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()