def main(): # pragma: no cover
from AnyQt.QtWidgets import QApplication
a = QApplication([])
ow = OWGrep()
ow.show()
a.exec_()
ow.saveSettings()
def test():
import sklearn.cross_validation as skl_cross_validation
app = QApplication([])
w = OWVennDiagram()
data = Orange.data.Table("brown-selected")
data = append_column(data, "M", Orange.data.StringVariable("Test"), numpy.arange(len(data)).reshape(-1, 1) % 30)
indices = skl_cross_validation.ShuffleSplit(len(data), n_iter=5, test_size=0.7)
indices = iter(indices)
def select(data):
sample, _ = next(indices)
return data[sample]
d1 = select(data)
d2 = select(data)
d3 = select(data)
d4 = select(data)
d5 = select(data)
for i, data in enumerate([d1, d2, d3, d4, d5]):
data.name = chr(ord("A") + i)
w.setData(data, key=i)
w.handleNewSignals()
w.show()
app.exec_()
del w
app.processEvents()
return app
def test():
from AnyQt.QtWidgets import QApplication
app = QApplication([])
w = OWSelectRows()
w.set_data(Table("zoo"))
w.show()
app.exec_()
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 main():
from AnyQt.QtWidgets import QApplication
app = QApplication(sys.argv)
ow = TestConnected()
ow.show()
ow.raise_()
app.exec_()
return 0
def main():
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWFile()
ow.show()
a.exec_()
ow.saveSettings()
def main():
app = QApplication([])
w = OWExplainPredictions()
data = Orange.data.Table("iris.tab")
data_subset = data[:20]
w.set_data(data_subset)
w.set_data(None)
w.show()
app.exec_()
def main():
app = QApplication([])
w = OWMergeData()
data = Orange.data.Table("tests/data-gender-region")
extra_data = Orange.data.Table("tests/data-regions")
w.setData(data)
w.setExtraData(extra_data)
w.handleNewSignals()
w.show()
app.exec_()
def main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
app = QApplication(list(argv))
ow = OWAverage()
ow.show()
ow.raise_()
dataset = Orange.data.Table("collagen.csv")
ow.set_data(dataset)
app.exec_()
return 0
def main():
from AnyQt.QtWidgets import QApplication
app = QApplication([])
w = OWContingencyTable()
data = Table("titanic")
w.set_data(data)
w.handleNewSignals()
w.show()
app.exec_()
def main():
app = QApplication([])
w = OWSelectByDataIndex()
data = Table("iris.tab")
data_subset = data[:20]
w.set_data(data)
w.set_data_subset(data_subset)
w.handleNewSignals()
w.show()
app.exec_()
def main():
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWMosaicDisplay()
ow.show()
data = Table("zoo.tab")
ow.set_data(data)
ow.handleNewSignals()
a.exec_()
def _test():
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWClassificationTree()
d = Table('iris')
ow.set_data(d)
ow.show()
a.exec_()
ow.saveSettings()
def main():
# pylint: disable=missing-docstring
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWSieveDiagram()
ow.show()
data = Table(r"zoo.tab")
ow.set_data(data)
a.exec_()
ow.saveSettings()
def main():
app = QApplication([])
w = OWConcatenate()
data_a = Orange.data.Table("iris")
data_b = Orange.data.Table("zoo")
w.set_more_data(data_a, 0)
w.set_more_data(data_b, 1)
w.handleNewSignals()
w.show()
app.exec_()
def main():
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWTreeLearner()
d = Table(sys.argv[1] if len(sys.argv) > 1 else 'iris')
ow.set_data(d)
ow.show()
a.exec_()
ow.saveSettings()
def main():
if getattr(sys, 'frozen', False):
import httplib2
httplib2.CA_CERTS = os.environ['REQUESTS_CA_BUNDLE'] = \
os.path.join(os.path.dirname(sys.executable), 'cacert.pem')
QApplication.setAttribute(Qt.AA_X11InitThreads)
app = QApplication(sys.argv)
app.setQuitOnLastWindowClosed(False)
app.clipboard().mimeData() # Sometimes first try to acquire clipboard fails. So be it.
