def __init__(self, **traits): super(HistDemo, self).__init__(**traits) img = cv.imread("lena.jpg") gray_img = cv.Mat() cv.cvtColor(img, gray_img, cv.CV_BGR2GRAY) self.img = gray_img self.img2 = self.img.clone() result = cv.MatND() r = cv.vector_float32([0, 256]) ranges = cv.vector_vector_float32([r, r]) cv.calcHist(cv.vector_Mat([self.img]), channels=cv.vector_int([0, 1]), mask=cv.Mat(), hist=result, histSize=cv.vector_int([256]), ranges=ranges) data = ArrayPlotData(x=np.arange(0, len(result[:])), y=result[:]) self.plot = Plot(data, padding=10) line = self.plot.plot(("x", "y"))[0] self.select_tool = RangeSelection(line, left_button_selects=True) line.tools.append(self.select_tool) self.select_tool.on_trait_change(self._selection_changed, "selection") line.overlays.append(RangeSelectionOverlay(component=line)) cv.imshow("Hist Demo", self.img) self.timer = Timer(50, self.on_timer)
def __init__(self, **kwargs): super(VariableMeshPannerView, self).__init__(**kwargs) # Create the plot self.add_trait("field", DelegatesTo("vm_plot")) plot = self.vm_plot.plot img_plot = self.vm_plot.img_plot if self.use_tools: plot.tools.append(PanTool(img_plot)) zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False) plot.overlays.append(zoom) 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) image_value_range = DataRange1D(self.vm_plot.fid) cbar_index_mapper = LinearMapper(range=image_value_range) self.colorbar = ColorBar(index_mapper=cbar_index_mapper, plot=img_plot, padding_right=40, resizable='v', width=30) self.colorbar.tools.append( PanTool(self.colorbar, constrain_direction="y", constrain=True)) zoom_overlay = ZoomTool(self.colorbar, axis="index", tool_mode="range", always_on=True, drag_button="right") self.colorbar.overlays.append(zoom_overlay) # create a range selection for the colorbar range_selection = RangeSelection(component=self.colorbar) self.colorbar.tools.append(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 range_selection.listeners.append(img_plot) self.full_container = HPlotContainer(padding=30) self.container = OverlayPlotContainer(padding=0) self.full_container.add(self.colorbar) self.full_container.add(self.container) self.container.add(self.vm_plot.plot)
def __init__(self, **traits): super(SelectionDemo, self).__init__(**traits) x = np.linspace(-140, 140, 1000) y = np.sin(x) * x**3 y /= np.max(y) data = ArrayPlotData(x=x, y=y) self.plot1 = plot1 = Plot(data, padding=25) self.line1 = line1 = plot1.plot(("x", "y"), type="line")[0] self.select_tool = select_tool = RangeSelection(line1) line1.tools.append(select_tool) select_tool.on_trait_change(self._selection_changed, "selection") line1.overlays.append(RangeSelectionOverlay(component=line1)) self.plot2 = plot2 = Plot(data, padding=25) plot2.plot(("x", "y"), type="line")[0] self.plot = VPlotContainer(plot2, plot1) self.data = data
def 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 _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]) # 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=(0,10), #ybounds=(0,10), colormap=jet) # Tweak some of the plot properties self.tplot.title = "Matrix" 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 # 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 __init__(self): super(CyclesPlot, self).__init__() # Normally you'd pass in the data, but I'll hardwire things for this # one-off plot. srecs = read_time_series_from_csv("./biz_cycles2.csv", date_col=0, date_format="%Y-%m-%d") dt = srecs["Date"] # Industrial production compared with trend (plotted on value axis) iprod_vs_trend = srecs["Metric 1"] # Industrial production change in last 6 Months (plotted on index axis) iprod_delta = srecs["Metric 2"] self._dates = dt self._series1 = self._selected_s1 = iprod_delta self._series2 = self._selected_s2 = iprod_vs_trend end_x = np.array([self._selected_s1[-1]]) end_y = np.array([self._selected_s2[-1]]) plotdata = ArrayPlotData(x=self._series1, y=self._series2, dates=self._dates, selected_x=self._selected_s1, selected_y=self._selected_s2, endpoint_x=end_x, endpoint_y=end_y) cycles = Plot(plotdata, padding=20) cycles.plot(("x", "y"), type="line", color=(.2, .4, .5, .4)) cycles.plot(("selected_x", "selected_y"), type="line", marker="circle", line_width=3, color=(.2, .4, .5, .9)) cycles.plot(("endpoint_x", "endpoint_y"), type="scatter", marker_size=4, marker="circle", color=(.2, .4, .5, .2), outline_color=(.2, .4, .5, .6)) cycles.index_range = DataRange1D(low_setting=80., high_setting=120.) cycles.value_range = DataRange1D(low_setting=80., high_setting=120.) # dig down to use actual Plot object cyc_plot = cycles.components[0] # Add the labels in the quadrants cyc_plot.overlays.append( PlotLabel("\nSlowdown" + 40 * " " + "Expansion", component=cyc_plot, font="swiss 24", color=(.2, .4, .5, .6), overlay_position="inside top")) cyc_plot.overlays.append( PlotLabel("Downturn" + 40 * " " + "Recovery\n ", component=cyc_plot, font="swiss 24", color=(.2, .4, .5, .6), overlay_position="inside bottom")) timeline = Plot(plotdata, resizable='h', height=50, padding=20) timeline.plot(("dates", "x"), type="line", color=(.2, .4, .5, .8), name='x') timeline.plot(("dates", "y"), type="line", color=(.5, .4, .2, .8), name='y') # Snap on the tools zoomer = ZoomTool(timeline, drag_button="right", always_on=True, tool_mode="range", axis="index", max_zoom_out_factor=1.1) panner = PanTool(timeline, constrain=True, constrain_direction="x") # dig down to get Plot component I want x_plt = timeline.plots['x'][0] range_selection = RangeSelection(x_plt, left_button_selects=True) range_selection.on_trait_change(self.update_interval, 'selection') x_plt.tools.append(range_selection) x_plt.overlays.append(RangeSelectionOverlay(x_plt)) # Set the plot's bottom axis to use the Scales ticking system scale_sys = CalendarScaleSystem( fill_ratio=0.4, default_numlabels=5, default_numticks=10, ) tick_gen = ScalesTickGenerator(scale=scale_sys) bottom_axis = ScalesPlotAxis(timeline, orientation="bottom", tick_generator=tick_gen) # Hack to remove default axis - FIXME: how do I *replace* an axis? del (timeline.underlays[-2]) timeline.overlays.append(bottom_axis) container = GridContainer(padding=20, fill_padding=True, bgcolor="lightgray", use_backbuffer=True, shape=(2, 1), spacing=(30, 30)) # add a central "x" and "y" axis x_line = LineInspector(cyc_plot, is_listener=True, color="gray", width=2) y_line = LineInspector(cyc_plot, is_listener=True, color="gray", width=2, axis="value") cyc_plot.overlays.append(x_line) cyc_plot.overlays.append(y_line) cyc_plot.index.metadata["selections"] = 100.0 cyc_plot.value.metadata["selections"] = 100.0 container.add(cycles) container.add(timeline) container.title = "Business Cycles" self.plot = container
def setup_plots(self): ''' Args: Returns: Raises: ''' ext = self.extension if ext == "S2": color = "green" else: color = "blue" self.create_plot("AAE " + ext, ylabel="[ ]", color=color) self.create_plot("ARE " + ext, ylabel="[ ]", color=color) self.create_plot("Jerk " + ext, ylabel="[m/s2/s]", color=color) self.create_plot("SI " + ext, ylabel="[ ]", color=color) self.create_plot("VI " + ext, ylabel="[ ]", color=color) p = self.plots["VI " + ext] null_ds = ArrayDataSource([]) self.time_plot = LinePlot( index=self.time_src, value=null_ds, index_mapper=LinearMapper(range=DataRange1D(self.time_src)), value_mapper=LinearMapper(range=DataRange1D(null_ds)), color="black", border_visible=True, bgcolor="white", height=10, resizable="h", padding_top=50, padding_bottom=40, padding_left=50, padding_right=20) self.ticker = ScalesTickGenerator() self.x_axis = LabelAxis(self.time_plot, orientation="bottom", title="Time [sec]", label_rotation=0) #self.x_axis = PlotAxis(self.time_plot, orientation="bottom", tick_generator = self.ticker, title="Time [sec]") self.time_plot.underlays.append(self.x_axis) self.container.add(self.time_plot) # Add a range overlay to the miniplot that is hooked up to the range # of the main price_plot range_tool = RangeSelection(self.time_plot) self.time_plot.tools.append(range_tool) range_overlay = RangeSelectionOverlay(self.time_plot, metadata_name="selections") self.time_plot.overlays.append(range_overlay) range_tool.on_trait_change(self._range_selection_handler, "selection") self.range_tool = range_tool p.plot_conactory.index_range.on_trait_change(self._plot_range_handler, "updated") self.zoom_overlay = ZoomOverlay(source=self.time_plot, destination=p.plot_conactory) self.container.overlays.append(self.zoom_overlay)