Exemplo n.º 1
0
def _do_plot_boilerplate(kwargs, image=False):
    """ Used by various plotting functions.  Checks/handles hold state,
    returns a Plot object for the plotting function to use.
    """

    if "hold" in kwargs:
        hold(kwargs["hold"])
        del kwargs["hold"]

    # Check for an active window; if none, open one.
    if len(session.windows) == 0:
        if image:
            win = session.new_window(is_image=True)
            activate(win)
        else:
            figure()

    cont = session.active_window.get_container()

    if not cont:
        cont = Plot(session.data)
        session.active_window.set_container(cont)

    existing_tools = [type(t) for t in (cont.tools + cont.overlays)]
    if not PanTool in existing_tools:
        cont.tools.append(PanTool(cont))
    if not ZoomTool in existing_tools:
        cont.overlays.append(ZoomTool(cont, tool_mode="box", always_on=True, drag_button="right"))

    if not session.hold:
        cont.delplot(*list(cont.plots.keys()))

    return cont
Exemplo n.º 2
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def _create_plot_component():
    # Create a scalar field to colormap
    xs = linspace(0, 10, 600)
    ys = linspace(0, 5, 600)
    x, y = meshgrid(xs,ys)
    z = exp(-(x**2+y**2)/100)

    # Create a plot data obect and give it this data
    pd = ArrayPlotData()
    pd.set_data("imagedata", z)

    # Create the plot
    plot = Plot(pd)
    img_plot = plot.img_plot("imagedata",
                             xbounds=(0, 10),
                             ybounds=(0, 5),
                             colormap=jet)[0]

    # Tweak some of the plot properties
    plot.title = "My First Image Plot"
    plot.padding = 50

    # Attach some tools to the plot
    plot.tools.append(PanTool(plot))
    zoom = ZoomTool(component=img_plot, tool_mode="box", always_on=False)
    img_plot.overlays.append(zoom)
    return plot
Exemplo n.º 3
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    def _create_plot_component():
        # Create a fake dataset from which 2 dimensions will be displayed in a
        # scatter plot:
        x = np.random.uniform(0.0, 10.0, 50)
        y = np.random.uniform(0.0, 5.0, 50)
        data = pd.DataFrame({"x": x, "y": y,
                             "dataset": np.random.choice(list("abcdefg"), 50)})
        plot_data = ArrayPlotData(x=x, y=y)
        plot = Plot(plot_data)
        scatter = plot.plot(("x", "y"), type="scatter")[0]

        # Attach the inspector and its overlays
        inspector = DataframeScatterInspector(
            component=scatter, data=data
        )
        scatter.tools.append(inspector)

        text_overlay = DataframeScatterOverlay(component=plot,
                                               inspector=inspector,
                                               bgcolor="black", alpha=0.6,
                                               text_color="white",
                                               border_color='none')
        plot.overlays.append(text_overlay)

        # Optional: add an overlay on the point to confirm what is hovered over
        # Note that this overlay magically knows about hovered points by
        # listening to renderer events rather than inspector events:
        point_overlay = ScatterInspectorOverlay(component=scatter,
                                                hover_color="red",
                                                hover_marker_size=6)
        scatter.overlays.append(point_overlay)
        return plot
Exemplo n.º 4
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def _create_plot_component():  # 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 = sin(x) * y

    # Create a plot data obect and give it this data
    pd = ArrayPlotData()
    pd.set_data("imagedata", z)

    # Create the plot
    plot = Plot(pd)
    img_plot = plot.img_plot("imagedata", xbounds=xbounds[:2], ybounds=ybounds[:2], colormap=jet)[0]

    # Tweak some of the plot properties
    plot.title = "Image Plot with Lasso"
    plot.padding = 50

    lasso_selection = LassoSelection(component=img_plot)
    lasso_selection.on_trait_change(lasso_updated, "disjoint_selections")
    lasso_overlay = LassoOverlay(lasso_selection=lasso_selection, component=img_plot)
    img_plot.tools.append(lasso_selection)
    img_plot.overlays.append(lasso_overlay)
    return plot
Exemplo n.º 5
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 def updateplots(self):
     x = np.sort(np.random.random(100))
     y = np.random.random(100)
     color = np.exp(-(x ** 2 + y ** 2))  # np.random.random(100)
     pd = ArrayPlotData()
     pd.set_data("index", x)
     pd.set_data("value", y)
     pd.set_data("color", color)
     # Create some line plots of some of the data
     plot = Plot(pd)
     # Create a scatter plot and get a reference to it (separate from the
     # Plot object) because we'll need it for the regression tool below.
     scatterplot = plot.plot(
         ("index", "value", "color"),
         type="cmap_scatter",
         color_mapper=reverse(Spectral),
         marker="square",
         fill_alpha=0.9,
         marker_size=6,
         bgcolor=QtGui.QColor(240, 240, 240),
     )[0]
     # Tweak some of the plot properties
     plot.padding = 50
     # Attach some tools to the plot
     plot.tools.append(PanTool(plot, drag_button="right"))
     plot.overlays.append(ZoomTool(plot))
     # Add the regression tool and overlay.  These need to be added
     # directly to the scatterplot instance (and not the Plot instance).
     regression = RegressionLasso(scatterplot, selection_datasource=scatterplot.index)
     scatterplot.tools.append(regression)
     scatterplot.overlays.append(RegressionOverlay(scatterplot, lasso_selection=regression))
     self.chaco_widget1.visualization.plot = plot
     self.chaco_widget2.visualization.plot = plot
Exemplo n.º 6
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def createWindow(widget):
    ''' Example on creating a new plot window in the
        main window MDI-Area
    '''
    import plotWidget
    from PySide import QtGui
    from numpy import linspace
    from scipy.special import jn
    from chaco.api import ArrayPlotData, Plot

    window = widget.createNewWindow()
    container = plotWidget.plotContainer(window)
    plotWidget = plotWidget.PlotWidget(container)
    container.setPlotWidget(plotWidget)

    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))
    plot = Plot(pd, title=None, padding_left=60, padding_right=5, padding_top=5, padding_bottom=30, border_visible=True)
    plot.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
    plotWidget.setPlot(plot)

    layout = QtGui.QBoxLayout(QtGui.QBoxLayout.TopToBottom)
    layout.addWidget(container)
    window.setLayout(layout)
    window.show()
Exemplo n.º 7
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    def _plot_default(self):
        plotter = Plot(data=self.data)
        main_plot = plotter.plot(['x','y'])[0]
        self.configure_plot(main_plot, xlabel='')

        plotter2 = Plot(data=self.data)
        zoom_plot = plotter2.plot(['x','y'])[0]
        self.configure_plot(zoom_plot)
        
        outer_container = VPlotContainer(padding=20,
                                         fill_padding=True,
                                         spacing=0,
                                         stack_order='top_to_bottom',
                                         bgcolor='lightgray',
                                         use_backbuffer=True)

        outer_container.add(main_plot)
        outer_container.add(zoom_plot)
        # FIXME: This is set to the windows bg color.  Should get from the system.
        #outer_container.bgcolor = (236/255., 233/255., 216/255., 1.0)

        main_plot.controller = RangeSelection(main_plot)
        
        zoom_overlay = ZoomOverlay(source=main_plot, destination=zoom_plot)
        outer_container.overlays.append(zoom_overlay)

        return outer_container
    def _plot_default(self):
        # Create a GridContainer to hold all of our plots: 2 rows, 4 columns:
        container = GridContainer(fill_padding=True,
                                  bgcolor="lightgray", use_backbuffer=True,
                                  shape=(2, 4))

        arrangements = [('top left', 'h'),
                        ('top right', 'h'),
                        ('top left', 'v'),
                        ('top right', 'v'),
                        ('bottom left', 'h'),
                        ('bottom right', 'h'),
                        ('bottom left', 'v'),
                        ('bottom right', 'v')]
        orientation_name = {'h': 'horizontal', 'v': 'vertical'}

        pd = ArrayPlotData(image=lena())
        # Plot some bessel functions and add the plots to our container
        for origin, orientation in arrangements:
            plot = Plot(pd, default_origin=origin, orientation=orientation)
            plot.img_plot('image')

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

            title = '{0}, {1}'
            plot.title = title.format(orientation_name[orientation],
                                      origin.replace(' ', '-'))

            # Add to the grid container
            container.add(plot)

        return container
Exemplo n.º 9
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def _create_plot_component():

    # Create a random scattering of XY pairs
    x = random.uniform(0.0, 10.0, 50)
    y = random.uniform(0.0, 5.0, 50)
    pd = ArrayPlotData(x=x, y=y)
    plot = Plot(pd, border_visible=True, overlay_border=True)

    scatter = plot.plot(("x", "y"), type="scatter", color="lightblue")[0]

    # Tweak some of the plot properties
    plot.set(title="Scatter Inspector Demo", padding=50)

    # Attach some tools to the plot
    plot.tools.append(PanTool(plot))
    plot.overlays.append(ZoomTool(plot))

    # Attach the inspector and its overlay
    scatter.tools.append(ScatterInspector(scatter))
    overlay = ScatterInspectorOverlay(
        scatter,
        hover_color="red",
        hover_marker_size=6,
        selection_marker_size=6,
        selection_color="yellow",
        selection_outline_color="purple",
        selection_line_width=3,
    )
    scatter.overlays.append(overlay)

    return plot
Exemplo n.º 10
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 def _sig_waveform_plot_default(self):
     plot = Plot(self.plot_data, padding=[75, 25, 25, 50],
                 title='Signal Waveform')
     plot.plot(('time', 'sig_waveform'), color='black')
     plot.index_axis.title = 'Time (sec)'
     plot.value_axis.title = 'Signal (V)'
     return plot
Exemplo n.º 11
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 def _sig_phase_plot_default(self):
     plot = Plot(self.plot_data, padding=[75, 25, 25, 50],
                 title='Signal Spectrum')
     plot.plot(('frequency', 'sig_phase'), index_scale='log', color='black')
     plot.index_axis.title = 'Frequency (Hz)'
     plot.value_axis.title = 'Power (dB)'
     return plot
Exemplo n.º 12
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def _create_plot_component():
    pd = ArrayPlotData(x=random(100), y=random(100))

    # Create some line plots of some of the data
    plot = Plot(pd)

    # Create a scatter plot and get a reference to it (separate from the
    # Plot object) because we'll need it for the regression tool below.
    scatterplot = plot.plot(("x", "y"), color="blue", type="scatter")[0]

    # Tweak some of the plot properties
    plot.padding = 50

    # Attach some tools to the plot
    plot.tools.append(PanTool(plot, drag_button="right"))
    plot.overlays.append(ZoomTool(plot))

    # Add the regression tool and overlay.  These need to be added
    # directly to the scatterplot instance (and not the Plot instance).
    regression = RegressionLasso(scatterplot,
        selection_datasource=scatterplot.index)
    scatterplot.tools.append(regression)
    scatterplot.overlays.append(RegressionOverlay(scatterplot,
                                                  lasso_selection=regression))
    return plot
Exemplo n.º 13
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    def _interp_data_button_fired(self):
        x = [row.x for row in self.rows]
        y = [row.y for row in self.rows]

        f = interp1d(asarray(x), asarray(y))
        y2 = Array
        print x, sorted(x)
        y2 = [f(i) for i in sorted(x)]

        print y2

        plotdata = ArrayPlotData(x=x, y=y)
        plotdata2 = ArrayPlotData(y=y2)

        test_plot = Plot(plotdata)
        test_plot.plot(("x", "y"), type="scatter")
        test_plot.plot(("x", "y"), type="line")
        test_plot_2 = Plot(plotdata2)
        # test_plot_2.plot("y2", type="line")
        # changing type to line w)ill make the
        # plot a line plot! not recommended!
        """
        test_plot = plot(x,y,"b-", bgcolor="white")
        test_plot.hold()
        test_plot = plot(x,y2,"g-")
        """
        container = HPlotContainer(test_plot)  # ,test_plot_2)
        self.aplot = container
Exemplo n.º 14
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    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
Exemplo n.º 15
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class Viewer1D(Viewer):
    image = Array
    result = Array

    def _reconstruction_default(self):
        rows, cols = self.image.shape[:2]
        self.plot_data = ArrayPlotData(original=self.image[0],
                                       reconstruction=self.result[0])

        aspect = cols/float(rows)

        old = Plot(self.plot_data)
        old.plot('original', )
        old.title = 'Old'

        self.new = Plot(self.plot_data)
        self.new.plot('reconstruction')
        self.new.title = 'New'

        container = HPlotContainer(bgcolor='none')
        container.add(old)
        container.add(self.new)

        return container

    def update_plot(self):
        self.plot_data.set_data('reconstruction', self.result[0])
        self.new.request_redraw()
Exemplo n.º 16
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    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
Exemplo n.º 17
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    def _plot_default(self):
        # Create a GridContainer to hold all of our plots: 2 rows, 3 columns
        container = GridPlotContainer(shape=(2,3),
                                      spacing=(10,5),
                                      valign='top',
                                      bgcolor='lightgray')

        # Create x data
        x = linspace(-5, 15.0, 100)
        pd = ArrayPlotData(index = x)

        # Plot some Bessel functions and add the plots to our container
        for i in range(6):
            data_name = 'y{}'.format(i)
            pd.set_data(data_name, jn(i,x))

            plot = Plot(pd)
            plot.plot(('index', data_name),
                      color=COLOR_PALETTE[i],
                      line_width=3.0)

            # Set each plot's aspect based on its position in the grid
            plot.set(height=((i % 3) + 1)*50,
                     resizable='h')

            # Add to the grid container
            container.add(plot)

        return container
Exemplo n.º 18
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    def plot_spectrum(self,x,y,field):
        for i in range(len(self.x_koords)):
            x_gap=abs(x-self.x_koords[i])
            y_gap=abs(y-self.y_koords[i])
            if x_gap <self.toleranz and y_gap<self.toleranz:
                spectrum=self.spectra[i]
                wavelength=self.ivCamera.create_wavelength_for_plotting()
                xm = [self.plotrangemarker,self.plotrangemarker] #for red line in plot
                ym = [0,16000] #self.plotrangey
                plotdata = ArrayPlotData(x=wavelength, y=spectrum,xm=xm,ym=ym)
                plot = Plot(plotdata)
                plot.plot(("x", "y"), type="line", color="blue")
                plot.x_axis.title="Wavelength [nm]"
                plot.y_axis.title="Counts"

                #catch error if apd counts not loaded
                try:
                    apd_counts = str(self.apd_counts[i])
                except:
                    apd_counts = str("0")

                plot.title = 'spectrum of QD ' +str(self.x_koords[i])+' '+str(self.y_koords[i])+' with APD at '+str(apd_counts)
                plot.overlays.append(ZoomTool(component=plot,tool_mode="box", always_on=False)) # damit man im Plot zoomen kann
                plot.tools.append(PanTool(plot, constrain_key="shift")) # damit man mit der Maus den Plot verschieben kann
                if field=='current':
                    self.plot_current=plot
                    if self.plotsetalways:
                        self._plotrangeset_fired()
                if field=='compare':
                    self.plot_compare=plot
                    if self.plotsetalways:
                        self._plotrangeset_fired()
Exemplo n.º 19
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    def _time_plot_default(self):
        time_plot = Plot(self.time_plot_data)

        time_plot.plot(('t', 'y'))

        time_plot.index_axis.title = "Time"

        time_plot.tools.append(PanTool(time_plot))

        zoomtool = ZoomTool(time_plot, drag_button='right',
                                                    always_on=True)
        time_plot.overlays.append(zoomtool)

        lines1 = CoordinateLineOverlay(component=time_plot,
                    index_data=self.x1,
                    value_data=self.y1,
                    color=(0.75, 0.25, 0.25, 0.75),
                    line_style='dash', line_width=1)
        time_plot.underlays.append(lines1)
        self.line_overlay1 = lines1

        lines2 = CoordinateLineOverlay(component=time_plot,
                    index_data=self.x2,
                    value_data=self.y2,
                    color=(0.2, 0.5, 1.0, 0.75),
                    line_width=3)
        time_plot.underlays.append(lines2)
        self.line_overlay2 = lines2

        return time_plot
Exemplo n.º 20
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 def _create_corr_plot(self):
     plot = Plot(self.plotdata, padding=0)
     plot.padding_left = 25
     plot.padding_bottom = 25
     plot.tools.append(PanTool(plot))
     plot.overlays.append(ZoomTool(plot))
     self.corr_plot = plot
Exemplo n.º 21
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    def _create_returns_plot(self):
        plot = Plot(self.plotdata)
        plot.legend.visible = True
        #FIXME: The legend move tool doesn't seem to quite work right now
        #plot.legend.tools.append(LegendTool(plot.legend))
        plot.x_axis = None
        x_axis = PlotAxis(plot, orientation="bottom",
                        tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
        plot.overlays.append(x_axis)
        plot.x_grid.tick_generator = x_axis.tick_generator
        for i, name in enumerate(self.plotdata.list_data()):
            if name == "times":
                continue
            renderer = plot.plot(("times", name), type="line", name=name,
                                  color=tuple(COLOR_PALETTE[i]))[0]

        # Tricky: need to set auto_handle_event on the RangeSelection
        # so that it passes left-clicks to the PanTool
        # FIXME: The range selection is still getting the initial left down
        renderer.tools.append(RangeSelection(renderer, left_button_selects = False,
            auto_handle_event = False))
        plot.tools.append(PanTool(plot, drag_button="left", constrain=True,
            constrain_direction="x"))
        plot.overlays.append(ZoomTool(plot, tool_mode="range", max_zoom_out=1.0))
        # Attach the range selection to the last renderer; any one will do
        self._range_selection_overlay = RangeSelectionOverlay(renderer,
                                    metadata_name="selections")
        renderer.overlays.append(self._range_selection_overlay)
        # Grab a reference to the Time axis datasource and add a listener to its
        # selections metadata
        self.times_ds = renderer.index
        self.times_ds.on_trait_change(self._selections_changed, 'metadata_changed')
        self.returns_plot = plot
Exemplo n.º 22
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    def _ch_plot_default(self):
        x = 0.26
        y = 0.
        h_cross_x = np.array([self.x_low, self.x_high])
        h_cross_y = np.array([y, y])
        v_cross_x = np.array([x, x])
        v_cross_y = np.array([self.y_low, self.y_high])

        cross_hair_data = ArrayPlotData(h_cross_x=h_cross_x,
                                        h_cross_y=h_cross_y,
                                        v_cross_x=v_cross_x,
                                        v_cross_y=v_cross_y)
        ch_plot = Plot(cross_hair_data)
        ch_plot.plot(("h_cross_x", "h_cross_y"), type="line", color="green",)
        ch_plot.plot(("v_cross_x", "v_cross_y"), type="line", color="green",)
        while len(ch_plot.underlays) > 0:
            ch_plot.underlays.pop(0)
        ch_plot.range2d = self.img_plot.range2d
        cross_hair_tool = CrossHairs(ch_plot)
        ch_plot.tools.append(cross_hair_tool)
        self.crosshair = cross_hair_tool
        self.on_trait_change(self.draw_cross_hairs,
                             "crosshair.selected_x, crosshair.selected_y")
        self.on_trait_change(self.render_julia,
                             "crosshair.selected_x, crosshair.selected_y")
        return ch_plot
Exemplo n.º 23
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def create_chaco_plot(parent, data, args, type=''):
    #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))
    #if not type:
    #    type = ['plot']
    #else:
    #    type = [type, 'plot']
    # Create some line plots of some of the data
    plot = Plot(data, title="Line Plot", padding=50, border_visible=True)
    plot.legend.visible = True
    #plot_fn = getattr(plot, '_'.join(type))
    if type:
        renderers = plot.plot(args, plot=type)
    else:
        renderers = plot.plot(args)
    #plot.plot(("index", "y3"), name="j_3", color="blue")

    # 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)
    plot.tools.append(TraitsTool(component=plot))

    # This Window object bridges the Enable and Qt4 worlds, and handles events
    # and drawing.  We can create whatever hierarchy of nested containers we
    # want, as long as the top-level item gets set as the .component attribute
    # of a Window.
    return Window(parent, -1, component = plot)
Exemplo n.º 24
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    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))
Exemplo n.º 25
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class PlotExample(HasTraits):
    plot = Instance(Plot)
    traits_view = View(UItem('plot', editor=ComponentEditor()),
                       width=400, height=400, resizable=True, 
                      )

    def __init__(self, index, series_a, series_b, series_c, **kw):
        super(PlotExample, self).__init__(**kw)

        plot_data = ArrayPlotData(index=index)
        plot_data.set_data('series_a', series_a)
        plot_data.set_data('series_b', series_b)
        plot_data.set_data('series_c', series_c)
        self.plot = Plot(plot_data)
        self.plot.plot(('index', 'series_a'), type='bar', bar_width=0.8, color='auto')
        self.plot.plot(('index', 'series_b'), type='bar', bar_width=0.8, color='auto')
        self.plot.plot(('index', 'series_c'), type='bar', bar_width=0.8, color='auto')

        # set the plot's value range to 0, otherwise it may pad too much
        self.plot.value_range.low = 0

        # replace the index values with some nicer labels
        label_axis = LabelAxis(self.plot, orientation='bottom',
                               title='Months',
                               positions = list(range(1, 10)),
                               labels = ['jan', 'feb', 'march', 'april', 'may'],
                               small_haxis_style=True)

        self.plot.underlays.remove(self.plot.index_axis)
        self.plot.index_axis = label_axis
        self.plot.underlays.append(label_axis)
 def _plot_default(self):
     data = ArrayPlotData(x=self.model.x, y=self.model.y)
     plot = Plot(data)
     plot.plot(('x', 'y'), style='line', color='green')
     plot.value_range.set_bounds(-self.model.a, self.model.a)
     plot.title = "a * exp(-b*x) * cos(omega*x + phase)"
     return plot
Exemplo n.º 27
0
def _create_plot_component():# 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 = sin(x)*y

    # Create a plot data obect and give it this data
    pd = ArrayPlotData()
    pd.set_data("imagedata", z)

    # Create the plot
    plot = Plot(pd)
    img_plot = plot.img_plot("imagedata",
                             xbounds = xbounds[:2],
                             ybounds = ybounds[:2],
                             colormap=jet)[0]

    # Tweak some of the plot properties
    plot.title = "My First Image Plot"
    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)
    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)
    return plot
Exemplo n.º 28
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def _create_plot_component():

    # Create some RGBA image data
    image = zeros((200,400,4), dtype=uint8)
    image[:,0:40,0] += 255     # Vertical red stripe
    image[0:25,:,1] += 255     # Horizontal green stripe; also yellow square
    image[-80:,-160:,2] += 255 # Blue square
    image[:,:,3] = 255

    # Create a plot data obect and give it this data
    pd = ArrayPlotData()
    pd.set_data("imagedata", image)

    # Create the plot
    plot = Plot(pd, default_origin="top left")
    plot.x_axis.orientation = "top"
    img_plot = plot.img_plot("imagedata")[0]

    # Tweak some of the plot properties
    plot.bgcolor = "white"

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

    imgtool = ImageInspectorTool(img_plot)
    img_plot.tools.append(imgtool)
    plot.overlays.append(ImageInspectorOverlay(component=img_plot,
                                               image_inspector=imgtool))
    return plot
Exemplo n.º 29
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def _create_plot_component(obj):
    # Spectrogram plot

    obj.data = fr.getDataSets(".chi")
    frequencies = obj.data[0][0]
    index = ArrayDataSource(data=frequencies)

    values = [obj.data[i][1] for i in xrange(len(obj.data))]
    print len(obj.data[1][1])
    print len(obj.data)
    p = WaterfallRenderer(
        index=index,
        values=values,
        index_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
        value_mapper=LinearMapper(range=DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
        x2_mapper=LinearMapper(low_pos=0, high_pos=100, range=DataRange1D(low=10.0, high=101.0)),
        y2_mapper=LinearMapper(low_pos=0, high_pos=100, range=DataRange1D(low=0, high=600000)),
    )
    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)

    c2 = VPlotContainer()
    c2.add(dummy)

    return c2
Exemplo n.º 30
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 def update_show_curve(self):
     plotdata = ArrayPlotData(x=self.ct_hu, y=self.re_electronic_density)
     plot = Plot(plotdata)
     plot.plot(("x", "y"), type="line", color="blue")              
    
     plot.title = self.name
     self.plot = plot
Exemplo n.º 31
0
class SolutionView(HasTraits):
    python_console_cmds = Dict()

    lats = List()
    lngs = List()
    alts = List()

    table = List()
    dops_table = List()
    pos_table = List()
    vel_table = List()

    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)

    running = Bool(True)
    position_centered = Bool(False)

    clear_button = SVGButton(label='',
                             tooltip='Clear',
                             filename=os.path.join(os.path.dirname(__file__),
                                                   'images', 'iconic',
                                                   'x.svg'),
                             width=16,
                             height=16)
    zoomall_button = SVGButton(label='',
                               tooltip='Zoom All',
                               filename=os.path.join(os.path.dirname(__file__),
                                                     'images', 'iconic',
                                                     'fullscreen.svg'),
                               width=16,
                               height=16)
    center_button = SVGButton(label='',
                              tooltip='Center on Solution',
                              toggle=True,
                              filename=os.path.join(os.path.dirname(__file__),
                                                    'images', 'iconic',
                                                    'target.svg'),
                              width=16,
                              height=16)
    paused_button = SVGButton(
        label='',
        tooltip='Pause',
        toggle_tooltip='Run',
        toggle=True,
        filename=os.path.join(os.path.dirname(__file__), 'images', 'iconic',
                              'pause.svg'),
        toggle_filename=os.path.join(os.path.dirname(__file__), 'images',
                                     'iconic', 'play.svg'),
        width=16,
        height=16)

    traits_view = View(
        HSplit(
            Item('table',
                 style='readonly',
                 editor=TabularEditor(adapter=SimpleAdapter()),
                 show_label=False,
                 width=0.3),
            VGroup(
                HGroup(
                    Item('paused_button', show_label=False),
                    Item('clear_button', show_label=False),
                    Item('zoomall_button', show_label=False),
                    Item('center_button', show_label=False),
                ),
                Item(
                    'plot',
                    show_label=False,
                    editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8)),
                ))))

    def _zoomall_button_fired(self):
        self.plot.index_range.low_setting = 'auto'
        self.plot.index_range.high_setting = 'auto'
        self.plot.value_range.low_setting = 'auto'
        self.plot.value_range.high_setting = 'auto'

    def _center_button_fired(self):
        self.position_centered = not self.position_centered

    def _paused_button_fired(self):
        self.running = not self.running

    def _clear_button_fired(self):
        self.lats = []
        self.lngs = []
        self.alts = []
        self.plot_data.set_data('lat', [])
        self.plot_data.set_data('lng', [])
        self.plot_data.set_data('alt', [])
        self.plot_data.set_data('t', [])

    def _pos_llh_callback(self, data):
        # Updating an ArrayPlotData isn't thread safe (see chaco issue #9), so
        # actually perform the update in the UI thread.
        if self.running:
            GUI.invoke_later(self.pos_llh_callback, data)

    def update_table(self):
        self._table_list = self.table.items()

    def pos_llh_callback(self, data):
        soln = sbp_messages.PosLLH(data)
        self.pos_table = []

        if self.log_file is None:
            self.log_file = open(
                time.strftime("position_log_%Y%m%d-%H%M%S.csv"), 'w')

        tow = soln.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        if self.week is not None:
            t = datetime.datetime(1980, 1, 6) + \
                datetime.timedelta(weeks=self.week) + \
                datetime.timedelta(seconds=tow)
            self.pos_table.append(('GPS Time', t))
            self.pos_table.append(('GPS Week', str(self.week)))

            self.log_file.write(
                '%s,%.10f,%.10f,%.4f,%d\n' %
                (str(t), soln.lat, soln.lon, soln.height, soln.n_sats))
            self.log_file.flush()

        self.pos_table.append(('GPS ToW', tow))

        self.pos_table.append(('Num. sats', soln.n_sats))

        self.pos_table.append(('Lat', soln.lat))
        self.pos_table.append(('Lng', soln.lon))
        self.pos_table.append(('Alt', soln.height))

        self.lats.append(soln.lat)
        self.lngs.append(soln.lon)
        self.alts.append(soln.height)

        self.lats = self.lats[-1000:]
        self.lngs = self.lngs[-1000:]
        self.alts = self.alts[-1000:]

        self.plot_data.set_data('lat', self.lats)
        self.plot_data.set_data('lng', self.lngs)
        self.plot_data.set_data('alt', self.alts)
        t = range(len(self.lats))
        self.plot_data.set_data('t', t)

        self.table = self.pos_table + self.vel_table + self.dops_table

        if self.position_centered:
            d = (self.plot.index_range.high - self.plot.index_range.low) / 2.
            self.plot.index_range.set_bounds(soln.pos_llh[0] - d,
                                             soln.pos_llh[0] + d)
            d = (self.plot.value_range.high - self.plot.value_range.low) / 2.
            self.plot.value_range.set_bounds(soln.pos_llh[1] - d,
                                             soln.pos_llh[1] + d)

    def dops_callback(self, data):
        dops = sbp_messages.Dops(data)
        self.dops_table = [('PDOP', '%.1f' % (dops.pdop * 0.01)),
                           ('GDOP', '%.1f' % (dops.gdop * 0.01)),
                           ('TDOP', '%.1f' % (dops.tdop * 0.01)),
                           ('HDOP', '%.1f' % (dops.hdop * 0.01)),
                           ('VDOP', '%.1f' % (dops.vdop * 0.01))]
        self.table = self.pos_table + self.vel_table + self.dops_table

    def vel_ned_callback(self, data):
        vel_ned = sbp_messages.VelNED(data)

        if self.vel_log_file is None:
            self.vel_log_file = open(
                time.strftime("velocity_log_%Y%m%d-%H%M%S.csv"), 'w')

        tow = vel_ned.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        if self.week is not None:
            t = datetime.datetime(1980, 1, 6) + \
                datetime.timedelta(weeks=self.week) + \
                datetime.timedelta(seconds=tow)

            self.vel_log_file.write(
                '%s,%.6f,%.6f,%.6f,%.6f,%d\n' %
                (str(t), vel_ned.n * 1e-3, vel_ned.e * 1e-3, vel_ned.d * 1e-3,
                 math.sqrt(vel_ned.n * vel_ned.n + vel_ned.e * vel_ned.e) *
                 1e-3, vel_ned.n_sats))
            self.vel_log_file.flush()

        self.vel_table = [
            ('Vel. N', '% 8.4f' % (vel_ned.n * 1e-3)),
            ('Vel. E', '% 8.4f' % (vel_ned.e * 1e-3)),
            ('Vel. D', '% 8.4f' % (vel_ned.d * 1e-3)),
        ]
        self.table = self.pos_table + self.vel_table + self.dops_table

    def gps_time_callback(self, data):
        self.week = sbp_messages.GPSTime(data).wn
        self.nsec = sbp_messages.GPSTime(data).ns

    def __init__(self, link):
        super(SolutionView, self).__init__()

        self.log_file = None
        self.vel_log_file = None

        self.plot_data = ArrayPlotData(lat=[0.0],
                                       lng=[0.0],
                                       alt=[0.0],
                                       t=[0.0],
                                       ref_lat=[0.0],
                                       ref_lng=[0.0],
                                       region_lat=[0.0],
                                       region_lng=[0.0])
        self.plot = Plot(self.plot_data)

        self.plot.plot(('lng', 'lat'),
                       type='line',
                       name='line',
                       color=(0, 0, 0, 0.1))
        self.plot.plot(('lng', 'lat'),
                       type='scatter',
                       name='points',
                       color='blue',
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.padding = (0, 1, 0, 1)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.link = link
        self.link.add_callback(sbp_messages.SBP_POS_LLH,
                               self._pos_llh_callback)
        self.link.add_callback(sbp_messages.SBP_VEL_NED, self.vel_ned_callback)
        self.link.add_callback(sbp_messages.SBP_DOPS, self.dops_callback)
        self.link.add_callback(sbp_messages.SBP_GPS_TIME,
                               self.gps_time_callback)

        self.week = None
        self.nsec = 0

        self.python_console_cmds = {'solution': self}
Exemplo n.º 32
0
    def create_plot(self):

