List of usage examples for javafx.collections FXCollections observableArrayList
public static <E> ObservableList<E> observableArrayList(Collection<? extends E> col)
From source file:org.dataconservancy.packaging.gui.presenter.impl.EditPackageContentsPresenterImplTest.java
@Test public void testUnignoreDataItemUnignoresParent() { presenter.rebuildTreeView();/* www. j a v a 2 s.c o m*/ presenter.toggleItemIgnore(FXCollections.observableArrayList(presenter.findItem(collection)), true); assertTrue(collection.isIgnored()); assertTrue(dataItem.isIgnored()); assertTrue(dataFile.isIgnored()); presenter.toggleItemIgnore(FXCollections.observableArrayList(presenter.findItem(dataItem)), false); //Parent should not be ignored assertFalse(collection.isIgnored()); //Item and it's children should not be ignored assertFalse(dataItem.isIgnored()); assertFalse(dataFile.isIgnored()); }
From source file:com.danilafe.sbaccountmanager.StarboundServerAccountManager.java
private void createAccount(ListView<String> to_update) { Stage createAccountStage = new Stage(); createAccountStage.initModality(Modality.APPLICATION_MODAL); //Set the stage info createAccountStage.setTitle("Add Server Account"); //Create a layout GridPane gp = new GridPane(); gp.setAlignment(Pos.CENTER);//from ww w.j a v a2 s . c om gp.setVgap(10); gp.setHgap(10); gp.setPadding(new Insets(25, 25, 25, 25)); //Ads the important things Text welcome = new Text("Create Server Account"); welcome.setFont(Font.font("Century Gothic", FontWeight.NORMAL, 20)); gp.add(welcome, 0, 0, 2, 1); Label username = new Label("Username"); Label password = new Label("Password"); Label r_password = new Label("Repeat Password"); TextField usernamefield = new TextField(); PasswordField passwordfield = new PasswordField(); PasswordField r_passwordfield = new PasswordField(); gp.add(username, 0, 1); gp.add(password, 0, 2); gp.add(r_password, 0, 3); gp.add(usernamefield, 1, 1); gp.add(passwordfield, 1, 2); gp.add(r_passwordfield, 1, 3); Text error = new Text(""); HBox error_box = new HBox(10); error_box.setAlignment(Pos.CENTER); error_box.getChildren().add(error); gp.add(error_box, 0, 4, 2, 1); Button finish = new Button("Finish"); finish.setDisable(true); HBox center_button = new HBox(10); center_button.setAlignment(Pos.CENTER); center_button.getChildren().add(finish); gp.add(center_button, 0, 5, 2, 1); ChangeListener name = new ChangeListener<String>() { @Override public void changed(ObservableValue<? extends String> observable, String oldValue, String newValue) { finish.setDisable(true); if (usernamefield.getText().equals("")) { error.setFill(Color.RED); error.setText("Username can not be blank!"); } if (!passwordfield.getText().equals(r_passwordfield.getText())) { error.setFill(Color.RED); error.setText("Passwords do not match!"); } if (passwordfield.getText().equals("") && r_passwordfield.getText().equals("")) { error.setFill(Color.RED); error.setText("Passwords can not be blank!"); } if (passwordfield.getText().equals(r_passwordfield.getText()) && !usernamefield.getText().equals("") && !passwordfield.getText().equals("")) { error.setFill(Color.GREEN); error.setText("No issues."); finish.setDisable(false); } } }; usernamefield.textProperty().addListener(name); passwordfield.textProperty().addListener(name); r_passwordfield.textProperty().addListener(name); finish.setOnAction(new EventHandler<ActionEvent>() { @Override public void handle(ActionEvent e) { users.remove(usernamefield.getText()); users.add(usernamefield.getText()); userpasswords.put(usernamefield.getText(), passwordfield.getText()); to_update.setItems(FXCollections.observableArrayList(users)); createAccountStage.close(); } }); //Creates the scene Scene scene = new Scene(gp, 300, 275); scene.getStylesheets().add(this.getClass().getResource("login_css.css").toExternalForm()); createAccountStage.setScene(scene); createAccountStage.show(); }
From source file:net.sourceforge.pmd.util.fxdesigner.XPathPanelController.java
/** * Evaluate the contents of the XPath expression area * on the given compilation unit. This updates the xpath * result panel, and can log XPath exceptions to the * event log panel./*from w w w. java2s.co m*/ * * @param compilationUnit The AST root * @param version The language version */ public void evaluateXPath(Node compilationUnit, LanguageVersion version) { try { String xpath = getXpathExpression(); if (StringUtils.isBlank(xpath)) { invalidateResults(false); return; } ObservableList<Node> results = FXCollections.observableArrayList(xpathEvaluator.evaluateQuery( compilationUnit, version, getXpathVersion(), xpath, ruleBuilder.getRuleProperties())); xpathResultListView.setItems( results.stream().map(parent::wrapNode).collect(Collectors.toCollection(LiveArrayList::new))); parent.highlightXPathResults(results); violationsTitledPane.setText("Matched nodes\t(" + results.size() + ")"); } catch (XPathEvaluationException e) { invalidateResults(true); designerRoot.getLogger().logEvent(new LogEntry(e, Category.XPATH_EVALUATION_EXCEPTION)); } xpathResultListView.refresh(); }
