def redraw_selection(self, marks=None):
if self.grid_cells is None:
return
sel_pen = QPen(QBrush(QColor(128, 128, 128)), 2)
sel_pen.setCosmetic(True)
mark_pen = QPen(QBrush(QColor(128, 128, 128)), 4)
mark_pen.setCosmetic(True)
pens = [self._grid_pen, sel_pen]
mark_brush = QBrush(QColor(224, 255, 255))
sels = self.selection is not None and np.max(self.selection)
palette = LimitedDiscretePalette(number_of_colors=sels + 1)
brushes = [QBrush(Qt.NoBrush)] + \
[QBrush(palette[i].lighter(165)) for i in range(sels)]
for y in range(self.size_y):
for x in range(self.size_x - (y % 2) * self.hexagonal):
cell = self.grid_cells[y, x]
marked = marks is not None and marks[x, y]
sel_group = self.selection is not None and self.selection[x, y]
if marked:
cell.setBrush(mark_brush)
cell.setPen(mark_pen)
else:
cell.setBrush(brushes[sel_group])
cell.setPen(pens[bool(sel_group)])
cell.setZValue(marked or sel_group)
def get_palette(self):
if self.agg_func in ('Count', 'Count defined'):
return DefaultContinuousPalette
elif self.is_mode():
return LimitedDiscretePalette(MAX_COLORS)
else:
return self.agg_attr.palette
def get_palette(self):
if not self.color_by_cluster or not self.clusters.groups:
return super().get_palette()
if self.clustering_type not in ClusterDocuments.TYPES: # From variable
return self.cluster_var.palette
return LimitedDiscretePalette(len(self.clusters.groups))
def discrete_colors(x, attributes):
disc_colors = np.zeros(x.shape + (3, ))
for i, a in enumerate(attributes):
nonnan = ~np.isnan(x[:, i])
colors = (a.colors if hasattr(a, "colors") else
LimitedDiscretePalette(len(a.values)).palette)
disc_colors[nonnan, i] = colors[x[nonnan, i].astype(int)]
return disc_colors
def get_cluster_labels(self):
if not self.clusters.groups:
return None
if self.clustering_type not in ClusterDocuments.TYPES: # From variable
colors = self.cluster_var.colors
else:
colors = LimitedDiscretePalette(len(self.clusters.groups)).palette
return [(ann, centroid, colors[key])
for key, (ann, centroid, _) in self.clusters.groups.items()]
def get_colors_sel(self):
white_pen = self._make_pen(QColor(Qt.white), 1)
if self.selection is None:
pen = [white_pen] * self.n_ids
else:
sels = np.max(self.selection)
if sels == 1:
orange_pen = self._make_pen(QColor(255, 190, 0, 255), 3)
pen = np.where(self.selection, orange_pen, white_pen)
else:
palette = LimitedDiscretePalette(number_of_colors=sels + 1)
pens = [white_pen] + [self._make_pen(palette[i], 3)
for i in range(sels)]
pen = np.choose(self.selection, pens)
return pen
def _get_colors(self):
"""
Defines colors for values. If colors match in all models use the union
otherwise use standard colors.
"""
all_colors_values = self._all_color_values()
base_color, base_values = all_colors_values[0]
for c, v in all_colors_values[1:]:
if not self._colors_match(base_color, base_values, c, v):
base_color = []
break
# replace base_color if longer
if len(v) > len(base_color):
base_color = c
base_values = v
if len(base_color) != len(self.class_values):
return LimitedDiscretePalette(len(self.class_values)).palette
# reorder colors to widgets order
colors = [None] * len(self.class_values)
for c, v in zip(base_color, base_values):
colors[self.class_values.index(v)] = c
return colors
def test_colors_diff_domain(self):
"""
Test whether the color selection for values is correct.
"""
# pylint: disable=protected-access
self.send_signal(self.widget.Inputs.data, self.iris)
# case 1: two domains one subset other
idom = self.iris.domain
dom1 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values)
)
dom2 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values[:2])
)
iris1 = self.iris[:100].transform(dom1)
iris2 = self.iris[:100].transform(dom2)
predictor_iris1 = ConstantLearner()(iris1)
predictor_iris2 = ConstantLearner()(iris2)
self.send_signal(self.widget.Inputs.predictors, predictor_iris1)
self.send_signal(self.widget.Inputs.predictors, predictor_iris2, 1)
colors = self.widget._get_colors()
np.testing.assert_array_equal(colors, iris1.domain.class_var.colors)
# case 2: two domains one subset other - different color order
idom = self.iris.domain
colors = idom.class_var.colors[::-1]
dom1 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values)
)
dom2 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values[:2])
)
dom1.class_var.colors = colors
dom2.class_var.colors = colors[:2]
iris1 = self.iris[:100].transform(dom1)
iris2 = self.iris[:100].transform(dom2)
predictor_iris1 = ConstantLearner()(iris1)
predictor_iris2 = ConstantLearner()(iris2)
self.send_signal(self.widget.Inputs.predictors, predictor_iris1)
self.send_signal(self.widget.Inputs.predictors, predictor_iris2, 1)
colors = self.widget._get_colors()
np.testing.assert_array_equal(colors, iris1.domain.class_var.colors)
# case 3: domain color, values miss-match - use default colors
idom = self.iris.domain
dom1 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values)
)
dom2 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values)
)
dom1.class_var.colors = dom1.class_var.colors[::-1]
iris1 = self.iris.transform(dom1)
iris2 = self.iris.transform(dom2)
predictor_iris1 = ConstantLearner()(iris1)
predictor_iris2 = ConstantLearner()(iris2)
self.send_signal(self.widget.Inputs.predictors, predictor_iris1)
self.send_signal(self.widget.Inputs.predictors, predictor_iris2, 1)
colors = self.widget._get_colors()
np.testing.assert_array_equal(colors, LimitedDiscretePalette(3).palette)
# case 4: two domains different values order, matching colors
idom = self.iris.domain
# this way we know that default colors are not used
colors = LimitedDiscretePalette(5).palette[2:]
dom1 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values)
)
dom2 = Domain(
idom.attributes,
DiscreteVariable(idom.class_var.name, idom.class_var.values[::-1])
)
dom1.class_var.colors = colors
dom2.class_var.colors = colors[::-1] # colors mixed same than values
iris1 = self.iris[:100].transform(dom1)
iris2 = self.iris[:100].transform(dom2)
predictor_iris1 = ConstantLearner()(iris1)
predictor_iris2 = ConstantLearner()(iris2)
self.send_signal(self.widget.Inputs.predictors, predictor_iris1)
self.send_signal(self.widget.Inputs.predictors, predictor_iris2, 1)
colors = self.widget._get_colors()
np.testing.assert_array_equal(colors, iris1.domain.class_var.colors)