def main():
# example from https://github.com/marinkaz/scikit-fusion
import numpy as np
from AnyQt.QtWidgets import QApplication
R12 = np.random.rand(50, 100)
R32 = np.random.rand(100, 150)
R33 = np.random.rand(150, 150)
R13 = np.random.rand(50, 150)
t1 = fusion.ObjectType('Users', 10)
t2 = fusion.ObjectType('Movies', 30)
t3 = fusion.ObjectType('Actors', 40)
relations = [fusion.Relation(R12, t1, t2, name='like'),
fusion.Relation(R13, t1, t3, name='are fans of'),
fusion.Relation(R12, t1, t2, name='don\'t like'),
fusion.Relation(R33, t3, t3, name='married to'),
fusion.Relation(R32, t2, t3, name='feature')]
G = fusion.FusionGraph()
for rel in relations:
G.add_relation(rel)
fuser = fusion.Dfmf()
fuser.fuse(G)
app = QApplication([])
w = OWChaining()
w.on_fuser_change(FittedFusionGraph(fuser))
w.show()
app.exec()
def main():
from AnyQt.QtWidgets import QApplication
t1 = fusion.ObjectType('Users', 10)
t2 = fusion.ObjectType('Movies', 30)
t3 = fusion.ObjectType('Actors', 40)
# test that MeanFuser completes correctly
R = np.ma.array([[1, 1, 0], [3, 0, 0]],
mask=[[0, 0, 1], [0, 1, 1]],
dtype=float)
rel = fusion.Relation(R, t1, t2)
assert (MeanFuser(0).complete(rel) == [[1, 1, 5 / 3], [3, 1, 5 / 3]]).all()
assert (MeanFuser(1).complete(rel) == [[1, 1, 1], [3, 3, 3]]).all()
assert (MeanFuser(2).complete(rel) == [[1, 1, 5 / 3], [3, 5 / 3,
5 / 3]]).all()
R1 = np.ma.array(np.random.random((20, 20)))
R2 = np.ma.array(np.random.random((40, 40)),
mask=np.random.random((40, 40)) > .8)
relations = [
fusion.Relation(R1, t1, t2, name='like'),
fusion.Relation(R2, t3, t2, name='feature in'),
]
G = fusion.FusionGraph()
G.add_relations_from(relations)
app = QApplication([])
w = OWMeanFuser()
w.on_fusion_graph_change(G)
w.show()
app.exec()
def main():
""" A simple test. """
app = QApplication([])
def _on_selected_points(points):
print(len(points), points)
xAxis = {
'type': 'category',
'data': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
}
yAxis = {
'type': 'value'
}
series = [{
'data': [820, 932, 901, 934, 1290, 1330, 1320],
'type': 'line'
}]
options = {
'xAxis': xAxis,
'yAxis': yAxis,
'series': series
}
w = Echarts(debug=True)
w.chart(options=options)
w.show()
app.exec()
def main():
from Orange.data import Table, Domain, ContinuousVariable, StringVariable
words = 'hey~mr. tallyman tally~me banana daylight come and me wanna go home'
words = np.array([w.replace('~', ' ') for w in words.split()], dtype=object, ndmin=2).T
weights = np.random.random((len(words), 1))
data = np.zeros((len(words), 0))
metas = []
for i, w in enumerate(weights.T):
data = np.column_stack((data, words, w))
metas = metas + [StringVariable('Topic' + str(i)),
ContinuousVariable('weights')]
domain = Domain([], metas=metas)
table = Table.from_numpy(domain,
X=np.zeros((len(words), 0)),
metas=data)
app = QApplication([''])
w = OWWordCloud()
w.on_topic_change(table)
domain = Domain([], metas=[StringVariable('text')])
data = Corpus(domain=domain, metas=np.array([[' '.join(words.flat)]]))
# data = Corpus.from_numpy(domain, X=np.zeros((1, 0)), metas=np.array([[' '.join(words.flat)]]))
w.on_corpus_change(data)
w.show()
app.exec()
def main():
from Orange.data import Table, Domain, ContinuousVariable, StringVariable
words = 'hey~mr. tallyman tally~me banana daylight come and me wanna go home'
words = np.array([w.replace('~', ' ') for w in words.split()],
dtype=object,
ndmin=2).T
weights = np.random.random((len(words), 1))