        # Create the mapper, etc
        self._image_index = GridDataSource(array([]),
                                           array([]),
                                           sort_order=("ascending",
                                                       "ascending"))
        image_index_range = DataRange2D(self._image_index)
        self._image_index.on_trait_change(self._metadata_changed,
                                          "metadata_changed")

        self._image_value = ImageData(data=array([]), value_depth=1)
        image_value_range = DataRange1D(self._image_value)

        # Create the contour plots
        self.polyplot = ContourPolyPlot(index=self._image_index,
                                        value=self._image_value,
                                        index_mapper=GridMapper(range=
                                            image_index_range),
                                        color_mapper=\
                                            self._cmap(image_value_range),
                                        levels=self.num_levels)

        self.lineplot = ContourLinePlot(
            index=self._image_index,
            value=self._image_value,
            index_mapper=GridMapper(range=self.polyplot.index_mapper.range),
            levels=self.num_levels)

        # Add a left axis to the plot
        left = PlotAxis(orientation='left',
                        title="y",
                        mapper=self.polyplot.index_mapper._ymapper,
                        component=self.polyplot)
        self.polyplot.overlays.append(left)

        # Add a bottom axis to the plot
        bottom = PlotAxis(orientation='bottom',
                          title="x",
                          mapper=self.polyplot.index_mapper._xmapper,
                          component=self.polyplot)
        self.polyplot.overlays.append(bottom)

        # Add some tools to the plot
        self.polyplot.tools.append(
            PanTool(self.polyplot, constrain_key="shift"))
        self.polyplot.overlays.append(
            ZoomTool(component=self.polyplot, tool_mode="box",
                     always_on=False))
        self.polyplot.overlays.append(
            LineInspector(component=self.polyplot,
                          axis='index_x',
                          inspect_mode="indexed",
                          write_metadata=True,
                          is_listener=True,
                          color="white"))
        self.polyplot.overlays.append(
            LineInspector(component=self.polyplot,
                          axis='index_y',
                          inspect_mode="indexed",
                          write_metadata=True,
                          color="white",
                          is_listener=True))

        # Add these two plots to one container
        contour_container = OverlayPlotContainer(padding=20,
                                                 use_backbuffer=True,
                                                 unified_draw=True)
        contour_container.add(self.polyplot)
        contour_container.add(self.lineplot)

        # Create a colorbar
        cbar_index_mapper = LinearMapper(range=image_value_range)
        self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
                                 plot=self.polyplot,
                                 padding_top=self.polyplot.padding_top,
                                 padding_bottom=self.polyplot.padding_bottom,
                                 padding_right=40,
                                 resizable='v',
                                 width=30)

        self.pd = ArrayPlotData(line_index=array([]),
                                line_value=array([]),
                                scatter_index=array([]),
                                scatter_value=array([]),
                                scatter_color=array([]))

        self.cross_plot = Plot(self.pd, resizable="h")
        self.cross_plot.height = 100
        self.cross_plot.padding = 20
        self.cross_plot.plot(("line_index", "line_value"), line_style="dot")
        self.cross_plot.plot(
            ("scatter_index", "scatter_value", "scatter_color"),
            type="cmap_scatter",
            name="dot",
            color_mapper=self._cmap(image_value_range),
            marker="circle",
            marker_size=8)

        self.cross_plot.index_range = self.polyplot.index_range.x_range

        self.pd.set_data("line_index2", array([]))
        self.pd.set_data("line_value2", array([]))
        self.pd.set_data("scatter_index2", array([]))
        self.pd.set_data("scatter_value2", array([]))
        self.pd.set_data("scatter_color2", array([]))

        self.cross_plot2 = Plot(self.pd,
                                width=140,
                                orientation="v",
                                resizable="v",
                                padding=20,
                                padding_bottom=160)
        self.cross_plot2.plot(("line_index2", "line_value2"), line_style="dot")
        self.cross_plot2.plot(
            ("scatter_index2", "scatter_value2", "scatter_color2"),
            type="cmap_scatter",
            name="dot",
            color_mapper=self._cmap(image_value_range),
            marker="circle",
            marker_size=8)

        self.cross_plot2.index_range = self.polyplot.index_range.y_range

        # Create a container and add components
        self.container = HPlotContainer(padding=40,
                                        fill_padding=True,
                                        bgcolor="white",
                                        use_backbuffer=False)
        inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
        inner_cont.add(self.cross_plot)
        inner_cont.add(contour_container)
        self.container.add(self.colorbar)
        self.container.add(inner_cont)
        self.container.add(self.cross_plot2)
Exemplo n.º 33
0
class PlotUI(HasTraits):

    # container for all plots
    container = Instance(HPlotContainer)

    # Plot components within this container:
    polyplot = Instance(ContourPolyPlot)
    lineplot = Instance(ContourLinePlot)
    cross_plot = Instance(Plot)
    cross_plot2 = Instance(Plot)
    colorbar = Instance(ColorBar)

    # plot data
    pd = Instance(ArrayPlotData)

    # view options
    num_levels = Int(15)
    colormap = Enum(colormaps)

    #Traits view definitions:
    traits_view = View(Group(
        UItem('container', editor=ComponentEditor(size=(800, 600)))),
                       resizable=True)

    plot_edit_view = View(Group(Item('num_levels'), Item('colormap')),
                          buttons=["OK", "Cancel"])

    #---------------------------------------------------------------------------
    # Private Traits
    #---------------------------------------------------------------------------

    _image_index = Instance(GridDataSource)
    _image_value = Instance(ImageData)

    _cmap = Trait(default_colormaps.viridis, Callable)

    #---------------------------------------------------------------------------
    # Public View interface
    #---------------------------------------------------------------------------

    def __init__(self, *args, **kwargs):
        super(PlotUI, self).__init__(*args, **kwargs)
        # FIXME: 'with' wrapping is temporary fix for infinite range in initial
        # color map, which can cause a distracting warning print. This 'with'
        # wrapping should be unnecessary after fix in color_mapper.py.
        with errstate(invalid='ignore'):
            self.create_plot()

    def create_plot(self):

        # Create the mapper, etc
        self._image_index = GridDataSource(array([]),
                                           array([]),
                                           sort_order=("ascending",
                                                       "ascending"))
        image_index_range = DataRange2D(self._image_index)
        self._image_index.on_trait_change(self._metadata_changed,
                                          "metadata_changed")

        self._image_value = ImageData(data=array([]), value_depth=1)
        image_value_range = DataRange1D(self._image_value)

        # Create the contour plots
        self.polyplot = ContourPolyPlot(index=self._image_index,
                                        value=self._image_value,
                                        index_mapper=GridMapper(range=
                                            image_index_range),
                                        color_mapper=\
                                            self._cmap(image_value_range),
                                        levels=self.num_levels)

        self.lineplot = ContourLinePlot(
            index=self._image_index,
            value=self._image_value,
            index_mapper=GridMapper(range=self.polyplot.index_mapper.range),
            levels=self.num_levels)

        # Add a left axis to the plot
        left = PlotAxis(orientation='left',
                        title="y",
                        mapper=self.polyplot.index_mapper._ymapper,
                        component=self.polyplot)
        self.polyplot.overlays.append(left)

        # Add a bottom axis to the plot
        bottom = PlotAxis(orientation='bottom',
                          title="x",
                          mapper=self.polyplot.index_mapper._xmapper,
                          component=self.polyplot)
        self.polyplot.overlays.append(bottom)

        # Add some tools to the plot
        self.polyplot.tools.append(
            PanTool(self.polyplot, constrain_key="shift"))
        self.polyplot.overlays.append(
            ZoomTool(component=self.polyplot, tool_mode="box",
                     always_on=False))
        self.polyplot.overlays.append(
            LineInspector(component=self.polyplot,
                          axis='index_x',
                          inspect_mode="indexed",
                          write_metadata=True,
                          is_listener=True,
                          color="white"))
        self.polyplot.overlays.append(
            LineInspector(component=self.polyplot,
                          axis='index_y',
                          inspect_mode="indexed",
                          write_metadata=True,
                          color="white",
                          is_listener=True))

        # Add these two plots to one container
        contour_container = OverlayPlotContainer(padding=20,
                                                 use_backbuffer=True,
                                                 unified_draw=True)
        contour_container.add(self.polyplot)
        contour_container.add(self.lineplot)

        # Create a colorbar
        cbar_index_mapper = LinearMapper(range=image_value_range)
        self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
                                 plot=self.polyplot,
                                 padding_top=self.polyplot.padding_top,
                                 padding_bottom=self.polyplot.padding_bottom,
                                 padding_right=40,
                                 resizable='v',
                                 width=30)

        self.pd = ArrayPlotData(line_index=array([]),
                                line_value=array([]),
                                scatter_index=array([]),
                                scatter_value=array([]),
                                scatter_color=array([]))

        self.cross_plot = Plot(self.pd, resizable="h")
        self.cross_plot.height = 100
        self.cross_plot.padding = 20
        self.cross_plot.plot(("line_index", "line_value"), line_style="dot")
        self.cross_plot.plot(
            ("scatter_index", "scatter_value", "scatter_color"),
            type="cmap_scatter",
            name="dot",
            color_mapper=self._cmap(image_value_range),
            marker="circle",
            marker_size=8)

        self.cross_plot.index_range = self.polyplot.index_range.x_range

        self.pd.set_data("line_index2", array([]))
        self.pd.set_data("line_value2", array([]))
        self.pd.set_data("scatter_index2", array([]))
        self.pd.set_data("scatter_value2", array([]))
        self.pd.set_data("scatter_color2", array([]))

        self.cross_plot2 = Plot(self.pd,
                                width=140,
                                orientation="v",
                                resizable="v",
                                padding=20,
                                padding_bottom=160)
        self.cross_plot2.plot(("line_index2", "line_value2"), line_style="dot")
        self.cross_plot2.plot(
            ("scatter_index2", "scatter_value2", "scatter_color2"),
            type="cmap_scatter",
            name="dot",
            color_mapper=self._cmap(image_value_range),
            marker="circle",
            marker_size=8)

        self.cross_plot2.index_range = self.polyplot.index_range.y_range

        # Create a container and add components
        self.container = HPlotContainer(padding=40,
                                        fill_padding=True,
                                        bgcolor="white",
                                        use_backbuffer=False)
        inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
        inner_cont.add(self.cross_plot)
        inner_cont.add(contour_container)
        self.container.add(self.colorbar)
        self.container.add(inner_cont)
        self.container.add(self.cross_plot2)

    def update(self, model):
        self.minz = model.minz
        self.maxz = model.maxz
        self.colorbar.index_mapper.range.low = self.minz
        self.colorbar.index_mapper.range.high = self.maxz
        self._image_index.set_data(model.xs, model.ys)
        self._image_value.data = model.zs
        self.pd.update_data(line_index=model.xs, line_index2=model.ys)
        self.container.invalidate_draw()
        self.container.request_redraw()

    #---------------------------------------------------------------------------
    # Event handlers
    #---------------------------------------------------------------------------

    def _metadata_changed(self, old, new):
        """ This function takes out a cross section from the image data, based
        on the line inspector selections, and updates the line and scatter
        plots."""

        self.cross_plot.value_range.low = self.minz
        self.cross_plot.value_range.high = self.maxz
        self.cross_plot2.value_range.low = self.minz
        self.cross_plot2.value_range.high = self.maxz
        if "selections" in self._image_index.metadata:
            x_ndx, y_ndx = self._image_index.metadata["selections"]
            if y_ndx and x_ndx:
                xdata, ydata = self._image_index.get_data()
                xdata, ydata = xdata.get_data(), ydata.get_data()
                self.pd.update_data(
                    line_value=self._image_value.data[y_ndx, :],
                    line_value2=self._image_value.data[:, x_ndx],
                    scatter_index=array([xdata[x_ndx]]),
                    scatter_index2=array([ydata[y_ndx]]),
                    scatter_value=array([self._image_value.data[y_ndx,
                                                                x_ndx]]),
                    scatter_value2=array(
                        [self._image_value.data[y_ndx, x_ndx]]),
                    scatter_color=array([self._image_value.data[y_ndx,
                                                                x_ndx]]),
                    scatter_color2=array(
                        [self._image_value.data[y_ndx, x_ndx]]))
        else:
            self.pd.update_data({
                "scatter_value": array([]),
                "scatter_value2": array([]),
                "line_value": array([]),
                "line_value2": array([])
            })

    def _colormap_changed(self):
        self._cmap = default_colormaps.color_map_name_dict[self.colormap]
        if self.polyplot is not None:
            value_range = self.polyplot.color_mapper.range
            self.polyplot.color_mapper = self._cmap(value_range)
            value_range = self.cross_plot.color_mapper.range
            self.cross_plot.color_mapper = self._cmap(value_range)
            # FIXME: change when we decide how best to update plots using
            # the shared colormap in plot object
            self.cross_plot.plots["dot"][0].color_mapper = self._cmap(
                value_range)
            self.cross_plot2.plots["dot"][0].color_mapper = self._cmap(
                value_range)
            self.container.request_redraw()

    def _num_levels_changed(self):
        if self.num_levels > 3:
            self.polyplot.levels = self.num_levels
            self.lineplot.levels = self.num_levels
Exemplo n.º 34
0
    def _create_window(self):
        self.model = model = Model()
        cmap = jet
        self._update_model(cmap)

        # Create the plot
        self.plotdata = ArrayPlotData()
        self._update_images()

        # Center Plot
        centerplot = Plot(self.plotdata, padding=0)
        imgplot = centerplot.img_plot("xy",
                                xbounds=(model.min_x, model.max_x),
                                ybounds=(model.min_y, model.max_y),
                                colormap=cmap)[0]
        self._add_plot_tools(imgplot, "xy")
        self.center = imgplot

        # Right Plot
        rightplot = Plot(self.plotdata, width=150, resizable="v", padding=0)
        rightplot.value_range = centerplot.value_range
        imgplot = rightplot.img_plot("yz",
                                xbounds=(model.min_z, model.max_z),
                                ybounds=(model.min_y, model.max_y),
                                colormap=cmap)[0]
        self._add_plot_tools(imgplot, "yz")
        self.right = imgplot

        # Bottom Plot.  Seismic plot axis1 (depth) down into earth 
        #               i.e. z is depth, to altitude.
        bottomplot = Plot(self.plotdata, height=150, resizable="h", 
                          padding=0, origin="top left")
        bottomplot.index_range = centerplot.index_range
        imgplot = bottomplot.img_plot("xz",
                                xbounds=(model.min_x, model.max_x),
                                ybounds=(model.min_z, model.max_z),
                                colormap=cmap)[0]
        self._add_plot_tools(imgplot, "xz")
        self.bottom = imgplot

        # Create Container and add all Plots
        container = GridPlotContainer(padding=20, fill_padding=True,
                                      bgcolor="white", use_backbuffer=True,
                                      shape=(2,2), spacing=(20,20))
        container.add(centerplot)
        container.add(rightplot)
        container.add(bottomplot)

        self.container = container
        return Window(self, -1, component=container)
Exemplo n.º 35
0
 def create_plot(self, parent):
     plot = Plot(self.plotdata, padding=50, border_visible=True)
     plot.plot(("x", "y"), name="data plot", color="green")
     return Window(parent, -1, component=plot)
Exemplo n.º 36
0
def make_plots(self, n_dfe_taps):
    """ Create the plots used by the PyBERT GUI."""

    post_chnl_str = "Channel"
    post_tx_str   = "Channel + Tx Preemphasis"
    post_ctle_str = "Channel + Tx Preemphasis + CTLE (+ AMI DFE)"
    post_dfe_str  = "Channel + Tx Preemphasis + CTLE (+ AMI DFE) + PyBERT DFE"

    plotdata = self.plotdata

    # - DFE tab
    plot2 = Plot(plotdata, padding_left=75)
    plot2.plot(("t_ns", "ui_ests"), type="line", color="blue")
    plot2.title = "CDR Adaptation"
    plot2.index_axis.title = "Time (ns)"
    plot2.value_axis.title = "UI (ps)"

    plot9 = Plot(plotdata, auto_colors=["red", "orange", "yellow", "green", "blue", "purple"], padding_left=75,)
    for i in range(n_dfe_taps):
        plot9.plot(
            ("tap_weight_index", "tap%d_weights" % (i + 1)), type="line", color="auto", name="tap%d" % (i + 1),
        )
    plot9.title = "DFE Adaptation"
    plot9.tools.append(PanTool(plot9, constrain=True, constrain_key=None, constrain_direction="x"))
    zoom9 = ZoomTool(plot9, tool_mode="range", axis="index", always_on=False)
    plot9.overlays.append(zoom9)
    plot9.legend.visible = True
    plot9.legend.align = "ul"

    plot_clk_per_hist = Plot(plotdata, padding_left=75)
    plot_clk_per_hist.plot(("clk_per_hist_bins", "clk_per_hist_vals"), type="line", color="blue")
    plot_clk_per_hist.title = "CDR Clock Period Histogram"
    plot_clk_per_hist.index_axis.title = "Clock Period (ps)"
    plot_clk_per_hist.value_axis.title = "Bin Count"

    plot_clk_per_spec = Plot(plotdata, padding_left=75)
    plot_clk_per_spec.plot(("clk_freqs", "clk_spec"), type="line", color="blue")
    plot_clk_per_spec.title = "CDR Clock Period Spectrum"
    plot_clk_per_spec.index_axis.title = "Frequency (bit rate)"
    plot_clk_per_spec.value_axis.title = "|H(f)| (dB mean)"
    plot_clk_per_spec.value_range.low_setting = -10
    zoom_clk_per_spec = ZoomTool(plot_clk_per_spec, tool_mode="range", axis="index", always_on=False)
    plot_clk_per_spec.overlays.append(zoom_clk_per_spec)

    container_dfe = GridPlotContainer(shape=(2, 2))
    container_dfe.add(plot2)
    container_dfe.add(plot9)
    container_dfe.add(plot_clk_per_hist)
    container_dfe.add(plot_clk_per_spec)
    self.plots_dfe = container_dfe
    self._dfe_plot = plot9

    # - EQ Tune tab
    plot_h_tune = Plot(plotdata, padding_left=75)
    plot_h_tune.plot(("t_ns_chnl", "ctle_out_h_tune"), type="line", color="blue")
    plot_h_tune.plot(("t_ns_chnl", "clocks_tune"), type="line", color="gray")
    plot_h_tune.title = "Channel + Tx Preemphasis + CTLE (+ AMI DFE) + Ideal DFE"
    plot_h_tune.index_axis.title = "Time (ns)"
    plot_h_tune.y_axis.title = "Pulse Response (V)"
    zoom_tune = ZoomTool(plot_h_tune, tool_mode="range", axis="index", always_on=False)
    plot_h_tune.overlays.append(zoom_tune)
    self.plot_h_tune = plot_h_tune

    # - Impulse Responses tab
    plot_h_chnl = Plot(plotdata, padding_left=75)
    plot_h_chnl.plot(("t_ns_chnl", "chnl_h"), type="line", color="blue", name="Incremental")
    plot_h_chnl.title = post_chnl_str
    plot_h_chnl.index_axis.title = "Time (ns)"
    plot_h_chnl.y_axis.title = "Impulse Response (V/ns)"
    plot_h_chnl.legend.visible = True
    plot_h_chnl.legend.align = "ur"
    zoom_h = ZoomTool(plot_h_chnl, tool_mode="range", axis="index", always_on=False)
    plot_h_chnl.overlays.append(zoom_h)

    plot_h_tx = Plot(plotdata, padding_left=75)
    plot_h_tx.plot(("t_ns_chnl", "tx_out_h"), type="line", color="red", name="Cumulative")
    plot_h_tx.title = post_tx_str
    plot_h_tx.index_axis.title = "Time (ns)"
    plot_h_tx.y_axis.title = "Impulse Response (V/ns)"
    plot_h_tx.legend.visible = True
    plot_h_tx.legend.align = "ur"
    plot_h_tx.index_range = plot_h_chnl.index_range  # Zoom x-axes in tandem.

    plot_h_ctle = Plot(plotdata, padding_left=75)
    plot_h_ctle.plot(("t_ns_chnl", "ctle_out_h"), type="line", color="red", name="Cumulative")
    plot_h_ctle.title = post_ctle_str
    plot_h_ctle.index_axis.title = "Time (ns)"
    plot_h_ctle.y_axis.title = "Impulse Response (V/ns)"
    plot_h_ctle.legend.visible = True
    plot_h_ctle.legend.align = "ur"
    plot_h_ctle.index_range = plot_h_chnl.index_range  # Zoom x-axes in tandem.

    plot_h_dfe = Plot(plotdata, padding_left=75)
    plot_h_dfe.plot(("t_ns_chnl", "dfe_out_h"), type="line", color="red", name="Cumulative")
    plot_h_dfe.title = post_dfe_str
    plot_h_dfe.index_axis.title = "Time (ns)"
    plot_h_dfe.y_axis.title = "Impulse Response (V/ns)"
    plot_h_dfe.legend.visible = True
    plot_h_dfe.legend.align = "ur"
    plot_h_dfe.index_range = plot_h_chnl.index_range  # Zoom x-axes in tandem.

    container_h = GridPlotContainer(shape=(2, 2))
    container_h.add(plot_h_chnl)
    container_h.add(plot_h_tx)
    container_h.add(plot_h_ctle)
    container_h.add(plot_h_dfe)
    self.plots_h = container_h

    # - Step Responses tab
    plot_s_chnl = Plot(plotdata, padding_left=75)
    plot_s_chnl.plot(("t_ns_chnl", "chnl_s"), type="line", color="blue", name="Incremental")
    plot_s_chnl.title = post_chnl_str
    plot_s_chnl.index_axis.title = "Time (ns)"
    plot_s_chnl.y_axis.title = "Step Response (V)"
    plot_s_chnl.legend.visible = True
    plot_s_chnl.legend.align = "lr"
    zoom_s = ZoomTool(plot_s_chnl, tool_mode="range", axis="index", always_on=False)
    plot_s_chnl.overlays.append(zoom_s)

    plot_s_tx = Plot(plotdata, padding_left=75)
    plot_s_tx.plot(("t_ns_chnl", "tx_s"), type="line", color="blue", name="Incremental")
    plot_s_tx.plot(("t_ns_chnl", "tx_out_s"), type="line", color="red", name="Cumulative")
    plot_s_tx.title = post_tx_str
    plot_s_tx.index_axis.title = "Time (ns)"
    plot_s_tx.y_axis.title = "Step Response (V)"
    plot_s_tx.legend.visible = True
    plot_s_tx.legend.align = "lr"
    plot_s_tx.index_range = plot_s_chnl.index_range  # Zoom x-axes in tandem.

    plot_s_ctle = Plot(plotdata, padding_left=75)
    plot_s_ctle.plot(("t_ns_chnl", "ctle_s"), type="line", color="blue", name="Incremental")
    plot_s_ctle.plot(("t_ns_chnl", "ctle_out_s"), type="line", color="red", name="Cumulative")
    plot_s_ctle.title = post_ctle_str
    plot_s_ctle.index_axis.title = "Time (ns)"
    plot_s_ctle.y_axis.title = "Step Response (V)"
    plot_s_ctle.legend.visible = True
    plot_s_ctle.legend.align = "lr"
    plot_s_ctle.index_range = plot_s_chnl.index_range  # Zoom x-axes in tandem.

    plot_s_dfe = Plot(plotdata, padding_left=75)
    plot_s_dfe.plot(("t_ns_chnl", "dfe_s"), type="line", color="blue", name="Incremental")
    plot_s_dfe.plot(("t_ns_chnl", "dfe_out_s"), type="line", color="red", name="Cumulative")
    plot_s_dfe.title = post_dfe_str
    plot_s_dfe.index_axis.title = "Time (ns)"
    plot_s_dfe.y_axis.title = "Step Response (V)"
    plot_s_dfe.legend.visible = True
    plot_s_dfe.legend.align = "lr"
    plot_s_dfe.index_range = plot_s_chnl.index_range  # Zoom x-axes in tandem.

    container_s = GridPlotContainer(shape=(2, 2))
    container_s.add(plot_s_chnl)
    container_s.add(plot_s_tx)
    container_s.add(plot_s_ctle)
    container_s.add(plot_s_dfe)
    self.plots_s = container_s

    # - Pulse Responses tab
    plot_p_chnl = Plot(plotdata, padding_left=75)
    plot_p_chnl.plot(("t_ns_chnl", "chnl_p"), type="line", color="blue", name="Incremental")
    plot_p_chnl.title = post_chnl_str
    plot_p_chnl.index_axis.title = "Time (ns)"
    plot_p_chnl.y_axis.title = "Pulse Response (V)"
    plot_p_chnl.legend.visible = True
    plot_p_chnl.legend.align = "ur"
    zoom_p = ZoomTool(plot_p_chnl, tool_mode="range", axis="index", always_on=False)
    plot_p_chnl.overlays.append(zoom_p)

    plot_p_tx = Plot(plotdata, padding_left=75)
    plot_p_tx.plot(("t_ns_chnl", "tx_out_p"), type="line", color="red", name="Cumulative")
    plot_p_tx.title = post_tx_str
    plot_p_tx.index_axis.title = "Time (ns)"
    plot_p_tx.y_axis.title = "Pulse Response (V)"
    plot_p_tx.legend.visible = True
    plot_p_tx.legend.align = "ur"
    plot_p_tx.index_range = plot_p_chnl.index_range  # Zoom x-axes in tandem.

    plot_p_ctle = Plot(plotdata, padding_left=75)
    plot_p_ctle.plot(("t_ns_chnl", "ctle_out_p"), type="line", color="red", name="Cumulative")
    plot_p_ctle.title = post_ctle_str
    plot_p_ctle.index_axis.title = "Time (ns)"
    plot_p_ctle.y_axis.title = "Pulse Response (V)"
    plot_p_ctle.legend.visible = True
    plot_p_ctle.legend.align = "ur"
    plot_p_ctle.index_range = plot_p_chnl.index_range  # Zoom x-axes in tandem.

    plot_p_dfe = Plot(plotdata, padding_left=75)
    plot_p_dfe.plot(("t_ns_chnl", "dfe_out_p"), type="line", color="red", name="Cumulative")
    plot_p_dfe.title = post_dfe_str
    plot_p_dfe.index_axis.title = "Time (ns)"
    plot_p_dfe.y_axis.title = "Pulse Response (V)"
    plot_p_dfe.legend.visible = True
    plot_p_dfe.legend.align = "ur"
    plot_p_dfe.index_range = plot_p_chnl.index_range  # Zoom x-axes in tandem.

    container_p = GridPlotContainer(shape=(2, 2))
    container_p.add(plot_p_chnl)
    container_p.add(plot_p_tx)
    container_p.add(plot_p_ctle)
    container_p.add(plot_p_dfe)
    self.plots_p = container_p

    # - Frequency Responses tab
    plot_H_chnl = Plot(plotdata, padding_left=75)
    plot_H_chnl.plot(("f_GHz", "chnl_H"), type="line", color="blue", name="Original Impulse", index_scale="log")
    plot_H_chnl.plot(("f_GHz", "chnl_trimmed_H"), type="line", color="red", name="Trimmed Impulse", index_scale="log")
    plot_H_chnl.title = post_chnl_str
    plot_H_chnl.index_axis.title = "Frequency (GHz)"
    plot_H_chnl.y_axis.title = "Frequency Response (dB)"
    plot_H_chnl.index_range.low_setting = 0.01
    plot_H_chnl.index_range.high_setting = 40.0
    plot_H_chnl.legend.visible = True
    plot_H_chnl.legend.align = "ll"

    plot_H_tx = Plot(plotdata, padding_left=75)
    plot_H_tx.plot(("f_GHz", "tx_H"), type="line", color="blue", name="Incremental", index_scale="log")
    plot_H_tx.plot(("f_GHz", "tx_out_H"), type="line", color="red", name="Cumulative", index_scale="log")
    plot_H_tx.title = post_tx_str
    plot_H_tx.index_axis.title = "Frequency (GHz)"
    plot_H_tx.y_axis.title = "Frequency Response (dB)"
    plot_H_tx.index_range.low_setting = 0.01
    plot_H_tx.index_range.high_setting = 40.0
    plot_H_tx.legend.visible = True
    plot_H_tx.legend.align = "ll"

    plot_H_ctle = Plot(plotdata, padding_left=75)
    plot_H_ctle.plot(("f_GHz", "ctle_H"), type="line", color="blue", name="Incremental", index_scale="log")
    plot_H_ctle.plot(("f_GHz", "ctle_out_H"), type="line", color="red", name="Cumulative", index_scale="log")
    plot_H_ctle.title = post_ctle_str
    plot_H_ctle.index_axis.title = "Frequency (GHz)"
    plot_H_ctle.y_axis.title = "Frequency Response (dB)"
    plot_H_ctle.index_range.low_setting = 0.01
    plot_H_ctle.index_range.high_setting = 40.0
    plot_H_ctle.value_range.low_setting = -40.0
    plot_H_ctle.legend.visible = True
    plot_H_ctle.legend.align = "ll"

    plot_H_chnl.value_range = plot_H_ctle.value_range
    plot_H_tx.value_range = plot_H_ctle.value_range

    plot_H_dfe = Plot(plotdata, padding_left=75)
    plot_H_dfe.plot(("f_GHz", "dfe_H"), type="line", color="blue", name="Incremental", index_scale="log")
    plot_H_dfe.plot(("f_GHz", "dfe_out_H"), type="line", color="red", name="Cumulative", index_scale="log")
    plot_H_dfe.title = post_dfe_str
    plot_H_dfe.index_axis.title = "Frequency (GHz)"
    plot_H_dfe.y_axis.title = "Frequency Response (dB)"
    plot_H_dfe.index_range.low_setting = 0.01
    plot_H_dfe.index_range.high_setting = 40.0
    plot_H_dfe.value_range = plot_H_ctle.value_range
    plot_H_dfe.legend.visible = True
    plot_H_dfe.legend.align = "ll"

    container_H = GridPlotContainer(shape=(2, 2))
    container_H.add(plot_H_chnl)
    container_H.add(plot_H_tx)
    container_H.add(plot_H_ctle)
    container_H.add(plot_H_dfe)
    self.plots_H = container_H

