Java Code Examples for org.apache.commons.lang3.text.WordUtils#wrap()

@Override
public Object filter(Object var, JinjavaInterpreter interpreter, String... args) {
  String str = Objects.toString(var, "");

  int wrapLength = 79;
  if (args.length > 0) {
    wrapLength = NumberUtils.toInt(args[0], 79);
  }

  boolean wrapLongWords = true;
  if (args.length > 1) {
    wrapLongWords = BooleanUtils.toBoolean(args[1]);
  }

  return WordUtils.wrap(str, wrapLength, "\n", wrapLongWords);
}
 

private void updateDefectsGroup() {
  defectsGroup.clear();

  defectsGroup.addActor(new Label("Defects", Main.skin));
  defectsGroup.addActor(new Label("", Main.skin));

  Label instructions = new Label(
      WordUtils.wrap(
          "[DARK_GRAY]Taking a major defect gives you 2 points, taking a minor gives you 1", 70
      ), Main.skin
  );

  defectsGroup.addActor(instructions);
  defectsGroup.addActor(new Label("", Main.skin));

  for (int i = 0; i < this.defects.size(); i++) {
    DefectData defectData = this.defects.get(i);

    defectsGroup.addActor(
        new Label(createDefectText(i, defectData), Main.skin)
    );
  }
}
 

private String generateCommentText(Comment element) {
	String body = element.getBody().trim().replaceAll("[\\n\\p{Space}]+", " ");
	int periodIndex = body.indexOf(".");
	int end = periodIndex == -1 ? body.length() : periodIndex;
	String wrapped = WordUtils.wrap(StringUtils.abbreviate(body.substring(0, end), 200), 30);
	return StringUtils.trimToNull(wrapped.replaceAll("\n", "\\\\n"));
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("Adamic-Adar measures the similarity between vertex neighborhoods and is " +
			"computed as the sum of the inverse logarithm of centerpoint degree over shared " +
			"neighbors.")
		.appendNewLine()
		.append("The algorithm result contains two vertex IDs and the similarity score.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("Hyperlink-Induced Topic Search computes two interdependent scores for " +
			"each vertex in a directed graph. A good \"hub\" links to good \"authorities\" " +
			"and good \"authorities\" are linked to from good \"hubs\".")
		.appendNewLine()
		.append("The result contains the vertex ID, hub score, and authority score.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("List all triangles graph.")
		.appendNewLine()
		.append("The algorithm result contains three vertex IDs. For the directed algorithm " +
			"the result contains an additional bitmask indicating the presence of the six " +
			"potential connecting edges.")
		.toString(), 80);
}
 

private static String toWrappedString(Object o, int wrap) {
  String s = "" + o;
  if(wrap <= 0) {
    return s;
  }
  return WordUtils.wrap(s, wrap);
}
 

private String createAbilityText(Ability ability) {
  String desc = WordUtils.wrap(ability.description, 70);

  if (ability.type == Ability.Type.PASSIVE) {
    return ability.name + "[LIGHT_GRAY] Passive\n[DARK_GRAY]" + desc;
  } else {
    return ability.name + "[LIGHT_GRAY] Usable every "
        + ability.recharge + " turns\n" + "[DARK_GRAY]" + desc;
  }
}
 

private String createTraitText(int index, TraitData traitData) {
  String selected = playerSetup.traits.contains(traitData.name, false) ? "[WHITE]x " : "";
  String type = traitData.cost == 1 ? "Minor" : "Major";
  String desc = WordUtils.wrap(traitData.description, 70);

  boolean hasConflictingDefect = false;

  for (int i = 0; i < playerSetup.defects.size; i++) {
    String defect = playerSetup.defects.get(i);

    if (traitData.cantHave != null && traitData.cantHave.contains(defect)) {
      hasConflictingDefect = true;
    }
  }

  if (hasConflictingDefect) {
    if (sectionSelected == Section.TRAITS && index == itemSelected) {
      return selected + "[DARK_GRAY]> " + type + " " + traitData.name + "\n" + desc;
    } else {
      return selected + "[DARK_GRAY]" + type + " " + traitData.name + "\n" + desc;
    }
  } else if (sectionSelected == Section.TRAITS && index == itemSelected) {
    return selected + "[DARK_GRAY]> [WHITE]"
        + type + " [WHITE]" + traitData.name + "\n[DARK_GRAY]" + desc;
  } else {
    return selected + "[WHITE]"
        + type + " [LIGHT_GRAY]" + traitData.name + "\n[DARK_GRAY]" + desc;
  }
}
 

