def main(argv=sys.argv): from AnyQt.QtWidgets import QApplication import Orange app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = 'iris' ow = OWPythagorasTree() data = Orange.data.Table(filename) if data.domain.has_discrete_class: from Orange.classification.tree import TreeLearner else: from Orange.regression.tree import TreeLearner model = TreeLearner(max_depth=1000)(data) model.instances = data ow.set_tree(model) ow.show() ow.raise_() ow.handleNewSignals() app.exec_() sys.exit(0)
def test(argv=sys.argv): app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" import sip import Orange.distance w = OWDistanceMap() w.show() w.raise_() data = Orange.data.Table(filename) dist = Orange.distance.Euclidean(data) w.set_distances(dist) w.handleNewSignals() rval = app.exec_() w.set_distances(None) w.saveSettings() w.onDeleteWidget() sip.delete(w) del w return rval
def main(argv=None): """ Test as separate Qt application :param argv: arguments from console - DataSet name if provided :return: None """ from AnyQt.QtWidgets import QApplication # PyQt changes argv list in-place app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "housing" ow = OWSplitInstancesWidget() ow.show() ow.raise_() dataset = Table(filename) ow.set_data(dataset) ow.handleNewSignals() app.exec_() ow.set_data(None) ow.handleNewSignals() ow.onDeleteWidget() return 0
def main(argv=None): if argv is None: argv = sys.argv import gc app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" data = Orange.data.Table(filename) w = OWMDS() w.set_data(data) w.set_subset_data(data[np.random.choice(len(data), 10)]) w.handleNewSignals() w.show() w.raise_() rval = app.exec_() w.set_subset_data(None) w.set_data(None) w.handleNewSignals() w.saveSettings() w.onDeleteWidget() w.deleteLater() del w gc.collect() app.processEvents() return rval
def main(argv=None): # pragma: no cover from AnyQt.QtWidgets import QApplication app = QApplication(argv or []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "zoo" w = OWSelectRows() w.set_data(Table(filename)) w.show() app.exec_()
def main(argv=None): # pragma: no cover app = QApplication(list(argv or sys.argv)) argv = app.arguments() w = OWFilter() if len(argv) > 1: filename = argv[1] else: filename = "brown-selected" # bad example data = Orange.data.Table(filename) w.set_data(data) w.show() w.raise_() app.exec() w.saveSettings() w.onDeleteWidget()
def main(argv=None): from AnyQt.QtWidgets import QApplication logging.basicConfig() app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" data = Orange.data.Table(filename) indices = numpy.random.permutation(len(data)) traindata = data[indices[:-20]] testdata = data[indices[-20:]] ow = OWLearningCurveC() ow.show() ow.raise_() ow.set_dataset(traindata) ow.set_testdataset(testdata) l1 = Orange.classification.NaiveBayesLearner() l1.name = "Naive Bayes" ow.set_learner(l1, 1) l2 = Orange.classification.LogisticRegressionLearner() l2.name = "Logistic Regression" ow.set_learner(l2, 2) l4 = Orange.classification.SklTreeLearner() l4.name = "Decision Tree" ow.set_learner(l4, 3) ow.handleNewSignals() app.exec_() ow.set_dataset(None) ow.set_testdataset(None) ow.set_learner(None, 1) ow.set_learner(None, 2) ow.set_learner(None, 3) ow.handleNewSignals() ow.onDeleteWidget() return 0
def main(argv=None): app = QApplication(argv or []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "brown-selected" w = OWSetEnrichment() data = Orange.data.Table(filename) w.setData(data) w.handleNewSignals() w.show() app.exec_() w.saveSettings() w.onDeleteWidget()