data = np.zeros((len(words), 0))
metas = []
for i, w in enumerate(weights.T):
data = np.column_stack((data, words, w))
metas = metas + [
StringVariable('Topic' + str(i)),
ContinuousVariable('weights')
]
domain = Domain([], metas=metas)
table = Table.from_numpy(domain, X=np.zeros((len(words), 0)), metas=data)
app = QApplication([''])
w = OWWordCloud()
w.on_topic_change(table)
domain = Domain([], metas=[StringVariable('text')])
data = Corpus(domain=domain, metas=np.array([[' '.join(words.flat)]]))
# data = Corpus.from_numpy(domain, X=np.zeros((1, 0)), metas=np.array([[' '.join(words.flat)]]))
w.on_corpus_change(data)
w.handleNewSignals()
w.show()
app.exec()
def main():
from AnyQt.QtWidgets import QApplication
t1 = fusion.ObjectType('Users', 10)
t2 = fusion.ObjectType('Movies', 30)
t3 = fusion.ObjectType('Actors', 40)
# test that MeanFuser completes correctly
R = np.ma.array([[1, 1, 0],
[3, 0, 0]], mask=[[0, 0, 1],
[0, 1, 1]], dtype=float)
rel = fusion.Relation(R, t1, t2)
assert (MeanFuser(0).complete(rel) == [[1, 1, 5/3],
[3, 1, 5/3]]).all()
assert (MeanFuser(1).complete(rel) == [[1, 1, 1],
[3, 3, 3]]).all()
assert (MeanFuser(2).complete(rel) == [[1, 1, 5/3],
[3, 5/3, 5/3]]).all()
R1 = np.ma.array(np.random.random((20, 20)))
R2 = np.ma.array(np.random.random((40, 40)),
mask=np.random.random((40,40)) > .8)
relations = [
fusion.Relation(R1, t1, t2, name='like'),
fusion.Relation(R2, t3, t2, name='feature in'),
]
G = fusion.FusionGraph()
G.add_relations_from(relations)
app = QApplication([])
w = OWMeanFuser()
w.on_fusion_graph_change(G)
w.show()
app.exec()
def main():
# example from https://github.com/marinkaz/scikit-fusion
import numpy as np
from AnyQt.QtWidgets import QApplication
R12 = np.random.rand(50, 100)
R32 = np.random.rand(100, 150)
R33 = np.random.rand(150, 150)
R13 = np.random.rand(50, 150)
t1 = fusion.ObjectType('Users', 10)
t2 = fusion.ObjectType('Movies', 30)
t3 = fusion.ObjectType('Actors', 40)
relations = [
fusion.Relation(R12, t1, t2, name='like'),
fusion.Relation(R13, t1, t3, name='are fans of'),
fusion.Relation(R12, t1, t2, name='don\'t like'),
fusion.Relation(R33, t3, t3, name='married to'),
fusion.Relation(R32, t2, t3, name='feature')
]
G = fusion.FusionGraph()
for rel in relations:
G.add_relation(rel)
fuser = fusion.Dfmf()
fuser.fuse(G)
app = QApplication([])
w = OWChaining()
w.on_fuser_change(FittedFusionGraph(fuser))
w.show()
app.exec()
def main():
from AnyQt.QtWidgets import QApplication
app = QApplication([])
w = OWOracleSQL()
w.show()
app.exec()
def main(args=sys.argv):
from AnyQt.QtWidgets import QApplication
app = QApplication(args)
w = SelectEncodingsWidget(
headingText="Select encodings visible in text encoding menus")
w.show()
w.activateWindow()
app.exec()
def main():
from AnyQt.QtWidgets import QApplication
a = QApplication([])
ow = GeoCodeFromFile()
ow.show()
ow.raise_()
a.exec()
ow.saveSettings()
def main():
app = QApplication([])
widget = OWSentimentAnalysis()
corpus = Corpus.from_file('book-excerpts')
corpus = corpus[:3]
widget.set_corpus(corpus)
widget.show()
app.exec()
def main():
app = QApplication([])
widget = OWSentimentAnalysis()
corpus = Corpus.from_file('bookexcerpts')
corpus = corpus[:3]
widget.set_corpus(corpus)
widget.show()
app.exec()
def main():
from sklearn.datasets import make_blobs
import numpy as np
from AnyQt.QtWidgets import QApplication
from orangecontrib.datafusion.models import FittedFusionGraph
from orangecontrib.datafusion.widgets.owmeanfuser import MeanFuser
