def paintEvent(self, QPaintEvent):
import math
if self.direction == 'x':
_angle = self.parent.rotationX
_x1 = self.width() / 2
_y1 = self.height() / 2
_x2 = math.cos(math.radians(_angle)) * (self.width() / 2)
_y2 = math.sin(math.radians(_angle)) * (self.width() / 2)
elif self.direction == 'y':
_angle = self.parent.rotationY
_x1 = self.width() / 2
_y1 = self.height() / 2
_x2 = math.cos(math.radians(_angle + 90)) * (self.height() / 2)
_y2 = math.sin(math.radians(_angle + 90)) * (self.height() / 2)
else:
_angle = 0
_x1 = 0
_y1 = 0
_x2 = 0
_y2 = 0
_painter = QPainter(self)
_pm = QPixmap(self.width(), self.height())
_pm_compass = QPixmap(self.compassImage).transformed(QTransform().rotate(self.compassRotation))
_pm_image = QPixmap(self.image).transformed(QTransform().rotate(_angle))
_painter.drawPixmap(0, 0, _pm_compass)
_painter.setPen(QColor(255, 160, 47))
_painter.drawLine(_x1, _y1, _x2, _y2)
_x = (self.width() - _pm_image.width()) / 2
_y = (self.height() - _pm_image.height()) / 2
_painter.drawPixmap(_x, _y, _pm_image)
_painter.end()
def splash_screen():
# type: () -> Tuple[QPixmap, QRect]
"""
Return a splash screen pixmap and an text area within it.
The text area is used for displaying text messages during application
startup.
The default implementation returns a bland rectangle splash screen.
Returns
-------
t : Tuple[QPixmap, QRect]
A QPixmap and a rect area within it.
"""
path = pkg_resources.resource_filename(
__name__, "icons/orange-canvas-core-splash.svg")
pm = QPixmap(path)
version = QCoreApplication.applicationVersion()
if version:
version_parsed = LooseVersion(version)
version_comp = version_parsed.version
version = ".".join(map(str, version_comp[:2]))
size = 21 if len(version) < 5 else 16
font = QFont()
font.setPixelSize(size)
font.setBold(True)
font.setItalic(True)
font.setLetterSpacing(QFont.AbsoluteSpacing, 2)
metrics = QFontMetrics(font)
br = metrics.boundingRect(version).adjusted(-5, 0, 5, 0)
br.moveBottomRight(QPoint(pm.width() - 15, pm.height() - 15))
p = QPainter(pm)
p.setRenderHint(QPainter.Antialiasing)
p.setRenderHint(QPainter.TextAntialiasing)
p.setFont(font)
p.setPen(QColor("#231F20"))
p.drawText(br, Qt.AlignCenter, version)
p.end()
textarea = QRect(15, 15, 170, 20)
return pm, textarea
def __startInternalDrag(self, frame, hotSpot=None):
drag = QDrag(self)
pixmap = QPixmap(frame.size())
frame.render(pixmap)
transparent = QPixmap(pixmap.size())
transparent.fill(Qt.transparent)
painter = QPainter(transparent)
painter.setOpacity(0.35)
painter.drawPixmap(0, 0, pixmap.width(), pixmap.height(), pixmap)
painter.end()
drag.setPixmap(transparent)
if hotSpot is not None:
drag.setHotSpot(hotSpot)
mime = QMimeData()
mime.setData("application/x-internal-move", b"")
drag.setMimeData(mime)
return drag.exec(Qt.MoveAction)
def __startInternalDrag(self, frame, hotSpot=None):
drag = QDrag(self)
pixmap = QPixmap(frame.size())
frame.render(pixmap)
transparent = QPixmap(pixmap.size())
transparent.fill(Qt.transparent)
painter = QPainter(transparent)
painter.setOpacity(0.35)
painter.drawPixmap(0, 0, pixmap.width(), pixmap.height(), pixmap)
painter.end()
drag.setPixmap(transparent)
if hotSpot is not None:
drag.setHotSpot(hotSpot)
mime = QMimeData()
mime.setData("application/x-internal-move", b"")
drag.setMimeData(mime)
return drag.exec_(Qt.MoveAction)
def splash_screen():
# type: () -> Tuple[QPixmap, QRect]
"""
Return a splash screen pixmap and an text area within it.
The text area is used for displaying text messages during application
startup.
The default implementation returns a bland rectangle splash screen.
Returns
-------
t : Tuple[QPixmap, QRect]
A QPixmap and a rect area within it.
