def test_rgb_to_hex(self):
# provided by http://en.labelpartners.com/pantone_coated_table.html
matches = [[(254, 221, 0), "#FEDD00"], [(0, 20, 137), "#001489"],
[(175, 152, 0), "#AF9800"], [(255, 127, 50), "#FF7F32"]]
for rgb, hex_code in matches:
self.assertEqual(rgb_to_hex(rgb).lower(), hex_code.lower())
def gradient_color(self):
if not self.is_logistic:
return self.default_background_color
else:
target_class_idx = self.data.domain.class_var.values.\
index(self.target_class)
color = self.data.domain.class_var.colors[target_class_idx]
return rgb_to_hex(tuple(color))
def gradient_color(self):
if not self.is_logistic:
return self.default_background_color
else:
target_class_idx = self.data.domain.class_var.values.\
index(self.target_class)
color = self.data.domain.class_var.colors[target_class_idx]
return rgb_to_hex(tuple(color))
def test_rgb_to_hex(self):
# provided by http://en.labelpartners.com/pantone_coated_table.html
matches = [[(254, 221, 0), "#FEDD00"],
[(0, 20, 137), "#001489"],
[(175, 152, 0), "#AF9800"],
[(255, 127, 50), "#FF7F32"]]
for rgb, hex_code in matches:
self.assertEqual(rgb_to_hex(rgb).lower(), hex_code.lower())
def replot(self):
"""
This function performs complete replot of the graph
"""
if self.data is None or self.selected_data is None:
self.set_empty_plot()
return
attr_x = self.data.domain[self.attr_x]
attr_y = self.data.domain[self.attr_y]
data_x = [v[0] for v in self.data[:, attr_x] if not isnan(v[0])]
data_y = [v[0] for v in self.data[:, attr_y] if not isnan(v[0])]
min_x = min(data_x)
max_x = max(data_x)
min_y = min(data_y)
max_y = max(data_y)
# just in cas that diff is 0
diff_x = (max_x - min_x) if abs(max_x - min_x) > 0.001 else 0.1
diff_y = (max_y - min_y) if abs(max_y - min_y) > 0.001 else 0.1
min_x, max_x = min_x - 0.03 * diff_x, max_x + 0.03 * diff_x
min_y, max_y = min_y - 0.03 * diff_y, max_y + 0.03 * diff_y
options = dict(series=[])
# gradient and contour
options['series'] += self.plot_gradient_and_contour(
min_x, max_x, min_y, max_y)
sd = self.selected_data
# data points
options['series'] += [
dict(
data=[list(p.attributes())
for p in sd
if (int(p.metas[0]) == _class and
all(v is not None for v in p.attributes()))],
type="scatter",
zIndex=10,
color=rgb_to_hex(tuple(
sd.domain.metas[0].colors[_class].tolist())),
showInLegend=True,
name=sd.domain.metas[0].values[_class])
for _class in range(len(sd.domain.metas[0].values))]
cls_domain = sd.domain.metas[0]
target_idx = cls_domain.values.index(self.target_class)
target_color = tuple(cls_domain.colors[target_idx].tolist())
other_color = (tuple(cls_domain.colors[(target_idx + 1) % 2].tolist())
if len(cls_domain.values) == 2 else (170, 170, 170))
# highcharts parameters
kwargs = dict(
xAxis_title_text=attr_x.name,
yAxis_title_text=attr_y.name,
xAxis_min=min_x,
xAxis_max=max_x,
yAxis_min=min_y,
yAxis_max=max_y,
colorAxis=dict(
labels=dict(enabled=False),
stops=[
[0, rgb_hash_brighter(rgb_to_hex(other_color), 0.5)],
[0.5, '#ffffff'],
[1, rgb_hash_brighter(rgb_to_hex(target_color), 0.5)]],
tickInterval=0.2, min=0, max=1),
plotOptions_contour_colsize=(max_y - min_y) / 1000,