X, y = make_blobs(100,
3,
centers=2,
center_box=(-100, 100),
cluster_std=10)
X = X.astype(int)
X += abs(X.min())
nrows, ncols, _ = X.max(0)
R1 = np.zeros((nrows + 1, ncols + 1))
R1[X[:, 0], X[:, 1]] = X[:, 2]
R1 = np.ma.array((R1 - R1.min()) / (R1.max() - R1.min()))
_, ncols, nrows = X.max(0)
R2 = np.zeros((nrows + 1, ncols + 1))
R2[X[:, 2], X[:, 1]] = X[:, 0]
R2 = np.ma.array((R2 - R2.min()) / (R2.max() - R2.min()))
t1 = fusion.ObjectType('Users', 10)
t2 = fusion.ObjectType('Movies', 30)
t3 = fusion.ObjectType('Actors', 40)
relations = [
fusion.Relation(R1, t1, t2, name='like'),
fusion.Relation(R2, t3, t2, name='feature in'),
]
G = fusion.FusionGraph()
for relation in relations:
relation.data.mask = np.random.rand(*relation.data.shape) > .8
G.add_relation(relation)
fuserF = fusion.Dfmf()
fuserF.fuse(G)
from copy import deepcopy
G = deepcopy(G)
fuserC = fusion.Dfmc()
fuserC.name = 'My dfmc<3'
fuserC.fuse(G)
app = QApplication([])
w = OWCompletionScoring()
w.on_fuser_change(FittedFusionGraph(fuserF), fuserF.__class__.__name__)
w.on_fuser_change(FittedFusionGraph(fuserC), fuserC.__class__.__name__)
w.on_fuser_change(MeanFuser(0), 'meanfuser0')
w.on_fuser_change(MeanFuser(1), 'meanfuser1')
w.on_fuser_change(MeanFuser(2), 'meanfuser2')
for i, relation in enumerate(relations, 1):
w.on_relation_change(Relation(relation), i)
w.show()
app.exec()
def main(): # pragma: no cover
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWKMeans()
d = Table("iris.tab")
ow.set_data(d)
ow.show()
a.exec()
ow.saveSettings()
def ephys_session_form(session_dict: dict) -> dict:
root = QApplication(sys.argv)
sForm0 = EphysSessionForm()
sForm0.session_dict = dict_to_dstr(session_dict)
sForm0.build()
sForm = pyforms.start_app(sForm0,
parent_win=root,
geometry=(200, 200, 600, 400))
# sForm.build()
root.exec()
return dstring_to_dict(sForm.form_data)
def session_form(mouse_name: str = '') -> dict:
root = QApplication(sys.argv)
sForm = pyforms.start_app(SessionForm, parent_win=root,
geometry=(200, 200, 600, 400))
sForm._mouseWeight.label = sForm._mouseWeight.label.format(mouse_name)
root.exec()
if sForm.valid_form_data:
return sForm.form_data
else:
return -1
def main(): # pragma: no cover
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWKMeans()
d = Table(sys.argv[1] if len(sys.argv) > 1 else "iris.tab")
ow.set_data(d)
ow.show()
a.exec()
ow.saveSettings()
def main(): # pragma: no cover
"""Simple test for manual inspection of the widget"""
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
table = Table("zoo")
ow = OWCreateClass()
ow.show()
ow.set_data(table)
a.exec()
ow.saveSettings()
def main():
from AnyQt.QtWidgets import QApplication
a = QApplication([])
ow = OWChoropleth()
ow.show()
ow.raise_()
data = Table("India_census_district_population")
ow.set_data(data)
a.exec()
ow.saveSettings()
def test_main():
from AnyQt.QtWidgets import QApplication
a = QApplication([])
ow = OWChoropleth()
ow.show()
ow.raise_()
data = Table('philadelphia-crime')
ow.set_data(data)
a.exec()
ow.saveSettings()
def main(): # pragma: no cover
"""Simple test for manual inspection of the widget"""
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
table = Table("zoo")
ow = OWCreateClass()
ow.show()
ow.set_data(table)
a.exec()
ow.saveSettings()
def main():
from AnyQt.QtWidgets import QApplication
a = QApplication([])
ow = OWGeocoding()
ow.show()
ow.raise_()
data = Table('philadelphia-crime')