wnd = MainWindow()
wnd.hide()
app.exec_()
def _test():
import sys
from AnyQt.QtWidgets import QApplication
from Orange.data import Table
a = QApplication(sys.argv)
ow = OWRegressionTree()
d = Table('housing')
ow.set_data(d)
ow.show()
a.exec_()
ow.saveSettings()
def main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
app = QApplication(list(argv))
ow = OWInterpolate()
ow.show()
ow.raise_()
dataset = Orange.data.Table("collagen")
ow.set_data(dataset)
ow.handleNewSignals()
app.exec_()
return 0
def test():
from AnyQt.QtWidgets import QApplication
app = QApplication([])
w = OWMergeData()
zoo = Orange.data.Table("zoo")
A = zoo[:, [0, 1, 2, "type", -1]]
B = zoo[:, [3, 4, 5, "type", -1]]
w.setDataA(A)
w.setDataB(B)
w.handleNewSignals()
w.show()
app.exec_()
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=sys.argv):
app = QApplication(list(argv))
filename = "IFG_single.dpt"
ow = OWFFT()
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 test1():
app = QApplication([])
w = OWVennDiagram()
data1 = Orange.data.Table("brown-selected")
data2 = Orange.data.Table("brown-selected")
w.setData(data1, 1)
w.setData(data2, 2)
w.handleNewSignals()
w.show()
w.raise_()
app.exec_()
del w
return app
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():
"""Standalone test"""
import sys
from AnyQt.QtWidgets import QApplication
from Orange.modelling.tree import TreeLearner
a = QApplication(sys.argv)
ow = OWTreeGraph()
data = Table(sys.argv[1] if len(sys.argv) > 1 else "titanic")
clf = TreeLearner()(data)
clf.instances = data
ow.ctree(clf)
ow.show()
ow.raise_()
a.exec_()
ow.saveSettings()
def main(argv=sys.argv):
if len(argv) == 1:
filename = None
else:
if argv[1] in ["-h", "--help"]:
usage(argv)
return 0
else:
filename = argv[1]
app = QApplication(argv)
app.setAttribute(Qt.AA_EnableHighDpiScaling)
app.setAttribute(Qt.AA_UseHighDpiPixmaps)
if filename is None:
filename, _ = QFileDialog.getOpenFileName(
None, "Image file", os.path.expanduser("~/Documents"),
"Image (*.png)")
if not filename:
return 1
print(filename)
form = MainForm(filename=filename)
rect = QApplication.desktop().availableGeometry()
form.show()
form.raise_()
return app.exec_()
def main():
import gc
import sip
from AnyQt.QtWidgets import QApplication
from Orange.classification import (LogisticRegressionLearner, SVMLearner,
NuSVMLearner)
app = QApplication([])
w = OWROCAnalysis()
w.show()
w.raise_()
# data = Orange.data.Table("iris")
data = Orange.data.Table("ionosphere")
results = Orange.evaluation.CrossValidation(
data,
[LogisticRegressionLearner(),
LogisticRegressionLearner(penalty="l1"),
SVMLearner(probability=True),
NuSVMLearner(probability=True)],
k=5,
store_data=True,
)
results.learner_names = ["Logistic", "Logistic (L1 reg.)", "SVM", "NuSVM"]
w.set_results(results)
rval = app.exec_()
w.deleteLater()
sip.delete(w)
del w
app.processEvents()
sip.delete(app)
del app
gc.collect()
return rval
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 test_main():
app = QApplication([])
data = Table("iris")
w = OWDataSampler()
w.set_data(data)
w.show()
return app.exec_()
def main():
import sip
from AnyQt.QtWidgets import QApplication
from Orange.classification import (LogisticRegressionLearner, SVMLearner,
NuSVMLearner)
app = QApplication([])
w = OWCalibrationPlot()
w.show()
w.raise_()
data = Orange.data.Table("ionosphere")
results = Orange.evaluation.CrossValidation(
data,
[LogisticRegressionLearner(penalty="l2"),
LogisticRegressionLearner(penalty="l1"),
SVMLearner(probability=True),
NuSVMLearner(probability=True)
],
