class LinePlot(HasTraits):
plot = Instance(Plot)
data = Instance(ArrayPlotData)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="Chaco Plot")
def __init__(self, **traits):
super(LinePlot, self).__init__(**traits)
x = np.linspace(-14, 14, 100)
y = np.sin(x) * x**3
data = ArrayPlotData(x=x, y=y)
plot = Plot(data)
plot.plot(("x", "y"), type="line", color="blue")
plot.title = "sin(x) * x^3"
plot.tools.append(PanTool(plot))
plot.tools.append(
DragZoom(plot, drag_button="right", maintain_aspect_ratio=False))
plot.overlays.append(ZoomTool(plot))
# plot.overlays.append(ZoomTool(plot, tool_mode="range", axis = "index",
# always_on=True, always_on_modifier="control"))
self.plot = plot
self.data = data
class ScatterPlotTraits(HasTraits):
plot = Instance(Plot)
data = Instance(ArrayPlotData)
color = Color("blue")
marker = marker_trait
traits_view = View(Group(Item('color', label="Color"),
Item('marker', label="Marker"),
Item('object.line.marker_size', label="Size"),
Item('plot',
editor=ComponentEditor(),
show_label=False),
orientation="vertical"),
width=800,
height=600,
resizable=True,
title="Chaco Plot")
def __init__(self, **traits):
super(ScatterPlotTraits, self).__init__(**traits)
x = np.linspace(-14, 14, 100)
y = np.sin(x) * x**3
data = ArrayPlotData(x=x, y=y)
plot = Plot(data)
self.line = plot.plot(("x", "y"), type="scatter", color="blue")[0]
self.plot = plot
self.data = data
def _color_changed(self):
self.line.color = self.color
def _marker_changed(self):
self.line.marker = self.marker
class ProbePlot(HasTraits):
def __init__(self):
super(Probe, self).__init__()
x = linspace(0, self.N, self.N / self.d)
y = linspace(0, self.N, self.N / self.d)
xgrid, ygrid = meshgrid(x[1:], y[1:])
z = exp(-(xgrid * xgrid + ygrid * ygrid) / 10000)
plotdata = ArrayPlotData(imagedata=z)
plot = Plot(plotdata)
self.renderer = plot.img_plot("imagedata",
xbounds=x,
ybounds=y,
colormap=bone)
#self.renderer = plot.plot(("x", "y"), type="scatter", color="blue")[0]
self.plot = plot
traits_view = View(VGroup(
HGroup(
Item('HT',
label="High Tension, kV",
help='The microscope accelerating voltage'), spring,
Item('wl', label="Wavelength, nm", style='readonly')),
HGroup(spring, Item('alpha', label="Conv. Angle")),
HGroup(VGroup(Item('noms', label="Nomenclature")),
Item('notations[self.noms]', label='Labels')),
HGroup(Item('plot', editor=ComponentEditor(), show_label=False))),
width=800,
height=600,
resizable=True,
title="Chaco Plot")
class MutiLinePlot(HasTraits):
plot = Instance(Plot)
data = Instance(ArrayPlotData)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="Chaco Plot")
def __init__(self, **traits):
super(MutiLinePlot, self).__init__(**traits)
x = np.linspace(-14, 14, 100)
y1 = np.sin(x) * x**3
y2 = np.cos(x) * x**3
data = ArrayPlotData(x=x, y1=y1, y2=y2)
plot = Plot(data)
plot.plot(("x", "y1"), type="line", color="blue", name="sin(x) * x**3")
plot.plot(("x", "y2"), type="line", color="red", name="cos(x) * x**3")
plot.plot(("x", "y2"),
type="scatter",
color="red",
marker="circle",
marker_size=2,
name="cos(x) * x**3 points")
plot.title = "Multiple Curves"
plot.legend.visible = True
self.plot = plot
self.data = data
class CircleSelectionDemo(HasTraits):
plot = Instance(Plot)
data = Instance(ArrayPlotData)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="Circle Selection Plot")
def __init__(self, **traits):
super(CircleSelectionDemo, self).__init__(**traits)
x = np.random.random(100) * 2
y = np.random.random(100)
data = ArrayPlotData(x=x, y=y)
plot = Plot(data)
scatter = plot.plot(("x", "y"), type="scatter", color="blue")[0]
scatter.tools.append(CircleSelection(scatter))
scatter.overlays.append(
ScatterInspectorOverlay(scatter,
selection_color="red",
selection_marker="circle",
selection_outline_color="black",
selection_marker_size=6))
scatter.overlays.append(CircleSelectionOverlay(scatter))
self.plot = plot
self.data = data
class LinePlot(HasTraits):
plot = Instance(Plot)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="Chaco Plot")
def __init__(self):
super(LinePlot, self).__init__()
x = linspace(-14, 14, 100)
y1 = sin(x) * x**3
y2 = cos(x) * x**3
plotdata = ArrayPlotData(x=x, y1=y1, y2=y2)
plot = Plot(plotdata)
plot.plot(("x", "y1"), type="line", color="blue", name="sin(x) * x**3")
plot.plot(("x", "y2"), type="line", color="red", name="cos(x) * x**3")
plot.plot(("x", "y2"),
type="scatter",
color="red",
marker="circle",
marker_size=2,
name="cos(x) * x**3 points")
plot.title = "Multiple Curves"
self.plot = plot
legend = Legend(padding=10, align="ur")
legend.plots = plot.plots
plot.overlays.append(legend)
class HistDemo(HasTraits):
plot = Instance(Plot)
timer = Instance(Timer)
need_update = Bool(False)
view = View(Item("plot", editor=ComponentEditor(), show_label=False),
resizable=True,
width=500,
height=250,
title="Hist Demo")
def __init__(self, **traits):
super(HistDemo, self).__init__(**traits)
img = cv.imread("lena.jpg")
gray_img = cv.Mat()
cv.cvtColor(img, gray_img, cv.CV_BGR2GRAY)
self.img = gray_img
self.img2 = self.img.clone()
result = cv.MatND()
r = cv.vector_float32([0, 256])
ranges = cv.vector_vector_float32([r, r])
cv.calcHist(cv.vector_Mat([self.img]),
channels=cv.vector_int([0, 1]),
mask=cv.Mat(),
hist=result,
histSize=cv.vector_int([256]),
ranges=ranges)
data = ArrayPlotData(x=np.arange(0, len(result[:])), y=result[:])
self.plot = Plot(data, padding=10)
line = self.plot.plot(("x", "y"))[0]
self.select_tool = RangeSelection(line, left_button_selects=True)
line.tools.append(self.select_tool)
self.select_tool.on_trait_change(self._selection_changed, "selection")
line.overlays.append(RangeSelectionOverlay(component=line))
cv.imshow("Hist Demo", self.img)
self.timer = Timer(50, self.on_timer)
def _selection_changed(self):
if self.select_tool.selection != None:
self.need_update = True
def on_timer(self):
if self.need_update:
x0, x1 = self.select_tool.selection
self.img2[:] = self.img[:]
np.clip(self.img2[:], x0, x1, out=self.img2[:])
self.img2[:] -= x0