private String createDefectText(int index, DefectData defectData) {
  String selected = playerSetup.defects.contains(defectData.name, false) ? "[WHITE]x " : "";
  String type = defectData.reward == 1 ? "Minor" : "Major";
  String desc = WordUtils.wrap(defectData.description, 70);

  boolean hasConflictingTrait = false;

  for (int i = 0; i < playerSetup.traits.size; i++) {
    String trait = playerSetup.traits.get(i);

    if (defectData.cantHave != null && defectData.cantHave.contains(trait)) {
      hasConflictingTrait = true;
    }
  }

  if (hasConflictingTrait) {
    if (sectionSelected == Section.TRAITS && index == itemSelected) {
      return selected + "[DARK_GRAY]> " + type + " " + defectData.name + "\n" + desc;
    } else {
      return selected + "[DARK_GRAY]" + type + " " + defectData.name + "\n" + desc;
    }
  } else if (sectionSelected == Section.DEFECTS && index == itemSelected) {
    return selected + "[DARK_GRAY]> [WHITE]" + type
        + " [WHITE]" + defectData.name + "\n[DARK_GRAY]" + desc;
  } else {
    return selected + "[WHITE]" + type
        + " [LIGHT_GRAY]" + defectData.name + "\n[DARK_GRAY]" + desc;
  }
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("The local clustering coefficient measures the connectedness of each " +
			"vertex's neighborhood. The global clustering coefficient measures the " +
			"connected of the graph. The average clustering coefficient is the mean local " +
			"clustering coefficient. Each score ranges from 0.0 (no edges between vertex " +
			"neighbors) to 1.0 (neighborhood or graph is a clique).")
		.appendNewLine()
		.append("The algorithm result contains the vertex ID, degree, and number of edges " +
			"connecting neighbors.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("Adamic-Adar measures the similarity between vertex neighborhoods and is " +
			"computed as the sum of the inverse logarithm of centerpoint degree over shared " +
			"neighbors.")
		.appendNewLine()
		.append("The algorithm result contains two vertex IDs and the similarity score.")
		.toString(), 80);
}
 

@Override
public String toString(){

    final int FIXED_WIDTH = 80;

    String s = "";
    Iterator<Attribute> it = this.iterator();
    if (it.hasNext()) {
        s += it.next().getName();
    }
    while (it.hasNext()) {
        s += ", " + it.next().getName();
    }
    return(WordUtils.wrap(s+"\n",FIXED_WIDTH));
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("List all triangles graph.")
		.appendNewLine()
		.append("The algorithm result contains three vertex IDs. For the directed algorithm " +
			"the result contains an additional bitmask indicating the presence of the six " +
			"potential connecting edges.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("Jaccard Index measures the similarity between vertex neighborhoods and " +
			"is computed as the number of shared neighbors divided by the number of " +
			"distinct neighbors. Scores range from 0.0 (no shared neighbors) to 1.0 (all " +
			"neighbors are shared).")
		.appendNewLine()
		.append("The result contains two vertex IDs, the number of shared neighbors, and " +
			"the number of distinct neighbors.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("The local clustering coefficient measures the connectedness of each " +
			"vertex's neighborhood. The global clustering coefficient measures the " +
			"connected of the graph. The average clustering coefficient is the mean local " +
			"clustering coefficient. Each score ranges from 0.0 (no edges between vertex " +
			"neighbors) to 1.0 (neighborhood or graph is a clique).")
		.appendNewLine()
		.append("The algorithm result contains the vertex ID, degree, and number of edges " +
			"connecting neighbors.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("Adamic-Adar measures the similarity between vertex neighborhoods and is " +
			"computed as the sum of the inverse logarithm of centerpoint degree over shared " +
			"neighbors.")
		.appendNewLine()
		.append("The algorithm result contains two vertex IDs and the similarity score.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("Hyperlink-Induced Topic Search computes two interdependent scores for " +
			"each vertex in a directed graph. A good \"hub\" links to good \"authorities\" " +
			"and good \"authorities\" are linked to from good \"hubs\".")
		.appendNewLine()
		.append("The result contains the vertex ID, hub score, and authority score.")
		.toString(), 80);
}
 

@Override
public String getLongDescription() {
	return WordUtils.wrap(new StrBuilder()
		.appendln("List all triangles graph.")
		.appendNewLine()
		.append("The algorithm result contains three vertex IDs. For the directed algorithm " +
			"the result contains an additional bitmask indicating the presence of the six " +
			"potential connecting edges.")
		.toString(), 80);
}
 

private void wrapAndPrint(String text) {
	String wrapped = WordUtils.wrap(text, 60);
	wrapped = "    " + wrapped.replace("\n", "\n    ");
	System.out.println(wrapped);
}