def __init__(self):
# The delegates views don't work unless we caller the superclass __init__
super(CursorTest, self).__init__()
container = HPlotContainer(padding=0, spacing=20)
self.plot = container
# a subcontainer for the first plot.
# I'm not sure why this is required. Without it, the layout doesn't work right.
subcontainer = OverlayPlotContainer(padding=40)
container.add(subcontainer)
# make some data
index = numpy.linspace(-10, 10, 512)
value = numpy.sin(index)
# create a LinePlot instance and add it to the subcontainer
line = create_line_plot([index, value], add_grid=True, add_axis=True, index_sort="ascending", orientation="h")
subcontainer.add(line)
# here's our first cursor.
csr = CursorTool(line, drag_button="left", color="blue")
self.cursor1 = csr
# and set it's initial position (in data-space units)
csr.current_position = 0.0, 0.0
# this is a rendered component so it goes in the overlays list
line.overlays.append(csr)
# some other standard tools
line.tools.append(PanTool(line, drag_button="right"))
line.overlays.append(ZoomTool(line))
# make some 2D data for a colourmap plot
xy_range = (-5, 5)
x = numpy.linspace(xy_range[0], xy_range[1], 100)
y = numpy.linspace(xy_range[0], xy_range[1], 100)
X, Y = numpy.meshgrid(x, y)
Z = numpy.sin(X) * numpy.arctan2(Y, X)
# easiest way to get a CMapImagePlot is to use the Plot class
ds = ArrayPlotData()
ds.set_data("img", Z)
img = Plot(ds, padding=40)
cmapImgPlot = img.img_plot("img", xbounds=xy_range, ybounds=xy_range, colormap=jet)[0]
container.add(img)
# now make another cursor
csr2 = CursorTool(cmapImgPlot, drag_button="left", color="white", line_width=2.0)
self.cursor2 = csr2
csr2.current_position = 1.0, 1.5
cmapImgPlot.overlays.append(csr2)
# add some standard tools. Note, I'm assigning the PanTool to the
# right mouse-button to avoid conflicting with the cursors
cmapImgPlot.tools.append(PanTool(cmapImgPlot, drag_button="right"))
cmapImgPlot.overlays.append(ZoomTool(cmapImgPlot))
def load(self, an):
if an.peak_center_data:
g = self.graph
g.new_plot(xtitle='DAC', ytitle='Intensity')
xs, ys = an.peak_center_data
s, p = g.new_series(xs, ys)
s.index.sort_order = 'ascending'
t = CursorTool(s,
drag_button="left",
marker_size=5,
color='darkgreen')
s.overlays.append(t)
dto = CursorToolOverlay(
component=p,
tool=t)
p.overlays.append(dto)
t.current_position = an.peak_center, 0
v = nominal_value(an.peak_center)
g.add_vertical_rule(v)
g.add_plot_label('Center={:0.5f} V'.format(v),
font='modern 12',
color='darkgreen')
g.set_y_limits(pad='0.05', plotid=0)
return True
def render_image(self):
plot = super(CellCropper,self).render_image()
img=plot.img_plot("imagedata", colormap=gray)[0]
csr = CursorTool(img, drag_button='left', color='white',
line_width=2.0)
self.csr=csr
csr.current_position=self.left, self.top
img.overlays.append(csr)
self.img_plot=plot
return plot
def __init__(self, plot, pos_signal, drag_signal=None):
super(CrossHair, self).__init__()
self._pos_signal = pos_signal
self._drag_signal = drag_signal
csr = CursorTool(plot,
drag_button='left',
color='white',
line_width=2.0,
marker_size=2.0)
self.cursor = csr
csr.current_position = 0.0, 0.0
plot.overlays.append(csr)
self.is_being_dragged = False
def _plot_default(self):
plot_data = ArrayPlotData(index=self.sigma, value=self.mu)
self.plot_data = plot_data
plot = Plot(data=plot_data)
line = create_line_plot([self.sigma, self.mu],
add_grid=True,
value_bounds=(min(self.mean), max(self.mean)),
add_axis=True,
index_sort='ascending',
orientation='h')
scatter = create_scatter_plot(
[np.sqrt(np.diag(self.covar)),
np.squeeze(self.mean)],
index_bounds=(line.index_range.low, line.index_range.high),
value_bounds=(line.value_range.low, line.value_range.high),
marker='circle',
