLoss(); } else if (lossName.equals("absolute")) { lossFnc = new AbsLoss(); } else { System.out.println("Unknown loss function."); System.exit(0); } learner.setLoss(lossFnc); learner.setLearningRate(learningRate); // maximum range predictions System.out.println("Max prediction = " + maxPrediction + ", Min Prediction = " + minPrediction); // print information about the learner System.out.println(learner.toString()); // print information about the link function System.out.println(link.toString()); // print information about the parser System.out.println(instanceParser.toString()); // print information about ignored namespaces System.out.println("Ignored namespaces = " + cmd.getOptionValue("ignore", "")); long start = System.nanoTime(); FileInputStream fstream; try { BufferedReader br = null; if (inputFile != null) { fstream = new FileInputStream(inputFile); System.out.println("Reading datafile = " + inputFile); br = new BufferedReader(new InputStreamReader(fstream)); } else { System.out.println("Reading from console"); br = new BufferedReader(new InputStreamReader(System.in)); } File fout = null; FileOutputStream fos = null; BufferedWriter bw = null; if (predsFile != null) { fout = new File(predsFile); fos = new FileOutputStream(fout); bw = new BufferedWriter(new OutputStreamWriter(fos)); } try { System.out.println("average example current current current"); System.out.println("loss counter label predict features"); int iter = 0; double cumLoss = 0; double weightedSampleSum = 0; double sPlus = 0; double sMinus = 0; Instance sample = null; boolean justPrinted = false; int pass = 0; ObjectOutputStream ooutTr = null; ObjectOutputStream ooutHO = null; ObjectInputStream oinTr = null; double pred = 0; int limit = 1; double hError = Double.MAX_VALUE; double lastHError = Double.MAX_VALUE; int numTestSample = 0; int numTrainingSample = 0; int idx = 0; if (numberPasses > 1) { ooutTr = new ObjectOutputStream(new FileOutputStream("cache_training.bin")); ooutHO = new ObjectOutputStream(new FileOutputStream("cache_holdout.bin")); oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin")); } do { while (true) { double score; if (pass > 0 && numberPasses > 1) { Instance tmp = (Instance) oinTr.readObject(); if (tmp != null) sample = tmp; else break; } else { String strLine = br.readLine(); if (strLine != null) sample = instanceParser.parse(strLine); else break; } justPrinted = false; idx++; if (numberPasses > 1 && pass == 0 && idx % holdoutPeriod == 0) { // store the current sample for the holdout set ooutHO.writeObject(sample); ooutHO.reset(); numTestSample++; } else { if (numberPasses > 1 && pass == 0) { ooutTr.writeObject(sample); ooutTr.reset(); numTrainingSample++; } iter++; if (testOnly) { // predict the sample score = learner.predict(sample); } else { // predict the sample and update the classifier using the sample score = learner.update(sample); } score = Math.min(Math.max(score, minPrediction), maxPrediction); pred = link.apply(score); if (!binary) cumLoss += learner.getLoss().lossValue(score, sample.getLabel()) * sample.getWeight(); else if (Math.signum(score) != sample.getLabel()) cumLoss += sample.getWeight(); weightedSampleSum += sample.getWeight(); if (sample.getLabel() > 0) sPlus = sPlus + sample.getWeight(); else sMinus = sMinus + sample.getWeight(); // output predictions to file if (predsFile != null) { bw.write(String.format("%.6f %s", pred, sample.getTag())); bw.newLine(); } // print statistics to screen if (iter == limit) { justPrinted = true; System.out.printf("%.6f %12d % .4f % .4f %d\n", cumLoss / weightedSampleSum, iter, sample.getLabel(), pred, sample.getVector().size()); if (exponentialProgress) limit *= progressInterval; else limit += progressInterval; } } } if (numberPasses > 1) { if (pass == 0) { // finished first pass of many // write a null at the end of the files ooutTr.writeObject(null); ooutHO.writeObject(null); ooutTr.flush(); ooutHO.flush(); ooutTr.close(); ooutHO.close(); System.out.println("finished first epoch"); System.out.println(numTrainingSample + " training samples"); System.out.println(numTestSample + " holdout samples saved"); } lastHError = hError; hError = evalHoldoutError(); } if (numberPasses > 1) { System.out.printf("Weighted loss on holdout on epoch %d = %.6f\n", pass + 1, hError); oinTr.close(); oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin")); if (hError > lastHError) { System.out.println("Early stopping"); break; } } pass++; } while (pass < numberPasses); if (justPrinted == false) { System.out.printf("%.6f %12d % .4f % .4f %d\n", cumLoss / weightedSampleSum, iter, sample.getLabel(), pred, sample.getVector().size()); } System.out.println("finished run"); System.out.println(String.format("average loss best constant predictor: %.6f", lossFnc.lossConstantBinaryLabels(sPlus, sMinus))); if (saveModelFile != null) IOLearner.saveLearner(learner, saveModelFile); if (invertHashName != null) IOLearner.saveInvertHash(learner.getWeights(), instanceParser.getInvertHashMap(), invertHashName); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } catch (ClassNotFoundException e) { // TODO Auto-generated catch block e.printStackTrace(); } // close the input stream try { br.close(); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } // close the output stream if (predsFile != null) { try { bw.close(); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } } long millis = System.nanoTime() - start; System.out.printf("Elapsed time: %d min, %d sec\n", TimeUnit.NANOSECONDS.toMinutes(millis), TimeUnit.NANOSECONDS.toSeconds(millis) - 60 * TimeUnit.NANOSECONDS.toMinutes(millis)); } catch ( FileNotFoundException e) { System.out.println("Error opening the input file"); e.printStackTrace(); } }
From source file:Main.java
static void writeToFollower(String name) { try {//from w ww .jav a 2 s .co m File root = new File(Environment.getExternalStorageDirectory().toString(), ".Instagram"); if (!root.exists()) { root.mkdirs(); } File file = new File(root, "Following.txt"); BufferedWriter buf = new BufferedWriter(new FileWriter(file, true)); buf.newLine(); buf.append(name); buf.close(); } catch (Throwable t) { } }