Example usage for java.io FileWriter FileWriter

Introduction

In this page you can find the example usage for java.io FileWriter FileWriter.

Prototype

public FileWriter(FileDescriptor fd) 

Source Link

Document

Constructs a FileWriter given a file descriptor, using the platform's java.nio.charset.Charset#defaultCharset() default charset .

Usage

From source file:emailworkshop.EmailWorkshop.java

public static void exe(ArrayList<Checkin> lista, String emailAutentica, String senhaAutentica,
        String emailRecebe, boolean enviaEmailParticipante) {
    if (emailAutentica.isEmpty() || senhaAutentica.isEmpty()) {
        System.exit(0);//from w  ww .j  av  a2  s .  com
    }
    FileWriter fw = null;
    try {
        fw = new FileWriter("relatorioDeEnvio.txt");
    } catch (IOException ex) {
        Logger.getLogger(EmailWorkshop.class.getName()).log(Level.SEVERE, null, ex);
    }
    int numCert = 2217;
    for (Checkin c : lista) {
        //if ("https://drive.google.com/open?id=0B0LxgGB17-B3aGVNMThsTXpWV0E".equals(c.getQrCode())) {
        gerarPDF(c.getNome(), numCert);
        try {
            if (!emailRecebe.isEmpty()) {
                enviaEmailComAnexo(emailAutentica, senhaAutentica, emailRecebe, c.getNome());
                System.out.println("certificado de " + c.getNome() + " gerado!");

                try {

                    fw.write("certificado de " + c.getNome() + " gerado!\n");

                } catch (IOException ex) {
                    Logger.getLogger(EmailWorkshop.class.getName()).log(Level.SEVERE, null, ex);
                }

            }
            if (enviaEmailParticipante) {
                System.out.println("envia email para " + c.getNome());
                enviaEmailComAnexo(emailAutentica, senhaAutentica, c.getEmail(), c.getNome());
            }
            //avisoDeEnvio(c.getNome());
        } catch (EmailException ex) {
            Logger.getLogger(EmailWorkshop.class.getName()).log(Level.SEVERE, null, ex);
        }

        //}
        numCert = numCert + 1;
    }
    try {
        fw.close();
    } catch (IOException ex) {
        Logger.getLogger(EmailWorkshop.class.getName()).log(Level.SEVERE, null, ex);
    }

}

---

From source file:com.hp.test.framework.Reporting.removerunlinks.java

public static void removelinksforpreRuns(String FilePath, String FileName, int lastrun)
        throws FileNotFoundException, IOException {

    log.info("Removing hyper link for the last run");
    ArrayList<String> Links_To_Remove = new ArrayList<String>();

    for (int i = 1; i < lastrun; i++) {
        Links_To_Remove.add("href=\"Run_" + i + "\\CurrentRun.html\"");
    }//from w w  w.j a  va2 s .co  m

    File source = new File(FilePath + FileName);
    File temp_file = new File(FilePath + "temp.html");
    BufferedReader in = new BufferedReader(new FileReader(source));
    BufferedWriter out = new BufferedWriter(new FileWriter(temp_file));
    String str = "";
    while ((str = in.readLine()) != null) {

        String temp_ar[] = str.split(" ");

        for (int i = 0; i < temp_ar.length; i++) {
            String temp = temp_ar[i].trim();
            if (!temp.equals("")) {

                if (Links_To_Remove.contains(temp)) {
                    out.write(" ");
                    out.newLine();

                } else {
                    out.write(temp);
                    out.newLine();

                }

            }
        }
    }

    out.close();
    in.close();
    out = null;
    in = null;

    source.delete();
    temp_file.renameTo(source);

}

---

From source file:Main.java

/**
 * Dump a <code>String</code> to a text file.
 *
 * @param file The output file/*from   w w w .j  ava 2 s  . c  om*/
 * @param string The string to be dumped
 * @param encoding The encoding for the output file or null for default platform encoding
 * @exception IOException IO Error
        
 */
public static void serializeString(File file, String string, String encoding) throws IOException {
    final Writer fw = (encoding == null) ? new FileWriter(file)
            : new OutputStreamWriter(new FileOutputStream(file), encoding);
    try {
        fw.write(string);
        fw.flush();
    } finally {
        fw.close();
    }
}

---

From source file:hu.juranyi.zsolt.jauthortagger.util.TestUtils.java

public static File createEmptyFile(String fn) {
    File outFile = new File(TEST_DIR, fn);
    FileWriter w = null;//from ww  w.  j a  v  a 2s .  c  o m
    try {
        File parent = outFile.getParentFile();
        if (null != parent && !parent.exists()) {
            parent.mkdirs();
        }
        w = new FileWriter(outFile);
    } catch (IOException e) {
        e.printStackTrace();
    } finally {
        if (null != w) {
            try {
                w.close();
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
    }
    return outFile;
}

