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 _create_plot_component():
# Create some data
numpts = 1000
x = sort(random(numpts))
y = random(numpts)
color = randint(0, 7, numpts)
# 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=accent,
marker="square",
fill_alpha=0.5,
marker_size=6,
outline_color="black",
border_visible=True,
bgcolor="white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot with Range-selectable Data Points"
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 ColomappedScatterPlot 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)
selection = ColormappedSelectionOverlay(cmap_renderer,
fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
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
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 _create_plot_component():
# Load state data
states = pandas.read_csv('states.csv')
lon = (states['longitude'] + 180.) / 360.
lat = numpy.radians(states['latitude'])
lat = (1 - (1. - numpy.log(numpy.tan(lat) +
(1. / numpy.cos(lat))) / numpy.pi) / 2.0)
populations = pandas.read_csv('state_populations.csv')
data = populations['2010']
lon = lon.view(numpy.ndarray)
lat = lat.view(numpy.ndarray)
data = data.view(numpy.ndarray)
plot = Plot(ArrayPlotData(index=lon, value=lat, color=data))
renderers = plot.plot(
("index", "value", "color"),
type="cmap_scatter",
name="unfunded",
color_mapper=OrRd,
marker="circle",
outline_color='lightgray',
line_width=1.,
marker_size=10,
)
tile_cache = MBTileManager(filename='./map.mbtiles',
min_level=2,
max_level=4)
# Need a better way add the overlays
cmap = renderers[0]
map = Map(cmap, tile_cache=tile_cache, zoom_level=3)
cmap.underlays.append(map)
plot.title = "2010 Population"
plot.tools.append(PanTool(plot))
plot.tools.append(ZoomTool(plot))
plot.index_axis.title = "Longitude"
plot.index_axis.tick_label_formatter = convert_lon
plot.value_axis.title = "Latitude"
plot.value_axis.tick_label_formatter = convert_lat
cmap.overlays.append(
ColormappedSelectionOverlay(cmap,
fade_alpha=0.35,
selection_type="mask"))
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap
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 _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=jet,
marker = "square",
fill_alpha = 0.5,
marker_size = 6,
outline_color = "black",
border_visible = True,
bgcolor = "white")
# Tweak some of the plot properties
plot.title = "Colormapped Scatter Plot"
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 ColomappedScatterPlot 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)
selection = ColormappedSelectionOverlay(cmap_renderer, fade_alpha=0.35,
selection_type="mask")
cmap_renderer.overlays.append(selection)
# Create the colorbar, handing in the appropriate range and colormap
colorbar = create_colorbar(plot.color_mapper)
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
def _create_plot_component():
# Load state data
states = pandas.read_csv('states.csv')
lon = (states['longitude'] + 180.) / 360.
lat = numpy.radians(states['latitude'])
lat = (1 - (1. - numpy.log(numpy.tan(lat) +
(1./numpy.cos(lat)))/numpy.pi)/2.0)
populations = pandas.read_csv('state_populations.csv')
data = populations['2010']
lon = lon.view(numpy.ndarray)
lat = lat.view(numpy.ndarray)
data = data.view(numpy.ndarray)
plot = Plot(ArrayPlotData(index = lon, value=lat, color=data))
renderers = plot.plot(("index", "value", "color"),
type = "cmap_scatter",
name = "unfunded",
color_mapper = OrRd,
marker = "circle",
outline_color = 'lightgray',
line_width = 1.,
marker_size = 10,
)
tile_cache = MBTileManager(filename = './map.mbtiles',
min_level = 2,
max_level = 4)
# Need a better way add the overlays
cmap = renderers[0]
map = Map(cmap, tile_cache=tile_cache, zoom_level=3)
cmap.underlays.append(map)
plot.title = "2010 Population"
plot.tools.append(PanTool(plot))
plot.tools.append(ZoomTool(plot))
plot.index_axis.title = "Longitude"
plot.index_axis.tick_label_formatter = convert_lon
plot.value_axis.title = "Latitude"
plot.value_axis.tick_label_formatter = convert_lat
cmap.overlays.append(
ColormappedSelectionOverlay(cmap, fade_alpha=0.35,
