def draw_colorbar(self):
scatplot=self.scatplot
cmap_renderer = scatplot.plots["my_plot"][0]
selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35,
selection_type="range")
cmap_renderer.overlays.append(selection)
if self.thresh is not None:
cmap_renderer.color_data.metadata['selections']=self.thresh
cmap_renderer.color_data.metadata_changed={'selections':self.thresh}
# Create the colorbar, handing in the appropriate range and colormap
colormap=scatplot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=DataRange1D(low = 0.0,
high = 1.0)),
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar_selection=RangeSelection(component=colorbar)
colorbar.tools.append(colorbar_selection)
ovr=colorbar.overlays.append(RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray",
metadata_name='selections'))
#ipshell('colorbar, colorbar_selection and ovr available:')
self.cbar_selection=colorbar_selection
self.cmap_renderer=cmap_renderer
colorbar.plot = cmap_renderer
colorbar.padding_top = scatplot.padding_top
colorbar.padding_bottom = scatplot.padding_bottom
self.colorbar=colorbar
return colorbar
def control(self): """ A drawable control with a color bar. """ color_map = self.plot_obj.color_mapper linear_mapper = LinearMapper(range=color_map.range) color_bar = ColorBar(index_mapper=linear_mapper, color_mapper=color_map, plot=self.plot_obj, orientation='v', resizable='v', width=30) color_bar._axis.tick_label_formatter = self.sci_formatter color_bar.padding_top = self.padding_top color_bar.padding_bottom = self.padding_bottom color_bar.padding_left = 50 # Room for labels. color_bar.padding_right = 10 range_selection = RangeSelection(component=color_bar) range_selection.listeners.append(self.plot_obj) color_bar.tools.append(range_selection) range_selection_overlay = RangeSelectionOverlay(component=color_bar) color_bar.overlays.append(range_selection_overlay) container = HPlotContainer(use_backbuffer=True) container.add(self) container.add(color_bar) return Window(self.parent, component=container).control
def __init__(self):
# Create some data
x = np.random.random(N_POINTS)
y = np.random.random(N_POINTS)
color = np.exp(-(x**2 + y**2))
# Create a plot data object and give it this data
data = ArrayPlotData(index=x, value=y, color=color)
# Create the plot
plot = Plot(data)
plot.plot(("index", "value", "color"), type="cmap_scatter",
color_mapper=jet)
# Create the colorbar, handing in the appropriate range and colormap
colormap = plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(plot, colorbar)
self.plot = container
def _hist2d_default(self):
plot = Plot(self.hist2d_data, padding=(20, 0, 0, 40))
plot.img_plot("H", xbounds=self.xedges, ybounds=self.yedges, colormap=jet)
plot.index_axis.title = "Voxel dist."
plot.value_axis.title = "Root Square Error"
# Create a colorbar
colormap = plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=20,
padding=(20, 30, 0, 0))
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True, padding=0)
container.add(colorbar)
container.add(plot)
container.bgcolor = "lightgray"
return container
def _plot_default(self):
plot = Plot(self.plotdata)
plot.title = "Simplex on the Rosenbrock function"
plot.img_plot("background",
name="background",
xbounds=(0,1.5),
ybounds=(0,1.5),
colormap=jet(DataRange1D(low=0,high=100)),
)
plot.plot(("values_x", "values_y"), type="scatter", color="red")
background = plot.plots["background"][0]
colormap = background.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=background,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
container = HPlotContainer(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
def _SmoothPlot_default(self):
plotdata = ArrayPlotData(imagedata=self.Smooth)
plot = Plot(plotdata, width=500, height=500, resizable='hv')
SmoothImage = plot.img_plot(
'imagedata',
colormap=gray,
xbounds=(self.X[0], self.X[-1]),
ybounds=(self.Y[0], self.Y[-1]),
)[0]
SmoothImage.x_mapper.domain_limits = (self.X[0], self.X[-1])
SmoothImage.y_mapper.domain_limits = (self.Y[0], self.Y[-1])
SmoothImage.overlays.append(ZoomTool(SmoothImage))
colormap = SmoothImage.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=10,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
self.SmoothImage = SmoothImage
container = HPlotContainer(padding=20,
fill_padding=True,
use_backbuffer=True)
container.add(colorbar)
container.add(plot)
return container
def control(self):
"""
A drawable control with a color bar.