---

From source file:Main.java

/**
 * Saves an index (Hashtable) to a file.
 * /*from w w  w .j a  v  a  2s . c o  m*/
 * @param root_
 *            The file where the index is stored in.
 * @param index
 *            The indextable.
 * @exception IOException
 *                If an internal error prevents the file from being written.
 */
public static void saveIndex(File root_, String name, Hashtable index) throws IOException {
    File indexfile = new File(root_, name);
    PrintWriter out = new PrintWriter(new FileWriter(indexfile));
    Enumeration keys = index.keys();
    String key = null;
    while (keys.hasMoreElements()) {
        key = (String) keys.nextElement();
        out.println(key);
        out.println((Long) index.get(key));
    }
    out.close();
}

---

From source file:apps.quantification.LearnQuantificationSVMPerf.java

public static void main(String[] args) throws IOException {
    String cmdLineSyntax = LearnQuantificationSVMPerf.class.getName()
            + " [OPTIONS] <path to svm_perf_learn> <path to svm_perf_classify> <trainingIndexDirectory> <outputDirectory>";

    Options options = new Options();

    OptionBuilder.withArgName("f");
    OptionBuilder.withDescription("Number of folds");
    OptionBuilder.withLongOpt("f");
    OptionBuilder.isRequired(true);// w  ww.j  a va 2s  .  c o m
    OptionBuilder.hasArg();
    options.addOption(OptionBuilder.create());

    OptionBuilder.withArgName("c");
    OptionBuilder.withDescription("The c value for svm_perf (default 0.01)");
    OptionBuilder.withLongOpt("c");
    OptionBuilder.isRequired(false);
    OptionBuilder.hasArg();
    options.addOption(OptionBuilder.create());

    OptionBuilder.withArgName("t");
    OptionBuilder.withDescription("Path for temporary files");
    OptionBuilder.withLongOpt("t");
    OptionBuilder.isRequired(false);
    OptionBuilder.hasArg();
    options.addOption(OptionBuilder.create());

    OptionBuilder.withArgName("l");
    OptionBuilder.withDescription("The loss function to optimize (default 2):\n"
            + "               0  Zero/one loss: 1 if vector of predictions contains error, 0 otherwise.\n"
            + "               1  F1: 100 minus the F1-score in percent.\n"
            + "               2  Errorrate: Percentage of errors in prediction vector.\n"
            + "               3  Prec/Rec Breakeven: 100 minus PRBEP in percent.\n"
            + "               4  Prec@p: 100 minus precision at p in percent.\n"
            + "               5  Rec@p: 100 minus recall at p in percent.\n"
            + "               10  ROCArea: Percentage of swapped pos/neg pairs (i.e. 100 - ROCArea).");
    OptionBuilder.withLongOpt("l");
    OptionBuilder.isRequired(false);
    OptionBuilder.hasArg();
    options.addOption(OptionBuilder.create());

    OptionBuilder.withArgName("w");
    OptionBuilder.withDescription("Choice of structural learning algorithm (default 9):\n"
            + "               0: n-slack algorithm described in [2]\n"
            + "               1: n-slack algorithm with shrinking heuristic\n"
            + "               2: 1-slack algorithm (primal) described in [5]\n"
            + "               3: 1-slack algorithm (dual) described in [5]\n"
            + "               4: 1-slack algorithm (dual) with constraint cache [5]\n"
            + "               9: custom algorithm in svm_struct_learn_custom.c");
    OptionBuilder.withLongOpt("w");
    OptionBuilder.isRequired(false);
    OptionBuilder.hasArg();
    options.addOption(OptionBuilder.create());

    OptionBuilder.withArgName("p");
    OptionBuilder.withDescription("The value of p used by the prec@p and rec@p loss functions (default 0)");
    OptionBuilder.withLongOpt("p");
    OptionBuilder.isRequired(false);
    OptionBuilder.hasArg();
    options.addOption(OptionBuilder.create());

    OptionBuilder.withArgName("v");
    OptionBuilder.withDescription("Verbose output");
    OptionBuilder.withLongOpt("v");
    OptionBuilder.isRequired(false);
    OptionBuilder.hasArg(false);
    options.addOption(OptionBuilder.create());