selection_type="mask"))
colorbar = create_colorbar(plot.color_mapper)
colorbar.plot = cmap
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 _create_plot_component(max_pop, index_ds, value_ds, color_ds, paths):
tile_cache = HTTPTileManager(min_level=2, max_level=4,
server='tile.cloudmade.com',
url='/1a1b06b230af4efdbb989ea99e9841af/20760/256/%(zoom)d/%(col)d/%(row)d.png') # noqa
color_range = DataRange1D(color_ds, low_setting=0)
choro = ChoroplethPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
index_mapper=LinearMapper(range=DataRange1D(index_ds)),
value_mapper=LinearMapper(range=DataRange1D(value_ds)),
color_mapper=colormap(range=color_range),
outline_color='white',
line_width=1.5,
fill_alpha=1.,
compiled_paths=paths,
tile_cache=tile_cache,
zoom_level=3,
)
container = OverlayPlotContainer(
bgcolor='sys_window', padding=50, fill_padding=False,
border_visible=True,
)
container.add(choro)
for dir in ['left']:
axis = PlotAxis(tick_label_formatter=convert_lat,
mapper=choro.value_mapper, component=container,
orientation=dir)
container.overlays.append(axis)
for dir in ['top', 'bottom']:
axis = PlotAxis(tick_label_formatter=convert_lon,
mapper=choro.index_mapper, component=container,
orientation=dir)
container.overlays.append(axis)
choro.tools.append(PanTool(choro))
choro.tools.append(ZoomTool(choro))
colorbar = create_colorbar(choro)
colorbar.padding_top = container.padding_top
colorbar.padding_bottom = container.padding_bottom
plt = HPlotContainer(use_backbuffer=True)
plt.add(container)
plt.add(colorbar)
plt.bgcolor = "sys_window"
return plt
def add_colorbar(self, desc):
""" Colorbar generated: embed it together with plot & replace in desc.
"""
plot = desc["plot"]
container = HPlotContainer(
**self.plot_style.container_style.to_traits())
container.add(plot, self.colorbar)
container.padding_right = sum(
[comp.padding_right for comp in container.components])
container.bgcolor = "transparent"
desc["plot"] = container
def regenerate_plots(self, including_colorbar=False):
"""Function to regenerate entire plot (because many parts of each
renderer must be updated when product_component or time slice changes)
"""
active_anim_data = self.active_anim_data
# first plot
column_conc_plot = self.contour_plot_from_data(
active_anim_data,
COLUMN_CONC_PLOT_ARRAY_NAMES,
style_dict=COLUMN_CONC_PLOT_STYLE_DICT)
# second plot
liq_conc_plot = self.contour_plot_from_data(
active_anim_data,
LIQ_CONC_PLOT_ARRAY_NAMES,
style_dict=LIQ_CONC_PLOT_STYLE_DICT)
# Add colorbar to plot 2
if including_colorbar:
self._colorbar_liquid = self._color_bar_gen(
liq_conc_plot, LIQ_CONC_COLOR_BAR_STYLE_DICT)
# third plot
bound_conc_plot = self.contour_plot_from_data(
active_anim_data,
BOUND_CONC_PLOT_ARRAY_NAMES,
style_dict=BOUND_CONC_PLOT_STYLE_DICT)
# Add colorbar to plot 3
if including_colorbar:
self._colorbar_bound = self._color_bar_gen(
bound_conc_plot, BOUND_CONC_COLOR_BAR_STYLE_DICT)
container = HPlotContainer(column_conc_plot, self._colorbar_liquid,
liq_conc_plot, self._colorbar_bound,
bound_conc_plot)
container.bgcolor = "transparent"
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 _create_plot_component(max_pop, index_ds, value_ds, color_ds, paths):
tile_cache = HTTPTileManager(
min_level=2,
max_level=4,
server='tile.cloudmade.com',
url=
'/1a1b06b230af4efdbb989ea99e9841af/20760/256/%(zoom)d/%(row)d/%(col)d.png'
)
color_range = DataRange1D(color_ds,
low_setting=0) #, high_setting=max_pop)
choro = ChoroplethPlot(
index=index_ds,
value=value_ds,
color_data=color_ds,
index_mapper=LinearMapper(range=DataRange1D(index_ds)),
value_mapper=LinearMapper(range=DataRange1D(value_ds)),
color_mapper=colormap(range=color_range),
outline_color='white',
line_width=1.5,
fill_alpha=1.,
compiled_paths=paths,
tile_cache=tile_cache,
zoom_level=3,
)
container = OverlayPlotContainer(
bgcolor='sys_window',
padding=50,
fill_padding=False,
border_visible=True,
)
container.add(choro)
for dir in ['left']:
axis = PlotAxis(tick_label_formatter=convert_lat,
mapper=choro.value_mapper,
component=container,
orientation=dir)
container.overlays.append(axis)