"""
color_map = self.plot_obj.color_mapper
linear_mapper = LinearMapper(range=color_map.range)
color_bar = ColorBar(index_mapper=linear_mapper,
color_mapper=color_map,
plot=self.plot_obj,
orientation='v',
resizable='v',
width=30)
color_bar._axis.tick_label_formatter = self.sci_formatter
color_bar.padding_top = self.padding_top
color_bar.padding_bottom = self.padding_bottom
color_bar.padding_left = 50 # Room for labels.
color_bar.padding_right = 10
range_selection = RangeSelection(component=color_bar)
range_selection.listeners.append(self.plot_obj)
color_bar.tools.append(range_selection)
range_selection_overlay = RangeSelectionOverlay(component=color_bar)
color_bar.overlays.append(range_selection_overlay)
container = HPlotContainer(use_backbuffer=True)
container.add(self)
container.add(color_bar)
return Window(self.parent, component=container).control
def _create_plot_component(model):
# Create a plot data object and give it the model's data array.
pd = ArrayPlotData()
pd.set_data("imagedata", model.data)
# Create the "main" Plot.
plot = Plot(pd, padding=50)
# Use a TransformColorMapper for the color map.
tcm = TransformColorMapper.from_color_map(jet)
# Create the image plot renderer in the main plot.
renderer = plot.img_plot("imagedata",
xbounds=model.x_array,
ybounds=model.y_array,
colormap=tcm)[0]
# Create the colorbar.
lm = LinearMapper(range=renderer.value_range,
domain_limits=(renderer.value_range.low,
renderer.value_range.high))
colorbar = ColorBar(index_mapper=lm,
plot=plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
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(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
return container
def _create_plot_component(model):
# Create a plot data object and give it the model's data array.
pd = ArrayPlotData()
pd.set_data("imagedata", model.data)
# Create the "main" Plot.
plot = Plot(pd, padding=50)
# Use a TransformColorMapper for the color map.
tcm = TransformColorMapper.from_color_map(jet)
# Create the image plot renderer in the main plot.
renderer = plot.img_plot("imagedata",
xbounds=model.x_array,
ybounds=model.y_array,
colormap=tcm)[0]
# Create the colorbar.
lm = LinearMapper(range=renderer.value_range,
domain_limits=(renderer.value_range.low,
renderer.value_range.high))
colorbar = ColorBar(index_mapper=lm,
plot=plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
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(use_backbuffer = True)
container.add(plot)
container.add(colorbar)
return container
def _RegionsPlot_default(self):
plotdata = ArrayPlotData(imagedata=self.RegionsAndLabels[0],
x=self.XPositions,
y=self.YPositions)
plot = Plot(plotdata, width=500, height=500, resizable='hv')
RegionsImage = plot.img_plot(
'imagedata',
colormap=gray,
xbounds=(self.X[0], self.X[-1]),
ybounds=(self.Y[0], self.Y[-1]),
)[0]
RegionsImage.x_mapper.domain_limits = (self.X[0], self.X[-1])
RegionsImage.y_mapper.domain_limits = (self.Y[0], self.Y[-1])
RegionsImage.overlays.append(ZoomTool(RegionsImage))
scatterplot = plot.plot(('x', 'y'),
type='scatter',
marker='plus',
color='yellow')
colormap = RegionsImage.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=10,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
self.RegionsData = plotdata
self.RegionsImage = RegionsImage
container = HPlotContainer(padding=20,
fill_padding=True,
use_backbuffer=True)
container.add(colorbar)
container.add(plot)
return container
def _container_default(self):
plot = self.plot
colormap = plot.color_mapper
colorbar = ColorBar(index_mapper = LinearMapper(range=colormap.range),
color_mapper = colormap,
plot = plot,
orientation = 'v', resizable = 'v',
width = 30, padding = 20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
container = HPlotContainer(bgcolor = 'lightgray',
use_backbuffer = True)
container.add(plot)
container.add(colorbar)
inspector = plot.plots['image'][0].tools[0]
overlay = ImageInspectorOverlay(component = container,
image_inspector = inspector,
bgcolor = 'white',
border_visible = True)
container.overlays.append(overlay)
return container
def _create_plot_component_cmap(signals):