def main(argv=None): app = QApplication(list(argv if argv is not None else sys.argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris.tab" data = Orange.data.Table(filename) def pred_error(data, *args, **kwargs): raise ValueError pred_error.domain = data.domain pred_error.name = "To err is human" if data.domain.has_discrete_class: predictors = [ Orange.classification.SVMLearner(probability=True)(data), Orange.classification.LogisticRegressionLearner()(data), pred_error, ] elif data.domain.has_continuous_class: predictors = [ Orange.regression.RidgeRegressionLearner(alpha=1.0)(data), Orange.regression.LinearRegressionLearner()(data), pred_error, ] else: predictors = [pred_error] w = OWPredictions() w.show() w.raise_() w.set_data(data) for i, pred in enumerate(predictors): w.set_predictor(pred, i) w.handleNewSignals() app.exec() w.set_data(None) w.handleNewSignals() for i in range(len(predictors)): w.set_predictor(None, i) w.handleNewSignals() w.saveSettings() return 0
def main(argv=None): from AnyQt.QtWidgets import QApplication logging.basicConfig() app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" data = Orange.data.Table(filename) indices = numpy.random.permutation(len(data)) traindata = data[indices[:-20]] testdata = data[indices[-20:]] ow = OWLearningCurveC() ow.show() ow.raise_() ow.set_dataset(traindata) ow.set_testdataset(testdata) l1 = Orange.classification.NaiveBayesLearner() l1.name = 'Naive Bayes' ow.set_learner(l1, 1) l2 = Orange.classification.LogisticRegressionLearner() l2.name = 'Logistic Regression' ow.set_learner(l2, 2) l4 = Orange.classification.SklTreeLearner() l4.name = "Decision Tree" ow.set_learner(l4, 3) ow.handleNewSignals() app.exec_() ow.set_dataset(None) ow.set_testdataset(None) ow.set_learner(None, 1) ow.set_learner(None, 2) ow.set_learner(None, 3) ow.handleNewSignals() ow.onDeleteWidget() return 0
def main(argv=None): # pragma: no cover app = QApplication(list(argv or sys.argv)) argv = app.arguments() w = OWFilter() if len(argv) > 1: filename = argv[1] data = Orange.data.Table(filename) else: X = np.random.exponential(size=(1000, 1050)) - 1 X[X < 0] = 0 data = Orange.data.Table.from_numpy(None, X) w.set_data(data) w.show() w.raise_() app.exec() w.saveSettings() w.onDeleteWidget()
def main(argv=sys.argv): app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris.tab" data = Orange.data.Table(filename) def pred_error(data, *args, **kwargs): raise ValueError pred_error.domain = data.domain pred_error.name = "To err is human" if data.domain.has_discrete_class: predictors = [ Orange.classification.SVMLearner(probability=True)(data), Orange.classification.LogisticRegressionLearner()(data), pred_error ] elif data.domain.has_continuous_class: predictors = [ Orange.regression.RidgeRegressionLearner(alpha=1.0)(data), Orange.regression.LinearRegressionLearner()(data), pred_error ] else: predictors = [pred_error] w = OWPredictions() w.show() w.raise_() w.set_data(data) for i, pred in enumerate(predictors): w.set_predictor(pred, i) w.handleNewSignals() app.exec() w.set_data(None) w.handleNewSignals() for i in range(len(predictors)): w.set_predictor(None, i) w.handleNewSignals() w.saveSettings() return 0
def main(argv=sys.argv): app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: path = argv[1] else: path = None w = OWImportImages() w.show() w.raise_() if path is not None: w.setCurrentPath(path) app.exec_() w.saveSettings() w.onDeleteWidget() return 0
def main(argv=sys.argv): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" w = OWSilhouettePlot() w.show() w.raise_() w.set_data(Orange.data.Table(filename)) w.handleNewSignals() app.exec_() w.set_data(None) w.handleNewSignals() w.onDeleteWidget() return 0
def main(argv=None): # pragma: no cover from AnyQt.QtWidgets import QApplication app = QApplication(argv or []) argv = app.arguments() w = OWEditDomain() if len(argv) > 1: filename = argv[1] else: filename = "iris" data = Orange.data.Table(filename) w.set_data(data) w.show() w.raise_() rval = app.exec() w.set_data(None) w.saveSettings() w.onDeleteWidget() return rval
