def saveToFile(self, event): fileChooser = JFileChooser() if not (self.targetURL is None): fileChooser.setSelectedFile(File("Burp_SSL_Scanner_Result_%s.html" \ % (self.targetURL.getHost()))) else: fileChooser.setSelectedFile(File("Burp_SSL_Scanner_Result.html")) if (fileChooser.showSaveDialog(self.getUiComponent()) == JFileChooser.APPROVE_OPTION): fw = FileWriter(fileChooser.getSelectedFile()) fw.write(self.textPane.getText()) fw.flush() fw.close() print "Saved results to disk"
def writeLocations(self): f = Files.createExternalFile(Environment.DIRECTORY_DOWNLOADS, "WeatherForecast", "locations.txt", None, None) try: stream = FileWriter(f) for key in self.order: stream.write(self.locations[key] + "\n") stream.flush() stream.close() except FileNotFoundException: pass
if i.strip() != '' ] de_vocab = [ i.strip() for i in codecs.open(options.de_vocab, 'r', 'utf8').readlines() if i.strip() != '' ] for env in en_vocab: add_to_tags(env) uc_training = UnCachedFgList(training_instanes=training_ti, en_vocab=en_vocab) for idx, ti in enumerate(training_ti): print idx, uc_training.get(idx) trainer = CrfTrainer(get_trainer_prm()) exit(1) feature_ids, feature_labels = zip( *sorted([(v, k) for k, v in feature_label2id.iteritems()])) # initialize weight for each feature factor_graph_model = FgModel(len(feature_label2id), list(feature_labels)) for fid in list(feature_ids): factor_graph_model.add(fid, 0.0) trainer.train(factor_graph_model, uc_training) sw = FileWriter('feature.weights') factor_graph_model.printModel(sw) sw = codecs.open('feature.names', 'w', 'utf8') for k, i in feature_label2id.iteritems(): sw.write(str(i) + '\t' + str(k) + '\n') sw.flush() sw.close()
import de.embl.cba.metadata.MetaData as MetaData import de.embl.cba.metadata.MetadataCreator as MetadataCreator import ij.IJ as IJ import org.yaml.snakeyaml.DumperOptions as DumperOptions import org.yaml.snakeyaml.Yaml as Yaml import java.io.FileWriter as FileWriter file = "/Volumes/cba/exchange/OeyvindOedegaard/yaml_project/01_TestFiles/20180627_LSM780M2_208_ibidi1_fcs_B_Posx96.lsm" metadataCreator = MetadataCreator(file) metadata = metadataCreator.getMetadata() dumperOptions = DumperOptions() dumperOptions.setDefaultFlowStyle(DumperOptions.FlowStyle.BLOCK) outputPath = "/Volumes/cba/exchange/OeyvindOedegaard/yaml_project/test.yaml" yaml = Yaml(dumperOptions) writer = FileWriter(outputPath) yaml.dump(metadata, writer) writer.flush() writer.close() IJ.open(outputPath)
ti, obs, guess = get_instance(line) training_ti.append(ti) for line in open(options.test_file).readlines(): ti, obs, guess = get_instance(line) testing_ti.append(ti) en_vocab = [i.strip() for i in codecs.open(options.en_vocab, 'r', 'utf8').readlines() if i.strip() != ''] de_vocab = [i.strip() for i in codecs.open(options.de_vocab, 'r', 'utf8').readlines() if i.strip() != ''] for env in en_vocab: add_to_tags(env) uc_training = UnCachedFgList(training_instanes=training_ti, en_vocab=en_vocab) for idx, ti in enumerate(training_ti): print idx, uc_training.get(idx) trainer = CrfTrainer(get_trainer_prm()) exit(1) feature_ids, feature_labels = zip(*sorted([(v, k) for k, v in feature_label2id.iteritems()])) # initialize weight for each feature factor_graph_model = FgModel(len(feature_label2id), list(feature_labels)) for fid in list(feature_ids): factor_graph_model.add(fid, 0.0) trainer.train(factor_graph_model, uc_training) sw = FileWriter('feature.weights') factor_graph_model.printModel(sw) sw = codecs.open('feature.names', 'w', 'utf8') for k, i in feature_label2id.iteritems(): sw.write(str(i) + '\t' + str(k) + '\n') sw.flush() sw.close()