From source file:com.exalttech.trex.ui.views.PacketTableView.java
/** * Handle Add packet button clicked/*from w w w . ja v a 2s . c o m*/ * * @param streamName * @param type */ public void handleAddPacket(String streamName, StreamBuilderType type) { doUpdate = false; TableProfileStream newRow = new TableProfileStream(); newRow.setName(streamName); tabledata.getStreamsList().add(newRow); Profile newProfile = new Profile(); newProfile.setName(streamName); tabledata.getProfiles().add(newProfile); streamPacketTableView.setItems(FXCollections.observableArrayList(tabledata.getStreamsList())); streamPacketTableView.getSelectionModel().select(newRow); openStreamDialog(type); }
From source file:org.dataconservancy.packaging.gui.presenter.impl.EditPackageContentsPresenterImplTest.java
@Test public void testIgnoreDataFile() { presenter.rebuildTreeView();/*from w w w . j a va 2 s . co m*/ presenter.toggleItemIgnore(FXCollections.observableArrayList(presenter.findItem(dataFile)), true); //Parent should not be ignored assertFalse(collection.isIgnored()); assertFalse(dataItem.isIgnored()); //Item should be ignored assertTrue(dataFile.isIgnored()); }
From source file:com.ggvaidya.scinames.summary.NameStabilityView.java
public void init() { Project project = projectView.getProject(); // What do we actually need to do? boolean flag_calculateNameSimilarity = (toCalculate & NAME_SIMILARITY) == NAME_SIMILARITY; boolean flag_calculateClusterSimilarity = (toCalculate & CLUSTER_SIMILARITY) == CLUSTER_SIMILARITY; boolean flag_calculateCircumscriptionSimilarity = (toCalculate & CIRCUMSCRIPTIONAL_SIMILARITY) == CIRCUMSCRIPTIONAL_SIMILARITY; // Setup stage. stage.setTitle("Name stability between " + project.getDatasets().size() + " datasets"); // Setup table. controller.getTableEditableProperty().set(false); //controller.setTableColumnResizeProperty(TableView.CONSTRAINED_RESIZE_POLICY); ObservableList<TableColumn> cols = controller.getTableColumnsProperty(); cols.clear();/*w ww . ja v a 2s . co m*/ // Precalculating. Table<Dataset, String, String> precalc = HashBasedTable.create(); // Set up columns. cols.add(createTableColumnFromPrecalc(precalc, "dataset")); cols.add(createTableColumnFromPrecalc(precalc, "date")); cols.add(createTableColumnFromPrecalc(precalc, "year")); cols.add(createTableColumnFromPrecalc(precalc, "count_binomial")); cols.add(createTableColumnFromPrecalc(precalc, "count_genera")); cols.add(createTableColumnFromPrecalc(precalc, "count_monotypic_genera")); cols.add(createTableColumnFromPrecalc(precalc, "names_added")); //cols.add(createTableColumnFromPrecalc(precalc, "names_added_list")); cols.add(createTableColumnFromPrecalc(precalc, "names_deleted")); //cols.add(createTableColumnFromPrecalc(precalc, "names_deleted_list")); cols.add(createTableColumnFromPrecalc(precalc, "species_added")); //cols.add(createTableColumnFromPrecalc(precalc, "species_added_list")); cols.add(createTableColumnFromPrecalc(precalc, "species_deleted")); //cols.add(createTableColumnFromPrecalc(precalc, "species_deleted_list")); cols.add(createTableColumnFromPrecalc(precalc, "mean_binomials_per_genera")); cols.add(createTableColumnFromPrecalc(precalc, "median_binomials_per_genera")); cols.add(createTableColumnFromPrecalc(precalc, "mode_binomials_per_genera_list")); /* All them stability calculations */ if (flag_calculateNameSimilarity) { cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_prev")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_prev_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_prev_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_prev_pc_prev")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_next")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_next_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_next_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_next_pc_next")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_first")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_first_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_first_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_first_pc_first")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_last")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_last_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_last_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "names_identical_to_last_pc_last")); } if (flag_calculateClusterSimilarity) { cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_prev")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_prev_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_prev_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_prev_pc_prev")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_next")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_next_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_next_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_next_pc_next")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_first")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_first_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_first_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_first_pc_first")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_last")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_last_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_last_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "clusters_identical_to_last_pc_last")); } if (flag_calculateCircumscriptionSimilarity) { cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_prev")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_prev_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_prev_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_prev_pc_prev")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_next")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_next_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_next_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_next_pc_next")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_first")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_first_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_first_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_first_pc_first")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_last")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_last_pc_this")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_last_pc_union")); cols.add(createTableColumnFromPrecalc(precalc, "circumscriptions_identical_to_last_pc_last")); } Set<String> recognitionColumns = new HashSet<>(); // Calculate binomials per dataset. Map<Name, Set<Dataset>> datasetsPerName = new HashMap<>(); // Prepare to loop! List<Dataset> checklists = project.getChecklists(); // BIRD HACK! Include all datasets! // checklists = project.getDatasets(); // Set table items. We're only interested in checklists, because // there's no such thing as "name stability" between non-checklist datasets. controller.getTableItemsProperty().set(FXCollections.observableArrayList(checklists)); List<Dataset> prevChecklists = new LinkedList<>(); Dataset firstChecklist = checklists.get(0); Dataset lastChecklist = checklists.get(checklists.size() - 1); // TODO: This used to be prevDataset, but prevChecklist makes a lot more sense, since we // want to compare checklists with each other, ignoring datasets. Would be nice if someone // with copious free time could look over the calculations and make sure they don't assume // that the previous checklist is also the previous dataset? Dataset prevChecklist = null; int index = -1; for (Dataset ds : checklists) { index++; Dataset nextChecklist = (index < (checklists.size() - 1) ? checklists.get(index + 1) : null); precalc.put(ds, "dataset", ds.getName()); precalc.put(ds, "date", ds.getDate().asYYYYmmDD("-")); precalc.put(ds, "year", ds.getDate().getYearAsString()); Set<Name> recognizedBinomials = project.getRecognizedNames(ds).stream().flatMap(n -> n.asBinomial()) .collect(Collectors.toSet()); precalc.put(ds, "count_binomial", String.valueOf(recognizedBinomials.size())); Set<Name> recognizedGenera = recognizedBinomials.stream().flatMap(n -> n.asGenus()) .collect(Collectors.toSet()); precalc.put(ds, "count_genera", String.valueOf(recognizedGenera.size())); precalc.put(ds, "mean_binomials_per_genera", new BigDecimal(((double) recognizedBinomials.size()) / recognizedGenera.size()) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); Map<Name, List<Name>> countBinomialsPerGenus = recognizedBinomials.stream() // Eliminate names that have zero (or more than one?!) genus name. .filter(n -> (n.asGenus().count() == 