"""
path = pkg_resources.resource_filename(
__name__, "icons/orange-canvas-core-splash.svg")
pm = QPixmap(path)
version = QCoreApplication.applicationVersion()
if version:
version_parsed = LooseVersion(version)
version_comp = version_parsed.version
version = ".".join(map(str, version_comp[:2]))
size = 21 if len(version) < 5 else 16
font = QFont()
font.setPixelSize(size)
font.setBold(True)
font.setItalic(True)
font.setLetterSpacing(QFont.AbsoluteSpacing, 2)
metrics = QFontMetrics(font)
br = metrics.boundingRect(version).adjusted(-5, 0, 5, 0)
br.moveBottomRight(QPoint(pm.width() - 15, pm.height() - 15))
p = QPainter(pm)
p.setRenderHint(QPainter.Antialiasing)
p.setRenderHint(QPainter.TextAntialiasing)
p.setFont(font)
p.setPen(QColor("#231F20"))
p.drawText(br, Qt.AlignCenter, version)
p.end()
textarea = QRect(15, 15, 170, 20)
return pm, textarea
class OWParallelGraph(OWPlot, ScaleData):
show_distributions = Setting(False)
show_attr_values = Setting(True)
show_statistics = Setting(default=False)
group_lines = Setting(default=False)
number_of_groups = Setting(default=5)
number_of_steps = Setting(default=30)
use_splines = Setting(False)
alpha_value = Setting(150)
alpha_value_2 = Setting(150)
def __init__(self, widget, parent=None, name=None):
OWPlot.__init__(self, parent, name, axes=[], widget=widget)
ScaleData.__init__(self)
self.update_antialiasing(False)
self.widget = widget
self.last_selected_curve = None
self.enableGridXB(0)
self.enableGridYL(0)
self.domain_contingencies = None
self.auto_update_axes = 1
self.old_legend_keys = []
self.selection_conditions = {}
self.attributes = []
self.visualized_mid_labels = []
self.attribute_indices = []
self.valid_data = []
self.groups = {}
self.colors = None
self.selected_examples = []
self.unselected_examples = []
self.bottom_pixmap = QPixmap(
gui.resource_filename("icons/upgreenarrow.png"))
self.top_pixmap = QPixmap(
gui.resource_filename("icons/downgreenarrow.png"))
def set_data(self, data, subset_data=None, **args):
self.start_progress()
self.set_progress(1, 100)
self.data = data
self.have_data = True
self.domain_contingencies = None
self.groups = {}
OWPlot.setData(self, data)
ScaleData.set_data(self, data, no_data=True, **args)
self._compute_domain_data_stat()
self.end_progress()
def update_data(self, attributes, mid_labels=None):
old_selection_conditions = self.selection_conditions
self.clear()
if self.data is None:
return
if len(attributes) < 2:
return
if self.show_statistics:
self.alpha_value = TRANSPARENT
self.alpha_value_2 = VISIBLE
else:
self.alpha_value = VISIBLE
self.alpha_value_2 = TRANSPARENT
self.attributes = attributes
self.attribute_indices = [
self.domain.index(name) for name in self.attributes
]
self.valid_data = self.get_valid_list(self.attribute_indices)
self.visualized_mid_labels = mid_labels
self.add_relevant_selections(old_selection_conditions)
class_var = self.domain.class_var
if not class_var:
self.colors = None
elif class_var.is_discrete:
self.colors = class_var.colors
elif class_var.is_continuous:
self.colors = ContinuousPaletteGenerator(*class_var.colors)
if self.group_lines:
self.show_statistics = False
self.draw_groups()
else:
self.show_statistics = False
self.draw_curves()
self.draw_distributions()
self.draw_axes()
self.draw_statistics()
self.draw_mid_labels(mid_labels)
self.draw_legend()
self.replot()
def add_relevant_selections(self, old_selection_conditions):
"""Keep only conditions related to the currently visualized attributes"""
for name, value in old_selection_conditions.items():
if name in self.attributes:
self.selection_conditions[name] = value
def draw_axes(self):
self.remove_all_axes()
for i in range(len(self.attributes)):
axis_id = UserAxis + i
a = self.add_axis(axis_id,
line=QLineF(i, 0, i, 1),
arrows=AxisStart | AxisEnd,
zoomable=True)
a.always_horizontal_text = True
a.max_text_width = 100
a.title_margin = -10
a.text_margin = 0
a.setZValue(5)
self.set_axis_title(axis_id, self.domain[self.attributes[i]].name)
self.set_show_axis_title(axis_id, self.show_attr_values)
if self.show_attr_values:
attr = self.domain[self.attributes[i]]
if attr.is_continuous:
self.set_axis_scale(axis_id, self.attr_values[attr][0],
self.attr_values[attr][1])
elif attr.is_discrete:
attribute_values = get_variable_values_sorted(
self.domain[self.attributes[i]])
attr_len = len(attribute_values)
values = [
float(1.0 + 2.0 * j) / float(2 * attr_len)
for j in range(len(attribute_values))
]
a.set_bounds((0, 1))
self.set_axis_labels(axis_id,
labels=attribute_values,
values=values)
def draw_curves(self):
conditions = {
name: self.attributes.index(name)
for name in self.selection_conditions.keys()
}
def is_selected(example):
return all(self.selection_conditions[name][0] <= example[index] <=
self.selection_conditions[name][1]
for (name, index) in list(conditions.items()))
selected_curves = defaultdict(list)
background_curves = defaultdict(list)
diff, mins = [], []
for i in self.attribute_indices:
var = self.domain[i]
if var.is_discrete:
diff.append(len(var.values))