self.img2[:] *= 256.0 / (x1 - x0)
cv.imshow("Hist Demo", self.img2)
self.need_update = False
class IMUGloveDisplay(HasTraits):
plot = Instance(HPlotContainer)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=1000,
height=600,
resizable=True,
title="IMU Glove Display")
def __init__(self):
super(IMUGloveDisplay, self).__init__()
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x=x, y=y)
scatter = Plot(plotdata)
scatter.plot(("x", "y"), type="scatter", color="blue")
line = Plot(plotdata)
line.plot(("x", "y"), type="line", color="blue")
container = HPlotContainer(scatter, line)
#scatter.title = "sin(x) * x^3"
#line.title = 'line plot'
self.plot = container
self.InitGlove()
def InitGlove(self):
self.api = GloveAPI()
self.api.initHardware()
self.api.debug(False)
self.api.configimu()
def ReadData(self, numToRead):
''' Start streaming data '''
self.api.stream(numToRead)
self.api.clearIMUPacketEngine()
keys = [
'sentinal', 'footer', 'temp', 'gx', 'gy', 'gz', 'ax', 'ay', 'az',
'sentinal'
]
#keys = ['gx','gy','gz','ax','ay','az']
packets = []
t = time.time()
for x in range(0, numToRead):
p = self.api.getIMUPacket()
if p:
''' Update the data in the plot.. '''
showPacket(p, keys)
else:
self.api.configimu()
self.api.stream(numToRead - x)
tdiff = time.time() - t
print("Total time:%6.6f Time per Packet:%6.6f" % (tdiff, tdiff / x))
class ComponentViewer(HasTraits):
""" A viewer of components for testing purposes """
# The component being viewed
component = Instance(Component)
# The canvas to which the component is added
canvas = Instance(Canvas)
# A view into a subsection of the canvas
viewport = Instance(Viewport)
# Default view
traits_view = View(HSplit(
Group(Item("viewport", editor=ComponentEditor(), show_label=False)),
Group(Item(name="component", style="custom", show_label=False),
scrollable=True)),
resizable=True,
id="canvas_viewer",
width=.6,
height=.4,
title="Viewer")
def _canvas_default(self):
""" Trait initialiser """
canvas = Canvas(draw_axes=True, bgcolor="honeydew")
return canvas
def _viewport_default(self):
""" Trait initialiser """
viewport = Viewport(component=self.canvas, enable_zoom=True)
viewport.tools.append(ViewportPanTool(viewport))
return viewport
def _component_changed(self, old, new):
""" Handles the component being changed.
"""
canvas = self.canvas
if old is not None:
canvas.remove(old)
if new is not None:
canvas.add(new)
def _anytrait_changed_for_component(self, new):
""" Handles redrawing of the canvas. """
self.canvas.request_redraw()
class LassoDemoPlot(HasTraits):
plot = Instance(HPlotContainer)
data = Instance(ArrayPlotData)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=600,
height=320,
resizable=True,
title="Lasso Tool Demo")
def __init__(self, **traits):
super(LassoDemoPlot, self).__init__(**traits)
x = np.random.random(N)
y = np.random.random(N)
x2 = np.array([])
y2 = np.array([])
data = ArrayPlotData(x=x, y=y, x2=x2, y2=y2)
plot1 = Plot(data, padding=10)
scatter_plot1 = plot1.plot(("x", "y"),
type="scatter",
marker="circle",
color="blue")[0]
self.lasso = LassoSelection(scatter_plot1,
incremental_select=True,
selection_datasource=scatter_plot1.index)
self.lasso.on_trait_change(self._selection_changed,
'selection_changed')
scatter_plot1.tools.append(self.lasso)
scatter_plot1.overlays.append(
LassoOverlay(scatter_plot1, lasso_selection=self.lasso))
plot2 = Plot(data, padding=10)
plot2.index_range = plot1.index_range
plot2.value_range = plot1.value_range
plot2.plot(("x2", "y2"), type="scatter", marker="circle", color="red")
self.plot = HPlotContainer(plot1, plot2)
self.plot2 = plot2
self.data = data
def _selection_changed(self):
index = np.array(self.lasso.selection_datasource.metadata["selection"],
dtype=np.bool)
self.data["x2"] = self.data["x"][index]
self.data["y2"] = self.data["y"][index]
class Plot_i(HasTraits):
plot = Instance(Plot)
color = ColorTrait('blue')
marker = marker_trait
marker_size = Int(4)
line_width = Int(4)
traits_view = View(Group(Tabbed(Group( \
Group(Item('color', label="Color"), \
Item('marker', label="Marker"), \
orientation = 'vertical'), \
Group( \
Item('marker_size', label= "Size"), \
Item('line_width', label = 'Linewidth'), \
orientation = 'vertical'), \
dock = 'tab', orientation = 'vertical')), \
Item('plot', editor=ComponentEditor(), show_label=False), orientation = 'horizontal'), \
width=800, height=600, resizable=True, title="Chaco Plot")
def __init__(self,X,Y):
super(Plot_i, self).__init__()
self.load_data(X,Y)
self.start()
def load_data(self,X,Y) :
self.X = X
self.Y = Y
plotdata = ArrayPlotData(x = X, y = Y)
plot = Plot(plotdata)
self.renderer_line = plot.plot(('x','y'),type = 'line', color = "blue")[0]
self.renderer_scat = plot.plot(('x','y'),type = 'scatter', color = "blue")[0]
self.plot = plot
def start(self):
self.configure_traits()
def _color_changed(self):
self.renderer_line.color = self.color
self.renderer_scat.color = self.color
def _marker_changed(self):
self.renderer_scat.marker = self.marker
def _marker_size_changed(self):
self.renderer_scat.marker_size = self.marker_size
def _line_width_changed(self):
self.renderer_line.line_width = self.line_width
class LinePlot(HasTraits):
plot = Instance(Plot)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="QTLab Analysis Plot")
def __init__(self, title, xtitle, x, ytitle, y, type="line", color="blue"):
super(LinePlot, self).__init__()
plotdata = ArrayPlotData(x=x, y=y)
self.plotdata = plotdata
plot = Plot(self.plotdata)
plot.plot(('x', 'y'), type=type, color=color)
plot.title = title
plot.x_axis.title = xtitle
plot.y_axis.title = ytitle
self.plot = plot
self._hid = 0
self._colors = ['blue', 'red', 'black', 'green', 'magenta', 'yellow']
# Add some tools
self.plot.tools.append(PanTool(self.plot, constrain_key="shift"))
self.plot.overlays.append(
ZoomTool(component=self.plot, tool_mode="box", always_on=False))
def update_plot(self, x, y):
'''
Update plot
'''
self.plotdata.set_data('x', x)
self.plotdata.set_data('y', y)
