def _column1():
if role == Qt.DecorationRole:
continuous_palette = ContinuousPaletteGenerator(*var.colors)
line = continuous_palette.getRGB(np.arange(0, 1, 1 / 256))
data = np.arange(0, 256, dtype=np.int8).reshape(
(1, 256)).repeat(16, 0)
img = QImage(data, 256, 16, QImage.Format_Indexed8)
img.setColorCount(256)
img.setColorTable([qRgb(*x) for x in line])
img.data = data
return img
if role == Qt.ToolTipRole:
return "{} - {}".format(self._encode_color(var.colors[0]),
self._encode_color(var.colors[1]))
if role == ColorRole:
return var.colors
def _column1():
if role == Qt.DecorationRole:
continuous_palette = ContinuousPaletteGenerator(*var.colors)
line = continuous_palette.getRGB(np.arange(0, 1, 1 / 256))
data = np.arange(0, 256, dtype=np.int8). \
reshape((1, 256)). \
repeat(16, 0)
img = QImage(data, 256, 16, QImage.Format_Indexed8)
img.setColorCount(256)
img.setColorTable([qRgb(*x) for x in line])
img.data = data
return img
if role == Qt.ToolTipRole:
return "{} - {}".format(self._encode_color(var.colors[0]),
self._encode_color(var.colors[1]))
if role == ColorRole:
return var.colors
def _column1():
if role == Qt.DecorationRole:
continuous_palette = \
ContinuousPaletteGenerator(*desc.get_colors())
line = continuous_palette.getRGB(np.arange(0, 1, 1 / 256))
data = np.arange(0, 256, dtype=np.int8). \
reshape((1, 256)). \
repeat(16, 0)
img = QImage(data, 256, 16, QImage.Format_Indexed8)
img.setColorCount(256)
img.setColorTable([qRgb(*x) for x in line])
img.data = data
return img
if role == Qt.ToolTipRole:
colors = desc.get_colors()
return f"{self._encode_color(colors[0])} " \
f"- {self._encode_color(colors[1])}"
if role == ColorRole:
return desc.get_colors()
return None
class ProbabilitiesItem(orangeqt.PlotItem):
"""
Displays class probabilities in the background
:param classifier: The classifier for which the probabilities are calculated
:type classifier: orange.P2NN
:param granularity: The size of individual cells
:type granularity: int
:param scale: The data scale factor
:type scale: float
:param spacing: The space between cells
:param spacing: int
:param rect: The rectangle into which to draw the probabilities. If unspecified, the entire plot is used.
:type rect: QRectF
"""
def __init__(self, classifier, granularity, scale, spacing, rect=None):
orangeqt.PlotItem.__init__(self)
self.classifier = classifier
self.rect = rect
self.granularity = granularity
self.scale = scale
self.spacing = spacing
self.pixmap_item = QGraphicsPixmapItem(self)
self.set_in_background(True)
self.setZValue(ProbabilitiesZValue)
def update_properties(self):
## Mostly copied from OWScatterPlotGraph
if not self.plot():
return
if not self.rect:
x,y = self.axes()
self.rect = self.plot().data_rect_for_axes(x,y)
s = self.graph_transform().mapRect(self.rect).size().toSize()
if not s.isValid():
return
rx = s.width()
ry = s.height()
rx -= rx % self.granularity
ry -= ry % self.granularity
p = self.graph_transform().map(QPointF(0, 0)) - self.graph_transform().map(self.rect.topLeft())
p = p.toPoint()
ox = p.x()
oy = -p.y()
if self.classifier.classVar.is_continuous:
imagebmp = orangeom.potentialsBitmap(self.classifier, rx, ry, ox, oy, self.granularity, self.scale)
palette = [qRgb(255.*i/255., 255.*i/255., 255-(255.*i/255.)) for i in range(255)] + [qRgb(255, 255, 255)]
else:
imagebmp, nShades = orangeom.potentialsBitmap(self.classifier, rx, ry, ox, oy, self.granularity, self.scale, self.spacing)
palette = []
sortedClasses = get_variable_values_sorted(self.classifier.domain.classVar)
for cls in self.classifier.classVar.values:
color = self.plot().discPalette.getRGB(sortedClasses.index(cls))
towhite = [255-c for c in color]
for s in range(nShades):
si = 1-float(s)/nShades
palette.append(qRgb(*tuple([color[i]+towhite[i]*si for i in (0, 1, 2)])))
palette.extend([qRgb(255, 255, 255) for i in range(256-len(palette))])
self.potentialsImage = QImage(imagebmp, rx, ry, QImage.Format_Indexed8)
self.potentialsImage.setColorTable(palette)
self.potentialsImage.setNumColors(256)
self.pixmap_item.setPixmap(QPixmap.fromImage(self.potentialsImage))
self.pixmap_item.setPos(self.graph_transform().map(self.rect.bottomLeft()))
def data_rect(self):
return self.rect if self.rect else QRectF()