mins.append(-0.5)
else:
diff.append(
self.domain_data_stat[i].max - self.domain_data_stat[i].min
or 1)
mins.append(self.domain_data_stat[i].min)
def scale_row(row):
return [(x - m) / d for x, m, d in zip(row, mins, diff)]
for row_idx, row in enumerate(self.data[:, self.attribute_indices]):
if any(np.isnan(v) for v in row.x):
continue
color = tuple(self.select_color(row_idx))
if is_selected(row):
color += (self.alpha_value, )
selected_curves[color].extend(scale_row(row))
self.selected_examples.append(row_idx)
else:
color += (self.alpha_value_2, )
background_curves[color].extend(row)
self.unselected_examples.append(row_idx)
self._draw_curves(selected_curves)
self._draw_curves(background_curves)
def select_color(self, row_index):
domain = self.data.domain
if domain.class_var is None:
return 0, 0, 0
class_val = self.data[row_index, domain.index(domain.class_var)]
if domain.has_continuous_class:
return self.continuous_palette.getRGB(class_val)
else:
return self.colors[int(class_val)]
def _draw_curves(self, selected_curves):
n_attr = len(self.attributes)
for color, y_values in sorted(selected_curves.items()):
n_rows = int(len(y_values) / n_attr)
x_values = list(range(n_attr)) * n_rows
curve = OWCurve()
curve.set_style(OWCurve.Lines)
curve.set_color(QColor(*color))
curve.set_segment_length(n_attr)
curve.set_data(x_values, y_values)
curve.attach(self)
def draw_groups(self):
phis, mus, sigmas = self.compute_groups()
diff, mins = [], []
for i in self.attribute_indices:
var = self.domain[i]
if var.is_discrete:
diff.append(len(var.values))
mins.append(-0.5)
else:
diff.append(
self.domain_data_stat[i].max - self.domain_data_stat[i].min
or 1)
mins.append(self.domain_data_stat[i].min)
for j, (phi, cluster_mus,
cluster_sigma) in enumerate(zip(phis, mus, sigmas)):
for i, (mu1, sigma1, mu2, sigma2), in enumerate(
zip(cluster_mus, cluster_sigma, cluster_mus[1:],
cluster_sigma[1:])):
nmu1 = (mu1 - mins[i]) / diff[i]
nmu2 = (mu2 - mins[i + 1]) / diff[i + 1]
nsigma1 = math.sqrt(sigma1) / diff[i]
nsigma2 = math.sqrt(sigma2) / diff[i + 1]
polygon = ParallelCoordinatePolygon(
i, nmu1, nmu2, nsigma1, nsigma2, phi,
tuple(self.colors[j]) if self.colors else (0, 0, 0))
polygon.attach(self)
self.replot()
def compute_groups(self):
key = (tuple(self.attributes), self.number_of_groups,
self.number_of_steps)
if key not in self.groups:
def callback(i, n):
self.set_progress(i, 2 * n)
conts = create_contingencies(self.data[:, self.attribute_indices],
callback=callback)
self.set_progress(50, 100)
w, mu, sigma, phi = lac(conts, self.number_of_groups,
self.number_of_steps)
self.set_progress(100, 100)
self.groups[key] = list(map(np.nan_to_num, (phi, mu, sigma)))
return self.groups[key]
def draw_legend(self):
domain = self.data.domain
class_var = domain.class_var
if class_var:
if class_var.is_discrete:
self.legend().clear()
values = get_variable_values_sorted(class_var)
for i, value in enumerate(values):
self.legend().add_item(
class_var.name, value,
OWPoint(OWPoint.Rect, QColor(*self.colors[i]), 10))
else:
values = self.attr_values[class_var]
decimals = class_var.number_of_decimals
self.legend().add_color_gradient(
class_var.name, ["%%.%df" % decimals % v for v in values])
else:
self.legend().clear()
self.old_legend_keys = []
def draw_mid_labels(self, mid_labels):
if mid_labels:
for j in range(len(mid_labels)):
self.addMarker(mid_labels[j],
j + 0.5,
1.0,
alignment=Qt.AlignCenter | Qt.AlignTop)
def draw_statistics(self):
"""Draw lines that represent standard deviation or quartiles"""
return # TODO: Implement using BasicStats
if self.show_statistics and self.data is not None:
data = []
domain = self.data.domain
for attr_idx in self.attribute_indices:
if not self.domain[attr_idx].is_continuous:
data.append([()])
continue # only for continuous attributes
if not domain.class_var or domain.has_continuous_class:
if self.show_statistics == MEANS:
m = self.domain_data_stat[attr_idx].mean
dev = self.domain_data_stat[attr_idx].var
data.append([(m - dev, m, m + dev)])
elif self.show_statistics == MEDIAN:
data.append([(0, 0, 0)])
continue
sorted_array = np.sort(attr_values)
if len(sorted_array) > 0:
data.append([
(sorted_array[int(len(sorted_array) / 4.0)],
sorted_array[int(len(sorted_array) / 2.0)],
sorted_array[int(len(sorted_array) * 0.75)])
])
else:
data.append([(0, 0, 0)])
else:
curr = []
class_values = get_variable_values_sorted(
self.domain.class_var)
class_index = self.domain.index(self.domain.class_var)
for c in range(len(class_values)):
attr_values = self.data[attr_idx,
self.data[class_index] == c]
attr_values = attr_values[~np.isnan(attr_values)]
if len(attr_values) == 0:
curr.append((0, 0, 0))
continue
if self.show_statistics == MEANS:
m = attr_values.mean()
dev = attr_values.std()
curr.append((m - dev, m, m + dev))
elif self.show_statistics == MEDIAN:
sorted_array = np.sort(attr_values)
curr.append(
(sorted_array[int(len(attr_values) / 4.0)],
sorted_array[int(len(attr_values) / 2.0)],
sorted_array[int(len(attr_values) * 0.75)]))
data.append(curr)