1)) .collect(Collectors.groupingBy(n -> n.asGenus().findAny().get())); /* LOGGER.info("Debugging: list of " + recognizedGenera.size() + " genera: " + recognizedGenera.stream().map(n -> n.getFullName()).collect(Collectors.joining(", ")) ); */ precalc.put(ds, "count_monotypic_genera", String.valueOf(countBinomialsPerGenus.entrySet().stream() .filter(entry -> new HashSet<>(entry.getValue()).size() == 1).count())); /* LOGGER.info("Debugging: list of monotypic genera: " + countBinomialsPerGenus.entrySet().stream() .filter(entry -> new HashSet<>(entry.getValue()).size() == 1) .map(entry -> entry.getKey().getFullName()) .collect(Collectors.joining(", ")) ); */ // Species added and deleted Set<Name> namesAdded = ds.getChanges(project).filter(ch -> ch.getType().equals(ChangeType.ADDITION)) .flatMap(ch -> ch.getToStream()).collect(Collectors.toSet()); Set<Name> namesDeleted = ds.getChanges(project).filter(ch -> ch.getType().equals(ChangeType.DELETION)) .flatMap(ch -> ch.getFromStream()).collect(Collectors.toSet()); // TODO: This isn't so useful -- the more useful measure would be the number of all species added // and all species deleted, making sure there isn't a cluster-al overlap. precalc.put(ds, "names_added", String.valueOf(namesAdded.size())); //precalc.put(ds, "names_added_list", namesAdded.stream().sorted().map(n -> n.getFullName()).collect(Collectors.joining(", "))); precalc.put(ds, "names_deleted", String.valueOf(namesDeleted.size())); //precalc.put(ds, "names_deleted_list", namesDeleted.stream().sorted().map(n -> n.getFullName()).collect(Collectors.joining(", "))); // Eliminate names that have been added, but were previously recognized at the species level. Set<Name> speciesAdded = namesAdded; if (prevChecklist != null) { Set<Name> prevRecognizedNames = project.getNameClusterManager() .getClusters(project.getRecognizedNames(prevChecklist)).stream() .flatMap(nc -> nc.getNames().stream()).collect(Collectors.toSet()); speciesAdded = namesAdded.stream().filter(n -> !prevRecognizedNames.contains(n)) .collect(Collectors.toSet()); } // Eliminate names that are still represented in the checklist by a species cluster. // (Note that this includes cases where a subspecies is removed, but another subspecies // or the nominal species is still recognized!) Set<Name> currentlyRecognizedBinomialNames = project.getNameClusterManager() .getClusters(project.getRecognizedNames(ds)).stream().flatMap(nc -> nc.getNames().stream()) .flatMap(n -> n.asBinomial()).collect(Collectors.toSet()); Set<Name> speciesDeleted = namesDeleted.stream() .filter(n -> !n.asBinomial().anyMatch(bn -> currentlyRecognizedBinomialNames.contains(bn))) .collect(Collectors.toSet()); precalc.put(ds, "species_added", String.valueOf(speciesAdded.size())); precalc.put(ds, "species_added_list", speciesAdded.stream().sorted().map(n -> n.getFullName()).collect(Collectors.joining(", "))); precalc.put(ds, "species_deleted", String.valueOf(speciesDeleted.size())); precalc.put(ds, "species_deleted_list", speciesDeleted.stream().sorted().map(n -> n.getFullName()).collect(Collectors.joining(", "))); // Measures of species per genera java.util.Map<String, Set<Name>> binomialsPerGenera = recognizedBinomials.stream() .collect(Collectors.toMap(n -> n.getGenus(), n -> { Set<Name> set = new HashSet<Name>(); set.add(n); return set; }, (a, b) -> { a.addAll(b); return a; })); List<Integer> binomialsPerGeneraCounts = binomialsPerGenera.values().stream().map(set -> set.size()) .sorted().collect(Collectors.toList()); Frequency freq = new Frequency(); for (String genus : binomialsPerGenera.keySet()) { // Blech. for (Name binom : binomialsPerGenera.get(genus)) { freq.addValue(genus); } } List<Comparable<?>> modeGenera = freq.getMode(); precalc.put(ds, "mode_binomials_per_genera_list", modeGenera.stream().map(o -> o.toString() + ": " + freq.getCount(o) + " binomials") .collect(Collectors.joining("; "))); double[] binomialsPerGeneraCountsAsDouble = binomialsPerGeneraCounts.stream() .mapToDouble(Integer::doubleValue).toArray(); Median median = new Median(); precalc.put(ds, "median_binomials_per_genera", String.valueOf(median.evaluate(binomialsPerGeneraCountsAsDouble))); if (firstChecklist == null) { // precalc.put(ds, "names_identical_to_first", "NA"); // precalc.put(ds, "names_identical_to_first_pc", "NA"); } else { if (flag_calculateNameSimilarity) { precalc.put(ds, "names_identical_to_first", String.valueOf(getBinomialNamesIntersection(project, ds, firstChecklist).size())); precalc.put(ds, "names_identical_to_first_pc_this", new