print(data[:10])
ow.set_data(data)
a.exec()
ow.saveSettings()
def main(): # pragma: no cover
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
c = ColorPaletteDlg(None)
c.createContinuousPalette("continuousPalette", "Continuous Palette")
c.createDiscretePalette("discPalette", "Discrete Palette")
box = c.createBox("otherColors", "Colors")
c.createColorButton(box, "Canvas", "Canvas")
c.createColorButton(box, "Grid", "Grid")
c.setColorSchemas()
c.show()
a.exec()
def main():
from AnyQt.QtWidgets import QApplication
a = QApplication([])
ow = OWGeocoding()
ow.show()
ow.raise_()
data = Table('/home/jk/PycharmProjects/orange3/geo/small_airports.csv')
print(data[:10])
ow.set_data(data)
a.exec()
ow.saveSettings()
def main():
corpus = Corpus.from_file('book-excerpts')
vect = BowVectorizer()
corpus_vect = vect.transform(corpus)
app = QApplication([])
widget = OWWordEnrichment()
widget.set_data(corpus_vect)
subset_corpus = corpus_vect[:10]
widget.set_data_selected(subset_corpus)
widget.handleNewSignals()
widget.show()
app.exec()
def main(): # pragma: no cover
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
c = ColorPaletteDlg(None)
c.createContinuousPalette("continuousPalette", "Continuous Palette")
c.createDiscretePalette("discPalette", "Discrete Palette")
box = c.createBox("otherColors", "Colors")
c.createColorButton(box, "Canvas", "Canvas")
c.createColorButton(box, "Grid", "Grid")
c.setColorSchemas()
c.show()
a.exec()
def main():
from sklearn.datasets import make_blobs
import numpy as np
from AnyQt.QtWidgets import QApplication
from orangecontrib.datafusion.models import FittedFusionGraph
from orangecontrib.datafusion.widgets.owmeanfuser import MeanFuser
X, y = make_blobs(100, 3, centers=2, center_box=(-100, 100), cluster_std=10)
X = X.astype(int)
X += abs(X.min())
nrows, ncols, _ = X.max(0)
R1 = np.zeros((nrows + 1, ncols + 1))
R1[X[:, 0], X[:, 1]] = X[:, 2]
R1 = np.ma.array((R1 - R1.min()) / (R1.max() - R1.min()))
_, ncols, nrows = X.max(0)
R2 = np.zeros((nrows + 1, ncols + 1))
R2[X[:, 2], X[:, 1]] = X[:, 0]
R2 = np.ma.array((R2 - R2.min()) / (R2.max() - R2.min()))
t1 = fusion.ObjectType('Users', 10)
t2 = fusion.ObjectType('Movies', 30)
t3 = fusion.ObjectType('Actors', 40)
relations = [
fusion.Relation(R1, t1, t2, name='like'),
fusion.Relation(R2, t3, t2, name='feature in'),
]
G = fusion.FusionGraph()
for relation in relations:
relation.data.mask = np.random.rand(*relation.data.shape) > .8
G.add_relation(relation)
fuserF = fusion.Dfmf()
fuserF.fuse(G)
from copy import deepcopy
G = deepcopy(G)
fuserC = fusion.Dfmc()
fuserC.name = 'My dfmc<3'
fuserC.fuse(G)
app = QApplication([])
w = OWCompletionScoring()
w.on_fuser_change(FittedFusionGraph(fuserF), fuserF.__class__.__name__)
w.on_fuser_change(FittedFusionGraph(fuserC), fuserC.__class__.__name__)
w.on_fuser_change(MeanFuser(0), 'meanfuser0')
w.on_fuser_change(MeanFuser(1), 'meanfuser1')
w.on_fuser_change(MeanFuser(2), 'meanfuser2')
for i, relation in enumerate(relations, 1):
w.on_relation_change(Relation(relation), i)
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 test_main():
from AnyQt.QtWidgets import QApplication
from Orange.modelling import KNNLearner as Learner
a = QApplication([])
ow = OWMap()
ow.show()
ow.raise_()
data = Table('philadelphia-crime')
ow.set_data(data)
QTimer.singleShot(10, lambda: ow.set_learner(Learner()))
ow.handleNewSignals()
a.exec()
ow.saveSettings()
def main():
""" A simple test. """