store_data=True
)
results.learner_names = ["LR l2", "LR l1", "SVM", "Nu SVM"]
w.set_results(results)
rval = app.exec_()
sip.delete(w)
del w
app.processEvents()
del app
return rval
def main():
from AnyQt.QtWidgets import QApplication
app = QApplication([])
w = OWEditDomain()
data = Orange.data.Table("iris")
w.set_data(data)
w.show()
w.raise_()
return app.exec_()
def main():
from AnyQt.QtWidgets import QApplication
app = QApplication([])
w = OWGeneNetwork()
brown = Orange.data.Table("brown-selected")
w.set_data(Orange.data.Table(brown[:5]))
w.show()
rval = app.exec_()
w.saveSettings()
return rval
def main_test():
from AnyQt.QtWidgets import QApplication
app = QApplication([])
w = OWGEODatasets()
w.show()
w.raise_()
r = app.exec_()
w.saveSettings()
return r
def start_app(ClassObject, geometry=None, stylesheet=None):
from confapp import conf
app = QApplication(sys.argv)
conf += 'pyforms_gui.settings'
mainwindow = StandAloneContainer(ClassObject)
myapp = mainwindow.centralWidget()
if geometry is not None:
mainwindow.show()
mainwindow.setGeometry(*geometry)
else:
mainwindow.showMaximized()
if conf.PYFORMS_QUALITY_TESTS_PATH is not None:
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test", help="File with the tests script")
args = parser.parse_args()
if args.test:
TEST_PATH = os.path.join(conf.PYFORMS_QUALITY_TESTS_PATH,
args.test)
TEST_FILE_PATH = os.path.join(TEST_PATH, args.test + '.py')
DATA_PATH = os.path.join(TEST_PATH, 'data', sys.platform)
INPUT_DATA_PATH = os.path.join(DATA_PATH, 'input-data')
OUTPUT_DATA_PATH = os.path.join(DATA_PATH, 'output-data')
EXPECTED_DATA_PATH = os.path.join(DATA_PATH, 'expected-data')
with open(TEST_FILE_PATH) as f:
global_vars = {} # globals()
local_vars = locals()
code = compile(f.read(), TEST_FILE_PATH, 'exec')
exec(code, global_vars, local_vars)
if stylesheet: app.setStyleSheet(stylesheet)
app.exec_()
return myapp
def start(parent_win=None):
try:
res = pyforms.start_app(VideoAnnotator,
geometry=conf.MAIN_WINDOW_GEOMETRY,
parent_win=parent_win)
return res
except Exception as e:
report = traceback.format_exc()
app = QApplication(sys.argv)
m = QMessageBox(
QMessageBox.Question, "Send report",
"<h2>Would you like to send us a report of the bug?</h2>"
"This will help us improving the software."
"<p>{bug}</p>".format(bug=str(e)),
QMessageBox.Yes | QMessageBox.No)
reply = m.exec_()
if reply == QMessageBox.Yes:
try:
app_id = conf.USERSTATS_APP_ID
reg_id = get_mac()
os_name = platform.platform()
version = pythonvideoannotator.__version__
data = {
'app-id': app_id,
'reg-id': reg_id,
'os-name': os_name,
'version': version,
'report': report
}
url = "{}/register-bug".format(conf.USERSTATS_URL)
request = Request(url, urlencode(data).encode())
urlopen(request).read().decode()
except Exception as ex:
print("Could not register new access", ex)
exit()
app.exec_()
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 test_main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
app = QApplication(list(argv))
ow = OWBioMart()
ow.show()
ow.raise_()
r = app.exec_()
ow.saveSettings()
return r
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 main(argv=None):
from AnyQt.QtWidgets import QApplication
app = QApplication(argv or sys.argv)
w = OWMarkerGenes()
w.show()
w.activateWindow()
rv = app.exec_()
w.saveSettings()
w.onDeleteWidget()
return rv
def main(argv=None):
from AnyQt.QtWidgets import QApplication
if argv is None:
argv = sys.argv
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):
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():
