Python api.HPlotContainer类代码示例

    def __init__(self):
        # The delegates views don't work unless we caller the superclass __init__
        super(CursorTest, self).__init__()

        container = HPlotContainer(padding=0, spacing=20)
        self.plot = container
        # a subcontainer for the first plot.
        # I'm not sure why this is required. Without it, the layout doesn't work right.
        subcontainer = OverlayPlotContainer(padding=40)
        container.add(subcontainer)

        # make some data
        index = numpy.linspace(-10, 10, 512)
        value = numpy.sin(index)

        # create a LinePlot instance and add it to the subcontainer
        line = create_line_plot([index, value], add_grid=True, add_axis=True, index_sort="ascending", orientation="h")
        subcontainer.add(line)

        # here's our first cursor.
        csr = CursorTool(line, drag_button="left", color="blue")
        self.cursor1 = csr
        # and set it's initial position (in data-space units)
        csr.current_position = 0.0, 0.0

        # this is a rendered component so it goes in the overlays list
        line.overlays.append(csr)

        # some other standard tools
        line.tools.append(PanTool(line, drag_button="right"))
        line.overlays.append(ZoomTool(line))

        # make some 2D data for a colourmap plot
        xy_range = (-5, 5)
        x = numpy.linspace(xy_range[0], xy_range[1], 100)
        y = numpy.linspace(xy_range[0], xy_range[1], 100)
        X, Y = numpy.meshgrid(x, y)
        Z = numpy.sin(X) * numpy.arctan2(Y, X)

        # easiest way to get a CMapImagePlot is to use the Plot class
        ds = ArrayPlotData()
        ds.set_data("img", Z)

        img = Plot(ds, padding=40)
        cmapImgPlot = img.img_plot("img", xbounds=xy_range, ybounds=xy_range, colormap=jet)[0]

        container.add(img)

        # now make another cursor
        csr2 = CursorTool(cmapImgPlot, drag_button="left", color="white", line_width=2.0)
        self.cursor2 = csr2

        csr2.current_position = 1.0, 1.5

        cmapImgPlot.overlays.append(csr2)

        # add some standard tools. Note, I'm assigning the PanTool to the
        # right mouse-button to avoid conflicting with the cursors
        cmapImgPlot.tools.append(PanTool(cmapImgPlot, drag_button="right"))
        cmapImgPlot.overlays.append(ZoomTool(cmapImgPlot))

	def 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 _container_default(self):
        x = arange(-5.0, 15.0, 20.0/100)

        y = jn(0, x)
        left_plot = create_line_plot((x, y), bgcolor="white",
                                     add_grid=True, add_axis=True)
        left_plot.tools.append(PanTool(left_plot))
        self.left_plot = left_plot

        y = jn(1, x)
        right_plot = create_line_plot((x, y), bgcolor="white",
                                      add_grid=True, add_axis=True)
        right_plot.tools.append(PanTool(right_plot))
        right_plot.y_axis.orientation = "right"
        self.right_plot = right_plot

        # Tone down the colors on the grids
        right_plot.hgrid.line_color = (0.3, 0.3, 0.3, 0.5)
        right_plot.vgrid.line_color = (0.3, 0.3, 0.3, 0.5)
        left_plot.hgrid.line_color = (0.3, 0.3, 0.3, 0.5)
        left_plot.vgrid.line_color = (0.3, 0.3, 0.3, 0.5)

        container = HPlotContainer(spacing=20, padding=50, bgcolor="lightgray")
        container.add(left_plot)
        container.add(right_plot)
        return container

    def _create_plot_component(self):
        
        selected = Event()
    
        # Create some x-y data series to plot
        x = linspace(-2.0, 10.0, 100)
        pd = ArrayPlotData(index = x)
        for i in range(5):
            pd.set_data("y" + str(i), jn(i,x))
    
        # Create some line plots of some of the data
        plot1 = Plot(pd, title="Line Plot", padding=50, border_visible=True)
        plot1.legend.visible = True
        plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
    
        # Attach some tools to the plot
        plot1.tools.append(PanTool(plot1))
        
        self.zoom = BetterSelectingZoom(component=plot1, tool_mode="box", always_on=False, selection_completed = selected)
        plot1.overlays.append(self.zoom)

        container = HPlotContainer()
        container.add(plot1)
        
        self.zoom.on_trait_change(self.selection_changed, 'ratio')
    
        return container

def _create_plot_component():