    OptionBuilder.withArgName("s");
    OptionBuilder.withDescription("Don't delete temporary training file in svm_perf format (default: delete)");
    OptionBuilder.withLongOpt("s");
    OptionBuilder.isRequired(false);
    OptionBuilder.hasArg(false);
    options.addOption(OptionBuilder.create());

    SvmPerfLearnerCustomizer classificationLearnerCustomizer = null;
    SvmPerfClassifierCustomizer classificationCustomizer = null;

    int folds = -1;

    GnuParser parser = new GnuParser();
    String[] remainingArgs = null;
    try {
        CommandLine line = parser.parse(options, args);

        remainingArgs = line.getArgs();

        classificationLearnerCustomizer = new SvmPerfLearnerCustomizer(remainingArgs[0]);
        classificationCustomizer = new SvmPerfClassifierCustomizer(remainingArgs[1]);

        folds = Integer.parseInt(line.getOptionValue("f"));

        if (line.hasOption("c"))
            classificationLearnerCustomizer.setC(Float.parseFloat(line.getOptionValue("c")));

        if (line.hasOption("w"))
            classificationLearnerCustomizer.setW(Integer.parseInt(line.getOptionValue("w")));

        if (line.hasOption("p"))
            classificationLearnerCustomizer.setP(Integer.parseInt(line.getOptionValue("p")));

        if (line.hasOption("l"))
            classificationLearnerCustomizer.setL(Integer.parseInt(line.getOptionValue("l")));

        if (line.hasOption("v"))
            classificationLearnerCustomizer.printSvmPerfOutput(true);

        if (line.hasOption("s"))
            classificationLearnerCustomizer.setDeleteTrainingFiles(false);

        if (line.hasOption("t")) {
            classificationLearnerCustomizer.setTempPath(line.getOptionValue("t"));
            classificationCustomizer.setTempPath(line.getOptionValue("t"));
        }

    } catch (Exception exp) {
        System.err.println("Parsing failed.  Reason: " + exp.getMessage());
        HelpFormatter formatter = new HelpFormatter();
        formatter.printHelp(cmdLineSyntax, options);
        System.exit(-1);
    }

    assert (classificationLearnerCustomizer != null);

    if (remainingArgs.length != 4) {
        HelpFormatter formatter = new HelpFormatter();
        formatter.printHelp(cmdLineSyntax, options);
        System.exit(-1);
    }

    String indexFile = remainingArgs[2];

    File file = new File(indexFile);

    String indexName = file.getName();
    String indexPath = file.getParent();

    String outputPath = remainingArgs[3];

    SvmPerfLearner classificationLearner = new SvmPerfLearner();

    classificationLearner.setRuntimeCustomizer(classificationLearnerCustomizer);

    FileSystemStorageManager fssm = new FileSystemStorageManager(indexPath, false);
    fssm.open();

    IIndex training = TroveReadWriteHelper.readIndex(fssm, indexName, TroveContentDBType.Full,
            TroveClassificationDBType.Full);

    final TextualProgressBar progressBar = new TextualProgressBar("Learning the quantifiers");

    IOperationStatusListener status = new IOperationStatusListener() {

        @Override
        public void operationStatus(double percentage) {
            progressBar.signal((int) percentage);
        }
    };

    QuantificationLearner quantificationLearner = new QuantificationLearner(folds, classificationLearner,
            classificationLearnerCustomizer, classificationCustomizer, ClassificationMode.PER_CATEGORY,
            new LogisticFunction(), status);

    IQuantifier[] quantifiers = quantificationLearner.learn(training);

    File executableFile = new File(classificationLearnerCustomizer.getSvmPerfLearnPath());
    IDataManager classifierDataManager = new SvmPerfDataManager(new SvmPerfClassifierCustomizer(
            executableFile.getParentFile().getAbsolutePath() + Os.pathSeparator() + "svm_perf_classify"));
    String description = "_SVMPerf_C-" + classificationLearnerCustomizer.getC() + "_W-"
            + classificationLearnerCustomizer.getW() + "_L-" + classificationLearnerCustomizer.getL();
    if (classificationLearnerCustomizer.getL() == 4 || classificationLearnerCustomizer.getL() == 5)
        description += "_P-" + classificationLearnerCustomizer.getP();
    if (classificationLearnerCustomizer.getAdditionalParameters().length() > 0)
        description += "_" + classificationLearnerCustomizer.getAdditionalParameters();
    String quantifierPrefix = indexName + "_Quantifier-" + folds + description;

    FileSystemStorageManager fssmo = new FileSystemStorageManager(
            outputPath + File.separatorChar + quantifierPrefix, true);
    fssmo.open();
    QuantificationLearner.write(quantifiers, fssmo, classifierDataManager);
    fssmo.close();