self.plot.data = self.plotdata
self.plot.request_redraw()
def plot_hold_on(self, x, y, type="line"):
'''
Plot if hold on
'''
self._hid = self._hid + 1
self.plotdata.set_data('x' + str(self._hid), x)
self.plotdata.set_data('y' + str(self._hid), y)
self.plot.plot(('x' + str(self._hid), 'y' + str(self._hid)),
type=type,
color=self._colors[self._hid % len(self._colors)])
self.plot.request_redraw()
class LinePlot(HasTraits):
plot = Instance(Plot)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="Chaco Plot")
def __init__(self):
super(LinePlot, self).__init__()
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x=x, y=y)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="line", color="blue")
plot.title = "sin(x) * x^3"
self.plot = plot
class ImagePlot(HasTraits):
plot = Instance(Plot)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="Chaco Plot")
def __init__(self):
super(ImagePlot, self).__init__()
x = np.linspace(0, 10, 50)
y = np.linspace(0, 5, 50)
xgrid, ygrid = np.meshgrid(x, y)
z = np.exp(-(xgrid * xgrid + ygrid * ygrid) / 100)
plotdata = ArrayPlotData(imagedata=z)
plot = Plot(plotdata)
plot.img_plot("imagedata", xbounds=x, ybounds=y, colormap=jet)
self.plot = plot
class ContainerExample(HasTraits):
plot = Instance(HPlotContainer)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=1000,
height=600,
resizable=True,
title="Chaco Plot")
def __init__(self):
super(ContainerExample, self).__init__()
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x=x, y=y)
scatter = Plot(plotdata)
scatter.plot(("x", "y"), type="scatter", color="blue")
line = Plot(plotdata)
line.plot(("x", "y"), type="line", color="blue")
container = HPlotContainer(scatter, line)
self.plot = container
class ContainerExample(HasTraits):
plot = Instance(VPlotContainer)
data = Instance(ArrayPlotData)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=1000,
height=600,
resizable=True,
title="Chaco Plot")
def __init__(self, **traits):
super(ContainerExample, self).__init__(**traits)
x = np.linspace(-14, 14, 100)
y = np.sin(x) * x**3 / 1000
data = ArrayPlotData(x=x, y=y)
p1 = Plot(data, padding=30)
p1.plot(("x", "y"), type="scatter", color="blue")
p1.plot(("x", "y"), type="line", color="blue")
p2 = Plot(data, padding=30)
p2.plot(("x", "y"), type="line", color="blue")
p2.set(bounds=[200, 100],
position=[70, 150],
bgcolor=(0.9, 0.9, 0.9),
unified_draw=True,
resizable="")
p3 = Plot(data, padding=30)
p3.plot(("x", "y"), type="line", color="blue", line_width=2.0)
p4 = Plot(data, padding=30)
p4.plot(("x", "y"), type="scatter", color="red", marker="circle")
c1 = OverlayPlotContainer(p1, p2)
c1.fixed_preferred_size = p3.get_preferred_size()
c2 = HPlotContainer(c1, p3)
c3 = VPlotContainer(p4, c2)
self.plot = c3
class LassoDemoPlot(HasTraits):
plot = Instance(HPlotContainer)
traits_view = View(
Item('plot',editor=ComponentEditor(), show_label=False),
width=600, height=320, resizable=True, title="Lasso Tool Demo")
def __init__(self):
x, y = np.ogrid[-2*np.pi:2*np.pi:256j, -2*np.pi:2*np.pi:256j]
self.img_data = np.sin(x)*y
#self.img_mask = np.zeros((len(x), len(y[0]), 4), dtype=np.uint8)
#self.img_mask[:, :, 3] = 255
self.img_index = np.array(list((np.broadcast(y, x))))
plotdata = ArrayPlotData(img_data=self.img_data, mask_data=self.img_data)
plot1 = Plot(plotdata, padding=10)
img_plot = plot1.img_plot("img_data",
xbounds=(np.min(x), np.max(x)),
ybounds=(np.min(y), np.max(y)))[0]
self.lasso = LassoSelection(img_plot)
img_plot.tools.append(self.lasso)
self.ll = LassoOverlay(img_plot, lasso_selection=self.lasso)
img_plot.overlays.append(self.ll)
self.lasso.on_trait_change(self._selection_changed, 'selection_completed')
plot2 = Plot(plotdata, padding=10)
plot2.img_plot("mask_data")
self.plot = HPlotContainer(plot1, plot2)
self.plot1 = plot1
self.plot2 = plot2
self.plotdata = plotdata
def _selection_changed(self):
data = np.logical_not(points_in_polygon(self.img_index, self.lasso.dataspace_points, False).astype(np.bool))
data = data.reshape((256, 256))
copy = self.img_data.copy()
copy[data] = 0
self.plotdata["mask_data"] = copy
self.plot2.request_redraw()
class SelectionDemo(HasTraits):
plot = Instance(VPlotContainer)
data = Instance(ArrayPlotData)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="范围选择演示")
def __init__(self, **traits):
super(SelectionDemo, self).__init__(**traits)
x = np.linspace(-140, 140, 1000)
y = np.sin(x) * x**3
y /= np.max(y)
data = ArrayPlotData(x=x, y=y)
self.plot1 = plot1 = Plot(data, padding=25)
self.line1 = line1 = plot1.plot(("x", "y"), type="line")[0]
self.select_tool = select_tool = RangeSelection(line1)
line1.tools.append(select_tool)
select_tool.on_trait_change(self._selection_changed, "selection")
line1.overlays.append(RangeSelectionOverlay(component=line1))
self.plot2 = plot2 = Plot(data, padding=25)
plot2.plot(("x", "y"), type="line")[0]
self.plot = VPlotContainer(plot2, plot1)
self.data = data
def _selection_changed(self):
selection = self.select_tool.selection
if selection != None:
self.plot2.index_range.set_bounds(*selection)
else:
self.plot2.index_range.reset()
class LinePlot(HasTraits):
plot = Instance(Plot)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=500,
height=500,
resizable=True,
title="Chaco Plot")
def __init__(self):
x = np.linspace(-14, 14, 100)
y = np.sin(x) * x**3
y2 = np.cos(x) * x**3
plotdata = ArrayPlotData(x=x, y=y, y2=y2)
plot = Plot(plotdata)
plot.plot(("x", "y"), type="line", color="blue", name="sin")
plot.plot(("x", "y2"), type="line", color="red", name="cos")
#line.index.sort_order = "ascending"
plot.title = "sin(x) * x^3"
plot.legend.visible = True
plot.legend.tools.append(LegendHighlighter(plot.legend))
self.plot = plot
self.plotdata = plotdata
class ImagePlot(HasTraits):
plot = Instance(Plot)
colormap = Enum(sorted(color_map_name_dict.keys()))
data = Array
traits_view = View(Item('colormap'),
Item('plot', editor=ComponentEditor(),
show_label=False),
width=600,
height=400,
title="Color Map")
def __init__(self):