app = QApplication([])
def _on_selected_points(points):
print(len(points), points)
w = Highchart(enable_zoom=True, enable_select='xy+',
selection_callback=_on_selected_points,
debug=True)
w.chart(dict(series=[dict(data=np.random.random((100, 2)))]),
credits_text='BTYB Yours Truly',
title_text='Foo plot',
chart_type='scatter')
w.show()
app.exec()
def main(argv=None):
import sys
from orangecontrib.imageanalytics.import_images import ImportImages
from orangecontrib.imageanalytics.image_embedder import ImageEmbedder
if argv is None:
argv = sys.argv
argv = list(argv)
app = QApplication(argv)
if len(argv) > 1:
image_dir = argv[1]
else:
raise ValueError("Provide the image directory as the first argument.")
import_images = ImportImages()
images, err = import_images(image_dir)
image_embedder = ImageEmbedder()
embeddings, _, _ = image_embedder(images)
ow = OWImageGrid()
ow.show()
ow.raise_()
ow.set_data(Orange.data.Table(embeddings))
rval = app.exec()
ow.saveSettings()
ow.onDeleteWidget()
return rval
def main(argv=None):
import sys
from orangecontrib.imageanalytics.import_images import ImportImages
from orangecontrib.imageanalytics.image_embedder import ImageEmbedder
if argv is None:
argv = sys.argv
argv = list(argv)
app = QApplication(argv)
if len(argv) > 1:
image_dir = argv[1]
else:
raise ValueError("Provide the image directory as the first argument.")
import_images = ImportImages()
images, err = import_images(image_dir)
image_embedder = ImageEmbedder()
embeddings, _, _ = image_embedder(images)
ow = OWImageGrid()
ow.show()
ow.raise_()
ow.set_data(Orange.data.Table(embeddings))
rval = app.exec()
ow.saveSettings()
ow.onDeleteWidget()
return rval
def main(argv=None): # pragma: no cover
# pylint: disable=import-outside-toplevel
import sys
from AnyQt.QtWidgets import QGraphicsScene, QMenu
from AnyQt.QtGui import QBrush
app = QApplication(argv or sys.argv)
scene = QGraphicsScene()
view = GraphicsWidgetView(scene)
scene.setParent(view)
view.setContextMenuPolicy(Qt.CustomContextMenu)
def context(pos):
menu = QMenu(view)
menu.addActions(view.actions())
a = menu.addAction("Aspect mode")
am = QMenu(menu)
am.addAction("Ignore", lambda: view.setAspectMode(Qt.IgnoreAspectRatio))
am.addAction("Keep", lambda: view.setAspectMode(Qt.KeepAspectRatio))
am.addAction("Keep by expanding", lambda: view.setAspectMode(Qt.KeepAspectRatioByExpanding))
a.setMenu(am)
menu.popup(view.viewport().mapToGlobal(pos))
view.customContextMenuRequested.connect(context)
w = QGraphicsWidget()
w.setPreferredSize(500, 500)
palette = w.palette()
palette.setBrush(palette.Window, QBrush(Qt.red, Qt.BDiagPattern))
w.setPalette(palette)
w.setAutoFillBackground(True)
scene.addItem(w)
view.setCentralWidget(w)
view.show()
return app.exec()
def main(argv=None):
import sys
import sip
argv = sys.argv[1:] if argv is None else argv
if argv:
filename = argv[0]
else:
filename = "heart_disease"
data = Table(filename)
app = QApplication([])
w = OWRadviz()
w.set_data(data)
w.set_subset_data(data[::10])
w.handleNewSignals()
w.show()
w.raise_()
r = app.exec()
w.set_data(None)
w.saveSettings()
sip.delete(w)
del w
return r
def main(argv=None):
import sys
import sip
argv = sys.argv[1:] if argv is None else argv
if argv:
filename = argv[0]
else:
filename = "iris"
data = Table(filename)
app = QApplication([])
w = OWLinearProjection()
w.set_data(data)
w.set_subset_data(data[::10])
w.handleNewSignals()
w.show()
w.raise_()
r = app.exec()
w.set_data(None)