nSignal, nSample = np.shape(signals)
xbounds = (1, nSample, nSample)
ybounds = (1, nSignal, nSignal)
z = signals
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
plot.img_plot("imagedata",
name="my_plot",
xbounds=xbounds[:2],
ybounds=ybounds[:2],
colormap=jet)
# Tweak some of the plot properties
plot.title = "Selectable Image Plot"
# plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
my_plot = plot.plots["my_plot"][0]
# 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)
# Create the colorbar, handing in the appropriate range and colormap
colormap = my_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a range selection for the colorbar
range_selection = RangeSelection(component=colorbar)
colorbar.tools.append(range_selection)
colorbar.overlays.append(RangeSelectionOverlay(component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=gist_earth,
marker="square",
fill_alpha=0.5,
marker_size=8,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Pan/Zoom Color Bar"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Add pan and zoom to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(plot)
plot.overlays.append(zoom)
# Create the colorbar, handing in the appropriate range and colormap
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
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="lightgray")
return container
def _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = exp(-(x**2 + y**2))
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", x)
pd.set_data("value", y)
pd.set_data("color", color)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color"),
type="cmap_scatter",
name="my_plot",
color_mapper=gist_earth,
marker = "square",
fill_alpha = 0.5,
marker_size = 8,
outline_color = "black",
border_visible = True,
bgcolor = "white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Pan/Zoom Color Bar"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Add pan and zoom to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(plot)
plot.overlays.append(zoom)
# Create the colorbar, handing in the appropriate range and colormap
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
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="lightgray")
return container
def _create_plot_component():
# Create a scalar field to colormap# Create a scalar field to colormap
xbounds = (-2 * pi, 2 * pi, 600)
ybounds = (-1.5 * pi, 1.5 * pi, 300)
xs = linspace(*xbounds)
ys = linspace(*ybounds)
x, y = meshgrid(xs, ys)
z = jn(2, x) * y * x
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", z)
# Create the plot
plot = Plot(pd)
plot.img_plot(
"imagedata",
name="my_plot",
xbounds=xbounds[:2],
ybounds=ybounds[:2],
colormap=jet)
# Tweak some of the plot properties
plot.title = "Selectable Image Plot"
plot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
my_plot = plot.plots["my_plot"][0]
# 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)
# Create the colorbar, handing in the appropriate range and colormap
colormap = my_plot.color_mapper
colorbar = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# create a range selection for the colorbar
range_selection = RangeSelection(component=colorbar)
colorbar.tools.append(range_selection)
colorbar.overlays.append(
RangeSelectionOverlay(
component=colorbar,
border_color="white",
alpha=0.8,
fill_color="lightgray"))
# we also want to the range selection to inform the cmap plot of
# the selection, so set that up as well
range_selection.listeners.append(my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
#my_plot.set_value_selection((-1.3, 6.9))
return container
def _myTIC_default( self ):
# create an interesting scalar field for the image plot
# Eggholder function
limitF = 500.0
xA = linspace(-limitF, limitF, 600)
yA = linspace(-limitF, limitF, 600)
( xMG,yMG ) = meshgrid( xA,yA )
zMG = -(yMG + 47) * sin( sqrt(abs(yMG + xMG/2 + 47 )))
zMG = zMG - xMG * sin( sqrt(abs(xMG - (yMG + 47))))
# Create an ArrayPlotData object and give it this data
myAPD = ArrayPlotData()
myAPD.set_data( "Z", zMG )
myAPD.set_data( "X",xA )
myAPD.set_data( "Y",yA )
# Create the plot
myTP = Plot( myAPD )