    # - Outputs tab
    plot_out_chnl = Plot(plotdata, padding_left=75)
    plot_out_chnl.plot(("t_ns", "ideal_signal"), type="line", color="lightgrey")
    plot_out_chnl.plot(("t_ns", "chnl_out"), type="line", color="blue")
    plot_out_chnl.title = post_chnl_str
    plot_out_chnl.index_axis.title = "Time (ns)"
    plot_out_chnl.y_axis.title = "Output (V)"
    plot_out_chnl.tools.append(PanTool(plot_out_chnl, constrain=True, constrain_key=None, constrain_direction="x"))
    zoom_out_chnl = ZoomTool(plot_out_chnl, tool_mode="range", axis="index", always_on=False)
    plot_out_chnl.overlays.append(zoom_out_chnl)

    plot_out_tx = Plot(plotdata, padding_left=75)
    plot_out_tx.plot(("t_ns", "tx_out"), type="line", color="blue")
    plot_out_tx.title = post_tx_str
    plot_out_tx.index_axis.title = "Time (ns)"
    plot_out_tx.y_axis.title = "Output (V)"
    plot_out_tx.index_range = plot_out_chnl.index_range  # Zoom x-axes in tandem.

    plot_out_ctle = Plot(plotdata, padding_left=75)
    plot_out_ctle.plot(("t_ns", "ctle_out"), type="line", color="blue")
    plot_out_ctle.title = post_ctle_str
    plot_out_ctle.index_axis.title = "Time (ns)"
    plot_out_ctle.y_axis.title = "Output (V)"
    plot_out_ctle.index_range = plot_out_chnl.index_range  # Zoom x-axes in tandem.

    plot_out_dfe = Plot(plotdata, padding_left=75)
    plot_out_dfe.plot(("t_ns", "dfe_out"), type="line", color="blue")
    plot_out_dfe.title = post_dfe_str
    plot_out_dfe.index_axis.title = "Time (ns)"
    plot_out_dfe.y_axis.title = "Output (V)"
    plot_out_dfe.index_range = plot_out_chnl.index_range  # Zoom x-axes in tandem.

    container_out = GridPlotContainer(shape=(2, 2))
    container_out.add(plot_out_chnl)
    container_out.add(plot_out_tx)
    container_out.add(plot_out_ctle)
    container_out.add(plot_out_dfe)
    self.plots_out = container_out

    # - Eye Diagrams tab
    seg_map = dict(
        red=[
            (0.00, 0.00, 0.00),  # black
            (0.00001, 0.00, 0.00),  # blue
            (0.15, 0.00, 0.00),  # cyan
            (0.30, 0.00, 0.00),  # green
            (0.45, 1.00, 1.00),  # yellow
            (0.60, 1.00, 1.00),  # orange
            (0.75, 1.00, 1.00),  # red
            (0.90, 1.00, 1.00),  # pink
            (1.00, 1.00, 1.00),  # white
        ],
        green=[
            (0.00, 0.00, 0.00),  # black
            (0.00001, 0.00, 0.00),  # blue
            (0.15, 0.50, 0.50),  # cyan
            (0.30, 0.50, 0.50),  # green
            (0.45, 1.00, 1.00),  # yellow
            (0.60, 0.50, 0.50),  # orange
            (0.75, 0.00, 0.00),  # red
            (0.90, 0.50, 0.50),  # pink
            (1.00, 1.00, 1.00),  # white
        ],
        blue=[
            (0.00, 0.00, 0.00),  # black
            (1e-18, 0.50, 0.50),  # blue
            (0.15, 0.50, 0.50),  # cyan
            (0.30, 0.00, 0.00),  # green
            (0.45, 0.00, 0.00),  # yellow
            (0.60, 0.00, 0.00),  # orange
            (0.75, 0.00, 0.00),  # red
            (0.90, 0.50, 0.50),  # pink
            (1.00, 1.00, 1.00),  # white
        ],
    )
    clr_map = ColorMapper.from_segment_map(seg_map)
    self.clr_map = clr_map

    plot_eye_chnl = Plot(plotdata, padding_left=75)
    plot_eye_chnl.img_plot("eye_chnl", colormap=clr_map)
    plot_eye_chnl.y_direction = "normal"
    plot_eye_chnl.components[0].y_direction = "normal"
    plot_eye_chnl.title = post_chnl_str
    plot_eye_chnl.x_axis.title = "Time (ps)"
    plot_eye_chnl.x_axis.orientation = "bottom"
    plot_eye_chnl.y_axis.title = "Signal Level (V)"
    plot_eye_chnl.x_grid.visible = True
    plot_eye_chnl.y_grid.visible = True
    plot_eye_chnl.x_grid.line_color = "gray"
    plot_eye_chnl.y_grid.line_color = "gray"

    plot_eye_tx = Plot(plotdata, padding_left=75)
    plot_eye_tx.img_plot("eye_tx", colormap=clr_map)
    plot_eye_tx.y_direction = "normal"
    plot_eye_tx.components[0].y_direction = "normal"
    plot_eye_tx.title = post_tx_str
    plot_eye_tx.x_axis.title = "Time (ps)"
    plot_eye_tx.x_axis.orientation = "bottom"
    plot_eye_tx.y_axis.title = "Signal Level (V)"
    plot_eye_tx.x_grid.visible = True
    plot_eye_tx.y_grid.visible = True
    plot_eye_tx.x_grid.line_color = "gray"
    plot_eye_tx.y_grid.line_color = "gray"

    plot_eye_ctle = Plot(plotdata, padding_left=75)
    plot_eye_ctle.img_plot("eye_ctle", colormap=clr_map)
    plot_eye_ctle.y_direction = "normal"
    plot_eye_ctle.components[0].y_direction = "normal"
    plot_eye_ctle.title = post_ctle_str
    plot_eye_ctle.x_axis.title = "Time (ps)"
    plot_eye_ctle.x_axis.orientation = "bottom"
    plot_eye_ctle.y_axis.title = "Signal Level (V)"
    plot_eye_ctle.x_grid.visible = True
    plot_eye_ctle.y_grid.visible = True
    plot_eye_ctle.x_grid.line_color = "gray"
    plot_eye_ctle.y_grid.line_color = "gray"

    plot_eye_dfe = Plot(plotdata, padding_left=75)
    plot_eye_dfe.img_plot("eye_dfe", colormap=clr_map)
    plot_eye_dfe.y_direction = "normal"
    plot_eye_dfe.components[0].y_direction = "normal"
    plot_eye_dfe.title = post_dfe_str
    plot_eye_dfe.x_axis.title = "Time (ps)"
    plot_eye_dfe.x_axis.orientation = "bottom"
    plot_eye_dfe.y_axis.title = "Signal Level (V)"
    plot_eye_dfe.x_grid.visible = True
    plot_eye_dfe.y_grid.visible = True
    plot_eye_dfe.x_grid.line_color = "gray"
    plot_eye_dfe.y_grid.line_color = "gray"

    container_eye = GridPlotContainer(shape=(2, 2))
    container_eye.add(plot_eye_chnl)
    container_eye.add(plot_eye_tx)
    container_eye.add(plot_eye_ctle)
    container_eye.add(plot_eye_dfe)
    self.plots_eye = container_eye

    # - Jitter Distributions tab
    plot_jitter_dist_chnl = Plot(plotdata, padding_left=75)
    plot_jitter_dist_chnl.plot(("jitter_bins", "jitter_chnl"), type="line", color="blue", name="Measured")
    plot_jitter_dist_chnl.plot(("jitter_bins", "jitter_ext_chnl"), type="line", color="red", name="Extrapolated")
    plot_jitter_dist_chnl.title = post_chnl_str
    plot_jitter_dist_chnl.index_axis.title = "Time (ps)"
    plot_jitter_dist_chnl.value_axis.title = "Count"
    plot_jitter_dist_chnl.legend.visible = True
    plot_jitter_dist_chnl.legend.align = "ur"

    plot_jitter_dist_tx = Plot(plotdata, padding_left=75)
    plot_jitter_dist_tx.plot(("jitter_bins", "jitter_tx"), type="line", color="blue", name="Measured")
    plot_jitter_dist_tx.plot(("jitter_bins", "jitter_ext_tx"), type="line", color="red", name="Extrapolated")
    plot_jitter_dist_tx.title = post_tx_str
    plot_jitter_dist_tx.index_axis.title = "Time (ps)"
    plot_jitter_dist_tx.value_axis.title = "Count"
    plot_jitter_dist_tx.legend.visible = True
    plot_jitter_dist_tx.legend.align = "ur"

    plot_jitter_dist_ctle = Plot(plotdata, padding_left=75)
    plot_jitter_dist_ctle.plot(("jitter_bins", "jitter_ctle"), type="line", color="blue", name="Measured")
    plot_jitter_dist_ctle.plot(("jitter_bins", "jitter_ext_ctle"), type="line", color="red", name="Extrapolated")
    plot_jitter_dist_ctle.title = post_ctle_str
    plot_jitter_dist_ctle.index_axis.title = "Time (ps)"
    plot_jitter_dist_ctle.value_axis.title = "Count"
    plot_jitter_dist_ctle.legend.visible = True
    plot_jitter_dist_ctle.legend.align = "ur"

    plot_jitter_dist_dfe = Plot(plotdata, padding_left=75)
    plot_jitter_dist_dfe.plot(("jitter_bins", "jitter_dfe"), type="line", color="blue", name="Measured")
    plot_jitter_dist_dfe.plot(("jitter_bins", "jitter_ext_dfe"), type="line", color="red", name="Extrapolated")
    plot_jitter_dist_dfe.title = post_dfe_str
    plot_jitter_dist_dfe.index_axis.title = "Time (ps)"
    plot_jitter_dist_dfe.value_axis.title = "Count"
    plot_jitter_dist_dfe.legend.visible = True
    plot_jitter_dist_dfe.legend.align = "ur"

    container_jitter_dist = GridPlotContainer(shape=(2, 2))
    container_jitter_dist.add(plot_jitter_dist_chnl)
    container_jitter_dist.add(plot_jitter_dist_tx)
    container_jitter_dist.add(plot_jitter_dist_ctle)
    container_jitter_dist.add(plot_jitter_dist_dfe)
    self.plots_jitter_dist = container_jitter_dist

    # - Jitter Spectrums tab
    plot_jitter_spec_chnl = Plot(plotdata)
    plot_jitter_spec_chnl.plot(("f_MHz", "jitter_spectrum_chnl"), type="line", color="blue", name="Total")
    plot_jitter_spec_chnl.plot(
        ("f_MHz", "jitter_ind_spectrum_chnl"), type="line", color="red", name="Data Independent"
    )
    plot_jitter_spec_chnl.plot(("f_MHz", "thresh_chnl"), type="line", color="magenta", name="Pj Threshold")
    plot_jitter_spec_chnl.title = post_chnl_str
    plot_jitter_spec_chnl.index_axis.title = "Frequency (MHz)"
    plot_jitter_spec_chnl.value_axis.title = "|FFT(TIE)| (dBui)"
    plot_jitter_spec_chnl.tools.append(
        PanTool(plot_jitter_spec_chnl, constrain=True, constrain_key=None, constrain_direction="x")
    )
    zoom_jitter_spec_chnl = ZoomTool(plot_jitter_spec_chnl, tool_mode="range", axis="index", always_on=False)
    plot_jitter_spec_chnl.overlays.append(zoom_jitter_spec_chnl)
    plot_jitter_spec_chnl.legend.visible = True
    plot_jitter_spec_chnl.legend.align = "lr"

    plot_jitter_spec_tx = Plot(plotdata)
    plot_jitter_spec_tx.plot(("f_MHz", "jitter_spectrum_tx"), type="line", color="blue", name="Total")
    plot_jitter_spec_tx.plot(("f_MHz", "jitter_ind_spectrum_tx"), type="line", color="red", name="Data Independent")
    plot_jitter_spec_tx.plot(("f_MHz", "thresh_tx"), type="line", color="magenta", name="Pj Threshold")
    plot_jitter_spec_tx.title = post_tx_str
    plot_jitter_spec_tx.index_axis.title = "Frequency (MHz)"
    plot_jitter_spec_tx.value_axis.title = "|FFT(TIE)| (dBui)"
    plot_jitter_spec_tx.value_range.low_setting = -40.0
    plot_jitter_spec_tx.index_range = plot_jitter_spec_chnl.index_range  # Zoom x-axes in tandem.
    plot_jitter_spec_tx.legend.visible = True
    plot_jitter_spec_tx.legend.align = "lr"

    plot_jitter_spec_chnl.value_range = plot_jitter_spec_tx.value_range

    plot_jitter_spec_ctle = Plot(plotdata)
    plot_jitter_spec_ctle.plot(("f_MHz", "jitter_spectrum_ctle"), type="line", color="blue", name="Total")
    plot_jitter_spec_ctle.plot(
        ("f_MHz", "jitter_ind_spectrum_ctle"), type="line", color="red", name="Data Independent"
    )
    plot_jitter_spec_ctle.plot(("f_MHz", "thresh_ctle"), type="line", color="magenta", name="Pj Threshold")
    plot_jitter_spec_ctle.title = post_ctle_str
    plot_jitter_spec_ctle.index_axis.title = "Frequency (MHz)"
    plot_jitter_spec_ctle.value_axis.title = "|FFT(TIE)| (dBui)"
    plot_jitter_spec_ctle.index_range = plot_jitter_spec_chnl.index_range  # Zoom x-axes in tandem.
    plot_jitter_spec_ctle.legend.visible = True
    plot_jitter_spec_ctle.legend.align = "lr"
    plot_jitter_spec_ctle.value_range = plot_jitter_spec_tx.value_range

    plot_jitter_spec_dfe = Plot(plotdata)
    plot_jitter_spec_dfe.plot(("f_MHz_dfe", "jitter_spectrum_dfe"), type="line", color="blue", name="Total")
    plot_jitter_spec_dfe.plot(
        ("f_MHz_dfe", "jitter_ind_spectrum_dfe"), type="line", color="red", name="Data Independent"
    )
    plot_jitter_spec_dfe.plot(("f_MHz_dfe", "thresh_dfe"), type="line", color="magenta", name="Pj Threshold")
    plot_jitter_spec_dfe.title = post_dfe_str
    plot_jitter_spec_dfe.index_axis.title = "Frequency (MHz)"
    plot_jitter_spec_dfe.value_axis.title = "|FFT(TIE)| (dBui)"
    plot_jitter_spec_dfe.index_range = plot_jitter_spec_chnl.index_range  # Zoom x-axes in tandem.
    plot_jitter_spec_dfe.legend.visible = True
    plot_jitter_spec_dfe.legend.align = "lr"
    plot_jitter_spec_dfe.value_range = plot_jitter_spec_tx.value_range

    container_jitter_spec = GridPlotContainer(shape=(2, 2))
    container_jitter_spec.add(plot_jitter_spec_chnl)
    container_jitter_spec.add(plot_jitter_spec_tx)
    container_jitter_spec.add(plot_jitter_spec_ctle)
    container_jitter_spec.add(plot_jitter_spec_dfe)
    self.plots_jitter_spec = container_jitter_spec

    # - Bathtub Curves tab
    plot_bathtub_chnl = Plot(plotdata)
    plot_bathtub_chnl.plot(("jitter_bins", "bathtub_chnl"), type="line", color="blue")
    plot_bathtub_chnl.value_range.high_setting = 0
    plot_bathtub_chnl.value_range.low_setting = -18
    plot_bathtub_chnl.value_axis.tick_interval = 3
    plot_bathtub_chnl.title = post_chnl_str
    plot_bathtub_chnl.index_axis.title = "Time (ps)"
    plot_bathtub_chnl.value_axis.title = "Log10(P(Transition occurs inside.))"

    plot_bathtub_tx = Plot(plotdata)
    plot_bathtub_tx.plot(("jitter_bins", "bathtub_tx"), type="line", color="blue")
    plot_bathtub_tx.value_range.high_setting = 0
    plot_bathtub_tx.value_range.low_setting = -18
    plot_bathtub_tx.value_axis.tick_interval = 3
    plot_bathtub_tx.title = post_tx_str
    plot_bathtub_tx.index_axis.title = "Time (ps)"
    plot_bathtub_tx.value_axis.title = "Log10(P(Transition occurs inside.))"

    plot_bathtub_ctle = Plot(plotdata)
    plot_bathtub_ctle.plot(("jitter_bins", "bathtub_ctle"), type="line", color="blue")
    plot_bathtub_ctle.value_range.high_setting = 0
    plot_bathtub_ctle.value_range.low_setting = -18
    plot_bathtub_ctle.value_axis.tick_interval = 3
    plot_bathtub_ctle.title = post_ctle_str
    plot_bathtub_ctle.index_axis.title = "Time (ps)"
    plot_bathtub_ctle.value_axis.title = "Log10(P(Transition occurs inside.))"

    plot_bathtub_dfe = Plot(plotdata)
    plot_bathtub_dfe.plot(("jitter_bins", "bathtub_dfe"), type="line", color="blue")
    plot_bathtub_dfe.value_range.high_setting = 0
    plot_bathtub_dfe.value_range.low_setting = -18
    plot_bathtub_dfe.value_axis.tick_interval = 3
    plot_bathtub_dfe.title = post_dfe_str
    plot_bathtub_dfe.index_axis.title = "Time (ps)"
    plot_bathtub_dfe.value_axis.title = "Log10(P(Transition occurs inside.))"

    container_bathtub = GridPlotContainer(shape=(2, 2))
    container_bathtub.add(plot_bathtub_chnl)
    container_bathtub.add(plot_bathtub_tx)
    container_bathtub.add(plot_bathtub_ctle)
    container_bathtub.add(plot_bathtub_dfe)
    self.plots_bathtub = container_bathtub

    update_eyes(self)
Exemplo n.º 37
0
def update_results(self):
    """
    Updates all plot data used by GUI.

    Args:
        self(PyBERT): Reference to an instance of the *PyBERT* class.

    """

    # Copy globals into local namespace.
    ui            = self.ui
    samps_per_ui  = self.nspui
    eye_uis       = self.eye_uis
    num_ui        = self.nui
    clock_times   = self.clock_times
    f             = self.f
    t             = self.t
    t_ns          = self.t_ns
    t_ns_chnl     = self.t_ns_chnl
    conv_dly_ix   = self.conv_dly_ix
    n_taps        = self.n_taps

    Ts = t[1]
    ignore_until  = (num_ui - eye_uis) * ui
    ignore_samps  = (num_ui - eye_uis) * samps_per_ui

    # Misc.
    f_GHz         = f[:len(f) // 2] / 1.e9
    len_f_GHz     = len(f_GHz)
    self.plotdata.set_data("f_GHz",     f_GHz[1:])
    self.plotdata.set_data("t_ns",      t_ns)
    self.plotdata.set_data("t_ns_chnl", t_ns_chnl)

    # DFE.
    tap_weights = transpose(array(self.adaptation))
    i = 1
    for tap_weight in tap_weights:
        self.plotdata.set_data("tap%d_weights" % i, tap_weight)
        i += 1
    self.plotdata.set_data("tap_weight_index", range(len(tap_weight)))
    if(self._old_n_taps != n_taps):
        new_plot = Plot(self.plotdata, auto_colors=['red', 'orange', 'yellow', 'green', 'blue', 'purple'], padding_left=75)
        for i in range(self.n_taps):
            new_plot.plot(("tap_weight_index", "tap%d_weights" % (i + 1)), type="line", color="auto", name="tap%d"%(i+1))
        new_plot.title  = "DFE Adaptation"
        new_plot.tools.append(PanTool(new_plot, constrain=True, constrain_key=None, constrain_direction='x'))
        zoom9 = ZoomTool(new_plot, tool_mode="range", axis='index', always_on=False)
        new_plot.overlays.append(zoom9)
        new_plot.legend.visible = True
        new_plot.legend.align = 'ul'
        self.plots_dfe.remove(self._dfe_plot)
        self.plots_dfe.insert(1, new_plot)
        self._dfe_plot = new_plot
        self._old_n_taps = n_taps

    clock_pers = diff(clock_times)
    start_t = t[where(self.lockeds)[0][0]]
    start_ix = where(clock_times > start_t)[0][0]
    (bin_counts, bin_edges) = histogram(clock_pers[start_ix:], bins=100)
    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.
    clock_spec = fft(clock_pers[start_ix:])
    clock_spec = abs(clock_spec[:len(clock_spec) / 2])
    spec_freqs = arange(len(clock_spec)) / (2. * len(clock_spec))  # In this case, fNyquist = half the bit rate.
    clock_spec /= clock_spec[1:].mean()  # Normalize the mean non-d.c. value to 0 dB.
    self.plotdata.set_data("clk_per_hist_bins",  bin_centers * 1.e12)  # (ps)
    self.plotdata.set_data("clk_per_hist_vals",  bin_counts)
    self.plotdata.set_data("clk_spec",  10. * log10(clock_spec[1:]))  # Omit the d.c. value.
    self.plotdata.set_data("clk_freqs",  spec_freqs[1:])
    self.plotdata.set_data("dfe_out",  self.dfe_out)
    self.plotdata.set_data("ui_ests",  self.ui_ests)
    self.plotdata.set_data("clocks",   self.clocks)
    self.plotdata.set_data("lockeds",  self.lockeds)

    # Impulse responses
    self.plotdata.set_data("chnl_h",     self.chnl_h * 1.e-9 / Ts)  # Re-normalize to (V/ns), for plotting.
    self.plotdata.set_data("tx_h",       self.tx_h * 1.e-9 / Ts)
    self.plotdata.set_data("tx_out_h",   self.tx_out_h * 1.e-9 / Ts)
    self.plotdata.set_data("ctle_h",     self.ctle_h * 1.e-9 / Ts)
    self.plotdata.set_data("ctle_out_h", self.ctle_out_h * 1.e-9 / Ts)
    self.plotdata.set_data("dfe_h",      self.dfe_h * 1.e-9 / Ts)
    self.plotdata.set_data("dfe_out_h",  self.dfe_out_h * 1.e-9 / Ts)

    # Step responses
    self.plotdata.set_data("chnl_s",     self.chnl_s)
    self.plotdata.set_data("tx_s",       self.tx_s)
    self.plotdata.set_data("tx_out_s",   self.tx_out_s)
    self.plotdata.set_data("ctle_s",     self.ctle_s)
    self.plotdata.set_data("ctle_out_s", self.ctle_out_s)
    self.plotdata.set_data("dfe_s",      self.dfe_s)
    self.plotdata.set_data("dfe_out_s",  self.dfe_out_s)

    # Pulse responses
    self.plotdata.set_data("chnl_p",     self.chnl_p)
    self.plotdata.set_data("tx_out_p",   self.tx_out_p)
    self.plotdata.set_data("ctle_out_p", self.ctle_out_p)
    self.plotdata.set_data("dfe_out_p",  self.dfe_out_p)

    # Outputs
    self.plotdata.set_data("ideal_signal",   self.ideal_signal)
    self.plotdata.set_data("chnl_out",   self.chnl_out)
    self.plotdata.set_data("tx_out",     self.rx_in)
    self.plotdata.set_data("ctle_out",   self.ctle_out)
    self.plotdata.set_data("dfe_out",    self.dfe_out)
    self.plotdata.set_data("auto_corr",  self.auto_corr)

    # Frequency responses
    self.plotdata.set_data("chnl_H",     20. * log10(abs(self.chnl_H    [1 : len_f_GHz])))
    self.plotdata.set_data("chnl_trimmed_H", 20. * log10(abs(self.chnl_trimmed_H [1 : len_f_GHz])))
    self.plotdata.set_data("tx_H",       20. * log10(abs(self.tx_H      [1 : len_f_GHz])))
    self.plotdata.set_data("tx_out_H",   20. * log10(abs(self.tx_out_H  [1 : len_f_GHz])))
    self.plotdata.set_data("ctle_H",     20. * log10(abs(self.ctle_H    [1 : len_f_GHz])))
    self.plotdata.set_data("ctle_out_H", 20. * log10(abs(self.ctle_out_H[1 : len_f_GHz])))
    self.plotdata.set_data("dfe_H",      20. * log10(abs(self.dfe_H     [1 : len_f_GHz])))
    self.plotdata.set_data("dfe_out_H",  20. * log10(abs(self.dfe_out_H [1 : len_f_GHz])))

    # Jitter distributions
    jitter_ext_chnl = self.jitter_ext_chnl # These are used, again, in bathtub curve generation, below.
    jitter_ext_tx   = self.jitter_ext_tx
    jitter_ext_ctle = self.jitter_ext_ctle
    jitter_ext_dfe  = self.jitter_ext_dfe
    self.plotdata.set_data("jitter_bins",     array(self.jitter_bins)     * 1.e12)
    self.plotdata.set_data("jitter_chnl",     self.jitter_chnl)
    self.plotdata.set_data("jitter_ext_chnl", jitter_ext_chnl)
    self.plotdata.set_data("jitter_tx",       self.jitter_tx)
    self.plotdata.set_data("jitter_ext_tx",   jitter_ext_tx)
    self.plotdata.set_data("jitter_ctle",     self.jitter_ctle)
    self.plotdata.set_data("jitter_ext_ctle", jitter_ext_ctle)
    self.plotdata.set_data("jitter_dfe",      self.jitter_dfe)
    self.plotdata.set_data("jitter_ext_dfe",  jitter_ext_dfe)

    # Jitter spectrums
    log10_ui = log10(ui)
    self.plotdata.set_data("f_MHz",     self.f_MHz[1:])
    self.plotdata.set_data("f_MHz_dfe", self.f_MHz_dfe[1:])
    self.plotdata.set_data("jitter_spectrum_chnl",     10. * (log10(self.jitter_spectrum_chnl     [1:]) - log10_ui))
    self.plotdata.set_data("jitter_ind_spectrum_chnl", 10. * (log10(self.jitter_ind_spectrum_chnl [1:]) - log10_ui))
    self.plotdata.set_data("thresh_chnl",              10. * (log10(self.thresh_chnl              [1:]) - log10_ui))
    self.plotdata.set_data("jitter_spectrum_tx",       10. * (log10(self.jitter_spectrum_tx       [1:]) - log10_ui))
    self.plotdata.set_data("jitter_ind_spectrum_tx",   10. * (log10(self.jitter_ind_spectrum_tx   [1:]) - log10_ui))
    self.plotdata.set_data("thresh_tx",                10. * (log10(self.thresh_tx                [1:]) - log10_ui))
    self.plotdata.set_data("jitter_spectrum_ctle",     10. * (log10(self.jitter_spectrum_ctle     [1:]) - log10_ui))
    self.plotdata.set_data("jitter_ind_spectrum_ctle", 10. * (log10(self.jitter_ind_spectrum_ctle [1:]) - log10_ui))
    self.plotdata.set_data("thresh_ctle",              10. * (log10(self.thresh_ctle              [1:]) - log10_ui))
    self.plotdata.set_data("jitter_spectrum_dfe",      10. * (log10(self.jitter_spectrum_dfe      [1:]) - log10_ui))
    self.plotdata.set_data("jitter_ind_spectrum_dfe",  10. * (log10(self.jitter_ind_spectrum_dfe  [1:]) - log10_ui))
    self.plotdata.set_data("thresh_dfe",               10. * (log10(self.thresh_dfe               [1:]) - log10_ui))
    self.plotdata.set_data("jitter_rejection_ratio", self.jitter_rejection_ratio[1:])

    # Bathtubs
    half_len = len(jitter_ext_chnl) / 2
    #  - Channel
    bathtub_chnl    = list(cumsum(jitter_ext_chnl[-1 : -(half_len + 1) : -1]))
    bathtub_chnl.reverse()
    bathtub_chnl    = array(bathtub_chnl + list(cumsum(jitter_ext_chnl[:half_len + 1])))
    bathtub_chnl    = where(bathtub_chnl < MIN_BATHTUB_VAL, 0.1 * MIN_BATHTUB_VAL * ones(len(bathtub_chnl)),
                            bathtub_chnl) # To avoid Chaco log scale plot wierdness.
    self.plotdata.set_data("bathtub_chnl", log10(bathtub_chnl))
    #  - Tx
    bathtub_tx    = list(cumsum(jitter_ext_tx[-1 : -(half_len + 1) : -1]))
    bathtub_tx.reverse()
    bathtub_tx    = array(bathtub_tx + list(cumsum(jitter_ext_tx[:half_len + 1])))
    bathtub_tx    = where(bathtub_tx < MIN_BATHTUB_VAL, 0.1 * MIN_BATHTUB_VAL * ones(len(bathtub_tx)),
                          bathtub_tx) # To avoid Chaco log scale plot wierdness.
    self.plotdata.set_data("bathtub_tx", log10(bathtub_tx))
    #  - CTLE
    bathtub_ctle    = list(cumsum(jitter_ext_ctle[-1 : -(half_len + 1) : -1]))
    bathtub_ctle.reverse()
    bathtub_ctle    = array(bathtub_ctle + list(cumsum(jitter_ext_ctle[:half_len + 1])))
    bathtub_ctle    = where(bathtub_ctle < MIN_BATHTUB_VAL, 0.1 * MIN_BATHTUB_VAL * ones(len(bathtub_ctle)),
                            bathtub_ctle) # To avoid Chaco log scale plot wierdness.
    self.plotdata.set_data("bathtub_ctle", log10(bathtub_ctle))
    #  - DFE
    bathtub_dfe    = list(cumsum(jitter_ext_dfe[-1 : -(half_len + 1) : -1]))
    bathtub_dfe.reverse()
    bathtub_dfe    = array(bathtub_dfe + list(cumsum(jitter_ext_dfe[:half_len + 1])))
    bathtub_dfe    = where(bathtub_dfe < MIN_BATHTUB_VAL, 0.1 * MIN_BATHTUB_VAL * ones(len(bathtub_dfe)),
                           bathtub_dfe) # To avoid Chaco log scale plot wierdness.
    self.plotdata.set_data("bathtub_dfe", log10(bathtub_dfe))

    # Eyes
    width    = 2 * samps_per_ui
    xs       = linspace(-ui * 1.e12, ui * 1.e12, width)
    height   = 100
    y_max    = 1.1 * max(abs(array(self.chnl_out)))
    eye_chnl = calc_eye(ui, samps_per_ui, height, self.chnl_out[ignore_samps:], y_max)
    y_max    = 1.1 * max(abs(array(self.rx_in)))
    eye_tx   = calc_eye(ui, samps_per_ui, height, self.rx_in[ignore_samps:],   y_max)
    y_max    = 1.1 * max(abs(array(self.ctle_out)))
    eye_ctle = calc_eye(ui, samps_per_ui, height, self.ctle_out[ignore_samps:], y_max)
    i = 0
    while(clock_times[i] <= ignore_until):
        i += 1
        assert i < len(clock_times), "ERROR: Insufficient coverage in 'clock_times' vector."
    y_max    = 1.1 * max(abs(array(self.dfe_out)))
    eye_dfe  = calc_eye(ui, samps_per_ui, height, self.dfe_out, y_max, clock_times[i:])
    self.plotdata.set_data("eye_index", xs)
    self.plotdata.set_data("eye_chnl",  eye_chnl)
    self.plotdata.set_data("eye_tx",    eye_tx)
    self.plotdata.set_data("eye_ctle",  eye_ctle)
    self.plotdata.set_data("eye_dfe",   eye_dfe)
Exemplo n.º 38
0
class myImagePlot(HasTraits):
    # container for all plots
    container = Instance(HPlotContainer)
    
    # Plot components within this container:
    color_plot = Instance(CMapImagePlot)
    vertical_cross_plot = Instance(Plot)
    horizontal_cross_plot = Instance(Plot)
    colorbar = Instance(ColorBar)
    
    # plot data
    pd_all = Instance(ArrayPlotData)
    pd_horiz=Instance(ArrayPlotData)
    pd_vert=Instance(ArrayPlotData)
    #private data storage
    _imag_index=Instance(GridDataSource)
    _image_value=Instance(ImageData)   
    
    traits_view = View(
        Item('container', editor=ComponentEditor(), show_label=False),
        width=1000, height=700, resizable=True, title="Chaco Plot")

    def __init__(self, x,y,z):
        super(myImagePlot, self).__init__()
        self.pd_all = ArrayPlotData(imagedata = z)
        self.pd_horiz = ArrayPlotData(x=x, horiz=z[4, :])
        self.pd_vert = ArrayPlotData(y=y, vert=z[:,5])
    
        self._imag_index = GridDataSource(xdata=x, ydata=y, sort_order=("ascending","ascending"))
        index_mapper = GridMapper(range=DataRange2D(self._imag_index))
        self._imag_index.on_trait_change(self._metadata_changed,
                                          "metadata_changed")
        self._image_value = ImageData(data=z, value_depth=1)
        color_mapper = jet(DataRange1D(self._image_value))

        self.color_plot= CMapImagePlot(
            index=self._imag_index,
            index_mapper=index_mapper,
            value=self._image_value,
            value_mapper=color_mapper,
            padding=20,
            use_backbuffer=True,
            unified_draw=True)

        #Add axes to image plot            
        left = PlotAxis(orientation='left',
                        title= "Frequency (GHz)",
                        mapper=self.color_plot.index_mapper._ymapper,
                        component=self.color_plot)

        self.color_plot.overlays.append(left)
        
        bottom = PlotAxis(orientation='bottom',
                        title= "Time (us)",
                        mapper=self.color_plot.index_mapper._xmapper,
                        component=self.color_plot)
        self.color_plot.overlays.append(bottom)

        self.color_plot.tools.append(PanTool(self.color_plot,
                                           constrain_key="shift"))
        self.color_plot.overlays.append(ZoomTool(component=self.color_plot,
                                            tool_mode="box", always_on=False))
                                            
        #Add line inspector tool for horizontal and vertical
        self.color_plot.overlays.append(LineInspector(component=self.color_plot,
                                               axis='index_x',
                                               inspect_mode="indexed",
                                               write_metadata=True,
                                               is_listener=True,
                                               color="white"))

        self.color_plot.overlays.append(LineInspector(component=self.color_plot,
                                               axis='index_y',
                                               inspect_mode="indexed",
                                               write_metadata=True,
                                               color="white",
                                               is_listener=True))         

        myrange = DataRange1D(low=amin(z),
                              high=amax(z))
        cmap=jet                         
        self.colormap = cmap(myrange)

        # Create a colorbar
        cbar_index_mapper = LinearMapper(range=myrange)
        self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
                                 plot=self.color_plot,
                                 padding_top=self.color_plot.padding_top,
                                 padding_bottom=self.color_plot.padding_bottom,
                                 padding_right=40,
                                 resizable='v',
                                 width=30)#, ytitle="Magvec (mV)")

        #create horizontal line plot
        self.horiz_cross_plot = Plot(self.pd_horiz, resizable="h")
        self.horiz_cross_plot.height = 100
        self.horiz_cross_plot.padding = 20
        self.horiz_cross_plot.plot(("x", "horiz"))#,
                             #line_style="dot")
#        self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
#                             type="cmap_scatter",
#                             name="dot",
#                             color_mapper=self._cmap(image_value_range),
#                             marker="circle",
#                             marker_size=8)

        self.horiz_cross_plot.index_range = self.color_plot.index_range.x_range

        #create vertical line plot
        self.vert_cross_plot = Plot(self.pd_vert, width = 140, orientation="v", 
                                resizable="v", padding=20, padding_bottom=160)
        self.vert_cross_plot.plot(("y", "vert"))#,
#                             line_style="dot")
       # self.vert_cross_plot.xtitle="Magvec (mV)"
 #       self.vertica_cross_plot.plot(("vertical_scatter_index",
 #                              "vertical_scatter_value",
 #                              "vertical_scatter_color"),
 #                            type="cmap_scatter",
 #                            name="dot",
 #                            color_mapper=self._cmap(image_value_range),
 #                            marker="circle",
  #                           marker_size=8)

        self.vert_cross_plot.index_range = self.color_plot.index_range.y_range

        # Create a container and add components
        self.container = HPlotContainer(padding=40, fill_padding=True,
                                        bgcolor = "white", use_backbuffer=False)
        inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
        inner_cont.add(self.horiz_cross_plot)
        inner_cont.add(self.color_plot)
        self.container.add(self.colorbar)
        self.container.add(inner_cont)
        self.container.add(self.vert_cross_plot)
        
    def _metadata_changed(self, old, new):
        """ This function takes out a cross section from the image data, based
        on the line inspector selections, and updates the line and scatter
        plots."""