color='blue')
plot.add(line)
left, bottom = line.underlays[-2:]
left.title = 'Return'
bottom.title = 'Risk'
plot.add(scatter)
cursor = CursorTool(line, drag_button='left', color='blue')
self.cursor = cursor
#cursor.current_position = self.sigma[0], self.mu[0]
line.overlays.append(cursor)
line.tools.append(PanTool(line, drag_button='right'))
#line.overlays.append(ZoomTool(line))
return plot
def render_image(self):
plot = Plot(self.img_plotdata,default_origin="top left")
img=plot.img_plot("imagedata", colormap=gray)[0]
plot.title="%s of %s: "%(self.img_idx+1,self.numfiles)+self.titles[self.img_idx]
plot.aspect_ratio=float(self.sig.data.shape[2])/float(self.sig.data.shape[1])
csr = CursorTool(img, drag_button='left', color='white',
line_width=2.0)
self.csr=csr
csr.current_position=self.left, self.top
img.overlays.append(csr)
# attach the rectangle tool
plot.tools.append(PanTool(plot,drag_button="right"))
zoom = ZoomTool(plot, tool_mode="box", always_on=False, aspect_ratio=plot.aspect_ratio)
plot.overlays.append(zoom)
self.img_plot=plot
return plot
def create_plot_component(self): # Create a scalar field to colormap
# Create the plot
if self.shape == self.zdata.shape:
self.pd.set_data("imagedata", self.zdata)
return
self.pd = ArrayPlotData()
self.shape = self.zdata.shape
self.pd.set_data("imagedata", self.zdata)
plot = Plot(self.pd)
img_plot = plot.img_plot(
"imagedata",
colormap=jet,
)[0]
# Tweak some of the plot properties
plot.title = self.title
plot.padding = 50
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
csr = CursorTool(img_plot,
drag_button='right',
color='white',
line_width=2.0)
self.cursor = csr
csr.current_position = self.shape[0] / 2, self.shape[
1] / 2 #np.mean(self.xbounds), np.mean(self.ybounds)
img_plot.overlays.append(csr)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot,
image_inspector=imgtool,
bgcolor="white",
border_visible=True)
img_plot.overlays.append(overlay)
self.plot = plot
def _render_image(array_plot_data, title=None, tools=["zoom","pan"]):
plot = Plot(array_plot_data, default_origin="top left")
# the cursor tool, if any
csr = None
img_renderer = plot.img_plot("imagedata", colormap=gray, name="base_plot")[0]
# todo: generalize title and aspect ratio
plot.title = title
data_array = array_plot_data.arrays['imagedata']
plot.aspect_ratio=float(data_array.shape[1]) / float(data_array.shape[0])
# attach the rectangle tool
if "pan" in tools:
plot.tools.append(PanTool(plot,drag_button="right"))
if "zoom" in tools:
zoom = ZoomTool(plot, tool_mode="box", always_on=False, aspect_ratio=plot.aspect_ratio)
plot.overlays.append(zoom)
if "csr" in tools:
csr = CursorTool(img_renderer, drag_button='left', color='red',
line_width=2.0)
csr.current_position = 64, 64
img_renderer.overlays.append(csr)
return plot, csr
def create_plot_component(self):
if not self.data.size:
return
self.pd = ArrayPlotData()
self.shape = self.data.shape
self.pd.set_data("imagedata", self.data)
self.pd.set_data("left_line", self.data[:, self.shape[0]//2])
self.pd.set_data("top_line", self.data[self.shape[1]//2,:])
plot = Plot(self.pd)
cmap = default_colormaps.color_map_name_dict[self.colormap]
if self.rev_cmap:
cmap = default_colormaps.reverse(cmap)
drange = DataRange1D(ImageData(data=self.data, value_depth=1))
self.ccmap = cmap_constant_range(cmap, drange)(drange)
#from copy import copy
self.img_plot = plot.img_plot("imagedata",
colormap=cmap,
#xbounds=self.xbounds,
#ybounds=self.ybounds,
)[0]
self.cmap = self.img_plot.value_mapper
if not self.autoscale:
self.img_plot.value_mapper = self.ccmap
# Tweak some of the plot properties
plot.title = self.title
plot.padding = 10
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
csr = CursorTool(self.img_plot,
drag_button='right',