BigDecimal((double) getBinomialNamesIntersection(project, ds, firstChecklist).size() / recognizedBinomials.size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN) .toPlainString()); precalc.put(ds, "names_identical_to_first_pc_union", new BigDecimal((double) getBinomialNamesIntersection(project, ds, firstChecklist).size() / getBinomialNamesUnion(project, ds, firstChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "names_identical_to_first_pc_first", new BigDecimal((double) getBinomialNamesIntersection(project, ds, firstChecklist).size() / getBinomialNamesUnion(project, firstChecklist, firstChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateClusterSimilarity) { int clustersForDataset = project.getNameClusterManager().getClusters(recognizedBinomials) .size(); if (clustersForDataset != recognizedBinomials.size()) { throw new RuntimeException( "We have " + clustersForDataset + " clusters for this dataset, but " + recognizedBinomials.size() + " recognized binomials. What?"); } precalc.put(ds, "clusters_identical_to_first", String.valueOf(getBinomialClustersIntersection(project, ds, firstChecklist).size())); precalc.put(ds, "clusters_identical_to_first_pc_this", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, firstChecklist).size() / getBinomialClustersUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_first_pc_union", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, firstChecklist).size() / getBinomialClustersUnion(project, ds, firstChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_first_pc_first", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, firstChecklist).size() / getBinomialClustersUnion(project, firstChecklist, firstChecklist).size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateCircumscriptionSimilarity) { precalc.put(ds, "circumscriptions_identical_to_first", String .valueOf(getBinomialTaxonConceptsIntersection(project, ds, firstChecklist).size())); precalc.put(ds, "circumscriptions_identical_to_first_pc_this", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, firstChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_first_pc_union", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, firstChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, firstChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_first_pc_first", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, firstChecklist).size() / getBinomialTaxonConceptsUnion(project, firstChecklist, firstChecklist).size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } } if (lastChecklist == null) { // precalc.put(ds, "names_identical_to_first", "NA"); // precalc.put(ds, "names_identical_to_first_pc", "NA"); } else { if (flag_calculateNameSimilarity) { precalc.put(ds, "names_identical_to_last", String.valueOf(getBinomialNamesIntersection(project, ds, lastChecklist).size())); precalc.put(ds, "names_identical_to_last_pc_this", new BigDecimal((double) getBinomialNamesIntersection(project, ds, lastChecklist).size() / recognizedBinomials.size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN) .toPlainString()); precalc.put(ds, "names_identical_to_last_pc_union", new BigDecimal((double) getBinomialNamesIntersection(project, ds, lastChecklist).size() / getBinomialNamesUnion(project, ds, lastChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "names_identical_to_last_pc_last", new BigDecimal((double) getBinomialNamesIntersection(project, ds, lastChecklist).size() / getBinomialNamesUnion(project, lastChecklist, lastChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateClusterSimilarity) { int clustersForDataset = project.getNameClusterManager().getClusters(recognizedBinomials) .size(); if (clustersForDataset != recognizedBinomials.size()) { throw new RuntimeException( "We have " + clustersForDataset + " clusters for this dataset, but " + recognizedBinomials.size() + " recognized binomials. What?"); } precalc.put(ds, "clusters_identical_to_last", String.valueOf(getBinomialClustersIntersection(project, ds, lastChecklist).size())); precalc.put(ds, "clusters_identical_to_last_pc_this", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, lastChecklist).size() / getBinomialClustersUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_last_pc_union", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, lastChecklist).size() / getBinomialClustersUnion(project, ds, lastChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_last_pc_last", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, lastChecklist).size() / getBinomialClustersUnion(project, lastChecklist, lastChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateCircumscriptionSimilarity) { precalc.put(ds, "circumscriptions_identical_to_last", String .valueOf(getBinomialTaxonConceptsIntersection(project, ds, lastChecklist).size())); precalc.put(ds, "circumscriptions_identical_to_last_pc_this", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, lastChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_last_pc_union", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, lastChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, lastChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_last_pc_last", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, lastChecklist).size() / getBinomialTaxonConceptsUnion(project, lastChecklist, lastChecklist).size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } } if (prevChecklist == null) { // precalc.put(ds, "names_identical_to_prev", "NA"); // precalc.put(ds, "names_identical_to_prev_pc", "NA"); } else { if (flag_calculateNameSimilarity) { precalc.put(ds, "names_identical_to_prev", String.valueOf(getBinomialNamesIntersection(project, ds, prevChecklist).size())); precalc.put(ds, "names_identical_to_prev_pc_this", new BigDecimal((double) getBinomialNamesIntersection(project, ds, prevChecklist).size() / recognizedBinomials.size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN) .toPlainString()); precalc.put(ds, "names_identical_to_prev_pc_union", new BigDecimal((double) getBinomialNamesIntersection(project, ds, prevChecklist).size() / getBinomialNamesUnion(project, ds, prevChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "names_identical_to_prev_pc_prev", new BigDecimal((double) getBinomialNamesIntersection(project, ds, prevChecklist).size() / getBinomialNamesUnion(project, prevChecklist, prevChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateClusterSimilarity) { int clustersForDataset = project.getNameClusterManager().getClusters(recognizedBinomials) .size(); if (clustersForDataset != recognizedBinomials.size()) { throw new RuntimeException( "We have " + clustersForDataset + " clusters for this dataset, but " + recognizedBinomials.size() + " recognized binomials. What?"); } precalc.put(ds, "clusters_identical_to_prev", String.valueOf(getBinomialClustersIntersection(project, ds, prevChecklist).size())); precalc.put(ds, "clusters_identical_to_prev_pc_this", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, prevChecklist).size() / getBinomialClustersUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_prev_pc_union", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, prevChecklist).size() / getBinomialClustersUnion(project, ds, prevChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_prev_pc_prev", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, prevChecklist).size() / getBinomialClustersUnion(project, prevChecklist, prevChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateCircumscriptionSimilarity) { precalc.put(ds, "circumscriptions_identical_to_prev", String .valueOf(getBinomialTaxonConceptsIntersection(project, ds, prevChecklist).size())); precalc.put(ds, "circumscriptions_identical_to_prev_pc_this", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, prevChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_prev_pc_union", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, prevChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, prevChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_prev_pc_prev", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, prevChecklist).size() / getBinomialTaxonConceptsUnion(project, prevChecklist, prevChecklist).size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } // FYI, getBinomialTaxonConceptsUnion(project, ds, prevChecklist).size() should always be equal to the number of species in the dataset. } if (nextChecklist == null) { // precalc.put(ds, "names_identical_to_prev", "NA"); // precalc.put(ds, "names_identical_to_prev_pc", "NA"); } else { if (flag_calculateNameSimilarity) { precalc.put(ds, "names_identical_to_next", String.valueOf(getBinomialNamesIntersection(project, ds, nextChecklist).size())); precalc.put(ds, "names_identical_to_next_pc_this", new BigDecimal((double) getBinomialNamesIntersection(project, ds, nextChecklist).size() / recognizedBinomials.size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN) .toPlainString()); precalc.put(ds, "names_identical_to_next_pc_union", new BigDecimal((double) getBinomialNamesIntersection(project, ds, nextChecklist).size() / getBinomialNamesUnion(project, ds, nextChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "names_identical_to_next_pc_next", new