        #self.cross_plot.value_range.low = self.minz
        #self.cross_plot.value_range.high = self.maxz
        #self.cross_plot2.value_range.low = self.minz
        #self.cross_plot2.value_range.high = self.maxz
        if self._imag_index.metadata.has_key("selections"):
            x_ndx, y_ndx = self._imag_index.metadata["selections"]
            if y_ndx and x_ndx:
#                xdata, ydata = self._image_index.get_data()
#                xdata, ydata = xdata.get_data(), ydata.get_data()
                self.pd_horiz.set_data("horiz", self._image_value.data[y_ndx,:])
                self.pd_vert.set_data("vert", self._image_value.data[:,x_ndx])
Exemplo n.º 39
0
    def __init__(self, link):
        super(SolutionView, self).__init__()

        self.log_file = None
        self.vel_log_file = None

        self.plot_data = ArrayPlotData(lat=[],
                                       lng=[],
                                       alt=[],
                                       t=[],
                                       cur_lat=[],
                                       cur_lng=[],
                                       cur_lat_ps=[],
                                       cur_lng_ps=[],
                                       lat_ps=[],
                                       lng_ps=[],
                                       alt_ps=[],
                                       t_ps=[])
        self.plot = Plot(self.plot_data)

        # 1000 point buffer
        self.plot.plot(('lng', 'lat'),
                       type='line',
                       name='',
                       color=(0, 0, 0.9, 0.1))
        self.plot.plot(('lng', 'lat'),
                       type='scatter',
                       name='',
                       color='blue',
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_ps', 'lat_ps'),
                       type='line',
                       name='',
                       color=(1, 0.4, 0, 0.1))
        self.plot.plot(('lng_ps', 'lat_ps'),
                       type='scatter',
                       name='',
                       color='orange',
                       marker='diamond',
                       line_width=0.0,
                       marker_size=1.0)
        # current values
        spp = self.plot.plot(('cur_lng', 'cur_lat'),
                             type='scatter',
                             name='SPP',
                             color='blue',
                             marker='plus',
                             line_width=1.5,
                             marker_size=5.0)
        rtk = self.plot.plot(('cur_lng_ps', 'cur_lat_ps'),
                             type='scatter',
                             name='RTK',
                             color='orange',
                             marker='plus',
                             line_width=1.5,
                             marker_size=5.0)
        plot_labels = ['SPP', 'RTK']
        plots_legend = dict(zip(plot_labels, [spp, rtk]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'Longitude (degrees)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'Latitude (degrees)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.link = link
        self.link.add_callback(self._pos_llh_callback, SBP_MSG_POS_LLH)
        self.link.add_callback(self.vel_ned_callback, SBP_MSG_VEL_NED)
        self.link.add_callback(self.dops_callback, SBP_MSG_DOPS)
        self.link.add_callback(self.gps_time_callback, SBP_MSG_GPS_TIME)

        self.week = None
        self.nsec = 0

        self.python_console_cmds = {
            'solution': self,
        }
Exemplo n.º 40
0
    def __init__(self, x,y,z):
        super(myImagePlot, self).__init__()
        self.pd_all = ArrayPlotData(imagedata = z)
        self.pd_horiz = ArrayPlotData(x=x, horiz=z[4, :])
        self.pd_vert = ArrayPlotData(y=y, vert=z[:,5])
    
        self._imag_index = GridDataSource(xdata=x, ydata=y, sort_order=("ascending","ascending"))
        index_mapper = GridMapper(range=DataRange2D(self._imag_index))
        self._imag_index.on_trait_change(self._metadata_changed,
                                          "metadata_changed")
        self._image_value = ImageData(data=z, value_depth=1)
        color_mapper = jet(DataRange1D(self._image_value))

        self.color_plot= CMapImagePlot(
            index=self._imag_index,
            index_mapper=index_mapper,
            value=self._image_value,
            value_mapper=color_mapper,
            padding=20,
            use_backbuffer=True,
            unified_draw=True)

        #Add axes to image plot            
        left = PlotAxis(orientation='left',
                        title= "Frequency (GHz)",
                        mapper=self.color_plot.index_mapper._ymapper,
                        component=self.color_plot)

        self.color_plot.overlays.append(left)
        
        bottom = PlotAxis(orientation='bottom',
                        title= "Time (us)",
                        mapper=self.color_plot.index_mapper._xmapper,
                        component=self.color_plot)
        self.color_plot.overlays.append(bottom)

        self.color_plot.tools.append(PanTool(self.color_plot,
                                           constrain_key="shift"))
        self.color_plot.overlays.append(ZoomTool(component=self.color_plot,
                                            tool_mode="box", always_on=False))
                                            
        #Add line inspector tool for horizontal and vertical
        self.color_plot.overlays.append(LineInspector(component=self.color_plot,
                                               axis='index_x',
                                               inspect_mode="indexed",
                                               write_metadata=True,
                                               is_listener=True,
                                               color="white"))

        self.color_plot.overlays.append(LineInspector(component=self.color_plot,
                                               axis='index_y',
                                               inspect_mode="indexed",
                                               write_metadata=True,
                                               color="white",
                                               is_listener=True))         

        myrange = DataRange1D(low=amin(z),
                              high=amax(z))
        cmap=jet                         
        self.colormap = cmap(myrange)

        # Create a colorbar
        cbar_index_mapper = LinearMapper(range=myrange)
        self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
                                 plot=self.color_plot,
                                 padding_top=self.color_plot.padding_top,
                                 padding_bottom=self.color_plot.padding_bottom,
                                 padding_right=40,
                                 resizable='v',
                                 width=30)#, ytitle="Magvec (mV)")

        #create horizontal line plot
        self.horiz_cross_plot = Plot(self.pd_horiz, resizable="h")
        self.horiz_cross_plot.height = 100
        self.horiz_cross_plot.padding = 20
        self.horiz_cross_plot.plot(("x", "horiz"))#,
                             #line_style="dot")
#        self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
#                             type="cmap_scatter",
#                             name="dot",
#                             color_mapper=self._cmap(image_value_range),
#                             marker="circle",
#                             marker_size=8)

        self.horiz_cross_plot.index_range = self.color_plot.index_range.x_range

        #create vertical line plot
        self.vert_cross_plot = Plot(self.pd_vert, width = 140, orientation="v", 
                                resizable="v", padding=20, padding_bottom=160)
        self.vert_cross_plot.plot(("y", "vert"))#,
#                             line_style="dot")
       # self.vert_cross_plot.xtitle="Magvec (mV)"
 #       self.vertica_cross_plot.plot(("vertical_scatter_index",
 #                              "vertical_scatter_value",
 #                              "vertical_scatter_color"),
 #                            type="cmap_scatter",
 #                            name="dot",
 #                            color_mapper=self._cmap(image_value_range),
 #                            marker="circle",
  #                           marker_size=8)

        self.vert_cross_plot.index_range = self.color_plot.index_range.y_range

        # Create a container and add components
        self.container = HPlotContainer(padding=40, fill_padding=True,
                                        bgcolor = "white", use_backbuffer=False)
        inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
        inner_cont.add(self.horiz_cross_plot)
        inner_cont.add(self.color_plot)
        self.container.add(self.colorbar)
        self.container.add(inner_cont)
        self.container.add(self.vert_cross_plot)
Exemplo n.º 41
0
    def __init__(self, link, plot_history_max=1000, dirname=''):
        super(BaselineView, self).__init__()
        self.log_file = None
        self.directory_name_b = dirname
        self.num_hyps = 0
        self.last_hyp_update = 0
        self.last_btime_update = 0
        self.last_soln = None
        self.last_mode = 0
        self.plot_data = ArrayPlotData(n_fixed=[0.0],
                                       e_fixed=[0.0],
                                       d_fixed=[0.0],
                                       n_float=[0.0],
                                       e_float=[0.0],
                                       d_float=[0.0],
                                       n_dgnss=[0.0],
                                       e_dgnss=[0.0],
                                       d_dgnss=[0.0],
                                       t=[0.0],
                                       ref_n=[0.0],
                                       ref_e=[0.0],
                                       ref_d=[0.0],
                                       cur_fixed_e=[],
                                       cur_fixed_n=[],
                                       cur_fixed_d=[],
                                       cur_float_e=[],
                                       cur_float_n=[],
                                       cur_float_d=[],
                                       cur_dgnss_e=[],
                                       cur_dgnss_n=[],
                                       cur_dgnss_d=[])

        self.plot_history_max = plot_history_max
        self.n = np.zeros(plot_history_max)
        self.e = np.zeros(plot_history_max)
        self.d = np.zeros(plot_history_max)
        self.mode = np.zeros(plot_history_max)

        self.plot = Plot(self.plot_data)
        pts_float = self.plot.plot(('e_float', 'n_float'),
                                   type='scatter',
                                   color=color_dict[FLOAT_MODE],
                                   marker='dot',
                                   line_width=0.0,
                                   marker_size=1.0)
        pts_fixed = self.plot.plot(  # noqa: F841
            ('e_fixed', 'n_fixed'),
            type='scatter',
            color=color_dict[FIXED_MODE],
            marker='dot',
            line_width=0.0,
            marker_size=1.0)
        pts_dgnss = self.plot.plot(  # noqa: F841
            ('e_dgnss', 'n_dgnss'),
            type='scatter',
            color=color_dict[DGNSS_MODE],
            marker='dot',
            line_width=0.0,
            marker_size=1.0)
        ref = self.plot.plot(('ref_e', 'ref_n'),
                             type='scatter',
                             color='red',
                             marker='plus',
                             marker_size=5,
                             line_width=1.5)
        cur_fixed = self.plot.plot(('cur_fixed_e', 'cur_fixed_n'),
                                   type='scatter',
                                   color=color_dict[FIXED_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_float = self.plot.plot(('cur_float_e', 'cur_float_n'),
                                   type='scatter',
                                   color=color_dict[FLOAT_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_dgnss = self.plot.plot(('cur_dgnss_e', 'cur_dgnss_n'),
                                   type='scatter',
                                   color=color_dict[DGNSS_MODE],
                                   marker='plus',
                                   line_width=1.5,
                                   marker_size=5)
        plot_labels = [' Base Position', 'DGPS', 'RTK Float', 'RTK Fixed']
        plots_legend = dict(
            zip(plot_labels, [ref, cur_dgnss, cur_float, cur_fixed]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.labels = plot_labels  # sets order
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'E (meters)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'N (meters)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.week = None
        self.utc_time = None
        self.age_corrections = None
        self.heading = None
        self.nsec = 0

        self.link = link
        self.link.add_callback(
            self.baseline_callback,
            [SBP_MSG_BASELINE_NED, SBP_MSG_BASELINE_NED_DEP_A])
        self.link.add_callback(self.baseline_heading_callback,
                               [SBP_MSG_BASELINE_HEADING])
        self.link.add_callback(self.iar_state_callback, SBP_MSG_IAR_STATE)
        self.link.add_callback(self.gps_time_callback,
                               [SBP_MSG_GPS_TIME, SBP_MSG_GPS_TIME_DEP_A])
        self.link.add_callback(self.utc_time_callback, [SBP_MSG_UTC_TIME])
        self.link.add_callback(self.age_corrections_callback,
                               SBP_MSG_AGE_CORRECTIONS)

        call_repeatedly(0.2, self.solution_draw)

        self.python_console_cmds = {'baseline': self}
Exemplo n.º 42
0
class SolutionView(HasTraits):
    python_console_cmds = Dict()
    # we need to doubleup on Lists to store the psuedo absolutes separately
    # without rewriting everything
    lats = List()
    lngs = List()
    alts = List()

    lats_psuedo_abs = List()
    lngs_psuedo_abs = List()
    alts_psuedo_abs = List()

    table_spp = List()
    table_psuedo_abs = List()
    dops_table = List()
    pos_table_spp = List()
    vel_table = List()

    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)
    # Store plots we care about for legend

    running = Bool(True)
    zoomall = Bool(False)
    position_centered = Bool(False)

    clear_button = SVGButton(label='',
                             tooltip='Clear',
                             filename=os.path.join(os.path.dirname(__file__),
                                                   'images', 'iconic',
                                                   'x.svg'),
                             width=16,
                             height=16)
    zoomall_button = SVGButton(label='',
                               tooltip='Zoom All',
                               toggle=True,
                               filename=os.path.join(os.path.dirname(__file__),
                                                     'images', 'iconic',
                                                     'fullscreen.svg'),
                               width=16,
                               height=16)
    center_button = SVGButton(label='',
                              tooltip='Center on Solution',
                              toggle=True,
                              filename=os.path.join(os.path.dirname(__file__),
                                                    'images', 'iconic',
                                                    'target.svg'),
                              width=16,
                              height=16)
    paused_button = SVGButton(
        label='',
        tooltip='Pause',
        toggle_tooltip='Run',
        toggle=True,
        filename=os.path.join(os.path.dirname(__file__), 'images', 'iconic',
                              'pause.svg'),
        toggle_filename=os.path.join(os.path.dirname(__file__), 'images',
                                     'iconic', 'play.svg'),
        width=16,
        height=16)

    traits_view = View(
        HSplit(
            Tabbed(
                VGroup(Item('',
                            label='Single Point Position (SPP)',
                            emphasized=True),
                       Item('table_spp',
                            style='readonly',
                            editor=TabularEditor(adapter=SimpleAdapter()),
                            show_label=False,
                            width=0.3),
                       label='Single Point Position'),
                VGroup(Item('', label='RTK Position', emphasized=True),
                       Item('table_psuedo_abs',
                            style='readonly',
                            editor=TabularEditor(adapter=SimpleAdapter()),
                            show_label=False,
                            width=0.3),
                       label='RTK Position')),
            VGroup(
                HGroup(
                    Item('paused_button', show_label=False),
                    Item('clear_button', show_label=False),
                    Item('zoomall_button', show_label=False),
                    Item('center_button', show_label=False),
                ),
                Item('plot',
                     show_label=False,
                     editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8))),
            )))

    def _zoomall_button_fired(self):
        self.zoomall = not self.zoomall

    def _center_button_fired(self):
        self.position_centered = not self.position_centered

    def _paused_button_fired(self):
        self.running = not self.running

    def _clear_button_fired(self):
        self.lats = []
        self.lngs = []
        self.alts = []
        self.lats_psuedo_abs = []
        self.lngs_psuedo_abs = []
        self.alts_psuedo_abs = []
        self.plot_data.set_data('lat', [])
        self.plot_data.set_data('lng', [])
        self.plot_data.set_data('alt', [])
        self.plot_data.set_data('t', [])
        self.plot_data.set_data('lat_ps', [])
        self.plot_data.set_data('lng_ps', [])
        self.plot_data.set_data('alt_ps', [])
        self.plot_data.set_data('t_ps', [])

    def _pos_llh_callback(self, sbp_msg, **metadata):
        # Updating an ArrayPlotData isn't thread safe (see chaco issue #9), so
        # actually perform the update in the UI thread.
        if self.running:
            GUI.invoke_later(self.pos_llh_callback, sbp_msg)

    def update_table(self):
        self._table_list = self.table_spp.items()

    def pos_llh_callback(self, sbp_msg, **metadata):
        soln = MsgPosLLH(sbp_msg)
        masked_flag = soln.flags & 0x7
        if masked_flag == 0:
            psuedo_absolutes = False
        else:
            psuedo_absolutes = True
        pos_table = []

        if self.log_file is None:
            self.log_file = open(
                time.strftime("position_log_%Y%m%d-%H%M%S.csv"), 'w')
            self.log_file.write(
                "time,latitude(degrees),longitude(degrees),altitude(meters),n_sats,flags\n"
            )
        tow = soln.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        if self.week is not None:
            t = datetime.datetime(1980, 1, 6) + \
                datetime.timedelta(weeks=self.week) + \
                datetime.timedelta(seconds=tow)
            pos_table.append(('GPS Time', t))
            pos_table.append(('GPS Week', str(self.week)))

            self.log_file.write('%s,%.10f,%.10f,%.4f,%d,%d\n' %
                                (str(t), soln.lat, soln.lon, soln.height,
                                 soln.n_sats, soln.flags))
            self.log_file.flush()

        pos_table.append(('GPS ToW', tow))

        pos_table.append(('Num. sats', soln.n_sats))

        pos_table.append(('Lat', soln.lat))
        pos_table.append(('Lng', soln.lon))
        pos_table.append(('Alt', soln.height))
        pos_table.append(('Flags', '0x%02x' % soln.flags))
        if (soln.flags & 0xff) == 0:
            pos_table.append(('Mode', 'SPP (single point position)'))
        elif (soln.flags & 0xff) == 1:
            pos_table.append(('Mode', 'Fixed RTK'))
        elif (soln.flags & 0xff) == 2:
            pos_table.append(('Mode', 'Float RTK'))
        else:
            pos_table.append(('Mode', 'Unknown'))

        if psuedo_absolutes:
            # setup_plot variables
            self.lats_psuedo_abs.append(soln.lat)
            self.lngs_psuedo_abs.append(soln.lon)
            self.alts_psuedo_abs.append(soln.height)

            self.lats_psuedo_abs = self.lats_psuedo_abs[-1000:]
            self.lngs_psuedo_abs = self.lngs_psuedo_abs[-1000:]
            self.alts_psuedo_abs = self.alts_psuedo_abs[-1000:]

            self.plot_data.set_data('lat_ps', self.lats_psuedo_abs)
            self.plot_data.set_data('lng_ps', self.lngs_psuedo_abs)
            self.plot_data.set_data('alt_ps', self.alts_psuedo_abs)
            self.plot_data.set_data('cur_lat_ps', [soln.lat])
            self.plot_data.set_data('cur_lng_ps', [soln.lon])
            t_psuedo_abs = range(len(self.lats))
            self.plot_data.set_data('t', t)
            self.plot_data.set_data('t_ps', t_psuedo_abs)
            # set-up table variables
            self.table_psuedo_abs = pos_table

        else:
            # setup_plot variables
            self.lats.append(soln.lat)
            self.lngs.append(soln.lon)
            self.alts.append(soln.height)

            self.lats = self.lats[-1000:]
            self.lngs = self.lngs[-1000:]
            self.alts = self.alts[-1000:]

            self.plot_data.set_data('lat', self.lats)
            self.plot_data.set_data('lng', self.lngs)
            self.plot_data.set_data('alt', self.alts)
            self.plot_data.set_data('cur_lat', [soln.lat])
            self.plot_data.set_data('cur_lng', [soln.lon])
            t = range(len(self.lats))
            self.plot_data.set_data('t', t)

            # set-up table variables
            self.pos_table_spp = pos_table
            self.table_spp = self.pos_table_spp + self.vel_table + self.dops_table
            # TODO: figure out how to center the graph now that we have two separate messages
            # when we selectivtely send only SPP, the centering function won't work anymore
            if self.position_centered:
                d = (self.plot.index_range.high -
                     self.plot.index_range.low) / 2.
                self.plot.index_range.set_bounds(soln.lon - d, soln.lon + d)
                d = (self.plot.value_range.high -
                     self.plot.value_range.low) / 2.
                self.plot.value_range.set_bounds(soln.lat - d, soln.lat + d)
        if self.zoomall:
            plot_square_axes(self.plot, 'lng', 'lat')

    def dops_callback(self, sbp_msg, **metadata):
        dops = MsgDops(sbp_msg)
        self.dops_table = [('PDOP', '%.1f' % (dops.pdop * 0.01)),
                           ('GDOP', '%.1f' % (dops.gdop * 0.01)),
                           ('TDOP', '%.1f' % (dops.tdop * 0.01)),
                           ('HDOP', '%.1f' % (dops.hdop * 0.01)),
                           ('VDOP', '%.1f' % (dops.vdop * 0.01))]
        self.table_spp = self.pos_table_spp + self.vel_table + self.dops_table

    def vel_ned_callback(self, sbp_msg, **metadata):
        vel_ned = MsgVelNED(sbp_msg)

        if self.vel_log_file is None:
            self.vel_log_file = open(
                time.strftime("velocity_log_%Y%m%d-%H%M%S.csv"), 'w')
            self.vel_log_file.write(
                'time,north(m/s),east(m/s),down(m/s),speed(m/s),num_sats\n')

        tow = vel_ned.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        if self.week is not None:
            t = datetime.datetime(1980, 1, 6) + \
                datetime.timedelta(weeks=self.week) + \
                datetime.timedelta(seconds=tow)

            self.vel_log_file.write(
                '%s,%.6f,%.6f,%.6f,%.6f,%d\n' %
                (str(t), vel_ned.n * 1e-3, vel_ned.e * 1e-3, vel_ned.d * 1e-3,
                 math.sqrt(vel_ned.n * vel_ned.n + vel_ned.e * vel_ned.e) *
                 1e-3, vel_ned.n_sats))
            self.vel_log_file.flush()

        self.vel_table = [
            ('Vel. N', '% 8.4f' % (vel_ned.n * 1e-3)),
            ('Vel. E', '% 8.4f' % (vel_ned.e * 1e-3)),
            ('Vel. D', '% 8.4f' % (vel_ned.d * 1e-3)),
        ]
        self.table_spp = self.pos_table_spp + self.vel_table + self.dops_table

    def gps_time_callback(self, sbp_msg, **metadata):
        self.week = MsgGPSTime(sbp_msg).wn
        self.nsec = MsgGPSTime(sbp_msg).ns

    def __init__(self, link):
        super(SolutionView, self).__init__()

        self.log_file = None
        self.vel_log_file = None

        self.plot_data = ArrayPlotData(lat=[],
                                       lng=[],
                                       alt=[],
                                       t=[],
                                       cur_lat=[],
                                       cur_lng=[],
                                       cur_lat_ps=[],
                                       cur_lng_ps=[],
                                       lat_ps=[],
                                       lng_ps=[],
                                       alt_ps=[],
                                       t_ps=[])
        self.plot = Plot(self.plot_data)

        # 1000 point buffer
        self.plot.plot(('lng', 'lat'),
                       type='line',
                       name='',
                       color=(0, 0, 0.9, 0.1))
        self.plot.plot(('lng', 'lat'),
                       type='scatter',
                       name='',
                       color='blue',
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_ps', 'lat_ps'),
                       type='line',
                       name='',
                       color=(1, 0.4, 0, 0.1))
        self.plot.plot(('lng_ps', 'lat_ps'),
                       type='scatter',
                       name='',
                       color='orange',
                       marker='diamond',
                       line_width=0.0,
                       marker_size=1.0)
        # current values
        spp = self.plot.plot(('cur_lng', 'cur_lat'),
                             type='scatter',
                             name='SPP',
                             color='blue',
                             marker='plus',
                             line_width=1.5,
                             marker_size=5.0)
        rtk = self.plot.plot(('cur_lng_ps', 'cur_lat_ps'),
                             type='scatter',
                             name='RTK',
                             color='orange',
                             marker='plus',
                             line_width=1.5,
                             marker_size=5.0)
        plot_labels = ['SPP', 'RTK']
        plots_legend = dict(zip(plot_labels, [spp, rtk]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'Longitude (degrees)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'Latitude (degrees)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.link = link
        self.link.add_callback(self._pos_llh_callback, SBP_MSG_POS_LLH)
        self.link.add_callback(self.vel_ned_callback, SBP_MSG_VEL_NED)
        self.link.add_callback(self.dops_callback, SBP_MSG_DOPS)
        self.link.add_callback(self.gps_time_callback, SBP_MSG_GPS_TIME)

        self.week = None
        self.nsec = 0

        self.python_console_cmds = {
            'solution': self,
        }
Exemplo n.º 43
0
class BaselineView(HasTraits):
    python_console_cmds = Dict()

    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)

    running = Bool(True)
    zoomall = Bool(False)

    clear_button = SVGButton(label='',
                             tooltip='Clear',
                             filename=os.path.join(determine_path(), 'images',
                                                   'iconic', 'x.svg'),
                             width=16,
                             height=16)
    zoomall_button = SVGButton(label='',
                               tooltip='Zoom All',
                               toggle=True,
                               filename=os.path.join(determine_path(),
                                                     'images', 'iconic',
                                                     'fullscreen.svg'),
                               width=16,
                               height=16)
    paused_button = SVGButton(label='',
                              tooltip='Pause',
                              toggle_tooltip='Run',
                              toggle=True,
                              filename=os.path.join(determine_path(), 'images',
                                                    'iconic', 'pause.svg'),
                              toggle_filename=os.path.join(
                                  determine_path(), 'images', 'iconic',
                                  'play.svg'),
                              width=16,
                              height=16)

    position_threshold = Str()
    depth_threshold = Str()
    time_threshold = Str()

    focused_dev = Str
    dev_all_list = List(['All', 'Preset'])

    traits_view = View(
        HSplit(
            VGroup(
                HGroup(
                    Item('paused_button', show_label=False),
                    Item('clear_button', show_label=False),
                    Item('zoomall_button', show_label=False),
                    Item('focused_dev',
                         editor=EnumEditor(name='dev_all_list'),
                         label=u'焦点'), Spring(),
                    HGroup(
                        Item('position_threshold',
                             editor=TextEditor(auto_set=False, enter_set=True),
                             label=u'位置阈值'),
                        Item('depth_threshold',
                             editor=TextEditor(),
                             label=u'深度阈值'),
                        Item('time_threshold',
                             editor=TextEditor(),
                             label=u'时间阈值'),
                    )),
                Item(
                    'plot',
                    show_label=False,
                    editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8)),
                ))), )

    def _position_threshold_changed(self):
        try:
            if int(self.position_threshold) < 0 or int(
                    self.position_threshold) > 1e6:
                self.position_threshold = str(0)
        except:
            self.position_threshold = str(0)
        self.settings_yaml.set_threshold_field('position',
                                               int(self.position_threshold))
        self.settings_yaml.dump()

    def _depth_threshold_changed(self):
        try:
            if int(self.depth_threshold) < 0 or int(
                    self.depth_threshold) > self.plot_history_max:
                self.plot_history_max = str(0)
        except:
            self.plot_history_max = str(0)
        self.settings_yaml.set_threshold_field('depth',
                                               int(self.depth_threshold))
        self.settings_yaml.dump()

    def _time_threshold_changed(self):
        try:
            if int(self.time_threshold) < 0:
                self.time_threshold = str(0)
        except:
            self.time_threshold = str(0)
        self.settings_yaml.set_threshold_field('time',
                                               int(self.time_threshold))
        self.settings_yaml.dump()

    def _focused_dev_changed(self):
        self.zoom_once = True

    def _zoomall_button_fired(self):
        self.zoomall = not self.zoomall

    def _paused_button_fired(self):
        self.running = not self.running

    def _clear_button_fired(self):
        self.neds[:] = np.NAN
        self.fixeds[:] = False
        self.devs[:] = 0
        self.times[:] = 0
        self.plot_data.set_data('n_fixed', [])
        self.plot_data.set_data('e_fixed', [])
        self.plot_data.set_data('d_fixed', [])
        self.plot_data.set_data('n_float', [])
        self.plot_data.set_data('e_float', [])
        self.plot_data.set_data('d_float', [])
        self.plot_data.set_data('n_satisfied', [])
        self.plot_data.set_data('e_satisfied', [])
        self.plot_data.set_data('n_focused', [])
        self.plot_data.set_data('e_focused', [])
        self.plot_data.set_data('t', [])

    def _baseline_callback_ned(self, sbp_msg, **metadata):
        # Updating an ArrayPlotData isn't thread safe (see chaco issue #9), so
        # actually perform the update in the UI thread.
        if self.running:
            #GUI.invoke_later(self.baseline_callback, sbp_msg)

            soln = MsgBaselineNED(sbp_msg)
            GUI.invoke_later(self.baseline_callback, soln)

            cnt = self.cnt % 4
            fake_sbp_msg = copy.copy(soln)
            if cnt == 3:
                fake_sbp_msg.e = 217371
                fake_sbp_msg.n = 100837 - (cnt + 1) * 10e3
            else:
                fake_sbp_msg.e = 217371 + cnt * 20e3
                fake_sbp_msg.n = 100837 - cnt * 20e3
            fake_sbp_msg.sender = 100 + cnt
            fake_sbp_msg.flags = cnt
            soln = fake_sbp_msg
            self.cnt += 1
            GUI.invoke_later(self.baseline_callback, soln)