color='white',
line_width=2.0
)
self.cursor = csr
csr.current_index = self.shape[0]//2, self.shape[1]//2
self.img_plot.overlays.append(csr)
imgtool = ImageInspectorTool(self.img_plot)
self.img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=self.img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True)
self.img_plot.overlays.append(overlay)
self.plot = plot
self.cross_plot = Plot(self.pd,resizable='h')
self.cross_plot.height = 40
self.cross_plot.padding = 15
self.cross_plot.plot("top_line",
line_style="dot")
self.cross_plot.index_range = self.img_plot.index_range.x_range
self.cross_plot2 = Plot(self.pd, width=40, orientation="v",
padding=15, padding_bottom=10, resizable='v')
self.cross_plot2.plot("left_line",
line_style="dot")
self.cross_plot2.index_range = self.img_plot.index_range.y_range
# Create a container and add components
#self.container = HPlotContainer(padding=10, fill_padding=False,
# bgcolor="none", use_backbuffer=False)
#inner_cont = VPlotContainer(padding=0, use_backbuffer=True, bgcolor="none")
#inner_cont.add(self.plot)
#inner_cont.add(self.cross_plot)
self.container = GridPlotContainer(padding=20, fill_padding=False,
bgcolor="none", use_backbuffer=True,
shape=(2, 2), spacing=(12, 20))
#self.container.add(self.colorbar)
self.container.add(self.plot)
self.container.add(self.cross_plot2)
self.container.add(self.cross_plot)
def create_plot_component(self):# Create a scalar field to colormap
# Create the plot
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.fxydata())
plot = Plot(self.pd)
cmap = default_colormaps.color_map_name_dict[self.colormap]
if self.rev_cmap:
cmap = default_colormaps.reverse(cmap)
img_plot = plot.img_plot("imagedata",
xbounds = self.xbounds,
ybounds = self.ybounds,
colormap=cmap,
)[0]
# Tweak some of the plot properties
plot.title = self.title
plot.padding = 50
# Attach some tools to the plot
#plot.tools.append(PanTool(plot))
#zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
#plot.overlays.append(zoom)
csr = CursorTool(img_plot,
drag_button='left',
color='white',
line_width=2.0
)
self.cursor = csr
csr.current_position = np.mean(self.xbounds), np.mean(self.ybounds)
img_plot.overlays.append(csr)
imgtool = ImageInspectorTool(img_plot)
img_plot.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=img_plot, image_inspector=imgtool,
bgcolor="white", border_visible=True,)
#img_plot.overlays.append(overlay)
colorbar = ColorBar(index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.plot = plot
colorbar.padding_top = plot.padding_top + 10
colorbar.padding_bottom = plot.padding_bottom
# Add pan and zoom tools to the colorbar
colorbar.tools.append(PanTool(colorbar, constrain_direction="y", constrain=True))
zoom_overlay = ZoomTool(colorbar, axis="index", tool_mode="range",
always_on=True, drag_button="right")
colorbar.overlays.append(zoom_overlay)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(plot, colorbar, use_backbuffer=True, bgcolor="transparent",)
container.overlays.append(overlay)
#self.plot = plot
self.plot = container
def __init__(self):
#The delegates views don't work unless we caller the superclass __init__
super(CursorTest, self).__init__()
container = HPlotContainer(padding=0, spacing=20)
self.plot = container
#a subcontainer for the first plot.
#I'm not sure why this is required. Without it, the layout doesn't work right.
subcontainer = OverlayPlotContainer(padding=40)
container.add(subcontainer)
#make some data
index = numpy.linspace(-10, 10, 512)
value = numpy.sin(index)
#create a LinePlot instance and add it to the subcontainer
line = create_line_plot([index, value],
add_grid=True,
add_axis=True,
index_sort='ascending',
orientation='h')
subcontainer.add(line)
#here's our first cursor.