# draw vertical lines
for i in range(len(data)):
for c in range(len(data[i])):
if data[i][c] == ():
continue
x = i - 0.03 * (len(data[i]) - 1) / 2.0 + c * 0.03
col = QColor(self.discrete_palette[c])
col.setAlpha(self.alpha_value_2)
self.add_curve(
"",
col,
col,
3,
OWCurve.Lines,
OWPoint.NoSymbol,
xData=[x, x, x],
yData=[data[i][c][0], data[i][c][1], data[i][c][2]],
lineWidth=4)
self.add_curve("",
col,
col,
1,
OWCurve.Lines,
OWPoint.NoSymbol,
xData=[x - 0.03, x + 0.03],
yData=[data[i][c][0], data[i][c][0]],
lineWidth=4)
self.add_curve("",
col,
col,
1,
OWCurve.Lines,
OWPoint.NoSymbol,
xData=[x - 0.03, x + 0.03],
yData=[data[i][c][1], data[i][c][1]],
lineWidth=4)
self.add_curve("",
col,
col,
1,
OWCurve.Lines,
OWPoint.NoSymbol,
xData=[x - 0.03, x + 0.03],
yData=[data[i][c][2], data[i][c][2]],
lineWidth=4)
# draw lines with mean/median values
if not domain.class_var or domain.has_continuous_class:
class_count = 1
else:
class_count = len(self.domain.class_var.values)
for c in range(class_count):
diff = -0.03 * (class_count - 1) / 2.0 + c * 0.03
ys = []
xs = []
for i in range(len(data)):
if data[i] != [()]:
ys.append(data[i][c][1])
xs.append(i + diff)
else:
if len(xs) > 1:
col = QColor(self.discrete_palette[c])
col.setAlpha(self.alpha_value_2)
self.add_curve("",
col,
col,
1,
OWCurve.Lines,
OWPoint.NoSymbol,
xData=xs,
yData=ys,
lineWidth=4)
xs = []
ys = []
col = QColor(self.discrete_palette[c])
col.setAlpha(self.alpha_value_2)
self.add_curve("",
col,
col,
1,
OWCurve.Lines,
OWPoint.NoSymbol,
xData=xs,
yData=ys,
lineWidth=4)
def draw_distributions(self):
"""Draw distributions with discrete attributes"""
if not (self.show_distributions and self.data is not None
and self.domain.has_discrete_class):
return
class_count = len(self.domain.class_var.values)
class_ = self.domain.class_var
# we create a hash table of possible class values (happens only if we have a discrete class)
if self.domain_contingencies is None:
self.domain_contingencies = dict(
zip([attr for attr in self.domain if attr.is_discrete],
get_contingencies(self.data, skipContinuous=True)))
self.domain_contingencies[class_] = get_contingency(
self.data, class_, class_)
max_count = max([
contingency.max()
for contingency in self.domain_contingencies.values()
] or [1])
sorted_class_values = get_variable_values_sorted(self.domain.class_var)
for axis_idx, attr_idx in enumerate(self.attribute_indices):
attr = self.domain[attr_idx]
if attr.is_discrete:
continue
contingency = self.domain_contingencies[attr]
attr_len = len(attr.values)
# we create a hash table of variable values and their indices
sorted_variable_values = get_variable_values_sorted(attr)
# create bar curve
for j in range(attr_len):
attribute_value = sorted_variable_values[j]
value_count = contingency[:, attribute_value]
for i in range(class_count):
class_value = sorted_class_values[i]
color = QColor(*self.colors[i])
color.setAlpha(self.alpha_value)
width = float(
value_count[class_value] * 0.5) / float(max_count)
y_off = float(1.0 + 2.0 * j) / float(2 * attr_len)
height = 0.7 / float(class_count * attr_len)
y_low_bottom = y_off + float(
class_count * height) / 2.0 - i * height
curve = PolygonCurve(QPen(color),
QBrush(color),
xData=[
axis_idx, axis_idx + width,
axis_idx + width, axis_idx
],
yData=[
y_low_bottom, y_low_bottom,
y_low_bottom - height,
y_low_bottom - height
],
tooltip=attr.name)
curve.attach(self)
# handle tooltip events
def event(self, ev):
if ev.type() == QEvent.ToolTip:
x = self.inv_transform(xBottom, ev.pos().x())
y = self.inv_transform(yLeft, ev.pos().y())
canvas_position = self.mapToScene(ev.pos())
x_float = self.inv_transform(xBottom, canvas_position.x())
contact, (index,
pos) = self.testArrowContact(int(round(x_float)),
canvas_position.x(),
canvas_position.y())
if contact:
attr = self.domain[self.attributes[index]]
if attr.is_continuous:
condition = self.selection_conditions.get(
attr.name, [0, 1])
val = self.attr_values[attr][0] + condition[pos] * (
self.attr_values[attr][1] - self.attr_values[attr][0])
str_val = attr.name + "= %%.%df" % attr.number_of_decimals % val
QToolTip.showText(ev.globalPos(), str_val)
else:
for curve in self.items():
if type(curve) == PolygonCurve and \
curve.boundingRect().contains(x, y) and \
getattr(curve, "tooltip", None):
(name, value, total, dist) = curve.tooltip
count = sum([v[1] for v in dist])
if count == 0:
continue
tooltip_text = "Attribute: <b>%s</b><br>Value: <b>%s</b><br>" \
"Total instances: <b>%i</b> (%.1f%%)<br>" \
"Class distribution:<br>" % (
name, value, count, 100.0 * count / float(total))
for (val, n) in dist:
tooltip_text += " <b>%s</b> : <b>%i</b> (%.1f%%)<br>" % (
val, n, 100.0 * float(n) / float(count))
QToolTip.showText(ev.globalPos(), tooltip_text[:-4])
elif ev.type() == QEvent.MouseMove:
QToolTip.hideText()
return OWPlot.event(self, ev)
def testArrowContact(self, indices, x, y):
if type(indices) != list: indices = [indices]
for index in indices:
if index >= len(self.attributes) or index < 0:
continue
int_x = self.transform(xBottom, index)