BigDecimal((double) getBinomialNamesIntersection(project, ds, nextChecklist).size() / getBinomialNamesUnion(project, nextChecklist, nextChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateClusterSimilarity) { int clustersForDataset = project.getNameClusterManager().getClusters(recognizedBinomials) .size(); if (clustersForDataset != recognizedBinomials.size()) { throw new RuntimeException( "We have " + clustersForDataset + " clusters for this dataset, but " + recognizedBinomials.size() + " recognized binomials. What?"); } precalc.put(ds, "clusters_identical_to_next", String.valueOf(getBinomialClustersIntersection(project, ds, nextChecklist).size())); precalc.put(ds, "clusters_identical_to_next_pc_this", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, nextChecklist).size() / getBinomialClustersUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_next_pc_union", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, nextChecklist).size() / getBinomialClustersUnion(project, ds, nextChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "clusters_identical_to_next_pc_next", new BigDecimal( (double) getBinomialClustersIntersection(project, ds, nextChecklist).size() / getBinomialClustersUnion(project, nextChecklist, nextChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } if (flag_calculateCircumscriptionSimilarity) { precalc.put(ds, "circumscriptions_identical_to_next", String .valueOf(getBinomialTaxonConceptsIntersection(project, ds, nextChecklist).size())); precalc.put(ds, "circumscriptions_identical_to_next_pc_this", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, nextChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, ds).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_next_pc_union", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, nextChecklist).size() / getBinomialTaxonConceptsUnion(project, ds, nextChecklist).size() * 100) .setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); precalc.put(ds, "circumscriptions_identical_to_next_pc_next", new BigDecimal( (double) getBinomialTaxonConceptsIntersection(project, ds, nextChecklist).size() / getBinomialTaxonConceptsUnion(project, nextChecklist, nextChecklist).size() * 100).setScale(2, BigDecimal.ROUND_HALF_EVEN).toPlainString()); } } /* // For the visualization thingie. int total = prevChecklists.size(); List<Integer> counts = new LinkedList<>(); for(Name name: recognizedBinomials) { int prevRecognized = 0; if(!datasetsPerName.containsKey(name)) { datasetsPerName.put(name, new HashSet<>()); } else { prevRecognized = datasetsPerName.get(name).size(); } datasetsPerName.get(name).add(ds); counts.add( (int)( ((double)prevRecognized)/total*100 ) ); } Map<Integer, List<Integer>> countsByPercentage = counts.stream().sorted().collect(Collectors.groupingBy(n -> (int)(n/10)*10)); for(int percentage: countsByPercentage.keySet()) { precalc.put(ds, "previously_recognized_" + percentage + "pc", String.valueOf(countsByPercentage.get(percentage).size())); recognitionColumns.add("previously_recognized_" + percentage + "pc"); } prevChecklists.add(ds); */ // Set up the previous checklist for the next loop. prevChecklist = ds; } /* LinkedList<String> recognitionColumnsList = new LinkedList<>(recognitionColumns); recognitionColumnsList.sort(null); for(String colName: recognitionColumnsList) { cols.add(createTableColumnFromPrecalc(precalc, colName)); }*/ }
From source file:org.dataconservancy.packaging.gui.presenter.impl.EditPackageContentsPresenterImplTest.java
@Test public void testUnIgnoreDataFile() { presenter.rebuildTreeView();//from ww w . j a va 2s .co m presenter.toggleItemIgnore(FXCollections.observableArrayList(presenter.findItem(dataFile)), true); //Parent should not be ignored assertFalse(collection.isIgnored()); assertFalse(dataItem.isIgnored()); //Item should be ignored assertTrue(dataFile.isIgnored()); presenter.toggleItemIgnore(FXCollections.observableArrayList(presenter.findItem(dataFile)), false); //Parent should not be ignored assertFalse(collection.isIgnored()); assertFalse(dataItem.isIgnored()); //Item should not be ignored assertFalse(dataFile.isIgnored()); }
From source file:acmi.l2.clientmod.l2smr.Controller.java