        # _threshold_satisfied()函数计算需要优化
        # 或者保持数据发送频率小于2(/s)
        time.sleep(0.5)

    def baseline_callback(self, sbp_msg):
        #soln = MsgBaselineNED(sbp_msg)
        soln = sbp_msg

        soln.n = soln.n * 1e-3
        soln.e = soln.e * 1e-3
        soln.d = soln.d * 1e-3

        dist = np.sqrt(soln.n**2 + soln.e**2 + soln.d**2)

        tow = soln.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        #row_data = [soln.sender, soln.n, soln.e, soln.d, soln.n_sats, soln.flags, soln.depth]
        row_data = [
            soln.sender, soln.n, soln.e, soln.d, soln.n_sats, soln.flags
        ]
        try:
            key = int(row_data[0])
            self.data_dict[key] = row_data
        except:
            pass
        self.utils.setDataViewTable(self.data_dict)
        if soln.sender not in self.dev_list:
            self.dev_list.append(soln.sender)
            self.dev_all_list.append(str(soln.sender))

        # Rotate array, deleting oldest entries to maintain
        # no more than N in plot
        self.neds[1:] = self.neds[:-1]
        self.fixeds[1:] = self.fixeds[:-1]
        self.devs[1:] = self.devs[:-1]
        self.times[1:] = self.times[:-1]

        # Insert latest position
        self.neds[0][:] = [soln.n, soln.e, soln.d]
        self.fixeds[0] = (soln.flags & 1) == 1
        self.devs[0] = int(soln.sender)
        self.times[0] = int(time.time())

        neds_all = []
        neds_fixed = []
        neds_float = []
        neds_satisfied = []
        neds_unsatisfied = []
        devs = np.unique(self.devs)
        if devs[0] == 0:
            devs = devs[1:]
        for dev in devs:
            is_dev = np.equal(dev, self.devs)
            neds_all.append(self.neds[is_dev][0])
            try:
                neds_fixed.append(self.neds[np.logical_and(
                    is_dev, self.fixeds)][0])
            except:
                pass
            try:
                neds_float.append(self.neds[np.logical_and(
                    is_dev, np.logical_not(self.fixeds))][0])
            except:
                pass
            position_satisfied, depth_satisfied, time_satisfied = self._threshold_satisfied(
            )
            is_satisfied = np.logical_and(position_satisfied, depth_satisfied,
                                          time_satisfied)
            try:
                neds_satisfied.append(self.neds[np.logical_and(
                    is_dev, is_satisfied)][0])
            except:
                pass
            try:
                neds_unsatisfied.append(self.neds[np.logical_and(
                    is_dev, np.logical_not(is_satisfied))][0])
            except:
                pass
        neds_all = np.array(neds_all)
        neds_fixed = np.array(neds_fixed)
        neds_float = np.array(neds_float)
        neds_satisfied = np.array(neds_satisfied)
        neds_unsatisfied = np.array(neds_unsatisfied)
        self.neds_all = neds_all
        self.neds_satisfied = neds_satisfied
        self.neds_unsatisfied = neds_unsatisfied

        neds_focused = np.empty((0, 3))
        if self.focused_dev == '':
            pass
        elif self.focused_dev == 'All':
            neds_focused = neds_all
        elif self.focused_dev != 'Preset':
            neds_focused = np.array(
                [self.neds[np.equal(self.devs, int(self.focused_dev))][0]])

        #if not all(map(any, np.isnan(neds_fixed))):
        if len(neds_fixed) > 0:
            self.plot_data.set_data('n_fixed', neds_fixed.T[0])
            self.plot_data.set_data('e_fixed', neds_fixed.T[1])
            self.plot_data.set_data('d_fixed', neds_fixed.T[2])
        #if not all(map(any, np.isnan(neds_float))):
        if len(neds_float) > 0:
            self.plot_data.set_data('n_float', neds_float.T[0])
            self.plot_data.set_data('e_float', neds_float.T[1])
            self.plot_data.set_data('d_float', neds_float.T[2])
        if len(neds_satisfied) > 0:
            self.plot_data.set_data('n_satisfied', neds_satisfied.T[0])
            self.plot_data.set_data('e_satisfied', neds_satisfied.T[1])
        if len(neds_unsatisfied) > 0:
            self.plot_data.set_data('n_unsatisfied', neds_unsatisfied.T[0])
            self.plot_data.set_data('e_unsatisfied', neds_unsatisfied.T[1])
        if len(self.presets) > 0:
            self.plot_data.set_data('n_preset', self.presets['n'])
            self.plot_data.set_data('e_preset', self.presets['e'])
        if len(neds_focused) > 0:
            self.plot_data.set_data('n_focused', neds_focused.T[0])
            self.plot_data.set_data('e_focused', neds_focused.T[1])

        if self.zoomall:
            self._zoomall()

        if self.zoom_once:
            if self.focused_dev == 'All':
                self._zoomall()
            elif self.focused_dev == 'Preset':
                plot_square_axes(self.plot, 'e_preset', 'n_preset')
            else:
                plot_square_axes(self.plot, 'e_focused', 'n_focused')
            self.zoom_once = False

    # 计算阈值,函数功能待测试
    def _threshold_satisfied(self):
        position_satisfieds = np.zeros(self.plot_history_max, dtype=bool)
        depth_satisfieds = np.ones(self.plot_history_max, dtype=bool)
        time_satisfieds = np.ones(self.plot_history_max, dtype=bool)

        devs = np.unique(self.devs)
        for dev in devs:
            dev_neds = self.neds[np.equal(self.devs, dev)]

            ned_mean = map(np.mean, zip(*dev_neds))
            dn = ned_mean[0] - self.presets['n']
            de = ned_mean[1] - self.presets['e']
            d = np.sqrt(np.add(np.square(dn), np.square(de)))
            dmin = np.min(d)
            if dmin < int(self.position_threshold):
                position_satisfieds[np.equal(dev, self.devs)] = True

        ne_depth = self.neds[0:int(self.depth_threshold)]
        for ned in ne_depth:
            dn = ned[0] - self.presets['n']
            de = ned[1] - self.presets['e']
            d = np.sqrt(np.add(np.square(dn), np.square(de)))
            dmin = np.min(d)
            if dmin > int(self.position_threshold):
                depth_satisfieds[np.equal(dev, self.devs)] = False
                break

        cur_time = time.time()
        for dev, t, ned in zip(self.devs, self.times, self.neds):
            dt = cur_time - t
            if dt > int(self.time_threshold):
                break
            dn = ned[0] - self.presets['n']
            de = ned[1] - self.presets['e']
            d = np.sqrt(np.add(np.square(dn), np.square(de)))
            dmin = np.min(d)
            if dmin > int(self.position_threshold):
                time_satisfieds[np.equal(dev, self.devs)] = False

        return (position_satisfieds, depth_satisfieds, time_satisfieds)

    def _zoomall(self):
        plot_square_axes(self.plot, ('e_fixed', 'e_float', 'e_preset'),
                         ('n_fixed', 'n_float', 'n_preset'))

    def _read_preset_points(self, filename='preset.csv'):
        preset_points = {}
        px = []
        py = []
        try:
            if os.path.isfile(filename):
                path_to_file = filename
            else:
                path_to_file = os.path.join(determine_path(), filename)
            f = open(path_to_file, 'r')
            for i in f.readlines():
                xy = i.split(',')
                if len(xy) < 2:
                    continue
                try:
                    x = float(xy[0]) * 1e-3
                    y = float(xy[1]) * 1e-3
                    px.append(x)
                    py.append(y)
                except:
                    continue
        except:
            pass
        preset_points['e'] = px
        preset_points['n'] = py
        return preset_points

    def set_utils(self, utils):
        self.utils = utils

    def __init__(self, link, plot_history_max=1000):
        super(BaselineView, self).__init__()
        self.plot_data = ArrayPlotData(n_fixed=[0.0],
                                       e_fixed=[0.0],
                                       d_fixed=[0.0],
                                       n_float=[0.0],
                                       e_float=[0.0],
                                       d_float=[0.0],
                                       n_satisfied=[0.0],
                                       e_satisfied=[0.0],
                                       n_unsatisfied=[0.0],
                                       e_unsatisfied=[0.0],
                                       n_focused=[0.0],
                                       e_focused=[0.0],
                                       t=[0.0],
                                       e_preset=[],
                                       n_preset=[])
        self.plot_history_max = plot_history_max

        self.neds = np.empty((plot_history_max, 3))
        self.neds[:] = np.NAN
        self.fixeds = np.zeros(plot_history_max, dtype=bool)
        self.devs = np.zeros(plot_history_max)
        self.times = np.zeros(plot_history_max)

        self.plot = Plot(self.plot_data)
        color_float = (0.5, 0.5, 1.0)
        color_fixed = 'orange'
        color_satisfied = (0.3, 1.0, 0.0)
        pts_float = self.plot.plot(('e_float', 'n_float'),
                                   type='scatter',
                                   color=color_float,
                                   marker='plus',
                                   line_width=2.0,
                                   marker_size=8.0)
        pts_fixed = self.plot.plot(('e_fixed', 'n_fixed'),
                                   type='scatter',
                                   color=color_fixed,
                                   marker='plus',
                                   line_width=2.0,
                                   marker_size=8.0)
        threshold_satisfied = self.plot.plot(('e_satisfied', 'n_satisfied'),
                                             type='scatter',
                                             color=color_satisfied,
                                             marker='dot',
                                             line_width=0.0,
                                             marker_size=4.5)
        threshold_unsatisfied = self.plot.plot(
            ('e_unsatisfied', 'n_unsatisfied'),
            type='scatter',
            color='red',
            marker='dot',
            line_width=0.0,
            marker_size=4.5)
        preset = self.plot.plot(('e_preset', 'n_preset'),
                                type='scatter',
                                color='black',
                                marker='plus',
                                marker_size=1.5,
                                line_width=0.0)
        pts_focused = self.plot.plot(('e_focused', 'n_focused'),
                                     type='scatter',
                                     color='black',
                                     marker='dot',
                                     line_width=0.0,
                                     marker_size=0.0)
        #plot_labels = ['RTK Fixed','RTK Float']
        #plots_legend = dict(zip(plot_labels, [pts_fixed, pts_float]))
        #self.plot.legend.plots = plots_legend
        #self.plot.legend.visible = True
        self.plot.legend.visible = False

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'E (meters)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'N (meters)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.week = None
        self.nsec = 0

        self.link = link
        self.link.add_callback(self._baseline_callback_ned,
                               SBP_MSG_BASELINE_NED)

        self.cnt = 0
        self.dev_list = []
        self.data_dict = {}
        self.presets = self._read_preset_points()

        self.settings_yaml = SettingsList()
        self.position_threshold = str(
            self.settings_yaml.get_threshold_field('position'))
        self.depth_threshold = str(
            self.settings_yaml.get_threshold_field('depth'))
        self.time_threshold = str(
            self.settings_yaml.get_threshold_field('time'))

        self.zoom_once = False

        self.python_console_cmds = {'baseline': self}
Exemplo n.º 44
0
class BaselineView(HasTraits):

    # This mapping should match the flag definitions in libsbp for
    # the MsgBaselineNED message. While this isn't strictly necessary
    # it helps avoid confusion

    python_console_cmds = Dict()

    table = List()

    logging_b = Bool(False)
    directory_name_b = File

    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)

    running = Bool(True)
    zoomall = Bool(False)
    position_centered = Bool(False)

    clear_button = SVGButton(label='',
                             tooltip='Clear',
                             filename=os.path.join(determine_path(), 'images',
                                                   'iconic', 'x.svg'),
                             width=16,
                             height=16)
    zoomall_button = SVGButton(label='',
                               tooltip='Zoom All',
                               toggle=True,
                               filename=os.path.join(determine_path(),
                                                     'images', 'iconic',
                                                     'fullscreen.svg'),
                               width=16,
                               height=16)
    center_button = SVGButton(label='',
                              tooltip='Center on Baseline',
                              toggle=True,
                              filename=os.path.join(determine_path(), 'images',
                                                    'iconic', 'target.svg'),
                              width=16,
                              height=16)
    paused_button = SVGButton(label='',
                              tooltip='Pause',
                              toggle_tooltip='Run',
                              toggle=True,
                              filename=os.path.join(determine_path(), 'images',
                                                    'iconic', 'pause.svg'),
                              toggle_filename=os.path.join(
                                  determine_path(), 'images', 'iconic',
                                  'play.svg'),
                              width=16,
                              height=16)

    reset_button = Button(label='Reset Filters')

    traits_view = View(
        HSplit(
            Item('table',
                 style='readonly',
                 editor=TabularEditor(adapter=SimpleAdapter()),
                 show_label=False,
                 width=0.3),
            VGroup(
                HGroup(
                    Item('paused_button', show_label=False),
                    Item('clear_button', show_label=False),
                    Item('zoomall_button', show_label=False),
                    Item('center_button', show_label=False),
                    Item('reset_button', show_label=False),
                ),
                Item(
                    'plot',
                    show_label=False,
                    editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8)),
                ))))

    def _zoomall_button_fired(self):
        self.zoomall = not self.zoomall

    def _center_button_fired(self):
        self.position_centered = not self.position_centered

    def _paused_button_fired(self):
        self.running = not self.running

    def _reset_button_fired(self):
        self.link(MsgResetFilters(filter=0))

    def _reset_remove_current(self):
        self.plot_data.set_data('cur_fixed_n', [])
        self.plot_data.set_data('cur_fixed_e', [])
        self.plot_data.set_data('cur_fixed_d', [])
        self.plot_data.set_data('cur_float_n', [])
        self.plot_data.set_data('cur_float_e', [])
        self.plot_data.set_data('cur_float_d', [])
        self.plot_data.set_data('cur_dgnss_n', [])
        self.plot_data.set_data('cur_dgnss_e', [])
        self.plot_data.set_data('cur_dgnss_d', [])

    def _clear_history(self):
        self.plot_data.set_data('n_fixed', [])
        self.plot_data.set_data('e_fixed', [])
        self.plot_data.set_data('d_fixed', [])
        self.plot_data.set_data('n_float', [])
        self.plot_data.set_data('e_float', [])
        self.plot_data.set_data('d_float', [])
        self.plot_data.set_data('n_dgnss', [])
        self.plot_data.set_data('e_dgnss', [])
        self.plot_data.set_data('d_dgnss', [])

    def _clear_button_fired(self):
        self.n[:] = np.NAN
        self.e[:] = np.NAN
        self.d[:] = np.NAN
        self.mode[:] = np.NAN
        self.plot_data.set_data('t', [])
        self._clear_history()
        self._reset_remove_current()

    def iar_state_callback(self, sbp_msg, **metadata):
        self.num_hyps = sbp_msg.num_hyps
        self.last_hyp_update = time.time()

    def age_corrections_callback(self, sbp_msg, **metadata):
        age_msg = MsgAgeCorrections(sbp_msg)
        if age_msg.age != 0xFFFF:
            self.age_corrections = age_msg.age / 10.0
        else:
            self.age_corrections = None

    def gps_time_callback(self, sbp_msg, **metadata):
        if sbp_msg.msg_type == SBP_MSG_GPS_TIME_DEP_A:
            time_msg = MsgGPSTimeDepA(sbp_msg)
            flags = 1
        elif sbp_msg.msg_type == SBP_MSG_GPS_TIME:
            time_msg = MsgGPSTime(sbp_msg)
            flags = time_msg.flags
            if flags != 0:
                self.week = time_msg.wn
                self.nsec = time_msg.ns_residual

    def utc_time_callback(self, sbp_msg, **metadata):
        tmsg = MsgUtcTime(sbp_msg)
        seconds = math.floor(tmsg.seconds)
        microseconds = int(tmsg.ns / 1000.00)
        if tmsg.flags & 0x1 == 1:
            dt = datetime.datetime(tmsg.year, tmsg.month, tmsg.day, tmsg.hours,
                                   tmsg.minutes, tmsg.seconds, microseconds)
            self.utc_time = dt
            self.utc_time_flags = tmsg.flags
            if (tmsg.flags >> 3) & 0x3 == 0:
                self.utc_source = "Factory Default"
            elif (tmsg.flags >> 3) & 0x3 == 1:
                self.utc_source = "Non Volatile Memory"
            elif (tmsg.flags >> 3) & 0x3 == 2:
                self.utc_source = "Decoded this Session"
            else:
                self.utc_source = "Unknown"
        else:
            self.utc_time = None
            self.utc_source = None

    def baseline_heading_callback(self, sbp_msg, **metadata):
        headingMsg = MsgBaselineHeading(sbp_msg)
        if headingMsg.flags & 0x7 != 0:
            self.heading = headingMsg.heading * 1e-3
        else:
            self.heading = None

    def baseline_callback(self, sbp_msg, **metadata):
        soln = MsgBaselineNEDDepA(sbp_msg)
        self.last_soln = soln
        table = []

        soln.n = soln.n * 1e-3
        soln.e = soln.e * 1e-3
        soln.d = soln.d * 1e-3
        soln.h_accuracy = soln.h_accuracy * 1e-3
        soln.v_accuracy = soln.v_accuracy * 1e-3

        dist = np.sqrt(soln.n**2 + soln.e**2 + soln.d**2)

        tow = soln.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        ((tloc, secloc), (tgps, secgps)) = log_time_strings(self.week, tow)

        if self.utc_time is not None:
            ((tutc, secutc)) = datetime_2_str(self.utc_time)

        if self.directory_name_b == '':
            filepath = time.strftime("baseline_log_%Y%m%d-%H%M%S.csv")
        else:
            filepath = os.path.join(
                self.directory_name_b,
                time.strftime("baseline_log_%Y%m%d-%H%M%S.csv"))

        if not self.logging_b:
            self.log_file = None

        if self.logging_b:
            if self.log_file is None:
                self.log_file = sopen(filepath, 'w')
                self.log_file.write(
                    'pc_time,gps_time,tow(msec),north(meters),east(meters),down(meters),h_accuracy(meters),v_accuracy(meters),'
                    'distance(meters),num_sats,flags,num_hypothesis\n')
            log_str_gps = ''
            if tgps != '' and secgps != 0:
                log_str_gps = "{0}:{1:06.6f}".format(tgps, float(secgps))
            self.log_file.write(
                '%s,%s,%.3f,%.4f,%.4f,%.4f,%.4f,%.4f,%.4f,%d,%d,%d\n' %
                ("{0}:{1:06.6f}".format(tloc, float(secloc)), log_str_gps, tow,
                 soln.n, soln.e, soln.d, soln.h_accuracy, soln.v_accuracy,
                 dist, soln.n_sats, soln.flags, self.num_hyps))
            self.log_file.flush()

        self.last_mode = get_mode(soln)

        if self.last_mode < 1:
            table.append(('GPS Week', EMPTY_STR))
            table.append(('GPS TOW', EMPTY_STR))
            table.append(('GPS Time', EMPTY_STR))
            table.append(('UTC Time', EMPTY_STR))
            table.append(('UTC Src', EMPTY_STR))
            table.append(('N', EMPTY_STR))
            table.append(('E', EMPTY_STR))
            table.append(('D', EMPTY_STR))
            table.append(('Horiz Acc', EMPTY_STR))
            table.append(('Vert Acc', EMPTY_STR))
            table.append(('Dist.', EMPTY_STR))
            table.append(('Sats Used', EMPTY_STR))
            table.append(('Flags', EMPTY_STR))
            table.append(('Mode', EMPTY_STR))
        else:
            self.last_btime_update = time.time()
            if self.week is not None:
                table.append(('GPS Week', str(self.week)))
            table.append(('GPS TOW', "{:.3f}".format(tow)))

            if self.week is not None:
                table.append(
                    ('GPS Time', "{0}:{1:06.3f}".format(tgps, float(secgps))))
            if self.utc_time is not None:
                table.append(
                    ('UTC Time', "{0}:{1:06.3f}".format(tutc, float(secutc))))
                table.append(('UTC Src', self.utc_source))

            table.append(('N', soln.n))
            table.append(('E', soln.e))
            table.append(('D', soln.d))
            table.append(('Horiz Acc', soln.h_accuracy))
            table.append(('Vert Acc', soln.v_accuracy))
            table.append(('Dist.', "{0:.3f}".format(dist)))

            table.append(('Sats Used', soln.n_sats))

        table.append(('Flags', '0x%02x' % soln.flags))
        table.append(('Mode', mode_dict[self.last_mode]))
        if self.heading is not None:
            table.append(('Heading', self.heading))
        if self.age_corrections is not None:
            table.append(('Corr. Age [s]', self.age_corrections))
        self.table = table
        # Rotate array, deleting oldest entries to maintain
        # no more than N in plot
        self.n[1:] = self.n[:-1]
        self.e[1:] = self.e[:-1]
        self.d[1:] = self.d[:-1]
        self.mode[1:] = self.mode[:-1]

        # Insert latest position
        if self.last_mode > 1:
            self.n[0], self.e[0], self.d[0] = soln.n, soln.e, soln.d
        else:
            self.n[0], self.e[0], self.d[0] = [np.NAN, np.NAN, np.NAN]
        self.mode[0] = self.last_mode

    def solution_draw(self):
        if self.running:
            GUI.invoke_later(self._solution_draw)

    def _solution_draw(self):
        self._clear_history()
        soln = self.last_soln
        if np.any(self.mode):
            float_indexer = (self.mode == FLOAT_MODE)
            fixed_indexer = (self.mode == FIXED_MODE)
            dgnss_indexer = (self.mode == DGNSS_MODE)

            if np.any(fixed_indexer):
                self.plot_data.set_data('n_fixed', self.n[fixed_indexer])
                self.plot_data.set_data('e_fixed', self.e[fixed_indexer])
                self.plot_data.set_data('d_fixed', self.d[fixed_indexer])
            if np.any(float_indexer):
                self.plot_data.set_data('n_float', self.n[float_indexer])
                self.plot_data.set_data('e_float', self.e[float_indexer])
                self.plot_data.set_data('d_float', self.d[float_indexer])
            if np.any(dgnss_indexer):
                self.plot_data.set_data('n_dgnss', self.n[dgnss_indexer])
                self.plot_data.set_data('e_dgnss', self.e[dgnss_indexer])
                self.plot_data.set_data('d_dgnss', self.d[dgnss_indexer])

            # Update our last solution icon
            if self.last_mode == FIXED_MODE:
                self._reset_remove_current()
                self.plot_data.set_data('cur_fixed_n', [soln.n])
                self.plot_data.set_data('cur_fixed_e', [soln.e])
                self.plot_data.set_data('cur_fixed_d', [soln.d])
            elif self.last_mode == FLOAT_MODE:
                self._reset_remove_current()
                self.plot_data.set_data('cur_float_n', [soln.n])
                self.plot_data.set_data('cur_float_e', [soln.e])
                self.plot_data.set_data('cur_float_d', [soln.d])
            elif self.last_mode == DGNSS_MODE:
                self._reset_remove_current()
                self.plot_data.set_data('cur_dgnss_n', [soln.n])
                self.plot_data.set_data('cur_dgnss_e', [soln.e])
                self.plot_data.set_data('cur_dgnss_d', [soln.d])
            else:
                pass
        # make the zoomall win over the position centered button
        # position centered button has no effect when zoom all enabled

        if not self.zoomall and self.position_centered:
            d = (self.plot.index_range.high - self.plot.index_range.low) / 2.
            self.plot.index_range.set_bounds(soln.e - d, soln.e + d)
            d = (self.plot.value_range.high - self.plot.value_range.low) / 2.
            self.plot.value_range.set_bounds(soln.n - d, soln.n + d)

        if self.zoomall:
            plot_square_axes(self.plot, ('e_fixed', 'e_float', 'e_dgnss'),
                             ('n_fixed', 'n_float', 'n_dgnss'))

    def __init__(self, link, plot_history_max=1000, dirname=''):
        super(BaselineView, self).__init__()
        self.log_file = None
        self.directory_name_b = dirname
        self.num_hyps = 0
        self.last_hyp_update = 0
        self.last_btime_update = 0
        self.last_soln = None
        self.last_mode = 0
        self.plot_data = ArrayPlotData(n_fixed=[0.0],
                                       e_fixed=[0.0],
                                       d_fixed=[0.0],
                                       n_float=[0.0],
                                       e_float=[0.0],
                                       d_float=[0.0],
                                       n_dgnss=[0.0],
                                       e_dgnss=[0.0],
                                       d_dgnss=[0.0],
                                       t=[0.0],
                                       ref_n=[0.0],
                                       ref_e=[0.0],
                                       ref_d=[0.0],
                                       cur_fixed_e=[],
                                       cur_fixed_n=[],
                                       cur_fixed_d=[],
                                       cur_float_e=[],
                                       cur_float_n=[],
                                       cur_float_d=[],
                                       cur_dgnss_e=[],
                                       cur_dgnss_n=[],
                                       cur_dgnss_d=[])

        self.plot_history_max = plot_history_max
        self.n = np.zeros(plot_history_max)
        self.e = np.zeros(plot_history_max)
        self.d = np.zeros(plot_history_max)
        self.mode = np.zeros(plot_history_max)

        self.plot = Plot(self.plot_data)
        pts_float = self.plot.plot(('e_float', 'n_float'),
                                   type='scatter',
                                   color=color_dict[FLOAT_MODE],
                                   marker='dot',
                                   line_width=0.0,
                                   marker_size=1.0)
        pts_fixed = self.plot.plot(  # noqa: F841
            ('e_fixed', 'n_fixed'),
            type='scatter',
            color=color_dict[FIXED_MODE],
            marker='dot',
            line_width=0.0,
            marker_size=1.0)
        pts_dgnss = self.plot.plot(  # noqa: F841
            ('e_dgnss', 'n_dgnss'),
            type='scatter',
            color=color_dict[DGNSS_MODE],
            marker='dot',
            line_width=0.0,
            marker_size=1.0)
        ref = self.plot.plot(('ref_e', 'ref_n'),
                             type='scatter',
                             color='red',
                             marker='plus',
                             marker_size=5,
                             line_width=1.5)
        cur_fixed = self.plot.plot(('cur_fixed_e', 'cur_fixed_n'),
                                   type='scatter',
                                   color=color_dict[FIXED_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_float = self.plot.plot(('cur_float_e', 'cur_float_n'),
                                   type='scatter',
                                   color=color_dict[FLOAT_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_dgnss = self.plot.plot(('cur_dgnss_e', 'cur_dgnss_n'),
                                   type='scatter',
                                   color=color_dict[DGNSS_MODE],
                                   marker='plus',
                                   line_width=1.5,
                                   marker_size=5)
        plot_labels = [' Base Position', 'DGPS', 'RTK Float', 'RTK Fixed']
        plots_legend = dict(
            zip(plot_labels, [ref, cur_dgnss, cur_float, cur_fixed]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.labels = plot_labels  # sets order
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'E (meters)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'N (meters)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.week = None
        self.utc_time = None
        self.age_corrections = None
        self.heading = None
        self.nsec = 0

        self.link = link
        self.link.add_callback(
            self.baseline_callback,
            [SBP_MSG_BASELINE_NED, SBP_MSG_BASELINE_NED_DEP_A])
        self.link.add_callback(self.baseline_heading_callback,
                               [SBP_MSG_BASELINE_HEADING])
        self.link.add_callback(self.iar_state_callback, SBP_MSG_IAR_STATE)
        self.link.add_callback(self.gps_time_callback,
                               [SBP_MSG_GPS_TIME, SBP_MSG_GPS_TIME_DEP_A])
        self.link.add_callback(self.utc_time_callback, [SBP_MSG_UTC_TIME])
        self.link.add_callback(self.age_corrections_callback,
                               SBP_MSG_AGE_CORRECTIONS)

        call_repeatedly(0.2, self.solution_draw)

        self.python_console_cmds = {'baseline': self}
Exemplo n.º 45
0
 def _tree_time_plot_default(self):
     plot = Plot(self.plot_data, title="Fractional area covered by trees")
     plot.plot(["time", "tree_history"])
     return plot
Exemplo n.º 46
0
    def __init__(self, link, plot_history_max=1000):
        super(BaselineView, self).__init__()
        self.plot_data = ArrayPlotData(n_fixed=[0.0],
                                       e_fixed=[0.0],
                                       d_fixed=[0.0],
                                       n_float=[0.0],
                                       e_float=[0.0],
                                       d_float=[0.0],
                                       n_satisfied=[0.0],
                                       e_satisfied=[0.0],
                                       n_unsatisfied=[0.0],
                                       e_unsatisfied=[0.0],
                                       n_focused=[0.0],
                                       e_focused=[0.0],
                                       t=[0.0],
                                       e_preset=[],
                                       n_preset=[])
        self.plot_history_max = plot_history_max

        self.neds = np.empty((plot_history_max, 3))
        self.neds[:] = np.NAN
        self.fixeds = np.zeros(plot_history_max, dtype=bool)
        self.devs = np.zeros(plot_history_max)
        self.times = np.zeros(plot_history_max)

        self.plot = Plot(self.plot_data)
        color_float = (0.5, 0.5, 1.0)
        color_fixed = 'orange'
        color_satisfied = (0.3, 1.0, 0.0)
        pts_float = self.plot.plot(('e_float', 'n_float'),
                                   type='scatter',
                                   color=color_float,
                                   marker='plus',
                                   line_width=2.0,
                                   marker_size=8.0)
        pts_fixed = self.plot.plot(('e_fixed', 'n_fixed'),
                                   type='scatter',
                                   color=color_fixed,
                                   marker='plus',
                                   line_width=2.0,
                                   marker_size=8.0)
        threshold_satisfied = self.plot.plot(('e_satisfied', 'n_satisfied'),
                                             type='scatter',
                                             color=color_satisfied,
                                             marker='dot',
                                             line_width=0.0,
                                             marker_size=4.5)
        threshold_unsatisfied = self.plot.plot(
            ('e_unsatisfied', 'n_unsatisfied'),
            type='scatter',
            color='red',
            marker='dot',
            line_width=0.0,
            marker_size=4.5)
        preset = self.plot.plot(('e_preset', 'n_preset'),
                                type='scatter',
                                color='black',
                                marker='plus',
                                marker_size=1.5,
                                line_width=0.0)
        pts_focused = self.plot.plot(('e_focused', 'n_focused'),
                                     type='scatter',
                                     color='black',
                                     marker='dot',
                                     line_width=0.0,
                                     marker_size=0.0)
        #plot_labels = ['RTK Fixed','RTK Float']
        #plots_legend = dict(zip(plot_labels, [pts_fixed, pts_float]))
        #self.plot.legend.plots = plots_legend
        #self.plot.legend.visible = True
        self.plot.legend.visible = False