# Create plot data.
row = linspace(0, 1, 100)
self.data = ones([10, 100]) * row
plotdata = ArrayPlotData(imagedata=self.data)
# Create a Plot and associate it with the PlotData
plot = Plot(plotdata)
# Create a line plot in the Plot
plot.img_plot("imagedata",
xbounds=(0, 1),
colormap=color_map_name_dict[self.colormap])[0]
plot.y_axis.visible = False
self.plot = plot
self.plot.aspect_ratio = 5
def _colormap_changed(self, new):
colormap = color_map_name_dict[self.colormap]
colormap = colormap(self.plot.color_mapper.range)
self.plot.color_mapper = colormap
self.plot.request_redraw()
class AnimationPlot(HasTraits):
plot = Instance(Plot)
data = Instance(ArrayPlotData)
phase = Float(0)
traits_view = View(
Item('plot',editor=ComponentEditor(), show_label=False),
width=500, height=500, resizable=True, title="Plot Animation",
handler = AnimationHandler())
def __init__(self, **traits):
super(AnimationPlot, self).__init__(**traits)
data = ArrayPlotData(x=[0], y=[0])
plot = Plot(data)
plot.plot(("x", "y"), type="line", color="blue")
plot.title = "sin(x)"
self.plot = plot
self.data = data
def on_timer(self):
x = np.linspace(self.phase, self.phase+np.pi*4, 100)
y = np.sin(x)
self.phase += 0.02
self.data["x"] = x
self.data["y"] = y
class LFapplication(HasTraits):
traits_view = View(Item('LF_img',
editor=ComponentEditor(),
show_label=False),
Item('X_angle', label='Angle in the X axis'),
Item('Y_angle', label='Angle in the Y axis'),
resizable=True,
title="LF Image")
def __init__(self, img_path):
super(LFapplication, self).__init__()
#
# Load image data
#
base_path = os.path.splitext(img_path)[0]
lenslet_path = base_path + '-lenslet.txt'
optics_path = base_path + '-optics.txt'
with open(lenslet_path, 'r') as f:
tmp = eval(f.readline())
x_offset, y_offset, right_dx, right_dy, down_dx, down_dy = \
np.array(tmp, dtype=np.float32)
with open(optics_path, 'r') as f:
for line in f:
name, val = line.strip().split()
try:
setattr(self, name, np.float32(val))
except:
pass
max_angle = math.atan(self.pitch / 2 / self.flen)
#
# Prepare image
#
im_pil = Image.open(img_path)
if im_pil.mode == 'RGB':
self.NCHANNELS = 3
w, h = im_pil.size
im = np.zeros((h, w, 4), dtype=np.float32)
im[:, :, :3] = np.array(im_pil).astype(np.float32)
self.LF_dim = (ceil(h / down_dy), ceil(w / right_dx), 3)
else:
self.NCHANNELS = 1
im = np.array(im_pil.getdata()).reshape(im_pil.size[::-1]).astype(
np.float32)
h, w = im.shape
self.LF_dim = (ceil(h / down_dy), ceil(w / right_dx))
x_start = x_offset - int(x_offset / right_dx) * right_dx
y_start = y_offset - int(y_offset / down_dy) * down_dy
x_ratio = self.flen * right_dx / self.pitch
y_ratio = self.flen * down_dy / self.pitch
#
# Generate the cuda kernel
#
mod_LFview = pycuda.compiler.SourceModule(
_kernel_tpl.render(newiw=self.LF_dim[1],
newih=self.LF_dim[0],
oldiw=w,
oldih=h,
x_start=x_start,
y_start=y_start,
x_ratio=x_ratio,
y_ratio=y_ratio,
x_step=right_dx,
y_step=down_dy,
NCHANNELS=self.NCHANNELS))
self.LFview_func = mod_LFview.get_function("LFview_kernel")
self.texref = mod_LFview.get_texref("tex")
#
# Now generate the cuda texture
#
if self.NCHANNELS == 3:
cuda.bind_array_to_texref(
cuda.make_multichannel_2d_array(im, order="C"), self.texref)
else:
cuda.matrix_to_texref(im, self.texref, order="C")
#
# We could set the next if we wanted to address the image
# in normalized coordinates ( 0 <= coordinate < 1.)
# texref.set_flags(cuda.TRSF_NORMALIZED_COORDINATES)
#
self.texref.set_filter_mode(cuda.filter_mode.LINEAR)
#
# Prepare the traits
#
self.add_trait('X_angle', Range(-max_angle, max_angle, 0.0))
self.add_trait('Y_angle', Range(-max_angle, max_angle, 0.0))
self.plotdata = ArrayPlotData(LF_img=self.sampleLF())
self.LF_img = Plot(self.plotdata)
if self.NCHANNELS == 3:
self.LF_img.img_plot("LF_img")
else:
self.LF_img.img_plot("LF_img", colormap=gray)
def sampleLF(self):
#
# Get the output image
#
output = np.zeros(self.LF_dim, dtype=np.uint8)
#
# Calculate the gridsize. This is entirely given by the size of our image.
#
blocks = (16, 16, 1)
gridx = ceil(self.LF_dim[1] / blocks[1])
gridy = ceil(self.LF_dim[0] / blocks[0])
grid = (gridx, gridy)
#
# Call the kernel
#
self.LFview_func(np.float32(self.X_angle),
np.float32(self.Y_angle),
cuda.Out(output),
texrefs=[self.texref],
block=blocks,
grid=grid)
return output
@on_trait_change('X_angle, Y_angle')
def updateImge(self):
self.plotdata.set_data('LF_img', self.sampleLF())
class PointSelectionDemo(HasTraits):
color = Enum(Colors.keys())
green_selection = List()
red_selection = List()
plot = Instance(Plot)
data = Instance(ArrayPlotData)
traits_view = View(HSplit(
Item('plot', editor=ComponentEditor(), show_label=False),
VGroup(
Item("color", show_label=False, style="custom"),
Heading(u"绿色选择点"),
Item("green_selection", show_label=False, style="readonly"),
Heading(u"红色选择点"),
Item("red_selection", show_label=False, style="readonly"),
)),
width=800,
height=400,
resizable=True,
title=u"数据点选择演示")
def __init__(self, **traits):
super(PointSelectionDemo, self).__init__(**traits)
x = np.random.rand(100)
y = np.random.rand(100)
data = ArrayPlotData(x=x, y=y)
plot = Plot(data, padding=25)
self.scatter = scatter = plot.plot(("x", "y"),
type="scatter",
marker_size=4)[0]
self.select_tools = {}
for i, c in enumerate(Colors.keys()):
hover_name = "hover_%s" % c
selection_name = "selections_%s" % c
self.select_tools[c] = ScatterInspector(
scatter,
hover_metadata_name=hover_name,
selection_metadata_name=selection_name)
scatter.overlays.append(
ScatterInspectorOverlay(
scatter,
hover_metadata_name=hover_name,
selection_metadata_name=selection_name,
hover_color="transparent",
hover_outline_color=c,
hover_marker_size=6,
hover_line_width=1,
selection_color=Colors[c],
))
scatter.active_tool = self.select_tools[self.color]
scatter.index.on_trait_change(self.selection_changed,
'metadata_changed')
self.plot = plot
self.data = data
def _color_changed(self):
self.scatter.active_tool = self.select_tools[self.color]
def selection_changed(self):
x = self.scatter.index.get_data()
y = self.scatter.value.get_data()
metadata = self.scatter.index.metadata
selection = metadata.get("selections_green", [])
self.green_selection = [
"%d, (%f, %f)" % (s, x[s], y[s]) for s in selection
]
selection = metadata.get("selections_red", [])
self.red_selection = [
"%d, (%f, %f)" % (s, x[s], y[s]) for s in selection
]
class Subgraph(BaseGraph):
""" Defines a representation of a subgraph in Graphviz's dot language.