csr = CursorTool(line, drag_button="left", color='blue')
self.cursor1 = csr
#and set it's initial position (in data-space units)
csr.current_position = 0.0, 0.0
#this is a rendered component so it goes in the overlays list
line.overlays.append(csr)
#some other standard tools
line.tools.append(PanTool(line, drag_button="right"))
line.overlays.append(ZoomTool(line))
#make some 2D data for a colourmap plot
xy_range = (-5, 5)
x = numpy.linspace(xy_range[0], xy_range[1], 100)
y = numpy.linspace(xy_range[0], xy_range[1], 100)
X, Y = numpy.meshgrid(x, y)
Z = numpy.sin(X) * numpy.arctan2(Y, X)
#easiest way to get a CMapImagePlot is to use the Plot class
ds = ArrayPlotData()
ds.set_data('img', Z)
img = Plot(ds, padding=40)
cmapImgPlot = img.img_plot("img",
xbounds=xy_range,
ybounds=xy_range,
colormap=jet)[0]
container.add(img)
#now make another cursor
csr2 = CursorTool(cmapImgPlot,
drag_button='left',
color='white',
line_width=2.0)
self.cursor2 = csr2
csr2.current_position = 1.0, 1.5
cmapImgPlot.overlays.append(csr2)
#add some standard tools. Note, I'm assigning the PanTool to the
#right mouse-button to avoid conflicting with the cursors
cmapImgPlot.tools.append(PanTool(cmapImgPlot, drag_button="right"))
cmapImgPlot.overlays.append(ZoomTool(cmapImgPlot))
def __init__(self, **kwtraits):
super(ResultExplorer, self).__init__(**kwtraits)
# containers
bgcolor = "sys_window" #(212/255.,208/255.,200/255.) # Windows standard background
self.plot_container = container = VPlotContainer(use_backbuffer=True,
padding=0,
fill_padding=False,
valign="center",
bgcolor=bgcolor)
subcontainer = HPlotContainer(use_backbuffer=True,
padding=0,
fill_padding=False,
halign="center",
bgcolor=bgcolor)
# freqs
self.synth = FreqSelector(parent=self)
# data source
self.plot_data = pd = ArrayPlotData()
self.set_result_data()
self.set_pict()
# map plot
self.map_plot = Plot(pd, padding=40)
self.map_plot.img_plot("image", name="image")
imgp = self.map_plot.img_plot("map_data", name="map", colormap=jet)[0]
self.imgp = imgp
t1 = self.map_plot.plot(("xpoly", "ypoly"),
name="sector",
type="polygon")
t1[0].face_color = (0, 0, 0, 0) # set face color to transparent
# map plot tools and overlays
imgtool = ImageInspectorTool(imgp)
imgp.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=imgp,
image_inspector=imgtool,
bgcolor="white",
border_visible=True)
self.map_plot.overlays.append(overlay)
self.zoom = RectZoomSelect(self.map_plot,
drag_button='right',
always_on=True,
tool_mode='box')
self.map_plot.overlays.append(self.zoom)
self.map_plot.tools.append(PanTool(self.map_plot))
# colorbar
colormap = imgp.color_mapper
self.drange = colormap.range
self.drange.low_setting = "track"
self.colorbar = cb = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.map_plot,
orientation='v',
resizable='v',
width=10,
padding=20)
# colorbar tools and overlays
range_selection = RangeSelection(component=cb)
cb.tools.append(range_selection)
cb.overlays.append(
RangeSelectionOverlay(component=cb,
border_color="white",
alpha=0.8,
fill_color=bgcolor))
range_selection.listeners.append(imgp)
# spectrum plot
self.spec_plot = Plot(pd, padding=25)
px = self.spec_plot.plot(("freqs", "spectrum"),
name="spectrum",
index_scale="log")[0]
self.yrange = self.spec_plot.value_range
px.index_mapper = MyLogMapper(range=self.spec_plot.index_range)
# spectrum plot tools
self.cursor = CursorTool(
px) #, drag_button="left", color='blue', show_value_line=False)
px.overlays.append(self.cursor)
self.cursor.current_position = 0.3, 0.5
px.index_mapper.map_screen(0.5)
# self.map_plot.tools.append(SaveTool(self.map_plot, filename='pic.png'))
# layout
self.set_map_details()
self.reset_sector()
subcontainer.add(self.map_plot)
subcontainer.add(self.colorbar)
# subcontainer.tools.append(SaveTool(subcontainer, filename='pic.png'))
container.add(self.spec_plot)
container.add(subcontainer)
container.tools.append(SaveTool(container, filename='pic.pdf'))
self.last_valid_digest = self.Beamformer.ext_digest