import OWNxFile
from AnyQt.QtWidgets import QApplication
a = QApplication([])
ow = OWNxExplorer()
ow.show()
def set_network(data, id=None):
ow.set_graph(data)
from os.path import join, dirname
owFile = OWNxFile.OWNxFile()
owFile.Outputs.network.send = set_network
owFile.openNetFile(
join(dirname(dirname(__file__)), 'networks', 'leu_by_genesets.net'))
#owFile.openNetFile(join(dirname(dirname(__file__)), 'networks', 'leu_by_pmid.net'))
#owFile.openNetFile(join(dirname(dirname(__file__)), 'networks', 'lastfm.net'))
ow.handleNewSignals()
a.exec_()
ow.saveSettings()
owFile.saveSettings()
def main(args=None):
if args is None:
args = sys.argv
app = QApplication(list(args))
w = OWDataSets()
w.show()
w.raise_()
rv = app.exec_()
w.saveSettings()
w.onDeleteWidget()
return rv
def main(argv=None):
from AnyQt.QtWidgets import QApplication
app = QApplication(list(argv) if argv else [])
w = OWClusterAnalysis()
data = Table("https://datasets.orange.biolab.si/sc/aml-1k.pickle")
w.show()
w.handle_input(data)
# w.cluster_indicator.initialize(data)
rval = app.exec_()
return rval
def main():
from AnyQt.QtWidgets import QApplication
import sys
app = QApplication(sys.argv)
ow = OWPythagorasTree()
data = Table(sys.argv[1] if len(sys.argv) > 1 else 'iris')
if data.domain.has_discrete_class:
from Orange.classification.tree import SklTreeLearner as TreeLearner
else:
from Orange.regression.tree import SklTreeRegressionLearner as TreeLearner
model = TreeLearner(max_depth=1000)(data)
model.instances = data
ow.set_tree(model)
ow.show()
ow.raise_()
ow.handleNewSignals()
app.exec_()
def test_main(argv=sys.argv):
argv = list(argv)
app = QApplication(argv)
w = OWIntegrate()
w.set_data(Orange.data.Table("collagen.csv"))
w.show()
w.raise_()
r = app.exec_()
w.set_data(None)
w.saveSettings()
w.onDeleteWidget()
return r
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): # pragma: no cover
from AnyQt.QtWidgets import QVBoxLayout, QCheckBox, QStatusBar
app = QApplication(list(argv) if argv else [])
l1 = QVBoxLayout()
l1.setContentsMargins(0, 0, 0, 0)
blayout = QVBoxLayout()
l1.addLayout(blayout)
sb = QStatusBar()
w = QWidget()
w.setLayout(l1)
messages = [
Message(Severity.Error,
text="Encountered a HCF",
detailedText="<em>AAA! It burns.</em>",
textFormat=Qt.RichText),
Message(
Severity.Warning,
text="ACHTUNG!",
detailedText=("<div style=\"color: red\">DAS KOMPUTERMASCHINE IST "
"NICHT FÜR DER GEFINGERPOKEN</div>"),
textFormat=Qt.RichText),
Message(Severity.Information,
text="The rain in spain falls mostly on the plain",
informativeText=(
"<a href=\"https://www.google.si/search?q="
"Average+Yearly+Precipitation+in+Spain\">Link</a>"),
textFormat=Qt.RichText),
Message(Severity.Error,
text="I did not do this!",
informativeText="The computer made suggestions...",
detailedText="... and the default options was yes."),
Message(),
]
mw = MessagesWidget(openExternalLinks=True)
for i, m in enumerate(messages):
cb = QCheckBox(m.text)
def toogled(state, i=i, m=m):
if state:
mw.setMessage(i, m)
else:
mw.removeMessage(i)
cb.toggled[bool].connect(toogled)
blayout.addWidget(cb)
sb.addWidget(mw)
w.layout().addWidget(sb, 0)
w.show()
return app.exec_()
def main_test():
from AnyQt.QtWidgets import QApplication
import sys
app = QApplication([])
w = OWGenes()
if len(sys.argv) > 1:
data = Table(sys.argv[1])
w.handle_input(data)
w.show()
w.raise_()
r = app.exec_()
w.saveSettings()
return r
def main(argv=None):
"""Show and test the widget"""
from AnyQt.QtWidgets import QApplication
logging.basicConfig(level=logging.DEBUG)