bottom = self.transform(
yLeft,
self.selection_conditions.get(self.attributes[index],
[0, 1])[0])
bottom_rect = QRect(int_x - self.bottom_pixmap.width() / 2, bottom,
self.bottom_pixmap.width(),
self.bottom_pixmap.height())
if bottom_rect.contains(QPoint(x, y)):
return 1, (index, 0)
top = self.transform(
yLeft,
self.selection_conditions.get(self.attributes[index],
[0, 1])[1])
top_rect = QRect(int_x - self.top_pixmap.width() / 2,
top - self.top_pixmap.height(),
self.top_pixmap.width(), self.top_pixmap.height())
if top_rect.contains(QPoint(x, y)):
return 1, (index, 1)
return 0, (0, 0)
def mousePressEvent(self, e):
canvas_position = self.mapToScene(e.pos())
x = self.inv_transform(xBottom, canvas_position.x())
contact, info = self.testArrowContact(int(round(x)),
canvas_position.x(),
canvas_position.y())
if contact:
self.pressed_arrow = info
else:
OWPlot.mousePressEvent(self, e)
def mouseMoveEvent(self, e):
if hasattr(self, "pressed_arrow"):
canvas_position = self.mapToScene(e.pos())
y = min(1, max(0, self.inv_transform(yLeft, canvas_position.y())))
index, pos = self.pressed_arrow
attr = self.domain[self.attributes[index]]
old_condition = self.selection_conditions.get(attr.name, [0, 1])
old_condition[pos] = y
self.selection_conditions[attr.name] = old_condition
self.update_data(self.attributes, self.visualized_mid_labels)
if attr.is_continuous:
val = self.attr_values[attr][0] + old_condition[pos] * (
self.attr_values[attr][1] - self.attr_values[attr][0])
strVal = attr.name + "= %.2f" % val
QToolTip.showText(e.globalPos(), strVal)
if self.sendSelectionOnUpdate and self.auto_send_selection_callback:
self.auto_send_selection_callback()
else:
OWPlot.mouseMoveEvent(self, e)
def mouseReleaseEvent(self, e):
if hasattr(self, "pressed_arrow"):
del self.pressed_arrow
else:
OWPlot.mouseReleaseEvent(self, e)
def zoom_to_rect(self, r):
r.setTop(self.graph_area.top())
r.setBottom(self.graph_area.bottom())
super().zoom_to_rect(r)
def removeAllSelections(self, send=1):
self.selection_conditions = {}
self.update_data(self.attributes, self.visualized_mid_labels)
# draw the curves and the selection conditions
def drawCanvas(self, painter):
OWPlot.drawCanvas(self, painter)
for i in range(
int(
max(
0,
math.floor(
self.axisScaleDiv(
xBottom).interval().minValue()))),
int(
min(
len(self.attributes),
math.ceil(
self.axisScaleDiv(xBottom).interval().maxValue()) +
1))):
bottom, top = self.selection_conditions.get(
self.attributes[i], (0, 1))
painter.drawPixmap(
self.transform(xBottom, i) - self.bottom_pixmap.width() / 2,
self.transform(yLeft, bottom), self.bottom_pixmap)
painter.drawPixmap(
self.transform(xBottom, i) - self.top_pixmap.width() / 2,
self.transform(yLeft, top) - self.top_pixmap.height(),
self.top_pixmap)
def auto_send_selection_callback(self):
pass
def clear(self):
super().clear()
self.attributes = []
self.visualized_mid_labels = []
self.selected_examples = []
self.unselected_examples = []
self.selection_conditions = {}
class OWParallelGraph(OWPlot, ScaleData):
show_distributions = Setting(False)
show_attr_values = Setting(True)
show_statistics = Setting(default=False)
group_lines = Setting(default=False)
number_of_groups = Setting(default=5)
number_of_steps = Setting(default=30)
use_splines = Setting(False)
alpha_value = Setting(150)
alpha_value_2 = Setting(150)
def __init__(self, widget, parent=None, name=None):
OWPlot.__init__(self, parent, name, axes=[], widget=widget)
ScaleData.__init__(self)
self.update_antialiasing(False)
self.widget = widget
self.last_selected_curve = None
self.enableGridXB(0)
self.enableGridYL(0)
self.domain_contingencies = None
self.auto_update_axes = 1
self.old_legend_keys = []
self.selection_conditions = {}
self.attributes = []
self.visualized_mid_labels = []
self.attribute_indices = []
self.valid_data = []
self.groups = {}
self.colors = None
self.selected_examples = []
self.unselected_examples = []
self.bottom_pixmap = QPixmap(gui.resource_filename("icons/upgreenarrow.png"))
self.top_pixmap = QPixmap(gui.resource_filename("icons/downgreenarrow.png"))
def set_data(self, data, subset_data=None, **args):
self.start_progress()
self.set_progress(1, 100)
self.data = data
self.have_data = True
self.domain_contingencies = None
self.groups = {}
OWPlot.setData(self, data)
ScaleData.set_data(self, data, no_data=True, **args)
self._compute_domain_data_stat()
self.end_progress()
def update_data(self, attributes, mid_labels=None):
old_selection_conditions = self.selection_conditions
self.clear()
if self.data is None:
return
if len(attributes) < 2:
return
if self.show_statistics:
self.alpha_value = TRANSPARENT
self.alpha_value_2 = VISIBLE
else:
self.alpha_value = VISIBLE
self.alpha_value_2 = TRANSPARENT
self.attributes = attributes
self.attribute_indices = [self.domain.index(name)
for name in self.attributes]
self.valid_data = self.get_valid_list(self.attribute_indices)
self.visualized_mid_labels = mid_labels
self.add_relevant_selections(old_selection_conditions)
class_var = self.domain.class_var
if not class_var:
self.colors = None
elif class_var.is_discrete:
self.colors = class_var.colors
elif class_var.is_continuous:
self.colors = ContinuousPaletteGenerator(*class_var.colors)
if self.group_lines:
self.show_statistics = False
self.draw_groups()
else:
self.show_statistics = False
self.draw_curves()
self.draw_distributions()
self.draw_axes()
self.draw_statistics()
self.draw_mid_labels(mid_labels)
self.draw_legend()
self.replot()
def add_relevant_selections(self, old_selection_conditions):
"""Keep only conditions related to the currently visualized attributes"""
for name, value in old_selection_conditions.items():
if name in self.attributes:
self.selection_conditions[name] = value
def draw_axes(self):
self.remove_all_axes()
for i in range(len(self.attributes)):
axis_id = UserAxis + i
a = self.add_axis(axis_id, line=QLineF(i, 0, i, 1), arrows=AxisStart | AxisEnd,
zoomable=True)
a.always_horizontal_text = True
a.max_text_width = 100
a.title_margin = -10
a.text_margin = 0
a.setZValue(5)
self.set_axis_title(axis_id, self.domain[self.attributes[i]].name)
self.set_show_axis_title(axis_id, self.show_attr_values)
if self.show_attr_values:
attr = self.domain[self.attributes[i]]
if attr.is_continuous:
self.set_axis_scale(axis_id, self.attr_values[attr][0],
self.attr_values[attr][1])
elif attr.is_discrete:
attribute_values = get_variable_values_sorted(self.domain[self.attributes[i]])
attr_len = len(attribute_values)
values = [float(1.0 + 2.0 * j) / float(2 * attr_len) for j in range(len(attribute_values))]
a.set_bounds((0, 1))
self.set_axis_labels(axis_id, labels=attribute_values, values=values)
def draw_curves(self):
conditions = {name: self.attributes.index(name) for name in self.selection_conditions.keys()}
def is_selected(example):
return all(self.selection_conditions[name][0] <= example[index] <= self.selection_conditions[name][1]
for (name, index) in list(conditions.items()))
selected_curves = defaultdict(list)
background_curves = defaultdict(list)
diff, mins = [], []
for i in self.attribute_indices:
var = self.domain[i]
if var.is_discrete:
diff.append(len(var.values))
mins.append(-0.5)
else:
diff.append(self.domain_data_stat[i].max - self.domain_data_stat[i].min or 1)
mins.append(self.domain_data_stat[i].min)
def scale_row(row):
return [(x - m) / d for x, m, d in zip(row, mins, diff)]
for row_idx, row in enumerate(self.data[:, self.attribute_indices]):
if any(np.isnan(v) for v in row.x):
continue
color = tuple(self.select_color(row_idx))
if is_selected(row):
color += (self.alpha_value,)
selected_curves[color].extend(scale_row(row))
self.selected_examples.append(row_idx)
else:
color += (self.alpha_value_2,)
background_curves[color].extend(row)
self.unselected_examples.append(row_idx)
self._draw_curves(selected_curves)
self._draw_curves(background_curves)
def select_color(self, row_index):
domain = self.data.domain
if domain.class_var is None:
return 0, 0, 0
class_val = self.data[row_index, domain.index(domain.class_var)]
if domain.has_continuous_class:
return self.continuous_palette.getRGB(class_val)
else:
return self.colors[int(class_val)]
def _draw_curves(self, selected_curves):
n_attr = len(self.attributes)
for color, y_values in sorted(selected_curves.items()):
n_rows = int(len(y_values) / n_attr)
x_values = list(range(n_attr)) * n_rows
curve = OWCurve()
curve.set_style(OWCurve.Lines)
curve.set_color(QColor(*color))
curve.set_segment_length(n_attr)
curve.set_data(x_values, y_values)
curve.attach(self)
def draw_groups(self):
phis, mus, sigmas = self.compute_groups()
diff, mins = [], []
for i in self.attribute_indices:
var = self.domain[i]
if var.is_discrete:
diff.append(len(var.values))
mins.append(-0.5)
else:
diff.append(self.domain_data_stat[i].max - self.domain_data_stat[i].min or 1)
mins.append(self.domain_data_stat[i].min)
for j, (phi, cluster_mus, cluster_sigma) in enumerate(zip(phis, mus, sigmas)):
for i, (mu1, sigma1, mu2, sigma2), in enumerate(
zip(cluster_mus, cluster_sigma, cluster_mus[1:], cluster_sigma[1:])):
nmu1 = (mu1 - mins[i]) / diff[i]
nmu2 = (mu2 - mins[i + 1]) / diff[i + 1]
nsigma1 = math.sqrt(sigma1) / diff[i]
nsigma2 = math.sqrt(sigma2) / diff[i + 1]
polygon = ParallelCoordinatePolygon(i, nmu1, nmu2, nsigma1, nsigma2, phi,
tuple(self.colors[j]) if self.colors
else (0, 0, 0))
polygon.attach(self)
self.replot()
def compute_groups(self):
key = (tuple(self.attributes), self.number_of_groups, self.number_of_steps)
if key not in self.groups:
def callback(i, n):
self.set_progress(i, 2*n)
conts = create_contingencies(self.data[:, self.attribute_indices], callback=callback)
self.set_progress(50, 100)
w, mu, sigma, phi = lac(conts, self.number_of_groups, self.number_of_steps)
self.set_progress(100, 100)
self.groups[key] = list(map(np.nan_to_num, (phi, mu, sigma)))
return self.groups[key]
def draw_legend(self):
domain = self.data.domain
class_var = domain.class_var
if class_var:
if class_var.is_discrete:
self.legend().clear()
values = get_variable_values_sorted(class_var)
for i, value in enumerate(values):
self.legend().add_item(