"""
#--------------------------------------------------------------------------
# Trait definitions:
#--------------------------------------------------------------------------
# An ID is one of the following:
# * Any string of alphabetic ([a-zA-Z\200-\377]) characters, underscores
# ('_') or digits ([0-9]), not beginning with a digit;
# * a number [-]?(.[0-9]+ | [0-9]+(.[0-9]*)? );
# * any double-quoted string ("...") possibly containing escaped
# quotes (\")1;
# * an HTML string (<...>).
# ID = Str
#
# name = Alias("ID", desc="synonym for ID") # Used by InstanceEditor
#
# # Subgraph nodes.
# nodes = List(Instance(Node))
#
# # Subgraph edges.
# edges = List(Instance(Edge))
#
# # Subgraphs of the subgraph.
# subgraphs = List(Instance("godot.subgraph.Subgraph"))
#
# # Separate rectangular layout regions.
# clusters = List(Instance("godot.cluster.Cluster"))
# Parent graph in the graph heirarchy.
# parent = Instance("godot.graph.Graph")
# Root graph instance.
# root = Instance("godot.graph.Graph")
#--------------------------------------------------------------------------
# Xdot trait definitions:
#--------------------------------------------------------------------------
# For a given graph object, one will typically a draw directive before the
# label directive. For example, for a node, one would first use the
# commands in _draw_ followed by the commands in _ldraw_.
# _draw_ = Str(desc="xdot drawing directive")
#
# # Label draw directive.
# _ldraw_ = Str(desc="xdot label drawing directive")
#--------------------------------------------------------------------------
# Dot trait definitions.
#--------------------------------------------------------------------------
# Rank constraints on the nodes in a subgraph. If rank="same", all nodes
# are placed on the same rank. If rank="min", all nodes are placed on the
# minimum rank. If rank="source", all nodes are placed on the minimum rank,
# and the only nodes on the minimum rank belong to some subgraph whose rank
# attribute is "source" or "min". Analogous criteria hold for rank="max"
# and rank="sink". (Note: the minimum rank is topmost or leftmost, and the
# maximum rank is bottommost or rightmost.)
rank = Enum("same",
"min",
"source",
"max",
"sink",
desc="rank constraints on the nodes in a subgraph",
graphviz=True)
#--------------------------------------------------------------------------
# Views:
#--------------------------------------------------------------------------
traits_view = View(
VGroup(
Group(
Item("vp",
editor=ComponentEditor(height=100),
show_label=False), Item("arrange", show_label=False)),
VGroup(
HGroup(Item("ID"), Item("rank")),
Tabbed(nodes_item, edges_item, dock="tab"),
# subgraphs_notebook_group
),
layout="split"),
title="Subgraph",
id="godot.subgraph",
buttons=["OK", "Cancel", "Help"],
resizable=True)
#--------------------------------------------------------------------------
# "object" interface:
#--------------------------------------------------------------------------
def __str__(self):
""" Returns a string representation of the cluster in dot language.
"""
s = "subgraph"
return "%s %s" % (s, super(Subgraph, self).__str__())
class DataSetBrowser(HasTraits):
"""
A class that allows browsing of a DataSet object with sliders
to navigate through plates, images within plates, and objects
within images.
"""
view = View(VGroup(
HGroup(
Item('image_plots',
editor=ComponentEditor(size=(50, 50)),
show_label=False), ),
HGroup(
Item('plots',
editor=ComponentEditor(size=(250, 300)),
show_label=False), ),
Group(
Item('object_index',
editor=RangeEditor(low=1,
high_name='num_objects',
mode='slider')),
Item('image_index',
editor=RangeEditor(low=1,
high_name='num_images',
mode='slider')),
Item('plate_index',
editor=RangeEditor(low=1,
high_name='num_plates',
mode='slider')),
),
HGroup(
Item('num_internal_knots',
label='Number of internal spline knots'),
Item('smoothing', label='Amount of smoothing applied'))),
height=700,
width=800,
resizable=True)
# Chaco plot
gfp_plot = Instance(Plot)
sil_plot = Instance(Plot)
image_plots = Instance(HPlotContainer)
rotated_plot = Instance(Plot)
plots = Instance(GridPlotContainer)
#legends = Instance(VPlotContainer)
# DataSet being viewed
dataset = Instance(DataSet)
# Plate object currently being examined
current_plate = Instance(Plate)
# ImageSilhouette object currently being examined
current_image = Instance(ImageSilhouette)
# ObjectSilhouette object currently being examined
current_object = Instance(ObjectSilhouette)
# Index traits that control the selected plate/image/object
plate_index = Int(1)
image_index = Int(1)
object_index = Int(1)
# Number of plates, images, and objects in the current context
num_plates = Property(Int, depends_on='dataset')
num_images = Property(Int, depends_on='current_plate')
num_objects = Property(Int, depends_on='current_image')
num_internal_knots = Range(1, 20, 3)
smoothing = Range(0.0, 2.0, 0)
def __init__(self, *args, **kwargs):
"""Construct a DataSetBrowser from the specified DataSet object."""
super(DataSetBrowser, self).__init__(*args, **kwargs)
self.current_plate = self.dataset[self.plate_index - 1]
self.current_image = self.current_plate[self.image_index - 1]
self.current_object = self.current_image[self.object_index - 1]
self.sil_plot = Plot()
self._object_index_changed()
######################### Private interface ##########################
def _plate_index_changed(self):
"""Handle the plate index changing."""
try:
self.current_plate = self.dataset[self.plate_index - 1]
except IndexError:
self.current_plate = None
self.image_index = 1
self._image_index_changed()
def _image_index_changed(self):
"""Handle the image index slider changing."""
try:
self.current_image = self.current_plate[self.image_index - 1]
except IndexError:
self.current_image = None
self.object_index = 1
self._object_index_changed()
def _object_index_changed(self):
"""Handle object index slider changing."""