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'E (meters)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'N (meters)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.week = None
        self.nsec = 0

        self.link = link
        self.link.add_callback(self._baseline_callback_ned,
                               SBP_MSG_BASELINE_NED)

        self.cnt = 0
        self.dev_list = []
        self.data_dict = {}
        self.presets = self._read_preset_points()

        self.settings_yaml = SettingsList()
        self.position_threshold = str(
            self.settings_yaml.get_threshold_field('position'))
        self.depth_threshold = str(
            self.settings_yaml.get_threshold_field('depth'))
        self.time_threshold = str(
            self.settings_yaml.get_threshold_field('time'))

        self.zoom_once = False

        self.python_console_cmds = {'baseline': self}
Exemplo n.º 47
0
 def _forest_plot_default(self):
     plot = Plot(self.plot_data)
     plot.img_plot("forest_image")
     plot.bounds = [0., 2.0]
     return plot
Exemplo n.º 48
0
    def test_process_2d_bounds_cell_plot(self):
        # behavior: _process_2d_bounds accepts all possible ways to set x and y
        # bounds in 2d plots and returns a 1d array with equally spaced
        # intervals between the lower and upper bound of the data. The number
        # of elements in the 1d array must be of one element larger than the
        # shape of the data, because this is cell data.

        height, width = 20, 10
        array_data = np.ones(shape=(height, width))
        plot = Plot()

        # bounds is None : infer from array_data shape
        xs = plot._process_2d_bounds(None, array_data, 1, cell_plot=True)
        self.assertEqual(xs.shape[0], width + 1)
        ys = plot._process_2d_bounds(None, array_data, 0, cell_plot=True)
        self.assertEqual(ys.shape[0], height + 1)

        # bounds is a tuple : it defines lower and upper range
        bounds = (1.0, 100.0)
        xs = plot._process_2d_bounds(bounds, array_data, 1, cell_plot=True)
        self.assertEqual(xs.shape[0], width + 1)
        self.assertEqual(xs[0], bounds[0])
        self.assertEqual(xs[-1], bounds[1])

        # bounds is a 1D array: the first and last elements are used to create
        # equally spaced intervals. Bounds must be of one element larger than the
        # corresponding axis in array_data, or it will raise a Value error
        bounds = np.zeros((height + 1, ))
        bounds[0], bounds[-1] = 0.2, 21.3
        ys = plot._process_2d_bounds(bounds, array_data, 0, cell_plot=True)
        self.assertEqual(ys.shape[0], height + 1)
        self.assertEqual(ys[0], bounds[0])
        self.assertEqual(ys[-1], bounds[-1])
        with assert_raises(ValueError):
            bounds = np.zeros((width // 2, ))
            plot._process_2d_bounds(bounds, array_data, 0, cell_plot=True)

        # bounds is a 2D array: the first and last elements along the appropriate
        # axis are used to create equally spaced intervals.
        # The size of the bounds must be the same as the data array, or this
        # sill raise a ValueError
        xbounds, ybounds = np.meshgrid(np.arange(width + 1),
                                       np.arange(height + 1))

        xs = plot._process_2d_bounds(xbounds, array_data, 1, cell_plot=True)
        self.assertEqual(xs.shape[0], width + 1)
        self.assertEqual(xs[0], xbounds[0, 0])
        self.assertEqual(xs[-1], xbounds[0, -1])
        with assert_raises(ValueError):
            plot._process_2d_bounds(xbounds[:, :5],
                                    array_data,
                                    1,
                                    cell_plot=True)

        ys = plot._process_2d_bounds(ybounds, array_data, 0, cell_plot=True)
        self.assertEqual(ys.shape[0], height + 1)
        self.assertEqual(ys[0], ybounds[0, 0])
        self.assertEqual(ys[-1], ybounds[-1, 0])
        with assert_raises(ValueError):
            plot._process_2d_bounds(ybounds[:5, :],
                                    array_data,
                                    0,
                                    cell_plot=True)
Exemplo n.º 49
0
class BaselineView(HasTraits):

    # This mapping should match the flag definitions in libsbp for
    # the MsgBaselineNED message. While this isn't strictly necessary
    # it helps avoid confusion

    python_console_cmds = Dict()
    last_plot_update_time = Float()
    last_stale_update_time = Float()

    table = List()

    logging_b = Bool(False)
    directory_name_b = File

    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)

    running = Bool(True)
    zoomall = Bool(False)
    position_centered = Bool(False)

    clear_button = SVGButton(
        label='',
        tooltip='Clear',
        filename=resource_filename('console/images/iconic/x.svg'),
        width=16,
        height=16)
    zoomall_button = SVGButton(
        label='',
        tooltip='Zoom All',
        toggle=True,
        filename=resource_filename('console/images/iconic/fullscreen.svg'),
        width=16,
        height=16)
    center_button = SVGButton(
        label='',
        tooltip='Center on Baseline',
        toggle=True,
        filename=resource_filename('console/images/iconic/target.svg'),
        width=16,
        height=16)
    paused_button = SVGButton(
        label='',
        tooltip='Pause',
        toggle_tooltip='Run',
        toggle=True,
        filename=resource_filename('console/images/iconic/pause.svg'),
        toggle_filename=resource_filename('console/images/iconic/play.svg'),
        width=16,
        height=16)

    reset_button = Button(label='Reset Filters')

    traits_view = View(
        HSplit(
            Item('table',
                 style='readonly',
                 editor=TabularEditor(adapter=SimpleAdapter()),
                 show_label=False,
                 width=0.3),
            VGroup(
                HGroup(
                    Item('paused_button', show_label=False),
                    Item('clear_button', show_label=False),
                    Item('zoomall_button', show_label=False),
                    Item('center_button', show_label=False),
                    Item('reset_button', show_label=False),
                ),
                Item(
                    'plot',
                    show_label=False,
                    editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8)),
                ))))

    def _zoomall_button_fired(self):
        self.zoomall = not self.zoomall

    def _center_button_fired(self):
        self.position_centered = not self.position_centered

    def _paused_button_fired(self):
        self.running = not self.running

    def _reset_button_fired(self):
        self.link(MsgResetFilters(filter=0))

    def _get_update_current(self, current_dict={}):
        out_dict = {
            'cur_n_fixed': [],
            'cur_e_fixed': [],
            'cur_d_fixed': [],
            'cur_n_float': [],
            'cur_e_float': [],
            'cur_d_float': [],
            'cur_n_dgnss': [],
            'cur_e_dgnss': [],
            'cur_d_dgnss': []
        }
        out_dict.update(current_dict)
        return out_dict

    def _synchronize_plot_data_by_mode(self,
                                       mode_string,
                                       update_current=False):
        # do all required plot_data updates for a single
        # new solution with mode defined by mode_string
        pending_update = {
            'n_' + mode_string:
            [n for n in self.slns['n_' + mode_string] if not np.isnan(n)],
            'e_' + mode_string:
            [e for e in self.slns['e_' + mode_string] if not np.isnan(e)]
        }
        if update_current:
            current = {}
            if len(pending_update['n_' + mode_string]) != 0:
                current = {
                    'cur_n_' + mode_string:
                    [pending_update['n_' + mode_string][-1]],
                    'cur_e_' + mode_string:
                    [pending_update['e_' + mode_string][-1]]
                }
            else:
                current = {
                    'cur_n_' + mode_string: [],
                    'cur_e_' + mode_string: []
                }
            pending_update.update(self._get_update_current(current))
        self.plot_data.update_data(pending_update)

    def _append_empty_sln_data(self, exclude_mode=None):
        for each_mode in mode_string_dict.values():
            if exclude_mode is None or each_mode != exclude_mode:
                self.slns['n_' + each_mode].append(np.nan)
                self.slns['e_' + each_mode].append(np.nan)

    def _update_sln_data_by_mode(self, soln, mode_string):
        # do backend deque updates for a new solution of type
        # mode string
        self.slns['n_' + mode_string].append(soln.n)
        self.slns['e_' + mode_string].append(soln.e)
        # Rotate old data out by appending to deque
        self._append_empty_sln_data(exclude_mode=mode_string)

    def _clr_sln_data(self):
        for each in self.slns:
            self.slns[each].clear()

    def _reset_remove_current(self):
        self.plot_data.update_data(self._get_update_current())

    def _clear_history(self):
        self._clr_sln_data()
        pending_update = {
            'n_fixed': [],
            'e_fixed': [],
            'd_fixed': [],
            'n_float': [],
            'e_float': [],
            'd_float': [],
            'n_dgnss': [],
            'e_dgnss': [],
            'd_dgnss': []
        }
        pending_update.update(self._get_update_current())
        self.plot_data.update(pending_update)

    def _clear_button_fired(self):
        self._clear_history()

    def age_corrections_callback(self, sbp_msg, **metadata):
        age_msg = MsgAgeCorrections(sbp_msg)
        if age_msg.age != 0xFFFF:
            self.age_corrections = age_msg.age / 10.0
        else:
            self.age_corrections = None
        self.last_age_corr_receipt_time = monotonic()

    def gps_time_callback(self, sbp_msg, **metadata):
        if sbp_msg.msg_type == SBP_MSG_GPS_TIME_DEP_A:
            time_msg = MsgGPSTimeDepA(sbp_msg)
            flags = 1
        elif sbp_msg.msg_type == SBP_MSG_GPS_TIME:
            time_msg = MsgGPSTime(sbp_msg)
            flags = time_msg.flags
            if flags != 0:
                self.week = time_msg.wn
                self.nsec = time_msg.ns_residual

    def utc_time_callback(self, sbp_msg, **metadata):
        tmsg = MsgUtcTime(sbp_msg)
        microseconds = int(tmsg.ns / 1000.00)
        if tmsg.flags & 0x1 == 1:
            dt = datetime.datetime(tmsg.year, tmsg.month, tmsg.day, tmsg.hours,
                                   tmsg.minutes, tmsg.seconds, microseconds)
            self.utc_time = dt
            self.utc_time_flags = tmsg.flags
            if (tmsg.flags >> 3) & 0x3 == 0:
                self.utc_source = "Factory Default"
            elif (tmsg.flags >> 3) & 0x3 == 1:
                self.utc_source = "Non Volatile Memory"
            elif (tmsg.flags >> 3) & 0x3 == 2:
                self.utc_source = "Decoded this Session"
            else:
                self.utc_source = "Unknown"
        else:
            self.utc_time = None
            self.utc_source = None

    def baseline_heading_callback(self, sbp_msg, **metadata):
        headingMsg = MsgBaselineHeading(sbp_msg)
        if headingMsg.flags & 0x7 != 0:
            self.heading = headingMsg.heading * 1e-3
        else:
            self.heading = "---"

    def baseline_callback(self, sbp_msg, **metadata):
        soln = MsgBaselineNEDDepA(sbp_msg)
        table = []

        soln.n = soln.n * 1e-3
        soln.e = soln.e * 1e-3
        soln.d = soln.d * 1e-3
        soln.h_accuracy = soln.h_accuracy * 1e-3
        soln.v_accuracy = soln.v_accuracy * 1e-3

        dist = np.sqrt(soln.n**2 + soln.e**2 + soln.d**2)

        tow = soln.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        ((tloc, secloc), (tgps, secgps)) = log_time_strings(self.week, tow)

        if self.utc_time is not None:
            ((tutc, secutc)) = datetime_2_str(self.utc_time)

        if self.directory_name_b == '':
            filepath = time.strftime("baseline_log_%Y%m%d-%H%M%S.csv")
        else:
            filepath = os.path.join(
                self.directory_name_b,
                time.strftime("baseline_log_%Y%m%d-%H%M%S.csv"))

        if not self.logging_b:
            self.log_file = None

        if self.logging_b:
            if self.log_file is None:
                self.log_file = sopen(filepath, 'w')
                self.log_file.write(
                    'pc_time,gps_time,tow(sec),north(meters),east(meters),down(meters),h_accuracy(meters),v_accuracy(meters),'
                    'distance(meters),num_sats,flags,num_hypothesis\n')
            log_str_gps = ''
            if tgps != '' and secgps != 0:
                log_str_gps = "{0}:{1:06.6f}".format(tgps, float(secgps))
            self.log_file.write(
                '%s,%s,%.3f,%.4f,%.4f,%.4f,%.4f,%.4f,%.4f,%d,%d,%d\n' %
                ("{0}:{1:06.6f}".format(tloc, float(secloc)), log_str_gps, tow,
                 soln.n, soln.e, soln.d, soln.h_accuracy, soln.v_accuracy,
                 dist, soln.n_sats, soln.flags, self.num_hyps))
            self.log_file.flush()

        self.last_mode = get_mode(soln)

        if self.last_mode < 1:
            table.append(('GPS Week', EMPTY_STR))
            table.append(('GPS TOW', EMPTY_STR))
            table.append(('GPS Time', EMPTY_STR))
            table.append(('UTC Time', EMPTY_STR))
            table.append(('UTC Src', EMPTY_STR))
            table.append(('N', EMPTY_STR))
            table.append(('E', EMPTY_STR))
            table.append(('D', EMPTY_STR))
            table.append(('Horiz Acc', EMPTY_STR))
            table.append(('Vert Acc', EMPTY_STR))
            table.append(('Dist.', EMPTY_STR))
            table.append(('Sats Used', EMPTY_STR))
            table.append(('Flags', EMPTY_STR))
            table.append(('Mode', EMPTY_STR))
            table.append(('Heading', EMPTY_STR))
            table.append(('Corr. Age [s]', EMPTY_STR))
        else:
            self.last_btime_update = monotonic()
            if self.week is not None:
                table.append(('GPS Week', str(self.week)))
            table.append(('GPS TOW', "{:.3f}".format(tow)))

            if self.week is not None:
                table.append(
                    ('GPS Time', "{0}:{1:06.3f}".format(tgps, float(secgps))))
            if self.utc_time is not None:
                table.append(
                    ('UTC Time', "{0}:{1:06.3f}".format(tutc, float(secutc))))
                table.append(('UTC Src', self.utc_source))

            table.append(('N', "{:.12g}".format(soln.n)))
            table.append(('E', "{:.12g}".format(soln.e)))
            table.append(('D', "{:.12g}".format(soln.d)))
            table.append(('Horiz Acc', "{:.12g}".format(soln.h_accuracy)))
            table.append(('Vert Acc', "{:.12g}".format(soln.v_accuracy)))
            table.append(('Dist.', "{0:.3f}".format(dist)))

            table.append(('Sats Used', soln.n_sats))

            table.append(('Flags', '0x%02x' % soln.flags))
            table.append(('Mode', mode_dict[self.last_mode]))
            if self.heading is not None:
                table.append(('Heading', self.heading))
            if self.age_corrections is not None:
                table.append(('Corr. Age [s]', self.age_corrections))
            else:
                table.append(('Corr. Age [s]', EMPTY_STR))
        self.table = table

        if self.last_mode != 0:
            self.last_soln = soln
            mode_string = mode_string_dict[self.last_mode]
            if mode_string not in self.pending_draw_modes:
                # if we don't already have a pending upate for that mode
                self.pending_draw_modes.append(mode_string)
            self.list_lock.acquire()
            self._update_sln_data_by_mode(soln, mode_string)
            self.list_lock.release()
        else:
            self.list_lock.acquire()
            self._append_empty_sln_data(soln)
            self.list_lock.release()

        if monotonic() - self.last_plot_update_time > GUI_UPDATE_PERIOD:
            self.update_scheduler.schedule_update('_solution_draw',
                                                  self._solution_draw)

    def _solution_draw(self):
        self.list_lock.acquire()
        current_time = monotonic()
        self.last_plot_update_time = current_time
        pending_draw_modes = self.pending_draw_modes
        current_mode = pending_draw_modes[-1] if len(
            pending_draw_modes) > 0 else None
        # Periodically, we make sure to redraw older data to expire old plot data
        if current_time - self.last_stale_update_time > STALE_DATA_PERIOD:
            # we don't update old solution modes every timestep to try and save CPU
            pending_draw_modes = list(mode_string_dict.values())
            self.last_stale_update_time = current_time
        for mode_string in pending_draw_modes:
            if self.running:
                update_current = mode_string == current_mode if current_mode else True
                self._synchronize_plot_data_by_mode(mode_string,
                                                    update_current)
                if mode_string in self.pending_draw_modes:
                    self.pending_draw_modes.remove(mode_string)

        self.list_lock.release()
        # make the zoomall win over the position centered button
        if not self.zoomall and self.position_centered and self.running and self.last_soln:
            d = (self.plot.index_range.high - self.plot.index_range.low) / 2.
            self.plot.index_range.set_bounds(self.last_soln.e - d,
                                             self.last_soln.e + d)
            d = (self.plot.value_range.high - self.plot.value_range.low) / 2.
            self.plot.value_range.set_bounds(self.last_soln.n - d,
                                             self.last_soln.n + d)

        if self.zoomall:
            plot_square_axes(self.plot, ('e_fixed', 'e_float', 'e_dgnss'),
                             ('n_fixed', 'n_float', 'n_dgnss'))

    def __init__(self, link, plot_history_max=1000, dirname=''):
        super(BaselineView, self).__init__()
        self.pending_draw_modes = []
        self.log_file = None
        self.directory_name_b = dirname
        self.num_hyps = 0
        self.last_hyp_update = 0
        self.last_btime_update = 0
        self.last_age_corections_receipt_time = 0
        self.last_soln = None
        self.last_mode = 0
        self.last_plot_update_time = 0
        self.last_stale_update_time = 0
        self.slns = {
            'n_fixed': deque(maxlen=PLOT_HISTORY_MAX),
            'e_fixed': deque(maxlen=PLOT_HISTORY_MAX),
            'd_fixed': deque(maxlen=PLOT_HISTORY_MAX),
            'n_float': deque(maxlen=PLOT_HISTORY_MAX),
            'e_float': deque(maxlen=PLOT_HISTORY_MAX),
            'd_float': deque(maxlen=PLOT_HISTORY_MAX),
            'n_dgnss': deque(maxlen=PLOT_HISTORY_MAX),
            'e_dgnss': deque(maxlen=PLOT_HISTORY_MAX),
            'd_dgnss': deque(maxlen=PLOT_HISTORY_MAX)
        }
        self.plot_data = ArrayPlotData(n_fixed=[],
                                       e_fixed=[],
                                       n_float=[],
                                       e_float=[],
                                       n_dgnss=[],
                                       e_dgnss=[],
                                       t=[0.0],
                                       ref_n=[0.0],
                                       ref_e=[0.0],
                                       cur_e_fixed=[],
                                       cur_n_fixed=[],
                                       cur_e_float=[],
                                       cur_n_float=[],
                                       cur_e_dgnss=[],
                                       cur_n_dgnss=[])

        self.list_lock = threading.Lock()
        self.plot = Plot(self.plot_data)
        self.plot.plot(('e_float', 'n_float'),
                       type='scatter',
                       color=color_dict[FLOAT_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('e_fixed', 'n_fixed'),
                       type='scatter',
                       color=color_dict[FIXED_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('e_dgnss', 'n_dgnss'),
                       type='scatter',
                       color=color_dict[DGNSS_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        ref = self.plot.plot(('ref_e', 'ref_n'),
                             type='scatter',
                             color='red',
                             marker='plus',
                             marker_size=5,
                             line_width=1.5)
        cur_fixed = self.plot.plot(('cur_e_fixed', 'cur_n_fixed'),
                                   type='scatter',
                                   color=color_dict[FIXED_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_float = self.plot.plot(('cur_e_float', 'cur_n_float'),
                                   type='scatter',
                                   color=color_dict[FLOAT_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_dgnss = self.plot.plot(('cur_e_dgnss', 'cur_n_dgnss'),
                                   type='scatter',
                                   color=color_dict[DGNSS_MODE],
                                   marker='plus',
                                   line_width=1.5,
                                   marker_size=5)
        plot_labels = [' Base Position', 'DGPS', 'RTK Float', 'RTK Fixed']
        plots_legend = dict(
            zip(plot_labels, [ref, cur_dgnss, cur_float, cur_fixed]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.labels = plot_labels  # sets order
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'E (meters)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'N (meters)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.week = None
        self.utc_time = None
        self.age_corrections = None
        self.heading = "---"
        self.nsec = 0

        self.link = link
        self.link.add_callback(
            self.baseline_callback,
            [SBP_MSG_BASELINE_NED, SBP_MSG_BASELINE_NED_DEP_A])
        self.link.add_callback(self.baseline_heading_callback,
                               [SBP_MSG_BASELINE_HEADING])
        self.link.add_callback(self.gps_time_callback,
                               [SBP_MSG_GPS_TIME, SBP_MSG_GPS_TIME_DEP_A])
        self.link.add_callback(self.utc_time_callback, [SBP_MSG_UTC_TIME])
        self.link.add_callback(self.age_corrections_callback,
                               SBP_MSG_AGE_CORRECTIONS)

        self.python_console_cmds = {'baseline': self}
        self.update_scheduler = UpdateScheduler()
Exemplo n.º 50
0
 def _histograms_default(self):
     plot = Plot(self.plot_data)
     plot.plot(["fractions", "density_function"], color="green")
     return plot
Exemplo n.º 51
0
class plot_window(HasTraits):
    _plot_data = Instance(ArrayPlotData)
    _plot = Instance(Plot)
    _click_tool = Instance(clicker_tool)
    _img_plot = Instance(ImagePlot)
    _right_click_avail = 0
    name = Str
    view = View(Item(name='_plot', editor=ComponentEditor(),
                     show_label=False), )

    def __init__(self):
        # -------------- Initialization of plot system ----------------
        padd = 25
        self._plot_data = ArrayPlotData()
        self._x = []
        self._y = []
        self.man_ori = [1, 2, 3, 4]
        self._plot = Plot(self._plot_data, default_origin="top left")
        self._plot.padding_left = padd
        self._plot.padding_right = padd
        self._plot.padding_top = padd
        self._plot.padding_bottom = padd
        self._quiverplots = []

        # -------------------------------------------------------------
    def left_clicked_event(self):
        print("left clicked")
        if len(self._x) < 4:
            self._x.append(self._click_tool.x)
            self._y.append(self._click_tool.y)
        print self._x
        print self._y
        self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
        self._plot.overlays = []
        self.plot_num_overlay(self._x, self._y, self.man_ori)

    def right_clicked_event(self):
        print("right clicked")
        if len(self._x) > 0:
            self._x.pop()
            self._y.pop()
            print self._x
            print self._y
            self.drawcross("coord_x", "coord_y", self._x, self._y, "red", 5)
            self._plot.overlays = []
            self.plot_num_overlay(self._x, self._y, self.man_ori)
        else:
            if (self._right_click_avail):
                print "deleting point"
                self.py_rclick_delete(self._click_tool.x, self._click_tool.y,
                                      self.cameraN)
                x = []
                y = []
                self.py_get_pix_N(x, y, self.cameraN)
                self.drawcross("x", "y", x[0], y[0], "blue", 4)

    def attach_tools(self):
        self._click_tool = clicker_tool(self._img_plot)
        self._click_tool.on_trait_change(self.left_clicked_event,
                                         'left_changed')
        self._click_tool.on_trait_change(self.right_clicked_event,
                                         'right_changed')
        self._img_plot.tools.append(self._click_tool)
        self._zoom_tool = SimpleZoom(component=self._plot,
                                     tool_mode="box",
                                     always_on=False)
        self._zoom_tool.max_zoom_out_factor = 1.0
        self._img_plot.tools.append(self._zoom_tool)
        if self._plot.index_mapper is not None:
            self._plot.index_mapper.on_trait_change(self.handle_mapper,
                                                    'updated',
                                                    remove=False)
        if self._plot.value_mapper is not None:
            self._plot.value_mapper.on_trait_change(self.handle_mapper,
                                                    'updated',
                                                    remove=False)

    def drawcross(self, str_x, str_y, x, y, color1, mrk_size):
        self._plot_data.set_data(str_x, x)
        self._plot_data.set_data(str_y, y)
        self._plot.plot((str_x, str_y),
                        type="scatter",
                        color=color1,
                        marker="plus",
                        marker_size=mrk_size)
        self._plot.request_redraw()

    def drawline(self, str_x, str_y, x1, y1, x2, y2, color1):
        self._plot_data.set_data(str_x, [x1, x2])
        self._plot_data.set_data(str_y, [y1, y2])
        self._plot.plot((str_x, str_y), type="line", color=color1)
        self._plot.request_redraw()

    def drawquiver(self, x1c, y1c, x2c, y2c, color, linewidth=1.0, scale=1.0):
        """ drawquiver draws multiple lines at once on the screen x1,y1->x2,y2 in the current camera window
        parameters:
            x1c - array of x1 coordinates
            y1c - array of y1 coordinates
            x2c - array of x2 coordinates
            y2c - array of y2 coordinates
            color - color of the line
            linewidth - linewidth of the line
        example usage:
            drawquiver ([100,200],[100,100],[400,400],[300,200],'red',linewidth=2.0)
            draws 2 red lines with thickness = 2 :  100,100->400,300 and 200,100->400,200

        """
        x1, y1, x2, y2 = self.remove_short_lines(x1c,
                                                 y1c,
                                                 x2c,
                                                 y2c,
                                                 min_length=0)
        if len(x1) > 0:
            xs = ArrayDataSource(x1)
            ys = ArrayDataSource(y1)

            quiverplot = QuiverPlot(
                index=xs,
                value=ys,
                index_mapper=LinearMapper(range=self._plot.index_mapper.range),
                value_mapper=LinearMapper(range=self._plot.value_mapper.range),
                origin=self._plot.origin,
                arrow_size=0,
                line_color=color,
                line_width=linewidth,
                ep_index=np.array(x2) * scale,
                ep_value=np.array(y2) * scale)
            self._plot.add(quiverplot)
            # we need this to track how many quiverplots are in the current
            # plot
            self._quiverplots.append(quiverplot)
            # import pdb; pdb.set_trace()

    def remove_short_lines(self, x1, y1, x2, y2, min_length=2):
        """ removes short lines from the array of lines
        parameters:
            x1,y1,x2,y2 - start and end coordinates of the lines
        returns:
            x1f,y1f,x2f,y2f - start and end coordinates of the lines, with short lines removed
        example usage:
            x1,y1,x2,y2=remove_short_lines([100,200,300],[100,200,300],[100,200,300],[102,210,320])
            3 input lines, 1 short line will be removed (100,100->100,102)
            returned coordinates:
            x1=[200,300]; y1=[200,300]; x2=[200,300]; y2=[210,320]
        """
        # dx, dy = 2, 2  # minimum allowable dx,dy
        x1f, y1f, x2f, y2f = [], [], [], []
        for i in range(len(x1)):
            if abs(x1[i] - x2[i]) > min_length or abs(y1[i] -
                                                      y2[i]) > min_length:
                x1f.append(x1[i])
                y1f.append(y1[i])
                x2f.append(x2[i])
                y2f.append(y2[i])
        return x1f, y1f, x2f, y2f

    def handle_mapper(self):
        for i in range(0, len(self._plot.overlays)):
            if hasattr(self._plot.overlays[i], 'real_position'):
                coord_x1, coord_y1 = self._plot.map_screen(
                    [self._plot.overlays[i].real_position])[0]
                self._plot.overlays[i].alternate_position = (coord_x1,
                                                             coord_y1)

    def plot_num_overlay(self, x, y, txt):
        for i in range(0, len(x)):
            coord_x, coord_y = self._plot.map_screen([(x[i], y[i])])[0]
            ovlay = TextBoxOverlay(component=self._plot,
                                   text=str(txt[i]),
                                   alternate_position=(coord_x, coord_y),
                                   real_position=(x[i], y[i]),
                                   text_color="white",
                                   border_color="red")
            self._plot.overlays.append(ovlay)

    def update_image(self, image, is_float):
        if is_float:
            self._plot_data.set_data('imagedata', image.astype(np.float))
        else:
            self._plot_data.set_data('imagedata', image.astype(np.byte))
        self._plot.request_redraw()
Exemplo n.º 52
0
 def _fire_time_plot_default(self):
     plot = Plot(self.plot_data, title="Fractional area with fires")
     plot.plot(["time", "fire_history"])
     return plot
Exemplo n.º 53
0
    def __init__(self, link, dirname=''):
        super(SolutionView, self).__init__()

        self.lats = np.zeros(self.plot_history_max)
        self.lngs = np.zeros(self.plot_history_max)
        self.alts = np.zeros(self.plot_history_max)
        self.tows = np.zeros(self.plot_history_max)
        self.modes = np.zeros(self.plot_history_max)
        self.log_file = None
        self.directory_name_v = dirname
        self.directory_name_p = dirname
        self.vel_log_file = None
        self.last_stime_update = 0
        self.last_soln = None

        self.counter = 0
        self.latitude_list = []
        self.longitude_list = []
        self.altitude_list = []
        self.altitude = 0
        self.longitude = 0
        self.latitude = 0
        self.last_pos_mode = 0

        self.plot_data = ArrayPlotData(lat_spp=[],
                                       lng_spp=[],
                                       alt_spp=[],
                                       cur_lat_spp=[],
                                       cur_lng_spp=[],
                                       lat_dgnss=[],
                                       lng_dgnss=[],
                                       alt_dgnss=[],
                                       cur_lat_dgnss=[],
                                       cur_lng_dgnss=[],
                                       lat_float=[],
                                       lng_float=[],
                                       alt_float=[],
                                       cur_lat_float=[],
                                       cur_lng_float=[],
                                       lat_fixed=[],
                                       lng_fixed=[],
                                       alt_fixed=[],
                                       cur_lat_fixed=[],
                                       cur_lng_fixed=[])
        self.plot = Plot(self.plot_data)