class_var.name, value,
OWPoint(OWPoint.Rect, QColor(*self.colors[i]), 10))
else:
values = self.attr_values[class_var]
decimals = class_var.number_of_decimals
self.legend().add_color_gradient(
class_var.name, ["%%.%df" % decimals % v for v in values])
else:
self.legend().clear()
self.old_legend_keys = []
def draw_mid_labels(self, mid_labels):
if mid_labels:
for j in range(len(mid_labels)):
self.addMarker(mid_labels[j], j + 0.5, 1.0, alignment=Qt.AlignCenter | Qt.AlignTop)
def draw_statistics(self):
"""Draw lines that represent standard deviation or quartiles"""
return # TODO: Implement using BasicStats
if self.show_statistics and self.data is not None:
data = []
domain = self.data.domain
for attr_idx in self.attribute_indices:
if not self.domain[attr_idx].is_continuous:
data.append([()])
continue # only for continuous attributes
if not domain.class_var or domain.has_continuous_class:
if self.show_statistics == MEANS:
m = self.domain_data_stat[attr_idx].mean
dev = self.domain_data_stat[attr_idx].var
data.append([(m - dev, m, m + dev)])
elif self.show_statistics == MEDIAN:
data.append([(0, 0, 0)]); continue
sorted_array = np.sort(attr_values)
if len(sorted_array) > 0:
data.append([(sorted_array[int(len(sorted_array) / 4.0)],
sorted_array[int(len(sorted_array) / 2.0)],
sorted_array[int(len(sorted_array) * 0.75)])])
else:
data.append([(0, 0, 0)])
else:
curr = []
class_values = get_variable_values_sorted(self.domain.class_var)
class_index = self.domain.index(self.domain.class_var)
for c in range(len(class_values)):
attr_values = self.data[attr_idx, self.data[class_index] == c]
attr_values = attr_values[~np.isnan(attr_values)]
if len(attr_values) == 0:
curr.append((0, 0, 0))
continue
if self.show_statistics == MEANS:
m = attr_values.mean()
dev = attr_values.std()
curr.append((m - dev, m, m + dev))
elif self.show_statistics == MEDIAN:
sorted_array = np.sort(attr_values)
curr.append((sorted_array[int(len(attr_values) / 4.0)],
sorted_array[int(len(attr_values) / 2.0)],
sorted_array[int(len(attr_values) * 0.75)]))
data.append(curr)
# draw vertical lines
for i in range(len(data)):
for c in range(len(data[i])):
if data[i][c] == ():
continue
x = i - 0.03 * (len(data[i]) - 1) / 2.0 + c * 0.03
col = QColor(self.discrete_palette[c])
col.setAlpha(self.alpha_value_2)
self.add_curve("", col, col, 3, OWCurve.Lines, OWPoint.NoSymbol, xData=[x, x, x],
yData=[data[i][c][0], data[i][c][1], data[i][c][2]], lineWidth=4)
self.add_curve("", col, col, 1, OWCurve.Lines, OWPoint.NoSymbol, xData=[x - 0.03, x + 0.03],
yData=[data[i][c][0], data[i][c][0]], lineWidth=4)
self.add_curve("", col, col, 1, OWCurve.Lines, OWPoint.NoSymbol, xData=[x - 0.03, x + 0.03],
yData=[data[i][c][1], data[i][c][1]], lineWidth=4)
self.add_curve("", col, col, 1, OWCurve.Lines, OWPoint.NoSymbol, xData=[x - 0.03, x + 0.03],
yData=[data[i][c][2], data[i][c][2]], lineWidth=4)
# draw lines with mean/median values
if not domain.class_var or domain.has_continuous_class:
class_count = 1
else:
class_count = len(self.domain.class_var.values)
for c in range(class_count):
diff = - 0.03 * (class_count - 1) / 2.0 + c * 0.03
ys = []
xs = []
for i in range(len(data)):
if data[i] != [()]:
ys.append(data[i][c][1])
xs.append(i + diff)
else:
if len(xs) > 1:
col = QColor(self.discrete_palette[c])
col.setAlpha(self.alpha_value_2)
self.add_curve("", col, col, 1, OWCurve.Lines,
OWPoint.NoSymbol, xData=xs, yData=ys, lineWidth=4)
xs = []
ys = []
col = QColor(self.discrete_palette[c])
col.setAlpha(self.alpha_value_2)
self.add_curve("", col, col, 1, OWCurve.Lines,
OWPoint.NoSymbol, xData=xs, yData=ys, lineWidth=4)
def draw_distributions(self):
"""Draw distributions with discrete attributes"""
if not (self.show_distributions and self.data is not None and self.domain.has_discrete_class):
return
class_count = len(self.domain.class_var.values)
class_ = self.domain.class_var
# we create a hash table of possible class values (happens only if we have a discrete class)
if self.domain_contingencies is None:
self.domain_contingencies = dict(
zip([attr for attr in self.domain if attr.is_discrete],
get_contingencies(self.data, skipContinuous=True)))
self.domain_contingencies[class_] = get_contingency(self.data, class_, class_)
max_count = max([contingency.max() for contingency in self.domain_contingencies.values()] or [1])
sorted_class_values = get_variable_values_sorted(self.domain.class_var)
for axis_idx, attr_idx in enumerate(self.attribute_indices):
attr = self.domain[attr_idx]
if attr.is_discrete:
continue
contingency = self.domain_contingencies[attr]
attr_len = len(attr.values)
# we create a hash table of variable values and their indices
sorted_variable_values = get_variable_values_sorted(attr)
# create bar curve
for j in range(attr_len):
attribute_value = sorted_variable_values[j]
value_count = contingency[:, attribute_value]
for i in range(class_count):
class_value = sorted_class_values[i]
color = QColor(*self.colors[i])
color.setAlpha(self.alpha_value)
width = float(value_count[class_value] * 0.5) / float(max_count)