try:
self.current_object = self.current_image[self.object_index - 1]
# Display
sil = self.current_object.image
self._update_img_plot('sil_plot', sil, 'Extracted mask')
# .T to get major axis horizontal
rotated = self.current_object.aligned_version.image.T
self._update_img_plot('rotated_plot', rotated, 'Aligned mask')
self.image_plots = HPlotContainer(self.sil_plot,
self.rotated_plot,
valign="top",
bgcolor="transparent")
self._update_spline_plot()
except IndexError:
self.current_object = None
def _get_num_plates(self):
"""Return the number of plates in the currently viewed dataset."""
return len(self.dataset)
def _get_num_images(self):
"""Return the number of images in the currently viewed plate."""
return len(self.current_plate)
def _get_num_objects(self):
"""Return the number of objects in the currently viewed image."""
return len(self.current_image)
def _update_img_plot(self, plot_name, image, title):
"""Update an image plot."""
plotdata = ArrayPlotData(imagedata=image)
xbounds = (0, image.shape[1] - 1)
ybounds = (0, image.shape[0] - 1)
plot = Plot(plotdata)
plot.aspect_ratio = float(xbounds[1]) / float(ybounds[1])
plot.img_plot("imagedata",
colormap=bone,
xbounds=xbounds,
ybounds=ybounds)
plot.title = title
setattr(self, plot_name, plot)
getattr(self, plot_name).request_redraw()
def _update_spline_plot(self):
"""Update the spline plot."""
knots = np.mgrid[0:1:((self.num_internal_knots + 2) * 1j)][1:-1]
medial_repr = self.current_object.aligned_version.medial_repr
dependent_variable = np.mgrid[0:1:(medial_repr.length * 1j)]
laplacian = ndimage.gaussian_laplace(medial_repr.width_curve,
self.smoothing,
mode='constant',
cval=np.nan)
m_spline = LSQUnivariateSpline(dependent_variable,
medial_repr.medial_axis, knots)
w_spline = LSQUnivariateSpline(dependent_variable,
medial_repr.width_curve, knots)
# sample at double the frequency
spl_dep_var = np.mgrid[0:1:(medial_repr.length * 2j)]
plots = self.plots
if plots is None:
# Render the plot for the first time.
plotdata = ArrayPlotData(
medial_x=dependent_variable,
medial_y=medial_repr.medial_axis,
width_x=dependent_variable,
width_y=medial_repr.width_curve,
medial_spline_x=spl_dep_var,
medial_spline_y=m_spline(spl_dep_var),
width_spline_x=spl_dep_var,
width_spline_y=w_spline(spl_dep_var),
laplacian_y=laplacian,
)
plot = Plot(plotdata)
# Width data
self._width_data_renderer, = plot.plot(
("width_x", "width_y"),
type="line",
color="blue",
name="Original width curve data")
filterdata = ArrayPlotData(x=dependent_variable,
laplacian=laplacian)
filterplot = Plot(filterdata)
self._laplacian_renderer, = filterplot.plot(
("x", "laplacian"),
type="line",
color="black",
name="Laplacian-of-Gaussian")
# Titles for plot & axes
plot.title = "Width curves"
plot.x_axis.title = "Normalized position on medial axis"
plot.y_axis.title = "Fraction of medial axis width"
# Legend mangling stuff
legend = plot.legend
plot.legend = None
legend.set(component=None,
visible=True,
resizable="",
auto_size=True,
bounds=[250, 70],
padding_top=plot.padding_top)
filterlegend = filterplot.legend
filterplot.legend = None
filterlegend.set(component=None,
visible=True,
resizable="",
auto_size=True,
bounds=[250, 50],
padding_top=filterplot.padding_top)
self.plots = GridPlotContainer(plot,
legend,
filterplot,
filterlegend,
shape=(2, 2),
valign="top",
bgcolor="transparent")
else:
# Update the real width curve
self._width_data_renderer.index.set_data(dependent_variable)
self._width_data_renderer.value.set_data(medial_repr.width_curve)
# Render the Laplacian
self._laplacian_renderer.index.set_data(dependent_variable)
self._laplacian_renderer.value.set_data(laplacian)
def _num_internal_knots_changed(self):
"""Hook to update the plot when we change the number of knots."""
self._update_spline_plot()
def _smoothing_changed(self):
"""Hook to update the plot when we change the smoothing parameter."""
self._update_spline_plot()
class SplineExplorer(traits.HasTraits):
"""A simple UI to adjust the parameters and view the resulting splines."""
v_min = traits.Float(0)
v_max = traits.Float(15)
a_min = traits.Float(-5)
a_max = traits.Float(5)
j_min = traits.Float(-2.5)
j_max = traits.Float(2.5)
mass = traits.Float(400)
q_i = traits.Float
v_i = traits.Float
a_i = traits.Float
t_i = traits.Float
q_f = traits.Float(100)
v_f = traits.Float(0)
a_f = traits.Float(0)
t_f = traits.Float(18)
plot_names = traits.List(
["Position", "Jerk", "Velocity", "Power", "Acceleration"])
active_plots = traits.List
target_type = traits.Enum(('Position', 'Velocity', 'Acceleration', 'Time'))
plot_container = traits.Instance(Container)
recalculate = menu.Action(name="Recalculate", action="recalc")
dump = menu.Action(name="Print", action="dump")
save = menu.Action(name="Save", action="save")
trait_view = ui.View(ui.HGroup(
ui.VGroup(
ui.Item(name='target_type', label='Target'),
ui.VGroup(ui.Item(name='active_plots',
show_label=False,
editor=ui.CheckListEditor(cols=3,
name='plot_names'),
style='custom'),
label='Show Plots',
show_border=True),
ui.VGroup(ui.Item(name='q_i', label='Position'),
ui.Item(name='v_i', label='Velocity'),
ui.Item(name='a_i', label='Acceleration'),
ui.Item(name='t_i', label='Time'),
label='Initial Conditions',
show_border=True),
ui.VGroup(ui.Item(
name='q_f',
label='Position',
enabled_when="target_type not in ('Velocity', 'Acceleration')"
),
ui.Item(name='v_f',
label='Velocity',
enabled_when="target_type != 'Acceleration'"),
ui.Item(name='a_f', label='Acceleration'),
ui.Item(name='t_f',
label='Time',
enabled_when="target_type == 'Time'"),
label='Final Conditions:',
show_border=True),
ui.VGroup(ui.Item(name='v_min', label='Min Velocity'),
ui.Item(name='v_max', label='Max Velocity'),
ui.Item(name='a_min', label='Min Acceleration'),
ui.Item(name='a_max', label='Max Acceleration'),
ui.Item(name='j_min', label='Min Jerk'),
ui.Item(name='j_max', label='Max Jerk'),
ui.Item(name='mass', label='Vehicle Mass'),
label='Constraints',
show_border=True)),
ui.Item('plot_container', editor=ComponentEditor(), show_label=False)),
title='Cubic Spline Explorer',
handler=SEButtonHandler(),
buttons=[recalculate, dump, save],
resizable=True,
width=1000)
def __init__(self):
super(SplineExplorer, self).__init__()
self.active_plots = self.plot_names[:]
self.active_plots.remove("Power")
self.calc()
def calc(self):
try:
self.solver = TrajectorySolver(self.v_max, self.a_max, self.j_max,
self.v_min, self.a_min, self.j_min)
self.initial = Knot(self.q_i, self.v_i, self.a_i, self.t_i)
self.final = Knot(self.q_f, self.v_f, self.a_f, self.t_f)
if self.target_type == 'Position':
self.spline = self.solver.target_position(
self.initial, self.final)
elif self.target_type == 'Velocity':
self.spline = self.solver.target_velocity(
self.initial, self.final)
elif self.target_type == 'Acceleration':
self.spline = self.solver.target_acceleration(
self.initial, self.final)
elif self.target_type == 'Time':
self.spline = self.solver.target_time(self.initial, self.final)
pos = vel = accel = jerk = power = False
if "Position" in self.active_plots: pos = True
if "Velocity" in self.active_plots: vel = True
if "Acceleration" in self.active_plots: accel = True
if "Jerk" in self.active_plots: jerk = True
if "Power" in self.active_plots: power = True
self.plotter = CSplinePlotter(self.spline,
self.v_max,
self.a_max,
self.j_max,
self.v_min,
self.a_min,
self.j_min,
mass=self.mass,
plot_pos=pos,
plot_vel=vel,
plot_accel=accel,
plot_jerk=jerk,
plot_power=power)
self.plot_container = self.plotter.container
except:
self.initial = None
self.final = None
self.spline = None
self.plot_container = Container()
def display(self):
self.configure_traits()
def get_save_filename(self):
"""Get a filename from the user via a FileDialog. Returns the filename."""