w.saveSettings()
sip.delete(w)
del w
return r
def main():
from AnyQt.QtWidgets import QApplication
from Orange.modelling import KNNLearner as Learner
a = QApplication([])
ow = OWMap()
ow.show()
ow.raise_()
data = Table("India_census_district_population")
ow.set_data(data)
QTimer.singleShot(10, lambda: ow.set_learner(Learner()))
ow.handleNewSignals()
a.exec()
ow.saveSettings()
def main(argv=None):
import sys
if argv is None:
argv = sys.argv
argv = list(argv)
a = QApplication(argv)
if len(argv) > 1:
filename = argv[1]
else:
filename = "heart_disease"
ow = OWScatterPlot()
ow.show()
ow.raise_()
data = Orange.data.Table(filename)
ow.set_data(data)
ow.set_subset_data(data[:30])
ow.handleNewSignals()
rval = a.exec()
ow.set_data(None)
ow.set_subset_data(None)
ow.handleNewSignals()
ow.saveSettings()
ow.onDeleteWidget()
return rval
def main(argv=None):
import sys
if argv is None:
argv = sys.argv
argv = list(argv)
a = QApplication(argv)
if len(argv) > 1:
filename = argv[1]
else:
filename = "heart_disease"
ow = OWScatterPlot()
ow.show()
ow.raise_()
data = Orange.data.Table(filename)
ow.set_data(data)
ow.set_subset_data(data[:30])
ow.handleNewSignals()
rval = a.exec()
ow.set_data(None)
ow.set_subset_data(None)
ow.handleNewSignals()
ow.saveSettings()
ow.onDeleteWidget()
return rval
def main(argv):
app = QApplication(argv)
mw = QMainWindow()
dock = CollapsibleDockWidget()
w1 = QTreeView()
w1.header().hide()
w2 = QToolButton()
w2.setFixedSize(38, 200)
dock.setExpandedWidget(w1)
dock.setCollapsedWidget(w2)
mw.addDockWidget(Qt.LeftDockWidgetArea, dock)
mw.setCentralWidget(QTextEdit())
mw.show()
a = QAction("Expand",
mw,
checkable=True,
shortcut=QKeySequence(Qt.ControlModifier | Qt.Key_D))
a.triggered[bool].connect(dock.setExpanded)
mw.addAction(a)
return app.exec()
def test_main():
from AnyQt.QtWidgets import QApplication
from Orange.modelling import KNNLearner as Learner
a = QApplication([])
ow = OWMap()
ow.show()
ow.raise_()
data = Table('philadelphia-crime')
ow.set_data(data)
QTimer.singleShot(10, lambda: ow.set_learner(Learner()))
ow.handleNewSignals()
a.exec()
ow.saveSettings()
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): # 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():
""" A simple test. """
app = QApplication([])
def _on_selected_points(points):
print(len(points), points)
w = Highchart(enable_zoom=True,
enable_select='xy+',
selection_callback=_on_selected_points,
debug=True)
w.chart(dict(series=[dict(data=np.random.random((100, 2)))]),
credits_text='BTYB Yours Truly',
title_text='Foo plot',
chart_type='scatter')
w.show()
app.exec()
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=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
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():
from Orange.data import Table, Domain, StringVariable
words = np.column_stack([
'Slovenia Slovenia SVN USA Iraq Iraq Iraq Iraq France FR'.split(),
'Slovenia Slovenia SVN France FR Austria NL GB GB GB'.split(),
'Alabama AL Texas TX TX TX MS Montana US-MT MT'.split(),
])
metas = [
StringVariable('World'),
StringVariable('Europe'),
StringVariable('USA'),
]
domain = Domain([], metas=metas)
table = Table.from_numpy(domain,
X=np.zeros((len(words), 0)),
metas=words)
app = QApplication([''])
w = OWGeoMap()
w.on_data(table)
w.show()
app.exec()
def main():
# example from https://github.com/marinkaz/scikit-fusion
import numpy as np
from AnyQt.QtWidgets import QApplication