for dir in ['top', 'bottom']:
axis = PlotAxis(tick_label_formatter=convert_lon,
mapper=choro.index_mapper,
component=container,
orientation=dir)
container.overlays.append(axis)
choro.tools.append(PanTool(choro))
choro.tools.append(ZoomTool(choro))
colorbar = create_colorbar(choro)
colorbar.padding_top = container.padding_top
colorbar.padding_bottom = container.padding_bottom
plt = HPlotContainer(use_backbuffer=True)
plt.add(container)
plt.add(colorbar)
plt.bgcolor = "sys_window"
return plt
def _create_plot_component(self):
# Create a plot data object and give it this data
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.data[self.data_name])
# find dimensions
xdim = self.data[self.data_name].shape[1]
ydim = self.data[self.data_name].shape[0]
# Create the plot
self.tplot = Plot(self.pd, default_origin="top left")
self.tplot.x_axis.orientation = "top"
self.tplot.img_plot("imagedata",
name="my_plot",
xbounds=(0.5, xdim + 0.5),
ybounds=(0.5, ydim + 0.5),
colormap=jet)
# Tweak some of the plot properties
self.tplot.title = "Connection Matrix for %s" % self.data_name
self.tplot.padding = 80
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
self.my_plot = self.tplot.plots["my_plot"][0]
# Attach some tools to the plot
self.tplot.tools.append(PanTool(self.tplot))
zoom = ZoomTool(component=self.tplot, tool_mode="box", always_on=False)
self.tplot.overlays.append(zoom)
# my custom tool to get the connection information
self.custtool = CustomTool(self.tplot)
self.tplot.tools.append(self.custtool)
# Create the colorbar, handing in the appropriate range and colormap
colormap = self.my_plot.color_mapper
self.colorbar = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
self.colorbar.padding_top = self.tplot.padding_top
self.colorbar.padding_bottom = self.tplot.padding_bottom
# create a range selection for the colorbar
self.range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(self.range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.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
self.range_selection.listeners.append(self.my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(self.tplot)
container.add(self.colorbar)
container.bgcolor = "white"
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_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(self):
# Create a plot data object and give it this data
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.data[self.data_name])
# find dimensions
xdim = self.data[self.data_name].shape[1]
ydim = self.data[self.data_name].shape[0]
# Create the plot
self.tplot = Plot(self.pd, default_origin="top left")
self.tplot.x_axis.orientation = "top"
self.tplot.img_plot("imagedata",
name="my_plot",
xbounds=(0.5,xdim + 0.5),
ybounds=(0.5,ydim + 0.5),
colormap=jet)
# Tweak some of the plot properties
self.tplot.title = "Connection Matrix for %s" % self.data_name
self.tplot.padding = 80
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
self.my_plot = self.tplot.plots["my_plot"][0]
# Attach some tools to the plot
self.tplot.tools.append(PanTool(self.tplot))
zoom = ZoomTool(component=self.tplot, tool_mode="box", always_on=False)
self.tplot.overlays.append(zoom)
# my custom tool to get the connection information
self.custtool = CustomTool(self.tplot)
self.tplot.tools.append(self.custtool)
# Create the colorbar, handing in the appropriate range and colormap
colormap = self.my_plot.color_mapper
self.colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
self.colorbar.padding_top = self.tplot.padding_top
self.colorbar.padding_bottom = self.tplot.padding_bottom
# create a range selection for the colorbar
self.range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(self.range_selection)
self.colorbar.overlays.append(RangeSelectionOverlay(component=self.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
self.range_selection.listeners.append(self.my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer = True)
container.add(self.tplot)
container.add(self.colorbar)
container.bgcolor = "white"
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
def _create_plot_component(self):