# contains a dict of default colormaps and their functions. We have to
# pass the colormapper the data range of interest to set up the private
# attributes
default_colormaps.color_map_name_dict
# the colormap method needs the range of the image data that we want to
# plot. We first put the image data (zMG) into an ImageData object. We
# then use DataRange1D on the ImageData instance to produce a DataRange1D
# instance describing the ImageData data. Finally, we feed the DataRange1D
# instance into the colormapper to produce a working colormapper.
myID = ImageData( )
myID.set_data( zMG )
myDR1D = DataRange1D( myID )
# pick a colormap
myColorMapperFn = default_colormaps.color_map_name_dict['copper']
# choose one or more modifications to the colormap function
#myColorMapperFn = default_colormaps.reverse( myColorMapperFn )
#myColorMapperFn = default_colormaps.center( myColorMapperFn,500 )
#myColorMapperFn = default_colormaps.fix( myColorMapperFn,(-500,500) )
# finally, build the colormapper function
myColorMapper = myColorMapperFn( myDR1D )
# add the image plot to this plot object
# specify the colormap explicitly
myTP.img_plot(
"Z",
xbounds = (xA[0],xA[-1]),
ybounds = (yA[0],yA[-1]),
colormap = myColorMapper,
)
# add the title and padding around the plot
myTP.title = "Eggholder Function"
myTP.padding = 50
# grids, fonts, etc
myTP.x_axis.title = "X"
myTP.y_axis.title = "Y"
# generate a ColorBar. pulls its colormapper from the myTP Plot object
myTCB = ColorBar(
plot = myTP,
index_mapper = LinearMapper( range = myTP.color_mapper.range ),
orientation = 'v',
resizable = 'v',
width = 40,
padding = 30,
)
# set the padding of the ColorBar to match the padding of the plot
myTCB.padding_top = myTP.padding_top
myTCB.padding_bottom = myTP.padding_bottom
# range of the colormapper. Changes the min/max values that are mapped
# to the ends of the color range. Try +/-2000 for poor contrast and +/-200 for
# saturated. Asymmetrical values work as well.
#myTP.color_mapper.range.low_setting = 0
#myTP.color_mapper.range.high_setting = 1000
# build up a single container for the colorbar and the image
myHPC = HPlotContainer( use_backbuffer = True )
myHPC.add( myTP )
myHPC.add( myTCB )
return( myHPC )
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 _create_plot_component():
# Create a scalar field to colormap
x_extents = (-2*pi, 2*pi)
y_extents = (-1.5*pi, 1.5*pi)
xs = linspace(-2*pi, 2*pi, 200)
ys = linspace(-1.5*pi, 1.5*pi, 100)
x, y = meshgrid(xs,ys)
zs = sin(log(abs((x+1)**4)+0.05))*cos(y)*1.1*(-y) + \
sin(((x+1)**2 + y**2)/4)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", zs)
# Create the left plot, a colormap and simple contours
lplot = Plot(pd)
lplot.img_plot("imagedata",
name="cm_plot",
xbounds=x_extents,
ybounds=y_extents,
colormap=gmt_drywet)
lplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents)
# Tweak some of the plot properties
lplot.title = "Colormap and contours"
lplot.padding = 20
lplot.bg_color = "white"
lplot.fill_padding = True
# Add some tools to the plot
zoom = ZoomTool(lplot, tool_mode="box", always_on=False)
lplot.overlays.append(zoom)
lplot.tools.append(PanTool(lplot, constrain_key="shift"))
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
cm_plot = lplot.plots["cm_plot"][0]
# Create the colorbar, handing in the appropriate range and colormap
colormap = cm_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=cm_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = lplot.padding_top
colorbar.padding_bottom = lplot.padding_bottom
# Create the left plot, contours of varying color and width
rplot = Plot(pd, range2d=lplot.range2d)
rplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents,
bgcolor="black",
levels=15,
styles="solid",
widths=list(linspace(4.0, 0.1, 15)),
colors=gmt_drywet)
# Add some tools to the plot
zoom = ZoomTool(rplot, tool_mode="box", always_on=False)
rplot.overlays.append(zoom)
rplot.tools.append(PanTool(rplot, constrain_key="shift"))
# Tweak some of the plot properties
rplot.title = "Varying contour lines"
rplot.padding = 20
rplot.bg_color = "white"
rplot.fill_padding = True
# Create a container and add our plots
container = HPlotContainer(padding=40, fill_padding=True,
bgcolor = "white", use_backbuffer=True)
container.add(colorbar)
container.add(lplot)
container.add(rplot)
return container
def _brain_default(self):
plot = Plot(self.brain_data, padding=0)
plot.width = self.brain_voxels.shape[1]
plot.height = self.brain_voxels.shape[0]
plot.aspect_ratio = 1.