dialog = FileDialog(action="save as",
default_filename="spline_00",
wildcard="*.png")
dialog.open()
if dialog.return_code == OK:
return dialog.path
def save(self, path):
"""Save an image of the plot. Does not catch any exceptions."""
# Create a graphics context of the right size
win_size = self.plot_container.outer_bounds
plot_gc = chaco.PlotGraphicsContext(win_size)
#plot_gc.set_fill_color("transparent")
# Place the plot component into it
plot_gc.render_component(self.plot_container)
# Save out to the user supplied filename
plot_gc.save(path)
def _active_plots_changed(self):
self.calc()
def _target_type_changed(self):
self.calc()
class SectionViewer(HasTraits):
section = Instance(Section)
plot = Instance(Plot)
plotdata = Instance(ArrayPlotData,
kw={
'x': numpy.array([]),
'y': numpy.array([])
})
#pointsx = Array(numpy.float, value=numpy.array([]))
#pointsy = Array(numpy.float, value=numpy.array([]))
data = Property(Array, depends_on='section.data.data_points')
@cached_property
def _get_data(self):
try:
ret = self.section.data.data_points
#self.pointsx = ret[:, 0]
#self.pointsy = ret[:, 1]
self.plotdata.set_data('x', ret[:, 0])
self.plotdata.set_data('y', ret[:, 1])
return ret
except AttributeError:
return numpy.Array([[], []])
@on_trait_change('data')
def replot(self):
# FIXME: handle aspect ratio correctness
print 'in SectionViewer.plot'
self.plot.title = 'Section : %s' % self.section.type
self.plot.request_redraw()
def __init__(self, *l, **kw):
# TODO: implement aspect ratio maintaining
HasTraits.__init__(self, *l, **kw)
#self.plotdata = ArrayPlotData(x=self.pointsx, y=self.pointsy)
plot = Plot(self.plotdata)
renderer = plot.plot(("x", "y"))
#lineplot = create_line_plot((self.pointsx,self.pointsy), width=2.0)
#lineplot.tools.append(PanTool(lineplot, drag_button='middle'))
#lineplot.tools.append(ZoomTool(lineplot, tool_mode='box'))
plot.tools.append(PanTool(plot, drag_button='left'))
plot.tools.append(ZoomTool(plot, tool_mode='box'))
plot.tools.append(DragZoom(plot, tool_mode='box', drag_button='right'))
plot.tools.append(CustomSaveTool(
plot)) #, filename='/home/pankaj/Desktop/file.png'))
#plot.overlays.append(PlotLabel('Section : %s' % self.section.type,component=plot))
#plot.tools.append(PlotToolbar(plot))
plot.tools.append(TraitsTool(plot))
#plot.tools.append(ZoomTool(plot, tool_mode='box', axis='index', drag_button='right', always_on=True))
#plot.aspect_ratio = 3
#plot.request_redraw()
#print plot.bounds
#plot.aspect_ratio = 1
#plot.bounds = [500,300]
#print plot.bounds
#plot.range2d = DataRange2D(low=(0,-.5), high=(1,0.5))
#print plot.bounds
for renderer in chain(*plot.plots.values()):
renderer.index_mapper.stretch_data = False
renderer.value_mapper.stretch_data = False
renderer.index_mapper.range.low = 0
renderer.index_mapper.range.high = 1
renderer.value_mapper.range.low = -3 / 8.
renderer.value_mapper.range.high = 3 / 8.
self.plot = plot
view = View(Item(
'plot',
editor=ComponentEditor(),
show_label=False,
resizable=True,
),
resizable=True)
class tcWindow(HasTraits):
project = tcProject
plot = tcPlot
def __init__(self, project):
self.project = project
self.plot = create_timechart_container(project)
self.plot_range_tools = self.plot.range_tools
self.plot_range_tools.on_trait_change(self._selection_time_changed,
"time")
self.trait_view().title = self.get_title()
def get_title(self):
if self.project.filename == "dummy":
return "PyTimechart: Please Open a File"