# we need the matrices!
# start with the currently selected one
#nr_nodes = self.matrix_data_ref[curr_edge].shape[0]
# Create a plot data obect and give it this data
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.matrix_data_ref[self.curr_edge])
# Create the plot
self.tplot = Plot(self.pd, default_origin="top left")
self.tplot.x_axis.orientation = "top"
self.tplot.img_plot("imagedata",
name="my_plot",
#xbounds=(0,nr_nodes),
#ybounds=(0,nr_nodes),
colormap=jet)
# Tweak some of the plot properties
self.tplot.title = self.curr_edge
self.tplot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
self.my_plot = self.tplot.plots["my_plot"][0]
# Attach some tools to the plot
self.tplot.tools.append(PanTool(self.tplot))
zoom = ZoomTool(component=self.tplot, tool_mode="box", always_on=False)
self.tplot.overlays.append(zoom)
# my custom tool to get the connection information
self.custtool = CustomTool(self.tplot)
self.tplot.tools.append(self.custtool)
# Create the colorbar, handing in the appropriate range and colormap
colormap = self.my_plot.color_mapper
self.colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
self.colorbar.padding_top = self.tplot.padding_top
self.colorbar.padding_bottom = self.tplot.padding_bottom
# TODO: the range selection gives a Segmentation Fault,
# but why, the matrix_viewer.py example works just fine!
# create a range selection for the colorbar
self.range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(self.range_selection)
self.colorbar.overlays.append(RangeSelectionOverlay(component=self.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
#self.range_selection.listeners.append(self.my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer = True)
container.add(self.tplot)
container.add(self.colorbar)
container.bgcolor = "white"
# my_plot.set_value_selection((-1.3, 6.9))
return container
def _create_plot_component(self):
# we need the matrices!
# start with the currently selected one
#nr_nodes = self.matrix_data_ref[curr_edge].shape[0]
# Create a plot data obect and give it this data
self.pd = ArrayPlotData()
self.pd.set_data("imagedata", self.matrix_data_ref[self.curr_edge])
# Create the plot
self.tplot = Plot(self.pd, default_origin="top left")
self.tplot.x_axis.orientation = "top"
self.tplot.img_plot(
"imagedata",
name="my_plot",
#xbounds=(0,nr_nodes),
#ybounds=(0,nr_nodes),
colormap=jet)
# Tweak some of the plot properties
self.tplot.title = self.curr_edge
self.tplot.padding = 50
# Right now, some of the tools are a little invasive, and we need the
# actual CMapImage object to give to them
self.my_plot = self.tplot.plots["my_plot"][0]
# Attach some tools to the plot
self.tplot.tools.append(PanTool(self.tplot))
zoom = ZoomTool(component=self.tplot, tool_mode="box", always_on=False)
self.tplot.overlays.append(zoom)
# my custom tool to get the connection information
self.custtool = CustomTool(self.tplot)
self.tplot.tools.append(self.custtool)
# Create the colorbar, handing in the appropriate range and colormap
colormap = self.my_plot.color_mapper
self.colorbar = ColorBar(
index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=self.my_plot,
orientation='v',
resizable='v',
width=30,
padding=20)
self.colorbar.padding_top = self.tplot.padding_top
self.colorbar.padding_bottom = self.tplot.padding_bottom
# TODO: the range selection gives a Segmentation Fault,
# but why, the matrix_viewer.py example works just fine!
# create a range selection for the colorbar
self.range_selection = RangeSelection(component=self.colorbar)
self.colorbar.tools.append(self.range_selection)
self.colorbar.overlays.append(
RangeSelectionOverlay(component=self.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
#self.range_selection.listeners.append(self.my_plot)
# Create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(self.tplot)
container.add(self.colorbar)
container.bgcolor = "white"
# my_plot.set_value_selection((-1.3, 6.9))
return container