    # Create some x-y data series to plot
    x = linspace(-2.0, 10.0, 100)
    pd = ArrayPlotData(index = x)
    for i in range(5):
        pd.set_data("y" + str(i), jn(i,x))

    # Create some line plots of some of the data
    plot1 = Plot(pd, title="Line Plot", padding=50, border_visible=True)
    plot1.legend.visible = True
    plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
    plot1.plot(("index", "y3"), name="j_3", color="blue")

    # Attach some tools to the plot
    plot1.tools.append(PanTool(plot1))
    zoom = ZoomTool(component=plot1, tool_mode="box", always_on=False)
    plot1.overlays.append(zoom)

    # Create a second scatter plot of one of the datasets, linking its
    # range to the first plot
    plot2 = Plot(pd, range2d=plot1.range2d, title="Scatter plot", padding=50,
                 border_visible=True)
    plot2.plot(('index', 'y3'), type="scatter", color="blue", marker="circle")

    # Create a container and add our plots
    container = HPlotContainer()
    container.add(plot1)
    container.add(plot2)

    return container

def _create_plot_component():

    # Create some x-y data series to plot
    x = linspace(-2.0, 10.0, 40)
    pd = ArrayPlotData(index = x, y0=jn(0,x))

    # Create some line plots of some of the data
    plot1 = Plot(pd, title="render_style = hold", padding=50, border_visible=True,
                 overlay_border = True)
    plot1.legend.visible = True
    lineplot = plot1.plot(("index", "y0"), name="j_0", color="red", render_style="hold")

    # Attach some tools to the plot
    attach_tools(plot1)

    # Create a second scatter plot of one of the datasets, linking its
    # range to the first plot
    plot2 = Plot(pd, range2d=plot1.range2d, title="render_style = connectedhold",
                 padding=50, border_visible=True, overlay_border=True)
    plot2.plot(('index', 'y0'), color="blue", render_style="connectedhold")
    attach_tools(plot2)

    # Create a container and add our plots
    container = HPlotContainer()
    container.add(plot1)
    container.add(plot2)
    return container

def _create_plot_component(obj):
    # Setup the spectrum plot
    frequencies = linspace(0.0, float(SAMPLING_RATE)/2, num=NUM_SAMPLES/2)
    obj.spectrum_data = ArrayPlotData(frequency=frequencies)
    empty_amplitude = zeros(NUM_SAMPLES/2)
    obj.spectrum_data.set_data('amplitude', empty_amplitude)

    obj.spectrum_plot = Plot(obj.spectrum_data)
    spec_renderer = obj.spectrum_plot.plot(("frequency", "amplitude"), name="Spectrum",
                           color="red")[0]
    obj.spectrum_plot.padding = 50
    obj.spectrum_plot.title = "Spectrum"
    spec_range = list(obj.spectrum_plot.plots.values())[0][0].value_mapper.range
    spec_range.low = 0.0
    spec_range.high = 5.0
    obj.spectrum_plot.index_axis.title = 'Frequency (hz)'
    obj.spectrum_plot.value_axis.title = 'Amplitude'

    # Time Series plot
    times = linspace(0.0, float(NUM_SAMPLES)/SAMPLING_RATE, num=NUM_SAMPLES)
    obj.time_data = ArrayPlotData(time=times)
    empty_amplitude = zeros(NUM_SAMPLES)
    obj.time_data.set_data('amplitude', empty_amplitude)

    obj.time_plot = Plot(obj.time_data)
    obj.time_plot.plot(("time", "amplitude"), name="Time", color="blue")
    obj.time_plot.padding = 50
    obj.time_plot.title = "Time"
    obj.time_plot.index_axis.title = 'Time (seconds)'
    obj.time_plot.value_axis.title = 'Amplitude'
    time_range = list(obj.time_plot.plots.values())[0][0].value_mapper.range
    time_range.low = -0.2
    time_range.high = 0.2

    # Spectrogram plot
    values = [zeros(NUM_SAMPLES/2) for i in range(SPECTROGRAM_LENGTH)]
    p = WaterfallRenderer(index = spec_renderer.index, values = values,
            index_mapper = LinearMapper(range = obj.spectrum_plot.index_mapper.range),
            value_mapper = LinearMapper(range = DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
            y2_mapper = LinearMapper(low_pos=0, high_pos=8,
                            range=DataRange1D(low=0, high=15)),
            )
    spectrogram_plot = p
    obj.spectrogram_plot = p
    dummy = Plot()
    dummy.padding = 50
    dummy.index_axis.mapper.range = p.index_mapper.range
    dummy.index_axis.title = "Frequency (hz)"
    dummy.add(p)

    container = HPlotContainer()
    container.add(obj.spectrum_plot)
    container.add(obj.time_plot)

    c2 = VPlotContainer()
    c2.add(dummy)
    c2.add(container)

    return c2

def _create_plot_component():

