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