plot.index_axis.visible = False
plot.value_axis.visible = False
renderer = plot.img_plot("axial", colormap=gray)[0]
plot.color_mapper.range = DataRange1D(low=0., high=1.0)
plot.bgcolor = 'pink'
# Brain tools
plot.tools.append(PanTool(plot, drag_button="right"))
plot.tools.append(ZoomTool(plot))
imgtool = ImageInspectorTool(renderer)
renderer.tools.append(imgtool)
overlay = ImageInspectorOverlay(component=renderer, image_inspector=imgtool,
bgcolor="white", border_visible=True)
renderer.overlays.append(overlay)
# Brain track cursor
self.cursor = CursorTool2D(renderer, drag_button='left', color='red', line_width=2.0)
#self.cursor.on_trait_change(self.update_stackedhist, 'current_index')
self.cursor.current_positionyou = (0., 0.)
renderer.overlays.append(self.cursor)
# Brain colorbar
colormap = plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=plot,
orientation='v',
resizable='v',
width=20,
padding=(30, 0, 0, 0))
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Noisy brain
plot2 = Plot(self.brain_data, padding=0)
plot2.width = self.brain_voxels.shape[1]
plot2.height = self.brain_voxels.shape[0]
plot2.aspect_ratio = 1.
plot2.index_axis.visible = False
plot2.value_axis.visible = False
renderer2 = plot2.img_plot("noisy_axial", colormap=gray)[0]
plot2.color_mapper.range = DataRange1D(low=0., high=1.0)
plot2.bgcolor = 'pink'
plot2.range2d = plot.range2d
# Brain_map tools
plot2.tools.append(PanTool(plot2, drag_button="right"))
plot2.tools.append(ZoomTool(plot2))
imgtool2 = ImageInspectorTool(renderer2)
renderer2.tools.append(imgtool2)
overlay2 = ImageInspectorOverlay(component=renderer2, image_inspector=imgtool2,
bgcolor="white", border_visible=True)
renderer2.overlays.append(overlay2)
# Brain_map track cursor
self.cursor2 = CursorTool2D(renderer2, drag_button='left', color='red', line_width=2.0)
#self.cursor2.on_trait_change(self.cursor2_changed, 'current_index')
self.cursor2.current_position = (0., 0.)