def main(argv=None): app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "brown-selected" data = Orange.data.Table(filename) w = OWReshape() w.show() w.raise_() w.set_data(data) w.handleNewSignals() app.exec() w.set_data(None) w.handleNewSignals() w.onDeleteWidget() return 0
def main(argv=None): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "geo-gds360" data = Orange.data.Table(filename) w = OWFeatureSelection() w.show() w.raise_() w.set_data(data) rval = app.exec_() w.set_data(None) w.saveSettings() w.onDeleteWidget() return rval
def main(argv=sys.argv): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" w = OWFeatureConstructor() w.show() w.raise_() data = Orange.data.Table(filename) w.setData(data) w.handleNewSignals() app.exec_() w.setData(None) w.handleNewSignals() w.saveSettings() return 0
def test_main(argv=sys.argv): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: data = Orange.data.Table(argv[1]) else: data = None w = OWGOEnrichmentAnalysis() w.show() w.raise_() w.setDataset(data) w.handleNewSignals() rval = app.exec_() w.setDataset(None) w.handleNewSignals() w.saveSettings() w.onDeleteWidget() return rval
def main(argv=sys.argv): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv)) args = app.arguments() if len(args) > 1: filename = args[1] else: filename = "iris" ow = OWDataSamplerA() ow.show() ow.raise_() dataset = Orange.data.Table(filename) ow.set_data(dataset) ow.handleNewSignals() app.exec_() ow.set_data(None) ow.handleNewSignals() return 0
def main(argv=None): # noqa import argparse from AnyQt.QtWidgets import QApplication app = QApplication(argv if argv is not None else []) argv = app.arguments() parser = argparse.ArgumentParser() parser.add_argument("--config", metavar="CLASSNAME", default="orangecanvas.config.default", help="The configuration namespace to use") args = parser.parse_args(argv[1:]) config_ = name_lookup(args.config) config_ = config_() config_.init() config.set_default(config_) dlg = AddonManagerDialog() dlg.start(config_) dlg.show() dlg.raise_() return app.exec()
def main(argv=None): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" data = Orange.data.Table(filename) ow = OWLearningCurveA() ow.show() ow.raise_() l1 = Orange.classification.NaiveBayesLearner() l1.name = "Naive Bayes" ow.set_learner(l1, 1) ow.set_dataset(data) l2 = Orange.classification.LogisticRegressionLearner() l2.name = "Logistic Regression" ow.set_learner(l2, 2) l4 = Orange.classification.SklTreeLearner() l4.name = "Decision Tree" ow.set_learner(l4, 3) ow.handleNewSignals() app.exec_() ow.set_dataset(None) ow.set_learner(None, 1) ow.set_learner(None, 2) ow.set_learner(None, 3) ow.handleNewSignals() ow.onDeleteWidget() return 0
def main(argv=None): from AnyQt.QtWidgets import QApplication logging.basicConfig(level=logging.DEBUG) app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "zoo-with-images" data = Table(filename) widget = OWImageEmbedding() widget.show() assert QSignalSpy(widget.blockingStateChanged).wait() widget.set_data(data) widget.handleNewSignals() app.exec() widget.set_data(None) widget.handleNewSignals() widget.saveSettings() widget.onDeleteWidget() return 0
def main(argv=sys.argv): import sip app = QApplication(argv) argv = app.arguments() w = OWImageViewer() w.show() w.raise_() if len(argv) > 1: data = Orange.data.Table(argv[1]) else: data = Orange.data.Table('zoo-with-images') w.setData(data) rval = app.exec_() w.saveSettings() w.onDeleteWidget() sip.delete(w) app.processEvents() return rval