private void initializeUnr() { mapsDirProperty().addListener((observable, oldValue, newValue) -> { unrChooser.getSelectionModel().clearSelection(); unrChooser.getItems().clear();/* ww w . j a v a 2s.c o m*/ unrChooser.setDisable(true); if (newValue == null) return; unrChooser.getItems().addAll(Arrays.stream(newValue.listFiles(MAP_FILE_FILTER)).map(File::getName) .collect(Collectors.toList())); unrChooser.setDisable(false); AutoCompleteComboBox.autoCompleteComboBox(unrChooser, AutoCompleteComboBox.AutoCompleteMode.CONTAINING); }); this.unrChooser.getSelectionModel().selectedItemProperty().addListener((observable, oldValue, newValue) -> { table.getSelectionModel().clearSelection(); filterPane.setDisable(true); actors.set(null); actorStaticMeshChooser.getItems().clear(); System.gc(); if (newValue == null) return; try (UnrealPackage up = new UnrealPackage(new File(getMapsDir(), newValue), true)) { longTask(progress -> { List<UnrealPackage.ImportEntry> staticMeshes = up.getImportTable().parallelStream() .filter(ie -> ie.getFullClassName().equalsIgnoreCase("Engine.StaticMesh")) .sorted((ie1, ie2) -> String.CASE_INSENSITIVE_ORDER .compare(ie1.getObjectInnerFullName(), ie2.getObjectInnerFullName())) .collect(Collectors.toList()); Platform.runLater(() -> { actorStaticMeshChooser.getItems().setAll(staticMeshes); AutoCompleteComboBox.autoCompleteComboBox(actorStaticMeshChooser, AutoCompleteComboBox.AutoCompleteMode.CONTAINING); }); List<Actor> actors = up .getExportTable().parallelStream().filter(e -> UnrealPackage.ObjectFlag .getFlags(e.getObjectFlags()).contains(UnrealPackage.ObjectFlag.HasStack)) .map(entry -> { try { return new Actor(entry.getIndex(), entry.getObjectInnerFullName(), entry.getObjectRawDataExternally(), up); } catch (Throwable e) { return null; } }).filter(Objects::nonNull) .filter(actor -> actor.getStaticMeshRef() != 0 && actor.getOffsets().location != 0) .collect(Collectors.toList()); Platform.runLater(() -> Controller.this.actors.set(FXCollections.observableArrayList(actors))); }, e -> onException("Import failed", e)); } catch (Exception e) { onException("Read failed", e); } resetFilter(); filterPane.setDisable(false); }); this.actorColumn.setCellValueFactory(actorStringCellDataFeatures -> new SimpleStringProperty( actorStringCellDataFeatures.getValue().getActorName())); this.staticMeshColumn.setCellValueFactory(actorStringCellDataFeatures -> new SimpleStringProperty( actorStringCellDataFeatures.getValue().getStaticMesh())); this.table.getSelectionModel().setSelectionMode(SelectionMode.MULTIPLE); this.table.getSelectionModel().selectedItemProperty().addListener((observable) -> updateSMAPane()); this.table.setOnMouseClicked(event -> { if (event.getButton() == MouseButton.PRIMARY && event.getClickCount() == 2) { String obj = table.getSelectionModel().getSelectedItem().getStaticMesh(); String file = obj.substring(0, obj.indexOf('.')) + ".usx"; showUmodel(obj, file); } }); }
From source file:com.danilafe.sbaccountmanager.StarboundServerAccountManager.java
private void createBannedUser(ListView<String> to_update) { Stage createBannedUser = new Stage(); createBannedUser.setTitle("Add Banned User"); createBannedUser.initModality(Modality.APPLICATION_MODAL); GridPane gp = new GridPane(); gp.setPadding(new Insets(25, 25, 25, 25)); gp.setAlignment(Pos.CENTER);// www . j ava 2 s . co m gp.setVgap(10); gp.setHgap(10); Text title = new Text("Add Banned Username"); title.setFont(Font.font("Century Gothic", FontWeight.NORMAL, 20)); gp.add(title, 0, 0, 2, 1); Label newusername = new Label("Ban Username"); TextField username = new TextField(); gp.add(newusername, 0, 1); gp.add(username, 1, 1); Button finish = new Button("Finish"); HBox finish_box = new HBox(10); finish_box.setAlignment(Pos.CENTER); finish_box.getChildren().add(finish); finish.setOnAction(new EventHandler<ActionEvent>() { @Override public void handle(ActionEvent event) { banned_usernames.remove(username.getText()); banned_usernames.add(username.getText()); to_update.setItems(FXCollections.observableArrayList(banned_usernames)); createBannedUser.close(); } }); gp.add(finish_box, 0, 2, 2, 1); Scene sc = new Scene(gp, 300, 175); createBannedUser.setScene(sc); createBannedUser.show(); }
From source file:org.dataconservancy.packaging.gui.presenter.impl.EditPackageContentsPresenterImplTest.java
@Test public void testUnIgnoreDataFileUnIgnoresParents() { presenter.rebuildTreeView();// w w w . j a va2 s .c o m presenter.toggleItemIgnore(FXCollections.observableArrayList(presenter.findItem(collection)), true); assertTrue(collection.isIgnored()); assertTrue(dataItem.isIgnored()); assertTrue(dataFile.isIgnored()); presenter.toggleItemIgnore(FXCollections.observableArrayList(presenter.findItem(dataFile)), false); //Parents should not be ignored assertFalse(collection.isIgnored()); assertFalse(dataItem.isIgnored()); //Item should not be ignored assertFalse(dataFile.isIgnored()); }