        # 1000 point buffer
        self.plot.plot(('lng_spp', 'lat_spp'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[SPP_MODE])
        self.plot.plot(('lng_spp', 'lat_spp'),
                       type='scatter',
                       name='',
                       color=color_dict[SPP_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_dgnss', 'lat_dgnss'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[DGNSS_MODE])
        self.plot.plot(('lng_dgnss', 'lat_dgnss'),
                       type='scatter',
                       name='',
                       color=color_dict[DGNSS_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_float', 'lat_float'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[FLOAT_MODE])
        self.plot.plot(('lng_float', 'lat_float'),
                       type='scatter',
                       name='',
                       color=color_dict[FLOAT_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_fixed', 'lat_fixed'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[FIXED_MODE])
        self.plot.plot(('lng_fixed', 'lat_fixed'),
                       type='scatter',
                       name='',
                       color=color_dict[FIXED_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        # current values
        spp = self.plot.plot(('cur_lng_spp', 'cur_lat_spp'),
                             type='scatter',
                             name=mode_dict[SPP_MODE],
                             color=color_dict[SPP_MODE],
                             marker='plus',
                             line_width=1.5,
                             marker_size=5.0)
        dgnss = self.plot.plot(('cur_lng_dgnss', 'cur_lat_dgnss'),
                               type='scatter',
                               name=mode_dict[DGNSS_MODE],
                               color=color_dict[DGNSS_MODE],
                               marker='plus',
                               line_width=1.5,
                               marker_size=5.0)
        rtkfloat = self.plot.plot(('cur_lng_float', 'cur_lat_float'),
                                  type='scatter',
                                  name=mode_dict[FLOAT_MODE],
                                  color=color_dict[FLOAT_MODE],
                                  marker='plus',
                                  line_width=1.5,
                                  marker_size=5.0)
        rtkfix = self.plot.plot(('cur_lng_fixed', 'cur_lat_fixed'),
                                type='scatter',
                                name=mode_dict[FIXED_MODE],
                                color=color_dict[FIXED_MODE],
                                marker='plus',
                                line_width=1.5,
                                marker_size=5.0)
        plot_labels = ['SPP', 'DGPS', "RTK float", "RTK fixed"]
        plots_legend = dict(zip(plot_labels, [spp, dgnss, rtkfloat, rtkfix]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.labels = plot_labels  # sets order
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'Longitude (degrees)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'Latitude (degrees)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.link = link
        self.link.add_callback(self._pos_llh_callback,
                               [SBP_MSG_POS_LLH_DEP_A, SBP_MSG_POS_LLH])
        self.link.add_callback(self.vel_ned_callback,
                               [SBP_MSG_VEL_NED_DEP_A, SBP_MSG_VEL_NED])
        self.link.add_callback(self.dops_callback,
                               [SBP_MSG_DOPS_DEP_A, SBP_MSG_DOPS])
        self.link.add_callback(self.gps_time_callback,
                               [SBP_MSG_GPS_TIME_DEP_A, SBP_MSG_GPS_TIME])
        self.link.add_callback(self.utc_time_callback, [SBP_MSG_UTC_TIME])
        self.link.add_callback(self.age_corrections_callback,
                               SBP_MSG_AGE_CORRECTIONS)

        self.week = None
        self.utc_time = None
        self.age_corrections = None
        self.nsec = 0

        self.python_console_cmds = {
            'solution': self,
        }
Exemplo n.º 54
0
    def __init__(self, link, plot_history_max=1000, dirname=''):
        super(BaselineView, self).__init__()
        self.pending_draw_modes = []
        self.log_file = None
        self.directory_name_b = dirname
        self.num_hyps = 0
        self.last_hyp_update = 0
        self.last_btime_update = 0
        self.last_age_corections_receipt_time = 0
        self.last_soln = None
        self.last_mode = 0
        self.last_plot_update_time = 0
        self.last_stale_update_time = 0
        self.slns = {
            'n_fixed': deque(maxlen=PLOT_HISTORY_MAX),
            'e_fixed': deque(maxlen=PLOT_HISTORY_MAX),
            'd_fixed': deque(maxlen=PLOT_HISTORY_MAX),
            'n_float': deque(maxlen=PLOT_HISTORY_MAX),
            'e_float': deque(maxlen=PLOT_HISTORY_MAX),
            'd_float': deque(maxlen=PLOT_HISTORY_MAX),
            'n_dgnss': deque(maxlen=PLOT_HISTORY_MAX),
            'e_dgnss': deque(maxlen=PLOT_HISTORY_MAX),
            'd_dgnss': deque(maxlen=PLOT_HISTORY_MAX)
        }
        self.plot_data = ArrayPlotData(n_fixed=[],
                                       e_fixed=[],
                                       n_float=[],
                                       e_float=[],
                                       n_dgnss=[],
                                       e_dgnss=[],
                                       t=[0.0],
                                       ref_n=[0.0],
                                       ref_e=[0.0],
                                       cur_e_fixed=[],
                                       cur_n_fixed=[],
                                       cur_e_float=[],
                                       cur_n_float=[],
                                       cur_e_dgnss=[],
                                       cur_n_dgnss=[])

        self.list_lock = threading.Lock()
        self.plot = Plot(self.plot_data)
        self.plot.plot(('e_float', 'n_float'),
                       type='scatter',
                       color=color_dict[FLOAT_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('e_fixed', 'n_fixed'),
                       type='scatter',
                       color=color_dict[FIXED_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('e_dgnss', 'n_dgnss'),
                       type='scatter',
                       color=color_dict[DGNSS_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        ref = self.plot.plot(('ref_e', 'ref_n'),
                             type='scatter',
                             color='red',
                             marker='plus',
                             marker_size=5,
                             line_width=1.5)
        cur_fixed = self.plot.plot(('cur_e_fixed', 'cur_n_fixed'),
                                   type='scatter',
                                   color=color_dict[FIXED_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_float = self.plot.plot(('cur_e_float', 'cur_n_float'),
                                   type='scatter',
                                   color=color_dict[FLOAT_MODE],
                                   marker='plus',
                                   marker_size=5,
                                   line_width=1.5)
        cur_dgnss = self.plot.plot(('cur_e_dgnss', 'cur_n_dgnss'),
                                   type='scatter',
                                   color=color_dict[DGNSS_MODE],
                                   marker='plus',
                                   line_width=1.5,
                                   marker_size=5)
        plot_labels = [' Base Position', 'DGPS', 'RTK Float', 'RTK Fixed']
        plots_legend = dict(
            zip(plot_labels, [ref, cur_dgnss, cur_float, cur_fixed]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.labels = plot_labels  # sets order
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'E (meters)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'N (meters)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.week = None
        self.utc_time = None
        self.age_corrections = None
        self.heading = "---"
        self.nsec = 0

        self.link = link
        self.link.add_callback(
            self.baseline_callback,
            [SBP_MSG_BASELINE_NED, SBP_MSG_BASELINE_NED_DEP_A])
        self.link.add_callback(self.baseline_heading_callback,
                               [SBP_MSG_BASELINE_HEADING])
        self.link.add_callback(self.gps_time_callback,
                               [SBP_MSG_GPS_TIME, SBP_MSG_GPS_TIME_DEP_A])
        self.link.add_callback(self.utc_time_callback, [SBP_MSG_UTC_TIME])
        self.link.add_callback(self.age_corrections_callback,
                               SBP_MSG_AGE_CORRECTIONS)

        self.python_console_cmds = {'baseline': self}
        self.update_scheduler = UpdateScheduler()
Exemplo n.º 55
0
    def __init__(self, link):
        super(IMUView, self).__init__()

        self.acc = np.empty((NUM_POINTS, 3))
        self.gyro = np.empty((NUM_POINTS, 3))

        self.plot_data = ArrayPlotData(t=np.arange(NUM_POINTS),
                                       acc_x=[0.0],
                                       acc_y=[0.0],
                                       acc_z=[0.0],
                                       gyr_x=[0.0],
                                       gyr_y=[0.0],
                                       gyr_z=[0.0])

        self.plot = Plot(self.plot_data,
                         auto_colors=colours_list,
                         emphasized=True)
        self.plot.title = 'Raw IMU Data'
        self.plot.title_color = [0, 0, 0.43]
        self.ylim = self.plot.value_mapper.range
        self.ylim.low = -32768
        self.ylim.high = 32767
        #self.plot.value_range.bounds_func = lambda l, h, m, tb: (0, h * (1 + m))
        self.plot.value_axis.orientation = 'right'
        self.plot.value_axis.axis_line_visible = False
        self.plot.value_axis.title = 'LSB count'

        self.legend_visible = True
        self.plot.legend.visible = True
        self.plot.legend.align = 'll'
        self.plot.legend.line_spacing = 1
        self.plot.legend.font = 'modern 8'
        self.plot.legend.draw_layer = 'overlay'
        self.plot.legend.tools.append(
            LegendTool(self.plot.legend, drag_button="right"))

        acc_x = self.plot.plot(('t', 'acc_x'),
                               type='line',
                               color='auto',
                               name='Accn. X')
        acc_x = self.plot.plot(('t', 'acc_y'),
                               type='line',
                               color='auto',
                               name='Accn. Y')
        acc_x = self.plot.plot(('t', 'acc_z'),
                               type='line',
                               color='auto',
                               name='Accn. Z')
        acc_x = self.plot.plot(('t', 'gyr_x'),
                               type='line',
                               color='auto',
                               name='Gyro X')
        acc_x = self.plot.plot(('t', 'gyr_y'),
                               type='line',
                               color='auto',
                               name='Gyro Y')
        acc_x = self.plot.plot(('t', 'gyr_z'),
                               type='line',
                               color='auto',
                               name='Gyro Z')

        self.link = link
        self.link.add_callback(self.imu_raw_callback, SBP_MSG_IMU_RAW)
        self.link.add_callback(self.imu_aux_callback, SBP_MSG_IMU_AUX)
        self.python_console_cmds = {'track': self}
Exemplo n.º 56
0
class ImagePlot(Atom):
    # container for all plots
    container = Typed(HPlotContainer)

    # Plot components within this container:
    color_plot = Typed(CMapImagePlot)
    vertical_cross_plot = Typed(Plot)
    horizontal_cross_plot = Typed(Plot)
    colorbar = Typed(ColorBar)

    # plot data
    pd_all = Typed(ArrayPlotData)
    #pd_horiz=Instance(ArrayPlotData)
    #pd_vert=Instance(ArrayPlotData)
    #private data storage
    _imag_index = Typed(GridDataSource)
    _image_value = Typed(ImageData)

    def __init__(self, x, y, z):
        super(ImagePlot, self).__init__()
        self.pd_all = ArrayPlotData(imagedata=z)
        #self.pd_horiz = ArrayPlotData(x=x, horiz=z[4, :])
        #self.pd_vert = ArrayPlotData(y=y, vert=z[:,5])

        self._imag_index = GridDataSource(xdata=x,
                                          ydata=y,
                                          sort_order=("ascending",
                                                      "ascending"))
        index_mapper = GridMapper(range=DataRange2D(self._imag_index))
        self._imag_index.on_trait_change(self._metadata_changed,
                                         "metadata_changed")
        self._image_value = ImageData(data=z, value_depth=1)
        color_mapper = jet(DataRange1D(self._image_value))

        self.color_plot = CMapImagePlot(index=self._imag_index,
                                        index_mapper=index_mapper,
                                        value=self._image_value,
                                        value_mapper=color_mapper,
                                        padding=20,
                                        use_backbuffer=True,
                                        unified_draw=True)

        #Add axes to image plot
        left = PlotAxis(orientation='left',
                        title="Frequency (GHz)",
                        mapper=self.color_plot.index_mapper._ymapper,
                        component=self.color_plot)

        self.color_plot.overlays.append(left)

        bottom = PlotAxis(orientation='bottom',
                          title="Time (us)",
                          mapper=self.color_plot.index_mapper._xmapper,
                          component=self.color_plot)
        self.color_plot.overlays.append(bottom)

        self.color_plot.tools.append(
            PanTool(self.color_plot, constrain_key="shift"))
        self.color_plot.overlays.append(
            ZoomTool(component=self.color_plot,
                     tool_mode="box",
                     always_on=False))

        #Add line inspector tool for horizontal and vertical
        self.color_plot.overlays.append(
            LineInspector(component=self.color_plot,
                          axis='index_x',
                          inspect_mode="indexed",
                          write_metadata=True,
                          is_listener=True,
                          color="white"))

        self.color_plot.overlays.append(
            LineInspector(component=self.color_plot,
                          axis='index_y',
                          inspect_mode="indexed",
                          write_metadata=True,
                          color="white",
                          is_listener=True))

        myrange = DataRange1D(low=amin(z), high=amax(z))
        cmap = jet
        self.colormap = cmap(myrange)

        # Create a colorbar
        cbar_index_mapper = LinearMapper(range=myrange)
        self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
                                 plot=self.color_plot,
                                 padding_top=self.color_plot.padding_top,
                                 padding_bottom=self.color_plot.padding_bottom,
                                 padding_right=40,
                                 resizable='v',
                                 width=30)  #, ytitle="Magvec (mV)")

        #create horizontal line plot
        self.horiz_cross_plot = Plot(self.pd_horiz, resizable="h")
        self.horiz_cross_plot.height = 100
        self.horiz_cross_plot.padding = 20
        self.horiz_cross_plot.plot(("x", "horiz"))  #,
        #line_style="dot")
        #        self.cross_plot.plot(("scatter_index","scatter_value","scatter_color"),
        #                             type="cmap_scatter",
        #                             name="dot",
        #                             color_mapper=self._cmap(image_value_range),
        #                             marker="circle",
        #                             marker_size=8)

        self.horiz_cross_plot.index_range = self.color_plot.index_range.x_range

        #create vertical line plot
        self.vert_cross_plot = Plot(self.pd_vert,
                                    width=140,
                                    orientation="v",
                                    resizable="v",
                                    padding=20,
                                    padding_bottom=160)
        self.vert_cross_plot.plot(("y", "vert"))  #,
        #                             line_style="dot")
        # self.vert_cross_plot.xtitle="Magvec (mV)"
        #       self.vertica_cross_plot.plot(("vertical_scatter_index",
        #                              "vertical_scatter_value",
        #                              "vertical_scatter_color"),
        #                            type="cmap_scatter",
        #                            name="dot",
        #                            color_mapper=self._cmap(image_value_range),
        #                            marker="circle",
        #                           marker_size=8)

        self.vert_cross_plot.index_range = self.color_plot.index_range.y_range

        # Create a container and add components
        self.container = HPlotContainer(padding=40,
                                        fill_padding=True,
                                        bgcolor="white",
                                        use_backbuffer=False)
        inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
        inner_cont.add(self.horiz_cross_plot)
        inner_cont.add(self.color_plot)
        self.container.add(self.colorbar)
        self.container.add(inner_cont)
        self.container.add(self.vert_cross_plot)

    def _metadata_changed(self, old, new):
        """ This function takes out a cross section from the image data, based
        on the line inspector selections, and updates the line and scatter
        plots."""

        #self.cross_plot.value_range.low = self.minz
        #self.cross_plot.value_range.high = self.maxz
        #self.cross_plot2.value_range.low = self.minz
        #self.cross_plot2.value_range.high = self.maxz
        if self._imag_index.metadata.has_key("selections"):
            x_ndx, y_ndx = self._imag_index.metadata["selections"]
            if y_ndx and x_ndx:
                #                xdata, ydata = self._image_index.get_data()
                #                xdata, ydata = xdata.get_data(), ydata.get_data()
                self.pd_horiz.set_data("horiz",
                                       self._image_value.data[y_ndx, :])
                self.pd_vert.set_data("vert", self._image_value.data[:, x_ndx])


#                    scatter_index=array([xdata[x_ndx]]),
#                    scatter_index2=array([ydata[y_ndx]]),
#                    scatter_value=array([self._image_value.data[y_ndx, x_ndx]]),
#                    scatter_value2=array([self._image_value.data[y_ndx, x_ndx]]),
#                    scatter_color=array([self._image_value.data[y_ndx, x_ndx]]),
#                    scatter_color2=array([self._image_value.data[y_ndx, x_ndx]])
#                )
#        else:
#            self.pd.update_data({"scatter_value": array([]),
#                "scatter_value2": array([]), "line_value": array([]),
#                "line_value2": array([])})

#if __name__ == "__main__":

#    filename="/Users/thomasaref/Dropbox/Dad stuff/sample3/digitizer/lt/sample3_digitizer_f_sweep_t_300mk_100nspulse.hdf5"
#
#    with h5py.File(filename, 'r') as f:
#
#        time=f["Traces"]["d - AvgTrace - t"][:]
#        Magvec=f["Traces"]["d - AvgTrace - Magvec"][:]
#        frequency=f["Data"]["Data"][:]
#    #    for name in f["Data"]:
#    #        print name
#
#    time=squeeze(time)
#    Magvec=squeeze(Magvec)
#    frequency=squeeze(frequency)
#
#    x = time[:,0]*1.0e6
#    y = frequency[0,:]/1.0e9
#    z=transpose(Magvec*1000.0)
#
#    ip=ImagePlot(xs,ys,z)
#    ip.configure_traits()

#class Image_Plot(Atom):
#    plot_control=Instance(Plot_Control)
#    xtitle=DelegatesTo('plot_control')
#    ytitle=DelegatesTo('plot_control')
#    ztitle=DelegatesTo('plot_control')
#    request_redraw=DelegatesTo('plot_control')
#    #ykeys=DelegatesTo('plot_control')
#    container = Typed(HPlotContainer)
#    color_plot = Typed(CMapImagePlot)
#    plot=Instance(Plot)
#    vertical_cross_plot = Typed(Plot)
#    horizontal_cross_plot = Typed(Plot)
#    colorbar = Typed(ColorBar)
#    pd_all = Instance(ArrayPlotData)
#    _image_index=Instance(GridDataSource)
#    _image_value=Instance(ImageData)
#    data=Dict()
#
#    pd=Instance(ArrayPlotData)
#
#    traits_view = View(Group(Item('container', editor=ComponentEditor(), show_label=False),
#                             orientation='horizontal'),
#        width=1000, height=700, resizable=True, title="Chaco Plot")
#
#    def _xtitle_changed(self):
#        self.horiz_cross_plot.x_axis.title=self.xtitle
#        self.plot.x_axis.title=self.xtitle
#
#    def _ytitle_changed(self):
#        self.vert_cross_plot.y_axis.title=self.ytitle
#        self.plot.y_axis.title=self.ytitle
#
#    def _request_redraw_fired(self):
#        self.color_plot.request_redraw()
#        self.horiz_cross_plot.request_redraw()
#        self.vert_cross_plot.request_redraw()
#
#    def __init__(self, data, plot_control):
#        super(Image_Plot, self).__init__()
#        self.plot_control=plot_control
#        z=zeros((len(data['y']['0']), len(data['x']['0'])))
#        z[:] = nan
#        for key, item in data['z'].iteritems():
#            z[int(key)]=item
#        x=data['x']['0']
#        y=data['y']['0']
#        self.pd = ArrayPlotData(z=z, x=x, y=y, horiz=z[0, :], vert=z[:, 0])
#        self.plot=Plot(self.pd, padding=50, fill_padding=True,
#                        bgcolor="white", use_backbuffer=True,  unified_draw=True)
#        xgrid, ygrid = meshgrid(x, y)
#
#        color_plot=self.plot.img_plot('z', name="img_plot", xbounds=xgrid, ybounds=ygrid)[0]
#        self._image_index = color_plot.index #GridDataSource(xdata=x, ydata=y, sort_order=("ascending","ascending"))
#        self._image_index.on_trait_change(self._metadata_changed, "metadata_changed")
#        self._image_value=color_plot.value
#        self.value_range=DataRange1D(self._image_value)
#        color_plot.color_mapper = jet(self.value_range)
#        color_plot.tools.append(PanTool(color_plot,
#                                           constrain_key="shift"))
#        color_plot.overlays.append(ZoomTool(component=color_plot,
#                                            tool_mode="box", always_on=False))
#
#        color_plot.overlays.append(LineInspector(component=color_plot,
#                                               axis='index_x',
#                                               inspect_mode="indexed",
#                                               write_metadata=True,
#                                               is_listener=True,
#                                               color="white"))
#
#        color_plot.overlays.append(LineInspector(component=color_plot,
#                                               axis='index_y',
#                                               inspect_mode="indexed",
#                                               write_metadata=True,
#                                               color="white",
#                                               is_listener=True))
#
#        cbar_index_mapper = LinearMapper(range=self.value_range)
#        self.colorbar = ColorBar(index_mapper=cbar_index_mapper,
#                                 plot=color_plot,
#                                 padding_top=color_plot.padding_top,
#                                 padding_bottom=color_plot.padding_bottom,
#                                 padding_right=40,
#                                 resizable='v',
#                                 width=30)#, ytitle="Magvec (mV)")
#
#        #create horizontal line plot
#        self.horiz_cross_plot = Plot(self.pd, resizable="h", height=100, padding=50)
#        self.horiz_cross_plot.plot(("x", "horiz"))#,
#        self.horiz_cross_plot.index_range = color_plot.index_range.x_range
#
#        #create vertical line plot
#        self.vert_cross_plot = Plot(self.pd, width = 100, orientation="v",
#                                resizable="v", padding=50, padding_bottom=250)
#        self.vert_cross_plot.plot(("y", "vert"))
#        self.vert_cross_plot.index_range = color_plot.index_range.y_range
#        #self.vert_cross_plot.x_axis.tick_label_formatter = lambda x: '%.2g'%x
#        self.color_plot=color_plot
#
#        self.container = HPlotContainer(padding=40, fill_padding=True,
#                                        bgcolor = "white", use_backbuffer=False)
#        inner_cont = VPlotContainer(padding=0, use_backbuffer=True)
#        inner_cont.add(self.horiz_cross_plot)
#        inner_cont.add(self.plot)
#        self.container.add(self.colorbar)
#        self.container.add(inner_cont)
#        self.container.add(self.vert_cross_plot)
#        #self.vert_cross_plot.y_axis.title="Frequency"
#        #self.horiz_cross_plot.x_axis.title="Time (us)"
#
#    def _metadata_changed(self, old, new):
#        if self._image_index.metadata.has_key("selections"):
#            x_ndx, y_ndx = self._image_index.metadata["selections"]
#            if y_ndx and x_ndx:
#                self.pd.set_data("horiz", self._image_value.data[y_ndx,:])
#                self.pd.set_data("vert", self._image_value.data[:,x_ndx])
#
#class Line_Plot(HasTraits):
#    plot_control=Instance(Plot_Control)
#    request_redraw=DelegatesTo('plot_control')
#    new_plot=DelegatesTo('plot_control')
#    xtitle=DelegatesTo('plot_control')
#    ytitle=DelegatesTo('plot_control')
#    title=DelegatesTo('plot_control')
#    show_legend=DelegatesTo('plot_control')
#    xyformat=DelegatesTo('plot_control')
#    plot=Instance(Plot)
#    keymap=DelegatesTo('plot_control') #Dict()
#    color_index=Int()
#    mycolors=List([ 'blue', 'red', 'green', 'purple',  'black', 'darkgray', 'cyan', 'magenta', 'orange'])
#    value_scale=DelegatesTo('plot_control')
#    index_scale=DelegatesTo('plot_control')
#    xcomplex=DelegatesTo('plot_control')
#    ycomplex=DelegatesTo('plot_control')
#
#    xkeys=DelegatesTo('plot_control')
#    zkeys=DelegatesTo('plot_control')
#    xindices=DelegatesTo('plot_control')
#    zindices=DelegatesTo('plot_control')
#    pd = Instance(ArrayPlotData)
#
#    def _value_scale_changed(self):
#         #if self.color_index!=0:
#             self.plot.value_scale = self.value_scale
#             self.plot.request_redraw()
#
#    def _index_scale_changed(self):
#         #if self.color_index!=0:
#             self.plot.index_scale = self.index_scale
#             self.plot.request_redraw()
#
#    def _show_legend_changed(self):
#        self.plot.legend.visible = self.show_legend
#        self.plot.request_redraw()
#
#    def _title_changed(self):
#        self.plot.title = self.title
#        self.plot.request_redraw()
#
#    def _xtitle_changed(self):
#        self.plot.x_axis.title=self.xtitle
#        self.plot.request_redraw()
#
#    def _ytitle_changed(self):
#        self.plot.y_axis.title=self.ytitle
#        self.plot.request_redraw()
#
#    def _request_redraw_fired(self):
#        self.plot.request_redraw()
#
#    def _new_plot_fired(self):
#        for key in self.plot.plots.keys():
#            self.remove_plot(key)
#        self.color_index=0
#        for n, name in enumerate(self.zkeys):
#            key='z'+str(name)
#            self.add_plot(key)
#
#    def _zkeys_changed(self,  name, old, new):
#        #print self.pd.list_data()
#        n=0
#        for key in self.pd.list_data():
#            if int(key[1:]) in new:
#                self.add_plot(key)
#                n=n+1
#            else:
#                self.remove_plot(key)
#
##        for key, plot in self.plot.plots.iteritems():
##            if int(key[1:]) in new:
##                if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
##                    color=self.mycolors[mod(n, len(self.mycolors))]
##                else:
##                    color=self.xyformat.t_color
##                plot[0].color=color
##                #plot[0].outline_color=self.xyformat.outline_color,
##                n=n+1
##
##            else:
##               plot[0].color="none"
#               #plot[0].outline_color="none"
#
#    def add_plot(self, key, z, xkey='x0', x=None):
#        if key not in self.plot.plots.keys() and key[0]!='x':
#            if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
#                color=self.mycolors[mod(self.color_index, len(self.mycolors))]
#            else:
#                color=self.xyformat.t_color
#
#            if x!=None:
#                self.pd.set_data(xkey, x)
#            self.pd.set_data(key, z)
#
#            #if self.color_index<len(self.xkeys):
#            #    xkey='x'+str(self.xkeys[self.color_index])
#            #else:
#            #    xkey='x'+str(self.xkeys[0])
#            self.plot.plot((xkey, key),
#                           name=key,
#                           type=self.xyformat.plot_type,
#                           line_width=self.xyformat.line_width,
#                           color=color,
#                           outline_color=self.xyformat.outline_color,
#                           marker = self.xyformat.marker,
#                           marker_size = self.xyformat.marker_size)
#            self.color_index=self.color_index+1
#
#    def remove_plot(self, key):
#        if key in self.plot.plots.keys():
#            self.plot.delplot(key)
#
#    def __init__(self, data, plot_control, *args, **kws):
#        super(Line_Plot, self).__init__(*args, **kws)
#        self.plot_control=plot_control
#        self.pd = ArrayPlotData()
#
#        for name, arr in sorted(data['z'].iteritems()):
#                self.pd.set_data('z'+str(name), arr)
#
#        for name, arr in sorted(data['x'].iteritems()):
#                self.pd.set_data('x'+str(name), arr)
#
#        plot = Plot(self.pd, padding=50, fill_padding=True,
#                        bgcolor="white", use_backbuffer=True)
#
#        # 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)
#        plot.legend.tools.append(LegendTool(plot.legend, drag_button="right"))
#        self.plot=plot
#
#        for n, item in enumerate(self.zkeys):
#                key='z'+str(item)
#                if self.xyformat.t_color=="transparent" or self.xyformat.t_color==(1.0, 1.0, 1.0, 1.0) :
#                    color=self.mycolors[mod(n, len(self.mycolors))]
#                else:
#                    color=self.xyformat.t_color
#                if n<len(self.xkeys):
#                    xkey='x'+str(self.xkeys[n])
#                else:
#                    xkey='x'+str(self.xkeys[0])
#                #n=n+1
#                #self.pd.set_data(key, magphase(self._image_value.data[int(item)], self.ycomplex))
#                self.plot.plot((xkey, key),
#                                name=key,
#                                type=self.xyformat.plot_type,
#                                line_width=self.xyformat.line_width,
#                                color=color,
#                                outline_color=self.xyformat.outline_color,
#                                marker = self.xyformat.marker,
#                                marker_size = self.xyformat.marker_size)
#        self.plot.value_scale = self.value_scale
#        self.plot.index_scale= self.index_scale
#
#
#    traits_view = View(Item('plot', style='custom',editor=ComponentEditor(),
#                             show_label=False),
#                    resizable=True, title="Chaco Plot",
#                    width=800, height=700, #kind='modal',
#                    buttons=[OKButton, CancelButton]
#                    )
Exemplo n.º 57
0
class SpectrumAnalyzerView(HasTraits):
    python_console_cmds = Dict()
    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)
    which_plot = Str("Channel 1")
    hint = Str("Enable with setting in \"System Monitor\" group.")
    traits_view = View(
        Item(
            'plot',
            editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8)),
            show_label=False),
        HGroup(
            Spring(width=20, springy=False),
            Item(
                '',
                label='Channel Selection:',
                emphasized=True),
            Item(
                name='which_plot',
                show_label=False,
                tooltip='Select the RF Channel for which to display Spectrum Analyzer',
                editor=EnumEditor(
                    values=["Channel 1", "Channel 2", "Channel 3", "Channel 4"])),
            Spring(width=20, springy=True),
            Item('hint', show_label=False, style='readonly', style_sheet='*{font-style:italic}'),
            Spring(width=20, springy=True)))

    def parse_payload(self, raw_payload):
        """
        Params
        ======
        payload: is an array of ints representing bytes from the SBP_MSG_USER_DATA message 'contents'

        Returns
        =======
        JSON dict of a payload based on this format, except all N of the diff_amplitude
        are together in a list under 'diff_amplitudes' and rx_time is split into TOW and week

        Frequency is in Hz, Amplitude is in dB

        FIELD               TYPE    OFFSET  SHORT EXPLANATION

        user_msg_tag        u16     0       bespoke preamble for spectrum message

        rx_time             struct  2       struct gps_time_t defined as double TOW + s16 WEEK

        starting_frequency  float  12       starting frequency for this packet

        frequency_step      float  16       frequency step for points in this packet

        min_amplitude       float  20       minimum level of amplitude

        amplitude_step      float  24       amplitude unit

        diff_amplitude      u8     28       N values in the above units
        """
        # Turn the array of ints representing uint8 bytes back to binary, so you can use struct
        # formatting to unpack it. Otherwise you would have to manually parse each byte.
        pack_fmt_str = 'B' * len(raw_payload)
        payload = struct.pack(pack_fmt_str, *raw_payload)
        payload_header_fmt_str = '<Hdhffff'
        payload_header_bytes = struct.calcsize(payload_header_fmt_str)
        diff_amplitude_n = (len(raw_payload) - payload_header_bytes)
        diff_amplitude_fmt_str = 'B' * diff_amplitude_n
        fmt_str = payload_header_fmt_str + diff_amplitude_fmt_str
        parsed_payload = struct.unpack(fmt_str, payload)
        fft_msg_header = [
            'user_msg_tag', 'TOW', 'week', 'starting_frequency',
            'frequency_step', 'min_amplitude', 'amplitude_step'
        ]
        payload_json = dict(
            zip(fft_msg_header, parsed_payload[:len(fft_msg_header)]))
        fft_msg_payload = parsed_payload[len(fft_msg_header):]
        payload_json['diff_amplitudes'] = fft_msg_payload
        return payload_json

    def _which_plot_changed(self):
        channel = int(self.which_plot[-1:])
        self.fftmonitor.enable_channel(channel)
        self.fftmonitor.disable_channel([ch for ch in CHANNELS if ch != channel])

    def spectrum_analyzer_state_callback(self, sbp_msg, **metadata):
        '''
        Params
        ======
        sbp_msg: sbp.msg.SBP object