y_off = float(1.0 + 2.0 * j) / float(2 * attr_len)
height = 0.7 / float(class_count * attr_len)
y_low_bottom = y_off + float(class_count * height) / 2.0 - i * height
curve = PolygonCurve(QPen(color),
QBrush(color),
xData=[axis_idx, axis_idx + width,
axis_idx + width, axis_idx],
yData=[y_low_bottom, y_low_bottom, y_low_bottom - height,
y_low_bottom - height],
tooltip=attr.name)
curve.attach(self)
# handle tooltip events
def event(self, ev):
if ev.type() == QEvent.ToolTip:
x = self.inv_transform(xBottom, ev.pos().x())
y = self.inv_transform(yLeft, ev.pos().y())
canvas_position = self.mapToScene(ev.pos())
x_float = self.inv_transform(xBottom, canvas_position.x())
contact, (index, pos) = self.testArrowContact(int(round(x_float)), canvas_position.x(),
canvas_position.y())
if contact:
attr = self.domain[self.attributes[index]]
if attr.is_continuous:
condition = self.selection_conditions.get(attr.name, [0, 1])
val = self.attr_values[attr][0] + condition[pos] * (
self.attr_values[attr][1] - self.attr_values[attr][0])
str_val = attr.name + "= %%.%df" % attr.number_of_decimals % val
QToolTip.showText(ev.globalPos(), str_val)
else:
for curve in self.items():
if type(curve) == PolygonCurve and \
curve.boundingRect().contains(x, y) and \
getattr(curve, "tooltip", None):
(name, value, total, dist) = curve.tooltip
count = sum([v[1] for v in dist])
if count == 0:
continue
tooltip_text = "Attribute: <b>%s</b><br>Value: <b>%s</b><br>" \
"Total instances: <b>%i</b> (%.1f%%)<br>" \
"Class distribution:<br>" % (
name, value, count, 100.0 * count / float(total))
for (val, n) in dist:
tooltip_text += " <b>%s</b> : <b>%i</b> (%.1f%%)<br>" % (
val, n, 100.0 * float(n) / float(count))
QToolTip.showText(ev.globalPos(), tooltip_text[:-4])
elif ev.type() == QEvent.MouseMove:
QToolTip.hideText()
return OWPlot.event(self, ev)
def testArrowContact(self, indices, x, y):
if type(indices) != list: indices = [indices]
for index in indices:
if index >= len(self.attributes) or index < 0:
continue
int_x = self.transform(xBottom, index)
bottom = self.transform(yLeft,
self.selection_conditions.get(self.attributes[index], [0, 1])[0])
bottom_rect = QRect(int_x - self.bottom_pixmap.width() / 2, bottom, self.bottom_pixmap.width(),
self.bottom_pixmap.height())
if bottom_rect.contains(QPoint(x, y)):
return 1, (index, 0)
top = self.transform(yLeft,
self.selection_conditions.get(self.attributes[index], [0, 1])[1])
top_rect = QRect(int_x - self.top_pixmap.width() / 2, top - self.top_pixmap.height(),
self.top_pixmap.width(),
self.top_pixmap.height())
if top_rect.contains(QPoint(x, y)):
return 1, (index, 1)
return 0, (0, 0)
def mousePressEvent(self, e):
canvas_position = self.mapToScene(e.pos())
x = self.inv_transform(xBottom, canvas_position.x())
contact, info = self.testArrowContact(int(round(x)), canvas_position.x(), canvas_position.y())
if contact:
self.pressed_arrow = info
else:
OWPlot.mousePressEvent(self, e)
def mouseMoveEvent(self, e):
if hasattr(self, "pressed_arrow"):
canvas_position = self.mapToScene(e.pos())
y = min(1, max(0, self.inv_transform(yLeft, canvas_position.y())))
index, pos = self.pressed_arrow
attr = self.domain[self.attributes[index]]
old_condition = self.selection_conditions.get(attr.name, [0, 1])
old_condition[pos] = y
self.selection_conditions[attr.name] = old_condition
self.update_data(self.attributes, self.visualized_mid_labels)
if attr.is_continuous:
val = self.attr_values[attr][0] + old_condition[pos] * (
self.attr_values[attr][1] - self.attr_values[attr][0])
strVal = attr.name + "= %.2f" % val
QToolTip.showText(e.globalPos(), strVal)
if self.sendSelectionOnUpdate and self.auto_send_selection_callback:
self.auto_send_selection_callback()
else:
OWPlot.mouseMoveEvent(self, e)
def mouseReleaseEvent(self, e):
if hasattr(self, "pressed_arrow"):
del self.pressed_arrow
else:
OWPlot.mouseReleaseEvent(self, e)
def zoom_to_rect(self, r):
r.setTop(self.graph_area.top())
r.setBottom(self.graph_area.bottom())
super().zoom_to_rect(r)
def removeAllSelections(self, send=1):
self.selection_conditions = {}
self.update_data(self.attributes, self.visualized_mid_labels)
# draw the curves and the selection conditions
def drawCanvas(self, painter):
OWPlot.drawCanvas(self, painter)
for i in range(
int(max(0, math.floor(self.axisScaleDiv(xBottom).interval().minValue()))),
int(min(len(self.attributes),
math.ceil(self.axisScaleDiv(xBottom).interval().maxValue()) + 1))):
bottom, top = self.selection_conditions.get(self.attributes[i], (0, 1))
painter.drawPixmap(self.transform(xBottom, i) - self.bottom_pixmap.width() / 2,
self.transform(yLeft, bottom), self.bottom_pixmap)
painter.drawPixmap(self.transform(xBottom, i) - self.top_pixmap.width() / 2,
self.transform(yLeft, top) - self.top_pixmap.height(), self.top_pixmap)
def auto_send_selection_callback(self):
pass
def clear(self):
super().clear()
self.attributes = []
self.visualized_mid_labels = []
self.selected_examples = []
self.unselected_examples = []
self.selection_conditions = {}