plotOptions_contour_rowsize=(max_x - min_x) / 1000,
legend=dict(
enabled=self.legend_enabled,
layout='vertical',
align='right',
verticalAlign='top',
floating=True,
backgroundColor='rgba(255, 255, 255, 0.3)',
symbolWidth=0,
symbolHeight=0),
tooltip_headerFormat="",
tooltip_pointFormat="<strong>%s:</strong> {point.x:.2f} <br/>"
"<strong>%s:</strong> {point.y:.2f}" %
(self.attr_x, self.attr_y))
self.scatter.chart(options, **kwargs)
self.plot_contour()
def replot(self):
"""
This function performs complete replot of the graph
"""
if self.data is None or self.selected_data is None:
self.set_empty_plot()
return
attr_x = self.data.domain[self.attr_x]
attr_y = self.data.domain[self.attr_y]
data_x = [v[0] for v in self.data[:, attr_x] if not isnan(v[0])]
data_y = [v[0] for v in self.data[:, attr_y] if not isnan(v[0])]
min_x = min(data_x)
max_x = max(data_x)
min_y = min(data_y)
max_y = max(data_y)
# just in cas that diff is 0
diff_x = (max_x - min_x) if abs(max_x - min_x) > 0.001 else 0.1
diff_y = (max_y - min_y) if abs(max_y - min_y) > 0.001 else 0.1
min_x, max_x = min_x - 0.03 * diff_x, max_x + 0.03 * diff_x
min_y, max_y = min_y - 0.03 * diff_y, max_y + 0.03 * diff_y
options = dict(series=[])
# gradient and contour
options['series'] += self.plot_gradient_and_contour(
min_x, max_x, min_y, max_y)
sd = self.selected_data
# data points
options['series'] += [
dict(data=[
list(p.attributes()) for p in sd
if (p.metas[0] == _class and all(v is not None
for v in p.attributes()))
],
type="scatter",
zIndex=10,
color=rgb_to_hex(
tuple(sd.domain.metas[0].colors[_class].tolist())),
showInLegend=True,
name=sd.domain.metas[0].values[_class])
for _class in range(len(sd.domain.metas[0].values))
]
# add nan values as a gray dots
options['series'] += [
dict(data=[
list(p.attributes()) for p in sd if np.isnan(p.metas[0])
],
type="scatter",
zIndex=10,
color=rgb_to_hex((160, 160, 160)),
showInLegend=False)
]
cls_domain = sd.domain.metas[0]
target_idx = cls_domain.values.index(self.target_class)
target_color = tuple(cls_domain.colors[target_idx].tolist())
other_color = (tuple(cls_domain.colors[(target_idx + 1) % 2].tolist())
if len(cls_domain.values) == 2 else (170, 170, 170))
# highcharts parameters
kwargs = dict(
xAxis_title_text=attr_x.name,
yAxis_title_text=attr_y.name,
xAxis_min=min_x,
xAxis_max=max_x,
yAxis_min=min_y,
yAxis_max=max_y,
colorAxis=dict(
labels=dict(enabled=False),
stops=[[0, rgb_hash_brighter(rgb_to_hex(other_color), 0.5)],
[0.5, '#ffffff'],
[1, rgb_hash_brighter(rgb_to_hex(target_color), 0.5)]],
tickInterval=0.2,
min=0,
max=1),
plotOptions_contour_colsize=(max_y - min_y) / 1000,
plotOptions_contour_rowsize=(max_x - min_x) / 1000,
legend=dict(enabled=self.legend_enabled,
layout='vertical',
align='right',
verticalAlign='top',
floating=True,
backgroundColor='rgba(255, 255, 255, 0.3)',
symbolWidth=0,
symbolHeight=0),
tooltip_headerFormat="",
tooltip_pointFormat="<strong>%s:</strong> {point.x:.2f} <br/>"
"<strong>%s:</strong> {point.y:.2f}" % (self.attr_x, self.attr_y))
self.scatter.chart(options, **kwargs)
self.plot_contour()