    # Create some x-y data series to plot
    plot_area = OverlayPlotContainer(border_visible=True)
    container = HPlotContainer(padding=50, bgcolor="transparent")
    #container.spacing = 15

    x = linspace(-2.0, 10.0, 100)
    for i in range(5):
        color = tuple(COLOR_PALETTE[i])
        y = jn(i, x)
        renderer = create_line_plot((x, y), color=color)
        plot_area.add(renderer)
        #plot_area.padding_left = 20

        axis = PlotAxis(orientation="left", resizable="v",
                    mapper = renderer.y_mapper,
                    axis_line_color=color,
                    tick_color=color,
                    tick_label_color=color,
                    title_color=color,
                    bgcolor="transparent",
                    title = "jn_%d" % i,
                    border_visible = True,)
        axis.bounds = [60,0]
        axis.padding_left = 10
        axis.padding_right = 10

        container.add(axis)

        if i == 4:
            # Use the last plot's X mapper to create an X axis and a
            # vertical grid
            x_axis = PlotAxis(orientation="bottom", component=renderer,
                        mapper=renderer.x_mapper)
            renderer.overlays.append(x_axis)
            grid = PlotGrid(mapper=renderer.x_mapper, orientation="vertical",
                    line_color="lightgray", line_style="dot")
            renderer.underlays.append(grid)

    # Add the plot_area to the horizontal container
    container.add(plot_area)

    # Attach some tools to the plot
    broadcaster = BroadcasterTool()
    for plot in plot_area.components:
        broadcaster.tools.append(PanTool(plot))

    # Attach the broadcaster to one of the plots.  The choice of which
    # plot doesn't really matter, as long as one of them has a reference
    # to the tool and will hand events to it.
    plot.tools.append(broadcaster)

    return container

class Graph(HasTraits):
    plot=None
    traits_view=View(Item('plot',editor=ComponentEditor(bgcolor="white"), show_label=False),
                     width=1200, height=1024, resizable=True, title="BacLog")
    
    def __init__(self):
        super(Graph,self).__init__()
        self.container = HPlotContainer(padding=20, bgcolor="transparent")
        self.plot_area = OverlayPlotContainer(border_visible=True)

    def add(self,series,limit=None):
        
        broadcaster = BroadcasterTool()
        
        for name,line in series._plot.line.items():
            if limit is not None and name not in limit:
                continue
            if line.time==[]:
                print "Graph.add> empty:", name
                continue
            plot=create_line_plot((seconds(line.time),line.data),color=line.color)
            self.plot_area.add(plot)

            axis = PlotAxis(orientation="left", resizable="v",
                            mapper = plot.y_mapper,
                            bgcolor="white",
                            title = name,
                            title_color = line.color,
                            title_spacing = -4.0,
                            border_visible = True,)
            ## Visual style
            axis.bounds = [60,0]
            axis.padding_left = 1
            axis.padding_right = 1
            self.container.add(axis)

            ## Tools (attach to all for now)
            plot.tools.append(broadcaster)
            broadcaster.tools.append(PanTool(plot))
            broadcaster.tools.append(DragZoom(plot,maintain_aspect_ratio=False,drag_button='right',restrict_domain=True))

        
    def run(self):

        ## Time axis (first one)
        plot=self.plot_area.components[0]
        time = PlotAxis(orientation="bottom", component=plot, mapper=plot.x_mapper)
        plot.overlays.append(time)