renderer2.overlays.append(self.cursor2)
# Brain_map colorbar
colormap2 = plot2.color_mapper
colorbar2 = ColorBar(index_mapper=LinearMapper(range=colormap2.range),
color_mapper=colormap2,
plot=plot2,
orientation='v',
resizable='v',
width=20,
padding=(30, 0, 0, 0))
colorbar2.padding_top = plot2.padding_top
colorbar2.padding_bottom = plot2.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True, padding=(0, 0, 10, 10))
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
container2 = HPlotContainer(use_backbuffer=True, padding=(0, 0, 10, 10))
container2.add(plot2)
container2.add(colorbar2)
container2.bgcolor = "lightgray"
Hcontainer = HPlotContainer(use_backbuffer=True)
Hcontainer.add(container)
Hcontainer.add(container2)
Hcontainer.bgcolor = "lightgray"
return Hcontainer
def _create_plot_component():
# Create a scalar field to colormap
x_extents = (-2 * pi, 2 * pi)
y_extents = (-1.5 * pi, 1.5 * pi)
xs = linspace(-2 * pi, 2 * pi, 200)
ys = linspace(-1.5 * pi, 1.5 * pi, 100)
x, y = meshgrid(xs, ys)
zs = sin(log(abs((x+1)**4)+0.05))*cos(y)*1.1*(-y) + \
sin(((x+1)**2 + y**2)/4)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", zs)
# Create the left plot, a colormap and simple contours
lplot = Plot(pd)
lplot.img_plot("imagedata",
name="cm_plot",
xbounds=x_extents,
ybounds=y_extents,
colormap=gmt_drywet)
lplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents)
# Tweak some of the plot properties
lplot.title = "Colormap and contours"
lplot.padding = 20
lplot.bg_color = "white"
lplot.fill_padding = True
# Add some tools to the plot
zoom = ZoomTool(lplot, tool_mode="box", always_on=False)
lplot.overlays.append(zoom)
lplot.tools.append(PanTool(lplot, constrain_key="shift"))
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
cm_plot = lplot.plots["cm_plot"][0]
# Create the colorbar, handing in the appropriate range and colormap
colormap = cm_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=cm_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
colorbar.padding_top = lplot.padding_top
colorbar.padding_bottom = lplot.padding_bottom
# Create the left plot, contours of varying color and width
rplot = Plot(pd, range2d=lplot.range2d)
rplot.contour_plot("imagedata",
type="line",
xbounds=x_extents,
ybounds=y_extents,
bgcolor="black",
levels=15,
styles="solid",
widths=list(linspace(4.0, 0.1, 15)),
colors=gmt_drywet)
# Add some tools to the plot
zoom = ZoomTool(rplot, tool_mode="box", always_on=False)
rplot.overlays.append(zoom)
rplot.tools.append(PanTool(rplot, constrain_key="shift"))
# Tweak some of the plot properties
rplot.title = "Varying contour lines"
rplot.padding = 20
rplot.bg_color = "white"
rplot.fill_padding = True
# Create a container and add our plots
container = HPlotContainer(padding=40,
fill_padding=True,
bgcolor="white",
use_backbuffer=True)
container.add(colorbar)
container.add(lplot)
container.add(rplot)
return container
def _create_plot_component():
# Create some data
numpts = 500
x1 = random(numpts)
y1 = random(numpts)
x2 = x1 + standard_normal(numpts) * 0.05
y2 = y1 + standard_normal(numpts) * 0.05
color = exp(-(x1**2 + y2**2))
widths = random(numpts)
# Create a plot data obect and give it this data
pd = ArrayPlotData()
pd.set_data("index", column_stack([x1, x2]).reshape(-1))
pd.set_data("value", column_stack([y1, y2]).reshape(-1))
pd.set_data("color", color)
pd.set_data("widths", widths)
# Create the plot
plot = Plot(pd)
plot.plot(("index", "value", "color", "widths"),
type="cmap_segment",
name="my_plot",
color_mapper=viridis,
border_visible=True,
render_style='cubic',
bgcolor="white",
size_min=0.5,
size_max=5.0)
# Tweak some of the plot properties
plot.title = "Colormapped Segment Plot with variable widths"
plot.padding = 50
plot.x_grid.visible = False
plot.y_grid.visible = False
plot.x_axis.font = "modern 16"
plot.y_axis.font = "modern 16"
# Right now, some of the tools are a little invasive, and we need the
# actual ColomappedSegmentPlot object to give to them
cmap_renderer = plot.plots["my_plot"][0]
# Attach some tools to the plot
plot.tools.append(PanTool(plot, constrain_key="shift"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
plot.overlays.append(zoom)
# Create the colorbar, handing in the appropriate range and colormap
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 = cmap_renderer
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = "lightgray"
return container