return "PyTimechart:" + self.project.filename
# Create an action that exits the application.
status = Str("Welcome to PyTimechart")
traits_view = View(
HSplit(
VSplit(
Item('project',
show_label=False,
editor=InstanceEditor(view='process_view'),
style='custom',
width=150),
# Item('plot_range_tools', show_label = False, editor=InstanceEditor(view = 'selection_view'), style='custom',width=150,height=100)
),
Item('plot', show_label=False, editor=ComponentEditor()),
),
toolbar=ToolBar(*_create_toolbar_actions(),
image_size=(24, 24),
show_tool_names=False),
menubar=MenuBar(*_create_menubar_actions()),
statusbar=[
StatusItem(name='status'),
],
resizable=True,
width=1280,
height=1024,
handler=tcActionHandler())
def _on_open_trace_file(self):
if open_file(None) and self.project.filename == "dummy":
self._ui.dispose()
def _on_view_properties(self):
self.plot.options.edit_traits()
def _on_exit(self, n=None):
self.close()
sys.exit(0)
def close(self, n=None):
pass
def _on_about(self):
aboutBox().edit_traits()
def _on_doc(self):
browse_doc()
def _selection_time_changed(self):
self.status = "selection time:%s" % (self.plot_range_tools.time)
plot = Plot(self.plotdata)
plot.plot(("x", "y"))
plot.plot(("x", "y"), type='scatter')
plot.tools.append(PanTool(plot, drag_button='left'))
plot.tools.append(ZoomTool(plot, tool_mode='box'))
plot.tools.append(DragZoom(plot, tool_mode='box', drag_button='right'))
plot.tools.append(CustomSaveTool(
plot)) #, filename='/home/pankaj/Desktop/file.png'))
plot.tools.append(TraitsTool(plot))
self.plot = plot
self.set_plotdata()
view_variables = View(Item(
'plot',
editor=ComponentEditor(),
show_label=False,
resizable=True,
),
HGroup(
Item('var_x',
editor=EnumEditor(name='var_names_view')),
Item('var_y',
editor=EnumEditor(name='var_names_view'))),
resizable=True)
class OutputVariablesAdapter(TabularAdapter):
def _columns_default(self):
var_names = sorted(self.object.variable_names, key=lambda x: x.lower())
ret = [(var_name, self.object.variable_names.index(var_name))
class CyclesPlot(HasTraits):
""" Simple plotting class with some attached controls"""
plot = Instance(GridContainer)
traits_view = View(Item('plot', editor=ComponentEditor(),
show_label=False),
width=800,
height=600,
resizable=True,
title="Business Cycles Plot")
# Private Traits
_file_path = Str
_dates = Array
_series1 = Array
_series2 = Array
_selected_s1 = Array
_selected_s2 = Array
def __init__(self):
super(CyclesPlot, self).__init__()
# Normally you'd pass in the data, but I'll hardwire things for this
# one-off plot.
srecs = read_time_series_from_csv("./biz_cycles2.csv",
date_col=0,
date_format="%Y-%m-%d")
dt = srecs["Date"]
# Industrial production compared with trend (plotted on value axis)
iprod_vs_trend = srecs["Metric 1"]
# Industrial production change in last 6 Months (plotted on index axis)
iprod_delta = srecs["Metric 2"]
self._dates = dt
self._series1 = self._selected_s1 = iprod_delta
self._series2 = self._selected_s2 = iprod_vs_trend
end_x = np.array([self._selected_s1[-1]])
end_y = np.array([self._selected_s2[-1]])
plotdata = ArrayPlotData(x=self._series1,
y=self._series2,
dates=self._dates,
selected_x=self._selected_s1,
selected_y=self._selected_s2,
endpoint_x=end_x,
endpoint_y=end_y)
cycles = Plot(plotdata, padding=20)
cycles.plot(("x", "y"), type="line", color=(.2, .4, .5, .4))
cycles.plot(("selected_x", "selected_y"),
type="line",
marker="circle",
line_width=3,
color=(.2, .4, .5, .9))
cycles.plot(("endpoint_x", "endpoint_y"),
type="scatter",
marker_size=4,
marker="circle",
color=(.2, .4, .5, .2),
outline_color=(.2, .4, .5, .6))
cycles.index_range = DataRange1D(low_setting=80., high_setting=120.)
cycles.value_range = DataRange1D(low_setting=80., high_setting=120.)
# dig down to use actual Plot object
cyc_plot = cycles.components[0]
# Add the labels in the quadrants
cyc_plot.overlays.append(
PlotLabel("\nSlowdown" + 40 * " " + "Expansion",
component=cyc_plot,
font="swiss 24",
color=(.2, .4, .5, .6),
overlay_position="inside top"))
cyc_plot.overlays.append(
PlotLabel("Downturn" + 40 * " " + "Recovery\n ",
component=cyc_plot,
font="swiss 24",
color=(.2, .4, .5, .6),
overlay_position="inside bottom"))
timeline = Plot(plotdata, resizable='h', height=50, padding=20)
timeline.plot(("dates", "x"),
type="line",
color=(.2, .4, .5, .8),
name='x')
timeline.plot(("dates", "y"),
type="line",
color=(.5, .4, .2, .8),
name='y')
# Snap on the tools
zoomer = ZoomTool(timeline,
drag_button="right",
always_on=True,
tool_mode="range",
axis="index",
max_zoom_out_factor=1.1)
panner = PanTool(timeline, constrain=True, constrain_direction="x")
# dig down to get Plot component I want
x_plt = timeline.plots['x'][0]
range_selection = RangeSelection(x_plt, left_button_selects=True)
range_selection.on_trait_change(self.update_interval, 'selection')
x_plt.tools.append(range_selection)
x_plt.overlays.append(RangeSelectionOverlay(x_plt))
# Set the plot's bottom axis to use the Scales ticking system
scale_sys = CalendarScaleSystem(
fill_ratio=0.4,
default_numlabels=5,
default_numticks=10,
)
tick_gen = ScalesTickGenerator(scale=scale_sys)
bottom_axis = ScalesPlotAxis(timeline,
orientation="bottom",
tick_generator=tick_gen)
# Hack to remove default axis - FIXME: how do I *replace* an axis?
del (timeline.underlays[-2])
timeline.overlays.append(bottom_axis)
container = GridContainer(padding=20,
fill_padding=True,
bgcolor="lightgray",
use_backbuffer=True,
shape=(2, 1),
spacing=(30, 30))
# add a central "x" and "y" axis
x_line = LineInspector(cyc_plot,
is_listener=True,
color="gray",
width=2)
y_line = LineInspector(cyc_plot,
is_listener=True,
color="gray",
width=2,
axis="value")
cyc_plot.overlays.append(x_line)
cyc_plot.overlays.append(y_line)
cyc_plot.index.metadata["selections"] = 100.0
cyc_plot.value.metadata["selections"] = 100.0
container.add(cycles)
container.add(timeline)
container.title = "Business Cycles"
self.plot = container
def update_interval(self, value):
# Reaching pretty deep here to get selections
sels = self.plot.plot_components[1].plots['x'][0].index.metadata[
'selections']
if not sels is None:
p = self._dates >= sels[0]
q = self._dates <= sels[1]
msk = p & q
self._selected_s1 = self._series1[msk]
self._selected_s2 = self._series2[msk]
# Find the index of the last point in the mask
last_idx = -(msk[::-1].argmax() + 1)
endpoint_x = np.array([self._series1[last_idx]])
endpoint_y = np.array([self._series2[last_idx]])
else:
self._selected_s1 = self._series1
self._selected_s2 = self._series2
endpoint_x = np.array([self._series1[-1]])
endpoint_y = np.array([self._series2[-1]])
self.plot.plot_components[0].data['selected_x'] = self._selected_s1
self.plot.plot_components[0].data['selected_y'] = self._selected_s2
self.plot.plot_components[0].data['endpoint_x'] = endpoint_x
self.plot.plot_components[0].data['endpoint_y'] = endpoint_y