if argv is None:
argv = sys.argv
argv = list(argv)
app = QApplication(argv)
if len(argv) > 1:
filename = argv[1]
else:
filename = "iris"
data = Table(filename)
class_var = data.domain.class_var
if class_var is None:
return 1
elif class_var.is_discrete:
learners = [
lambda data: 1 / 0,
Orange.classification.LogisticRegressionLearner(),
Orange.classification.MajorityLearner(),
Orange.classification.NaiveBayesLearner(),
]
else:
learners = [
lambda data: 1 / 0,
Orange.regression.MeanLearner(),
Orange.regression.KNNRegressionLearner(),
Orange.regression.RidgeRegressionLearner(),
]
w = OWTestLearners()
w.show()
w.set_train_data(data)
w.set_test_data(data)
for i, learner in enumerate(learners):
w.set_learner(learner, i)
w.handleNewSignals()
rval = app.exec_()
for i in range(len(learners)):
w.set_learner(None, i)
w.handleNewSignals()
w.saveSettings()
return rval
def main(args=None):
if args is None:
args = sys.argv
app = QApplication(list(args))
w = OWResolweDataObject()
res = ResolweHelper()
w.set_data(res.get_object(id=197))
# w.resetSettings()
w.show()
w.raise_()
rv = app.exec_()
w.saveSettings()
w.onDeleteWidget()
return rv
def test_main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
a = QApplication(argv)
if len(argv) > 1:
filename = argv[1]
else:
filename = "brown-selected"
w = OWLinePlot()
d = Table(filename)
w.set_data(d)
w.show()
r = a.exec_()
w.saveSettings()
return r
def test_main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
a = QApplication(argv)
if len(argv) > 1:
filename = argv[1]
else:
filename = "brown-selected"
w = OWDisplayProfiles()
d = Orange.data.Table(filename)
w.set_data(d)
w.show()
w.raise_()
r = a.exec_()
w.saveSettings()
return r
def main():
from AnyQt.QtWidgets import QApplication
import gc
import sip
app = QApplication([])
ow = OWPaintData()
ow.show()
ow.raise_()
rval = app.exec_()
ow.saveSettings()
ow.onDeleteWidget()
sip.delete(ow)
del ow
gc.collect()
app.processEvents()
return rval
def test_main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
if len(argv) > 1:
filename = argv[1]
else:
filename = os.path.expanduser("~/GDS1210.tab")
data = Orange.data.Table(filename)
app = QApplication(argv)
w = OWMAPlot()
w.setData(data)
w.show()
w.raise_()
r = app.exec_()
w.saveSettings()
return r
def test_main(argv=sys.argv):
from AnyQt.QtWidgets import QApplication
app = QApplication(argv)
if len(argv) > 1:
filename = argv[1]
else:
filename = "brown-selected"
data = Orange.data.Table(filename)
w = OWGeneInfo()
w.setData(data)
w.show()
w.raise_()
r = app.exec_()
w.saveSettings()
return r
def test_main(argv=None):
argv = sys.argv[1:] if argv is None else argv
if argv:
filename = argv[0]
else:
filename = "brown-selected"
app = QApplication([])
w = OWPreprocess()
w.set_data(Orange.data.Table(filename))
w.show()
w.raise_()
r = app.exec_()
w.set_data(None)
w.saveSettings()
w.onDeleteWidget()
return r
def main(argv=None):
from AnyQt.QtWidgets import QApplication
if argv is None:
argv = sys.argv
argv = list(argv)
app = QApplication(argv)
filename = "heart_disease"
data = Orange.data.Table(filename)
w = OWPieChart()
w.show()
w.raise_()
w.set_data(data)
w.handleNewSignals()
rval = app.exec_()
w.set_data(None)
w.handleNewSignals()
w.saveSettings()
return rval
def test_main(argv=sys.argv):
argv = list(argv)
app = QApplication(argv)
if len(argv) > 1:
filename = argv[1]
else:
filename = "brown-selected"
w = OWPreprocess()
w.set_data(Orange.data.Table(filename))
w.show()
w.raise_()
r = app.exec_()
w.set_data(None)
w.saveSettings()
w.onDeleteWidget()
return r