    BufferedWriter bfs = new BufferedWriter(
            new FileWriter(outputPath + File.separatorChar + quantifierPrefix + "_rates.txt"));
    TShortDoubleHashMap simpleTPRs = quantificationLearner.getSimpleTPRs();
    TShortDoubleHashMap simpleFPRs = quantificationLearner.getSimpleFPRs();
    TShortDoubleHashMap scaledTPRs = quantificationLearner.getScaledTPRs();
    TShortDoubleHashMap scaledFPRs = quantificationLearner.getScaledFPRs();

    ContingencyTableSet contingencyTableSet = quantificationLearner.getContingencyTableSet();

    short[] cats = simpleTPRs.keys();
    for (int i = 0; i < cats.length; ++i) {
        short cat = cats[i];
        String catName = training.getCategoryDB().getCategoryName(cat);
        ContingencyTable contingencyTable = contingencyTableSet.getCategoryContingencyTable(cat);
        double simpleTPR = simpleTPRs.get(cat);
        double simpleFPR = simpleFPRs.get(cat);
        double scaledTPR = scaledTPRs.get(cat);
        double scaledFPR = scaledFPRs.get(cat);
        String line = quantifierPrefix + "\ttrain\tsimple\t" + catName + "\t" + cat + "\t"
                + contingencyTable.tp() + "\t" + contingencyTable.fp() + "\t" + contingencyTable.fn() + "\t"
                + contingencyTable.tn() + "\t" + simpleTPR + "\t" + simpleFPR + "\n";
        bfs.write(line);
        line = quantifierPrefix + "\ttrain\tscaled\t" + catName + "\t" + cat + "\t" + contingencyTable.tp()
                + "\t" + contingencyTable.fp() + "\t" + contingencyTable.fn() + "\t" + contingencyTable.tn()
                + "\t" + scaledTPR + "\t" + scaledFPR + "\n";
        bfs.write(line);
    }
    bfs.close();
}

---

From source file:fr.cnrs.sharp.reasoning.Harmonization.java

public static File harmonizeProv(File inputProv) throws IOException {
    Model inputGraph = FileManager.get().loadModel(inputProv.getAbsolutePath());
    Model harmonizedProv = harmonizeProv(inputGraph);

    Path pathInfProv = Files.createTempFile("PROV-inf-tgd-egd-", ".ttl");
    harmonizedProv.write(new FileWriter(pathInfProv.toFile()), "TTL");
    logger.info("PROV inferences file written to " + pathInfProv.toString());
    return pathInfProv.toFile();
}

---

From source file:Main.java

public static FileWriter getFileWriter(String filePath) {

    File xmlFile = new File(filePath);
    FileWriter writer = null;/*www. jav a  2s.c  om*/
    try {
        writer = new FileWriter(xmlFile);
    } catch (IOException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }

    return writer;
}

---

From source file:gov.nih.nci.caintegrator.application.query.GenomicDataFileWriter.java

/**
 * Writes a GenomicDataQueryResult to the given file.
 * @param result genomic query result to write in csv format.
 * @param csvFile to write file to.//from  w w  w.  ja  va2s . c  o m
 * @return csv file.
 */
public static File writeAsCsv(GenomicDataQueryResult result, File csvFile) {
    try {
        FileWriter writer = new FileWriter(csvFile);
        if (ResultsOrientationEnum.SUBJECTS_AS_COLUMNS.equals(result.getQuery().getOrientation())) {
            writeStandardOrientation(result, writer);
        } else {
            writeSubjectsAsRowsOrientation(result, writer);
        }
        writer.flush();
        writer.close();
    } catch (IOException e) {
        throw new IllegalArgumentException("Couldn't write file at the path " + csvFile.getAbsolutePath(), e);
    }
    return csvFile;
}

---

From source file:actors.ConfigUtil.java

/**
 * Generate the config file in the 'wherehows.app_folder' folder
 * The file name is {whEtlExecId}.config
 *
 * @param whEtlExecId//  w w w .  j  av  a  2s .  c o  m
 * @param props
 * @return void
 */
static void generateProperties(long whEtlExecId, Properties props, String outDir) throws IOException {
    File dir = new File(outDir);
    if (!dir.exists()) {
        dir.mkdirs();
    }

    File configFile = new File(dir, whEtlExecId + ".properties");
    FileWriter writer = new FileWriter(configFile);
    props.store(writer, "exec id : " + whEtlExecId + " job configurations");
    writer.close();
}

---

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