R12 = np.random.rand(50, 100)
R22 = np.random.rand(100, 100)
R13 = np.random.rand(50, 40)
R31 = np.random.rand(40, 50)
R23 = np.random.rand(100, 40)
R23 = np.random.rand(100, 40)
R24 = np.random.rand(100, 40)
R34 = np.random.rand(40, 40)
t1 = fusion.ObjectType('Users', 10)
t2 = fusion.ObjectType('Actors', 20)
t3 = fusion.ObjectType('Movies', 30)
t4 = fusion.ObjectType('Genres', 40)
relations = [fusion.Relation(R12, t1, t2, name='like'),
fusion.Relation(R13, t1, t3, name='rated'),
fusion.Relation(R13, t1, t3, name='mated'),
fusion.Relation(R23, t2, t3, name='play in'),
fusion.Relation(R31, t3, t1),
fusion.Relation(R24, t2, t4, name='prefer'),
fusion.Relation(R34, t3, t4, name='belong to'),
fusion.Relation(R22, t2, t2, name='married to')]
app = QApplication(['asdf'])
w = OWFusionGraph()
w.show()
def _add_next_relation(event,
id=iter(range(len(relations))),
relation=iter(map(Relation, relations))):
try: w.on_relation_change(next(relation), next(id))
except StopIteration:
w.killTimer(w.timer_id)
w.on_relation_change(None, 4) # Remove relation #4
w.timerEvent = _add_next_relation
w.timer_id = w.startTimer(500)
app.exec()
def test_model():
app = QApplication([])
view = QTableView(
sortingEnabled=True
)
data = Orange.data.Table("lenses")
model = TableModel(data)
view.setModel(model)
view.show()
view.raise_()
return app.exec()
def main(argv=None):
import sys
argv = sys.argv[1:] if argv is None else argv
if argv:
filename = argv[0]
else:
filename = "iris"
data = Table(filename)
app = QApplication([])
w = OWLinearProjection()
w.set_data(data)
w.set_subset_data(data[::10])
w.handleNewSignals()
w.show()
w.raise_()
app.exec()
w.set_data(None)
w.saveSettings()
del w
def test_main():
a = QApplication(sys.argv)
ow = OWDataTable()
iris = Table("iris")
brown = Table("brown-selected")
housing = Table("housing")
ow.show()
ow.raise_()
ow.set_dataset(iris, iris.name)
ow.set_dataset(brown, brown.name)
ow.set_dataset(housing, housing.name)
rval = a.exec()
# ow.saveSettings()
return rval
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): # 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()
painter.setRenderHint(QPainter.Antialiasing)
for prop, color in zip(curr_class_dist, self.color_schema):
if prop == 0:
continue
painter.setPen(QPen(QBrush(color), pw))
to_x = x + prop * width
line = QLineF(x, baseline, to_x, baseline)
painter.drawLine(line)
x = to_x
painter.restore()
painter.restore()
if __name__ == "__main__":
from PyQt4.QtGui import QApplication
from Orange.classification import CN2Learner
data = Table("iris")
learner = CN2Learner()
model = learner(data)
model.instances = data
a = QApplication([])
ow = OWRuleViewer()
ow.set_classifier(model)
ow.show()
a.exec()
self.update_tweets_num(len(result) if result else 0)
self.corpus = result
self.set_text_features()
def update_tweets_num(self, num=0):
self.tweets_info_label.setText(self.tweets_info.format(num))
def set_text_features(self):
self.Warning.no_text_fields.clear()
if not self.text_includes:
self.Warning.no_text_fields()
if self.corpus is not None:
vars_ = [var for var in self.corpus.domain.metas
if var.name in self.text_includes]
self.corpus.set_text_features(vars_ or None)
self.send(IO.CORPUS, self.corpus)
@gui_require('api', 'key_missing')
def send_report(self):
for task in self.api.search_history:
self.report_items(task)
if __name__ == '__main__':
app = QApplication([])
widget = OWTwitter()
widget.show()
app.exec()
widget.saveSettings()