        ## Plot
        self.container.add(self.plot_area)
        self.plot=self.container

        self.configure_traits()

 def _plot_default(self):
     data = self.data < self.limit
     pd = self.pd = ArrayPlotData(imagedata=data,orig=self.data)
     plot1 = Plot(pd, default_origin='top left')
     plot2 = Plot(pd, default_origin='top left')
     img_plot1 = plot1.img_plot("imagedata",colormap=gray,padding=0)[0]
     img_plot2 = plot2.img_plot("orig",colormap=gray,padding=0)[0]
     container = HPlotContainer(plot1,plot2)
     container.spacing=0
     plot1.padding_right=0
     plot2.padding_left=0
     plot2.y_axis.orientation= 'right'
     return container

    def _load_image_data(self, data):
        cont = HPlotContainer()
        pd = ArrayPlotData()
        plot = Plot(data=pd, padding=[30, 5, 5, 30], default_origin="top left")

        pd.set_data("img", data)
        img_plot = plot.img_plot("img")[0]

        self._add_inspector(img_plot)
        self._add_tools(img_plot)

        cont.add(plot)
        cont.request_redraw()
        self.image_container.container = cont

def _create_plot_component():

    # Create some x-y data series to plot
    x = linspace(-2.0, 10.0, 100)
    pd = ArrayPlotData(index = x)
    for i in range(5):
        pd.set_data("y" + str(i), jn(i,x))

    # Create some line plots of some of the data
    plot = Plot(pd, title="Line Plot", padding=50, border_visible=True)
    plot.legend.visible = True
    plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="auto")
    plot.plot(("index", "y3"), name="j_3", color="auto")

    plot.x_grid.line_color = "black"
    plot.y_grid.line_color = "black"
    xmin, xmax = 1.0, 6.0
    ymin, ymax = 0.2, 0.80001
    plot.x_grid.set(data_min = xmin, data_max = xmax,
            transverse_bounds = (ymin, ymax),
            transverse_mapper = plot.y_mapper)

    plot.y_grid.set(data_min = ymin, data_max = ymax,
            transverse_bounds = (xmin, xmax),
            transverse_mapper = plot.x_mapper)

    # 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)

    # A second plot whose vertical grid lines are clipped to the jn(3) function
    def my_bounds_func(ticks):
        """ Returns y_low and y_high for each grid tick in the array **ticks** """
        tmp = array([zeros(len(ticks)),jn(3, ticks)]).T
        return tmp

    func_plot = Plot(pd, padding=50, border_visible=True)
    func_plot.plot(("index", "y3"), color="red")
    func_plot.x_grid.set(transverse_bounds = my_bounds_func,
                    transverse_mapper = func_plot.y_mapper,
                    line_color="black")
    func_plot.tools.append(PanTool(func_plot))

    container = HPlotContainer()
    container.add(plot)
    container.add(func_plot)

    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 test_valign(self):
     container = HPlotContainer(bounds=[300,200], valign="center")
     comp1 = StaticPlotComponent([200,100])
     container.add(comp1)
     container.do_layout()
     self.assertEqual(comp1.position, [0,50])
     container.valign="top"
     container.do_layout(force=True)
     self.assertEqual(comp1.position, [0,100])
     return

def _create_plot_component():

    # Create the index
    numpoints = 100
    low = -5
    high = 15.0
    x = arange(low, high, (high - low) / numpoints)
    plotdata = ArrayPlotData(x=x, y1=jn(0, x), y2=jn(1, x))

    # Create the left plot
    left_plot = Plot(plotdata)
    left_plot.x_axis.title = "X"
    left_plot.y_axis.title = "j0(x)"
    renderer = left_plot.plot(("x", "y1"), type="line", color="blue",
                              width=2.0)[0]
    renderer.overlays.append(LineInspector(renderer, axis='value',
                                            write_metadata=True,
                                            is_listener=True))
    renderer.overlays.append(LineInspector(renderer, axis="index",
                                            write_metadata=True,
                                            is_listener=True))
    left_plot.overlays.append(ZoomTool(left_plot, tool_mode="range"))
    left_plot.tools.append(PanTool(left_plot))

    # Create the right plot
    right_plot = Plot(plotdata)
    right_plot.index_range = left_plot.index_range
    right_plot.orientation = "v"
    right_plot.x_axis.title = "j1(x)"
    right_plot.y_axis.title = "X"
    renderer2 = right_plot.plot(("x", "y2"), type="line", color="red",
                                width=2.0)[0]
    renderer2.index = renderer.index
    renderer2.overlays.append(LineInspector(renderer2, write_metadata=True,
                              is_listener=True))
    renderer2.overlays.append(LineInspector(renderer2, axis="value",
                              is_listener=True))
    right_plot.overlays.append(ZoomTool(right_plot, tool_mode="range"))
    right_plot.tools.append(PanTool(right_plot))

    container = HPlotContainer(background="lightgray")
    container.add(left_plot)
    container.add(right_plot)

    return container