def main(argv=None): # noqa import argparse from AnyQt.QtWidgets import QApplication app = QApplication(argv if argv is not None else []) argv = app.arguments() parser = argparse.ArgumentParser() parser.add_argument( "--config", metavar="CLASSNAME", default="orangecanvas.config.default", help="The configuration namespace to use" ) args = parser.parse_args(argv[1:]) config_ = name_lookup(args.config) config_ = config_() config_.init() config.set_default(config_) dlg = AddonManagerDialog() dlg.start(config_) dlg.show() dlg.raise_() return app.exec()
def main(argv=None): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" data = Orange.data.Table(filename) ow = OWLearningCurveA() ow.show() ow.raise_() l1 = Orange.classification.NaiveBayesLearner() l1.name = 'Naive Bayes' ow.set_learner(l1, 1) ow.set_dataset(data) l2 = Orange.classification.LogisticRegressionLearner() l2.name = 'Logistic Regression' ow.set_learner(l2, 2) l4 = Orange.classification.SklTreeLearner() l4.name = "Decision Tree" ow.set_learner(l4, 3) ow.handleNewSignals() app.exec_() ow.set_dataset(None) ow.set_learner(None, 1) ow.set_learner(None, 2) ow.set_learner(None, 3) ow.handleNewSignals() ow.onDeleteWidget() return 0
def main(argv=None): from AnyQt.QtWidgets import QApplication # PyQt changes argv list in-place app = QApplication(list(argv) if argv else []) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "iris" ow = OWDataSamplerC() ow.show() ow.raise_() dataset = Orange.data.Table(filename) ow.set_data(dataset) ow.handleNewSignals() app.exec_() ow.set_data(None) ow.handleNewSignals() ow.onDeleteWidget() return 0
def main(argv=sys.argv): from AnyQt.QtWidgets import QApplication app = QApplication(list(argv)) argv = app.arguments() if len(argv) > 1: filename = argv[1] else: filename = "brown-selected" w = OWImpute() w.show() w.raise_() data = Orange.data.Table(filename) w.set_data(data) w.handleNewSignals() app.exec_() w.set_data(None) w.set_learner(None) w.handleNewSignals() w.onDeleteWidget() return 0
def main(argv=None): app = QApplication(list(argv) if argv else []) argv = app.arguments() parser = argparse.ArgumentParser(description=( "Run an orange workflow without showing a GUI and exit " "when it completes.\n\n" "WARNING: This is experimental as Orange is not designed to run " "non-interactive.")) parser.add_argument("--log-level", "-l", metavar="LEVEL", type=int, default=logging.CRITICAL, dest="log_level") parser.add_argument("--config", default="Orange.canvas.config.Config", type=str) parser.add_argument("file") args = parser.parse_args(argv[1:]) log_level = args.log_level filename = args.file logging.basicConfig(level=log_level) cfg_class = utils.name_lookup(args.config) cfg: config.Config = cfg_class() config.set_default(cfg) config.init() reg = WidgetRegistry() widget_discovery = cfg.widget_discovery( reg, cached_descriptions=cache.registry_cache()) widget_discovery.run(cfg.widgets_entry_points()) model = cfg.workflow_constructor() model.set_runtime_env("basedir", os.path.abspath(os.path.dirname(filename))) sigprop = model.findChild(signalmanager.SignalManager) sigprop.pause() # Pause signal propagation during load with open(filename, "rb") as f: model.load_from(f, registry=reg) # Ensure all widgets are created (this is required for the workflow # to even start - relies to much on OWWidget behaviour). for _ in map(model.widget_for_node, model.nodes): pass sigprop.resume() # Resume inter-widget signal propagation def on_finished(): severity = 0 for node in model.nodes: for msg in node.state_messages(): if msg.contents and msg.severity == msg.Error: print(msg.contents, msg.message_id, file=sys.stderr) severity = msg.Error if severity == UserMessage.Error: app.exit(1) else: app.exit() sigprop.finished.connect(on_finished) rval = app.exec_() model.clear() # Notify the workflow model to 'close'. QApplication.sendEvent(model, QEvent(QEvent.Close)) app.processEvents() return rval