        Updates the view's data for use in self.update_plot
        '''
        self.fftmonitor.capture_fft(sbp_msg, **metadata)
        channel = int(self.which_plot[-1:])
        if self.fftmonitor.num_ffts(channel) > 0:
            most_recent_fft = self.fftmonitor.get_ffts(channel).pop()
            self.fftmonitor.clear_ffts()
            self.most_recent_complete_data['frequencies'] = most_recent_fft['frequencies']
            self.most_recent_complete_data['amplitudes'] = most_recent_fft['amplitudes']
            self.update_scheduler.schedule_update('update_plot', self.update_plot)

    def update_plot(self):
        most_recent_fft = self.most_recent_complete_data
        if len(most_recent_fft['frequencies']) != 0:
            self.plot_data.set_data('frequency',
                                    most_recent_fft['frequencies'])
            self.plot_data.set_data('amplitude', most_recent_fft['amplitudes'])
            self.plot.value_mapper.range.low = min(
                most_recent_fft['amplitudes'])
            self.plot.value_mapper.range.high = max(
                most_recent_fft['amplitudes'])

    def __init__(self, link):
        super(SpectrumAnalyzerView, self).__init__()
        self.link = link
        self.link.add_callback(self.spectrum_analyzer_state_callback,
                               [SBP_MSG_SPECAN, SBP_MSG_SPECAN_DEP])
        self.python_console_cmds = {'spectrum': self}

        self.fftmonitor = FFTMonitor()
        self.fftmonitor.enable_channel(int(self.which_plot[-1:]))

        self.most_recent_complete_data = {
            'frequencies': np.array([]),
            'amplitudes': np.array([])
        }

        self.plot_data = ArrayPlotData()
        self.plot = Plot(self.plot_data, emphasized=True)

        self.plot.title = 'Spectrum Analyzer'
        self.plot.title_color = [0, 0, 0.43]

        self.plot.value_axis.orientation = 'right'
        self.plot.value_axis.title_spacing = 30
        self.plot.value_axis.title = 'Amplitude (dB)'

        self.plot.index_axis.title = 'Frequency (MHz)'
        self.plot_data.set_data('frequency', [0])
        self.plot_data.set_data('amplitude', [0])
        self.plot.plot(
            ('frequency', 'amplitude'), type='line', name='spectrum')

        self.update_scheduler = UpdateScheduler()
Exemplo n.º 58
0
class SolutionView(HasTraits):
    python_console_cmds = Dict()
    # we need to doubleup on Lists to store the psuedo absolutes separately
    # without rewriting everything
    """
  logging_v : toggle logging for velocity files
  directory_name_v : location and name of velocity files
  logging_p : toggle logging for position files
  directory_name_p : location and name of velocity files
  """
    plot_history_max = Int(1000)
    logging_v = Bool(False)
    directory_name_v = File

    logging_p = Bool(False)
    directory_name_p = File

    lats_psuedo_abs = List()
    lngs_psuedo_abs = List()
    alts_psuedo_abs = List()

    table = List()
    dops_table = List()
    pos_table = List()
    vel_table = List()

    rtk_pos_note = Str(
        "It is necessary to enter the \"Surveyed Position\" settings for the base station in order to view the RTK Positions in this tab."
    )

    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)
    # Store plots we care about for legend

    running = Bool(True)
    zoomall = Bool(False)
    position_centered = Bool(False)

    clear_button = SVGButton(label='',
                             tooltip='Clear',
                             filename=os.path.join(determine_path(), 'images',
                                                   'iconic', 'x.svg'),
                             width=16,
                             height=16)
    zoomall_button = SVGButton(label='',
                               tooltip='Zoom All',
                               toggle=True,
                               filename=os.path.join(determine_path(),
                                                     'images', 'iconic',
                                                     'fullscreen.svg'),
                               width=16,
                               height=16)
    center_button = SVGButton(label='',
                              tooltip='Center on Solution',
                              toggle=True,
                              filename=os.path.join(determine_path(), 'images',
                                                    'iconic', 'target.svg'),
                              width=16,
                              height=16)
    paused_button = SVGButton(label='',
                              tooltip='Pause',
                              toggle_tooltip='Run',
                              toggle=True,
                              filename=os.path.join(determine_path(), 'images',
                                                    'iconic', 'pause.svg'),
                              toggle_filename=os.path.join(
                                  determine_path(), 'images', 'iconic',
                                  'play.svg'),
                              width=16,
                              height=16)

    traits_view = View(
        HSplit(
            VGroup(
                Item('table',
                     style='readonly',
                     editor=TabularEditor(adapter=SimpleAdapter()),
                     show_label=False,
                     width=0.3),
                Item('rtk_pos_note',
                     show_label=False,
                     resizable=True,
                     editor=MultilineTextEditor(TextEditor(multi_line=True)),
                     style='readonly',
                     width=0.3,
                     height=-40),
            ),
            VGroup(
                HGroup(
                    Item('paused_button', show_label=False),
                    Item('clear_button', show_label=False),
                    Item('zoomall_button', show_label=False),
                    Item('center_button', show_label=False),
                ),
                Item('plot',
                     show_label=False,
                     editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8))),
            )))

    def _zoomall_button_fired(self):
        self.zoomall = not self.zoomall

    def _center_button_fired(self):
        self.position_centered = not self.position_centered

    def _paused_button_fired(self):
        self.running = not self.running

    def _reset_remove_current(self):
        self.plot_data.set_data('cur_lat_spp', [])
        self.plot_data.set_data('cur_lng_spp', [])
        self.plot_data.set_data('cur_alt_spp', [])
        self.plot_data.set_data('cur_lat_dgnss', [])
        self.plot_data.set_data('cur_lng_dgnss', [])
        self.plot_data.set_data('cur_alt_dgnss', [])
        self.plot_data.set_data('cur_lat_float', [])
        self.plot_data.set_data('cur_lng_float', [])
        self.plot_data.set_data('cur_alt_float', [])
        self.plot_data.set_data('cur_lat_fixed', [])
        self.plot_data.set_data('cur_lng_fixed', [])
        self.plot_data.set_data('cur_alt_fixed', [])

    def _clear_history(self):
        self.plot_data.set_data('lat_spp', [])
        self.plot_data.set_data('lng_spp', [])
        self.plot_data.set_data('alt_spp', [])
        self.plot_data.set_data('lat_dgnss', [])
        self.plot_data.set_data('lng_dgnss', [])
        self.plot_data.set_data('alt_dgnss', [])
        self.plot_data.set_data('lat_float', [])
        self.plot_data.set_data('lng_float', [])
        self.plot_data.set_data('alt_float', [])
        self.plot_data.set_data('lat_fixed', [])
        self.plot_data.set_data('lng_fixed', [])
        self.plot_data.set_data('alt_fixed', [])

    def _clear_button_fired(self):
        self.tows = np.empty(self.plot_history_max)
        self.lats = np.empty(self.plot_history_max)
        self.lngs = np.empty(self.plot_history_max)
        self.alts = np.empty(self.plot_history_max)
        self.modes = np.empty(self.plot_history_max)
        self._clear_history()
        self._reset_remove_current()

    def _pos_llh_callback(self, sbp_msg, **metadata):
        # Updating an ArrayPlotData isn't thread safe (see chaco issue #9), so
        # actually perform the update in the UI thread.
        if self.running:
            GUI.invoke_later(self.pos_llh_callback, sbp_msg)

    def age_corrections_callback(self, sbp_msg, **metadata):
        age_msg = MsgAgeCorrections(sbp_msg)
        if age_msg.age != 0xFFFF:
            self.age_corrections = age_msg.age / 10.0
        else:
            self.age_corrections = None

    def update_table(self):
        self._table_list = self.table_spp.items()

    def auto_survey(self):
        if self.last_soln.flags != 0:
            self.latitude_list.append(self.last_soln.lat)
            self.longitude_list.append(self.last_soln.lon)
            self.altitude_list.append(self.last_soln.height)
        if len(self.latitude_list) > 1000:
            self.latitude_list = self.latitude_list[-1000:]
            self.longitude_list = self.longitude_list[-1000:]
            self.altitude_list = self.altitude_list[-1000:]
        if len(self.latitude_list) != 0:
            self.latitude = sum(self.latitude_list) / len(self.latitude_list)
            self.altitude = sum(self.altitude_list) / len(self.latitude_list)
            self.longitude = sum(self.longitude_list) / len(self.latitude_list)

    def pos_llh_callback(self, sbp_msg, **metadata):
        if sbp_msg.msg_type == SBP_MSG_POS_LLH_DEP_A:
            soln = MsgPosLLHDepA(sbp_msg)
        else:
            soln = MsgPosLLH(sbp_msg)
        self.last_soln = soln

        self.last_pos_mode = get_mode(soln)
        pos_table = []
        soln.h_accuracy *= 1e-3
        soln.v_accuracy *= 1e-3

        tow = soln.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        # Return the best estimate of my local and receiver time in convenient
        # format that allows changing precision of the seconds
        ((tloc, secloc), (tgps, secgps)) = log_time_strings(self.week, tow)
        if self.utc_time:
            ((tutc, secutc)) = datetime_2_str(self.utc_time)

        if (self.directory_name_p == ''):
            filepath_p = time.strftime("position_log_%Y%m%d-%H%M%S.csv")
        else:
            filepath_p = os.path.join(
                self.directory_name_p,
                time.strftime("position_log_%Y%m%d-%H%M%S.csv"))

        if self.logging_p == False:
            self.log_file = None

        if self.logging_p:
            if self.log_file is None:
                self.log_file = sopen(filepath_p, 'w')
                self.log_file.write(
                    "pc_time,gps_time,tow(msec),latitude(degrees),longitude(degrees),altitude(meters),"
                    "h_accuracy(meters),v_accuracy(meters),n_sats,flags\n")
            log_str_gps = ""
            if tgps != "" and secgps != 0:
                log_str_gps = "{0}:{1:06.6f}".format(tgps, float(secgps))
            self.log_file.write(
                '%s,%s,%.3f,%.10f,%.10f,%.4f,%.4f,%.4f,%d,%d\n' %
                ("{0}:{1:06.6f}".format(tloc, float(secloc)), log_str_gps, tow,
                 soln.lat, soln.lon, soln.height, soln.h_accuracy,
                 soln.v_accuracy, soln.n_sats, soln.flags))
            self.log_file.flush()

        if self.last_pos_mode == 0:
            pos_table.append(('GPS Week', EMPTY_STR))
            pos_table.append(('GPS TOW', EMPTY_STR))
            pos_table.append(('GPS Time', EMPTY_STR))
            pos_table.append(('Num. Signals', EMPTY_STR))
            pos_table.append(('Lat', EMPTY_STR))
            pos_table.append(('Lng', EMPTY_STR))
            pos_table.append(('Height', EMPTY_STR))
            pos_table.append(('Horiz Acc', EMPTY_STR))
            pos_table.append(('Vert Acc', EMPTY_STR))
        else:
            self.last_stime_update = time.time()

            if self.week is not None:
                pos_table.append(('GPS Week', str(self.week)))
            pos_table.append(('GPS TOW', "{:.3f}".format(tow)))

            if self.week is not None:
                pos_table.append(
                    ('GPS Time', "{0}:{1:06.3f}".format(tgps, float(secgps))))
            if self.utc_time is not None:
                pos_table.append(
                    ('UTC Time', "{0}:{1:06.3f}".format(tutc, float(secutc))))
                pos_table.append(('UTC Src', self.utc_source))
            if self.utc_time is None:
                pos_table.append(('UTC Time', EMPTY_STR))
                pos_table.append(('UTC Src', EMPTY_STR))

            pos_table.append(('Sats Used', soln.n_sats))
            pos_table.append(('Lat', soln.lat))
            pos_table.append(('Lng', soln.lon))
            pos_table.append(('Height', soln.height))
            pos_table.append(('Horiz Acc', soln.h_accuracy))
            pos_table.append(('Vert Acc', soln.v_accuracy))

        pos_table.append(('Pos Flags', '0x%03x' % soln.flags))
        pos_table.append(('Pos Fix Mode', mode_dict[self.last_pos_mode]))
        if self.age_corrections != None:
            pos_table.append(('Corr. Age [s]', self.age_corrections))

        self.auto_survey()

        # setup_plot variables
        self.lats[1:] = self.lats[:-1]
        self.lngs[1:] = self.lngs[:-1]
        self.alts[1:] = self.alts[:-1]
        self.tows[1:] = self.tows[:-1]
        self.modes[1:] = self.modes[:-1]

        self.lats[0] = soln.lat
        self.lngs[0] = soln.lon
        self.alts[0] = soln.height
        self.tows[0] = soln.tow
        self.modes[0] = self.last_pos_mode

        self.lats = self.lats[-self.plot_history_max:]
        self.lngs = self.lngs[-self.plot_history_max:]
        self.alts = self.alts[-self.plot_history_max:]
        self.tows = self.tows[-self.plot_history_max:]
        self.modes = self.modes[-self.plot_history_max:]

        # SPP
        spp_indexer, dgnss_indexer, float_indexer, fixed_indexer = None, None, None, None
        self._clear_history()
        if np.any(self.modes):
            spp_indexer = (self.modes == SPP_MODE)
            dgnss_indexer = (self.modes == DGNSS_MODE)
            float_indexer = (self.modes == FLOAT_MODE)
            fixed_indexer = (self.modes == FIXED_MODE)

            # make sure that there is at least one true in indexer before setting
            if any(spp_indexer):
                self.plot_data.set_data('lat_spp', self.lats[spp_indexer])
                self.plot_data.set_data('lng_spp', self.lngs[spp_indexer])
                self.plot_data.set_data('alt_spp', self.alts[spp_indexer])
            if any(dgnss_indexer):
                self.plot_data.set_data('lat_dgnss', self.lats[dgnss_indexer])
                self.plot_data.set_data('lng_dgnss', self.lngs[dgnss_indexer])
                self.plot_data.set_data('alt_dgnss', self.alts[dgnss_indexer])
            if any(float_indexer):
                self.plot_data.set_data('lat_float', self.lats[float_indexer])
                self.plot_data.set_data('lng_float', self.lngs[float_indexer])
                self.plot_data.set_data('alt_float', self.alts[float_indexer])
            if any(fixed_indexer):
                self.plot_data.set_data('lat_fixed', self.lats[fixed_indexer])
                self.plot_data.set_data('lng_fixed', self.lngs[fixed_indexer])
                self.plot_data.set_data('alt_fixed', self.alts[fixed_indexer])

            # update our "current solution" icon
            if self.last_pos_mode == SPP_MODE:
                self._reset_remove_current()
                self.plot_data.set_data('cur_lat_spp', [soln.lat])
                self.plot_data.set_data('cur_lng_spp', [soln.lon])
            elif self.last_pos_mode == DGNSS_MODE:
                self._reset_remove_current()
                self.plot_data.set_data('cur_lat_dgnss', [soln.lat])
                self.plot_data.set_data('cur_lng_dgnss', [soln.lon])
            elif self.last_pos_mode == FLOAT_MODE:
                self._reset_remove_current()
                self.plot_data.set_data('cur_lat_float', [soln.lat])
                self.plot_data.set_data('cur_lng_float', [soln.lon])
            elif self.last_pos_mode == FIXED_MODE:
                self._reset_remove_current()
                self.plot_data.set_data('cur_lat_fixed', [soln.lat])
                self.plot_data.set_data('cur_lng_fixed', [soln.lon])
            else:
                pass

        # set-up table variables
        self.pos_table = pos_table
        self.table = self.pos_table + self.vel_table + self.dops_table

        # TODO: figure out how to center the graph now that we have two separate messages
        # when we selectively send only SPP, the centering function won't work anymore

        if not self.zoomall and self.position_centered:
            d = (self.plot.index_range.high - self.plot.index_range.low) / 2.
            self.plot.index_range.set_bounds(soln.lon - d, soln.lon + d)
            d = (self.plot.value_range.high - self.plot.value_range.low) / 2.
            self.plot.value_range.set_bounds(soln.lat - d, soln.lat + d)
        if self.zoomall:
            plot_square_axes(
                self.plot, ('lng_spp', 'lng_dgnss', 'lng_float', 'lng_fixed'),
                ('lat_spp', 'lat_dgnss', 'lat_float', 'lat_fixed'))

    def dops_callback(self, sbp_msg, **metadata):
        flags = 0
        if sbp_msg.msg_type == SBP_MSG_DOPS_DEP_A:
            dops = MsgDopsDepA(sbp_msg)
            flags = 1
        else:
            dops = MsgDops(sbp_msg)
            flags = dops.flags
        if flags != 0:
            self.dops_table = [('PDOP', '%.1f' % (dops.pdop * 0.01)),
                               ('GDOP', '%.1f' % (dops.gdop * 0.01)),
                               ('TDOP', '%.1f' % (dops.tdop * 0.01)),
                               ('HDOP', '%.1f' % (dops.hdop * 0.01)),
                               ('VDOP', '%.1f' % (dops.vdop * 0.01))]
        else:
            self.dops_table = [('PDOP', EMPTY_STR), ('GDOP', EMPTY_STR),
                               ('TDOP', EMPTY_STR), ('HDOP', EMPTY_STR),
                               ('VDOP', EMPTY_STR)]

        self.dops_table.append(('DOPS Flags', '0x%03x' % flags))
        self.table = self.pos_table + self.vel_table + self.dops_table

    def vel_ned_callback(self, sbp_msg, **metadata):
        flags = 0
        if sbp_msg.msg_type == SBP_MSG_VEL_NED_DEP_A:
            vel_ned = MsgVelNEDDepA(sbp_msg)
            flags = 1
        else:
            vel_ned = MsgVelNED(sbp_msg)
            flags = vel_ned.flags
        tow = vel_ned.tow * 1e-3
        if self.nsec is not None:
            tow += self.nsec * 1e-9

        ((tloc, secloc), (tgps, secgps)) = log_time_strings(self.week, tow)

        if self.directory_name_v == '':
            filepath_v = time.strftime("velocity_log_%Y%m%d-%H%M%S.csv")
        else:
            filepath_v = os.path.join(
                self.directory_name_v,
                time.strftime("velocity_log_%Y%m%d-%H%M%S.csv"))

        if self.logging_v == False:
            self.vel_log_file = None

        if self.logging_v:
            if self.vel_log_file is None:
                self.vel_log_file = sopen(filepath_v, 'w')
                self.vel_log_file.write(
                    'pc_time,gps_time,tow,north(m/s),east(m/s),down(m/s),speed(m/s),flags,num_signals\n'
                )
            log_str_gps = ''
            if tgps != "" and secgps != 0:
                log_str_gps = "{0}:{1:06.6f}".format(tgps, float(secgps))
            self.vel_log_file.write(
                '%s,%s,%.3f,%.6f,%.6f,%.6f,%.6f,%d,%d\n' %
                ("{0}:{1:06.6f}".format(tloc, float(secloc)), log_str_gps, tow,
                 vel_ned.n * 1e-3, vel_ned.e * 1e-3, vel_ned.d * 1e-3,
                 math.sqrt(vel_ned.n * vel_ned.n + vel_ned.e * vel_ned.e) *
                 1e-3, flags, vel_ned.n_sats))
            self.vel_log_file.flush()
        if flags != 0:
            self.vel_table = [
                ('Vel. N', '% 8.4f' % (vel_ned.n * 1e-3)),
                ('Vel. E', '% 8.4f' % (vel_ned.e * 1e-3)),
                ('Vel. D', '% 8.4f' % (vel_ned.d * 1e-3)),
            ]
        else:
            self.vel_table = [
                ('Vel. N', EMPTY_STR),
                ('Vel. E', EMPTY_STR),
                ('Vel. D', EMPTY_STR),
            ]
        self.vel_table.append(('Vel Flags', '0x%03x' % flags))
        self.table = self.pos_table + self.vel_table + self.dops_table

    def gps_time_callback(self, sbp_msg, **metadata):
        if sbp_msg.msg_type == SBP_MSG_GPS_TIME_DEP_A:
            time_msg = MsgGPSTimeDepA(sbp_msg)
            flags = 1
        elif sbp_msg.msg_type == SBP_MSG_GPS_TIME:
            time_msg = MsgGPSTime(sbp_msg)
            flags = time_msg.flags
            if flags != 0:
                self.week = time_msg.wn
                self.nsec = time_msg.ns_residual

    def utc_time_callback(self, sbp_msg, **metadata):
        tmsg = MsgUtcTime(sbp_msg)
        seconds = math.floor(tmsg.seconds)
        microseconds = int(tmsg.ns / 1000.00)
        if tmsg.flags & 0x1 == 1:
            dt = datetime.datetime(tmsg.year, tmsg.month, tmsg.day, tmsg.hours,
                                   tmsg.minutes, tmsg.seconds, microseconds)
            self.utc_time = dt
            self.utc_time_flags = tmsg.flags
            if (tmsg.flags >> 3) & 0x3 == 0:
                self.utc_source = "Factory Default"
            elif (tmsg.flags >> 3) & 0x3 == 1:
                self.utc_source = "Non Volatile Memory"
            elif (tmsg.flags >> 3) & 0x3 == 2:
                self.utc_source = "Decoded this Session"
            else:
                self.utc_source = "Unknown"
        else:
            self.utc_time = None
            self.utc_source = None

    def __init__(self, link, dirname=''):
        super(SolutionView, self).__init__()

        self.lats = np.zeros(self.plot_history_max)
        self.lngs = np.zeros(self.plot_history_max)
        self.alts = np.zeros(self.plot_history_max)
        self.tows = np.zeros(self.plot_history_max)
        self.modes = np.zeros(self.plot_history_max)
        self.log_file = None
        self.directory_name_v = dirname
        self.directory_name_p = dirname
        self.vel_log_file = None
        self.last_stime_update = 0
        self.last_soln = None

        self.counter = 0
        self.latitude_list = []
        self.longitude_list = []
        self.altitude_list = []
        self.altitude = 0
        self.longitude = 0
        self.latitude = 0
        self.last_pos_mode = 0

        self.plot_data = ArrayPlotData(lat_spp=[],
                                       lng_spp=[],
                                       alt_spp=[],
                                       cur_lat_spp=[],
                                       cur_lng_spp=[],
                                       lat_dgnss=[],
                                       lng_dgnss=[],
                                       alt_dgnss=[],
                                       cur_lat_dgnss=[],
                                       cur_lng_dgnss=[],
                                       lat_float=[],
                                       lng_float=[],
                                       alt_float=[],
                                       cur_lat_float=[],
                                       cur_lng_float=[],
                                       lat_fixed=[],
                                       lng_fixed=[],
                                       alt_fixed=[],
                                       cur_lat_fixed=[],
                                       cur_lng_fixed=[])
        self.plot = Plot(self.plot_data)

        # 1000 point buffer
        self.plot.plot(('lng_spp', 'lat_spp'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[SPP_MODE])
        self.plot.plot(('lng_spp', 'lat_spp'),
                       type='scatter',
                       name='',
                       color=color_dict[SPP_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_dgnss', 'lat_dgnss'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[DGNSS_MODE])
        self.plot.plot(('lng_dgnss', 'lat_dgnss'),
                       type='scatter',
                       name='',
                       color=color_dict[DGNSS_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_float', 'lat_float'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[FLOAT_MODE])
        self.plot.plot(('lng_float', 'lat_float'),
                       type='scatter',
                       name='',
                       color=color_dict[FLOAT_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        self.plot.plot(('lng_fixed', 'lat_fixed'),
                       type='line',
                       line_width=0.1,
                       name='',
                       color=color_dict[FIXED_MODE])
        self.plot.plot(('lng_fixed', 'lat_fixed'),
                       type='scatter',
                       name='',
                       color=color_dict[FIXED_MODE],
                       marker='dot',
                       line_width=0.0,
                       marker_size=1.0)
        # current values
        spp = self.plot.plot(('cur_lng_spp', 'cur_lat_spp'),
                             type='scatter',
                             name=mode_dict[SPP_MODE],
                             color=color_dict[SPP_MODE],
                             marker='plus',
                             line_width=1.5,
                             marker_size=5.0)
        dgnss = self.plot.plot(('cur_lng_dgnss', 'cur_lat_dgnss'),
                               type='scatter',
                               name=mode_dict[DGNSS_MODE],
                               color=color_dict[DGNSS_MODE],
                               marker='plus',
                               line_width=1.5,
                               marker_size=5.0)
        rtkfloat = self.plot.plot(('cur_lng_float', 'cur_lat_float'),
                                  type='scatter',
                                  name=mode_dict[FLOAT_MODE],
                                  color=color_dict[FLOAT_MODE],
                                  marker='plus',
                                  line_width=1.5,
                                  marker_size=5.0)
        rtkfix = self.plot.plot(('cur_lng_fixed', 'cur_lat_fixed'),
                                type='scatter',
                                name=mode_dict[FIXED_MODE],
                                color=color_dict[FIXED_MODE],
                                marker='plus',
                                line_width=1.5,
                                marker_size=5.0)
        plot_labels = ['SPP', 'DGPS', "RTK float", "RTK fixed"]
        plots_legend = dict(zip(plot_labels, [spp, dgnss, rtkfloat, rtkfix]))
        self.plot.legend.plots = plots_legend
        self.plot.legend.labels = plot_labels  # sets order
        self.plot.legend.visible = True

        self.plot.index_axis.tick_label_position = 'inside'
        self.plot.index_axis.tick_label_color = 'gray'
        self.plot.index_axis.tick_color = 'gray'
        self.plot.index_axis.title = 'Longitude (degrees)'
        self.plot.index_axis.title_spacing = 5
        self.plot.value_axis.tick_label_position = 'inside'
        self.plot.value_axis.tick_label_color = 'gray'
        self.plot.value_axis.tick_color = 'gray'
        self.plot.value_axis.title = 'Latitude (degrees)'
        self.plot.value_axis.title_spacing = 5
        self.plot.padding = (25, 25, 25, 25)

        self.plot.tools.append(PanTool(self.plot))
        zt = ZoomTool(self.plot,
                      zoom_factor=1.1,
                      tool_mode="box",
                      always_on=False)
        self.plot.overlays.append(zt)

        self.link = link
        self.link.add_callback(self._pos_llh_callback,
                               [SBP_MSG_POS_LLH_DEP_A, SBP_MSG_POS_LLH])
        self.link.add_callback(self.vel_ned_callback,
                               [SBP_MSG_VEL_NED_DEP_A, SBP_MSG_VEL_NED])
        self.link.add_callback(self.dops_callback,
                               [SBP_MSG_DOPS_DEP_A, SBP_MSG_DOPS])
        self.link.add_callback(self.gps_time_callback,
                               [SBP_MSG_GPS_TIME_DEP_A, SBP_MSG_GPS_TIME])
        self.link.add_callback(self.utc_time_callback, [SBP_MSG_UTC_TIME])
        self.link.add_callback(self.age_corrections_callback,
                               SBP_MSG_AGE_CORRECTIONS)

        self.week = None
        self.utc_time = None
        self.age_corrections = None
        self.nsec = 0

        self.python_console_cmds = {
            'solution': self,
        }
Exemplo n.º 59
0
class IMUView(HasTraits):
    python_console_cmds = Dict()
    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)
    imu_temp = Float(0)
    imu_conf = Int(0)
    rms_acc_x = Float(0)
    rms_acc_y = Float(0)
    rms_acc_z = Float(0)

    traits_view = View(
        VGroup(
            Item('plot',
                 editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8)),
                 show_label=False),
            HGroup(
                Item('imu_temp', format_str='%.2f C', height=-16, width=4),
                Item('imu_conf', format_str='0x%02X', height=-16, width=4),
                Item('rms_acc_x', format_str='%.2f g', height=-16, width=4),
                Item('rms_acc_y', format_str='%.2f g', height=-16, width=4),
                Item('rms_acc_z', format_str='%.2f g', height=-16, width=4),
            ),
        ))

    def update_plot(self):
        self.last_plot_update_time = monotonic()
        self.plot_data.set_data('acc_x', self.acc_x)
        self.plot_data.set_data('acc_y', self.acc_y)
        self.plot_data.set_data('acc_z', self.acc_z)
        self.plot_data.set_data('gyr_x', self.gyro_x)
        self.plot_data.set_data('gyr_y', self.gyro_y)
        self.plot_data.set_data('gyr_z', self.gyro_z)

    def imu_set_data(self):
        if monotonic() - self.last_plot_update_time < GUI_UPDATE_PERIOD:
            return
        if self.imu_conf is not None:
            acc_range = self.imu_conf & 0xF
            sf = 2.**(acc_range + 1) / 2.**15
            self.rms_acc_x = sf * np.sqrt(np.mean(np.square(self.acc_x)))
            self.rms_acc_y = sf * np.sqrt(np.mean(np.square(self.acc_y)))
            self.rms_acc_z = sf * np.sqrt(np.mean(np.square(self.acc_z)))
        self.update_scheduler.schedule_update('update_plot', self.update_plot)

    def imu_aux_callback(self, sbp_msg, **metadata):
        if sbp_msg.imu_type == 0:
            self.imu_temp = 23 + sbp_msg.temp / 2.**9
            self.imu_conf = sbp_msg.imu_conf
        elif sbp_msg.imu_type == 1:
            self.imu_temp = 25.0 + sbp_msg.temp / 256.0
            self.imu_conf = sbp_msg.imu_conf
        else:
            print("IMU type %d not known" % sbp_msg.imu_type)

    def imu_raw_callback(self, sbp_msg, **metadata):
        memoryview(self.acc_x)[:-1] = memoryview(self.acc_x)[1:]
        memoryview(self.acc_y)[:-1] = memoryview(self.acc_y)[1:]
        memoryview(self.acc_z)[:-1] = memoryview(self.acc_z)[1:]
        memoryview(self.gyro_x)[:-1] = memoryview(self.gyro_x)[1:]
        memoryview(self.gyro_y)[:-1] = memoryview(self.gyro_y)[1:]
        memoryview(self.gyro_z)[:-1] = memoryview(self.gyro_z)[1:]
        self.acc_x[-1] = sbp_msg.acc_x
        self.acc_y[-1] = sbp_msg.acc_y
        self.acc_z[-1] = sbp_msg.acc_z
        self.gyro_x[-1] = sbp_msg.gyr_x
        self.gyro_y[-1] = sbp_msg.gyr_y
        self.gyro_z[-1] = sbp_msg.gyr_z
        self.imu_set_data()

    def __init__(self, link):
        super(IMUView, self).__init__()

        self.acc_x = np.zeros(NUM_POINTS)
        self.acc_y = np.zeros(NUM_POINTS)
        self.acc_z = np.zeros(NUM_POINTS)
        self.gyro_x = np.zeros(NUM_POINTS)
        self.gyro_y = np.zeros(NUM_POINTS)
        self.gyro_z = np.zeros(NUM_POINTS)

        self.last_plot_update_time = 0

        self.plot_data = ArrayPlotData(t=np.arange(NUM_POINTS),
                                       acc_x=[0.0],
                                       acc_y=[0.0],
                                       acc_z=[0.0],
                                       gyr_x=[0.0],
                                       gyr_y=[0.0],
                                       gyr_z=[0.0])

        self.plot = Plot(self.plot_data,
                         auto_colors=colours_list,
                         emphasized=True)
        self.plot.title = 'Raw IMU Data'
        self.plot.title_color = [0, 0, 0.43]
        self.ylim = self.plot.value_mapper.range
        self.ylim.low = -32768
        self.ylim.high = 32767
        # self.plot.value_range.bounds_func = lambda l, h, m, tb: (0, h * (1 + m))
        self.plot.value_axis.orientation = 'right'
        self.plot.value_axis.axis_line_visible = False
        self.plot.value_axis.title = 'LSB count'

        self.legend_visible = True
        self.plot.legend.visible = True
        self.plot.legend.align = 'll'
        self.plot.legend.line_spacing = 1
        self.plot.legend.font = 'modern 8'
        self.plot.legend.draw_layer = 'overlay'
        self.plot.legend.tools.append(
            LegendTool(self.plot.legend, drag_button="right"))

        acc_x = self.plot.plot(('t', 'acc_x'),
                               type='line',
                               color='auto',
                               name='Accn. X')
        acc_x = self.plot.plot(('t', 'acc_y'),
                               type='line',
                               color='auto',
                               name='Accn. Y')
        acc_x = self.plot.plot(('t', 'acc_z'),
                               type='line',
                               color='auto',
                               name='Accn. Z')
        acc_x = self.plot.plot(('t', 'gyr_x'),
                               type='line',
                               color='auto',
                               name='Gyro X')
        acc_x = self.plot.plot(('t', 'gyr_y'),
                               type='line',
                               color='auto',
                               name='Gyro Y')
        acc_x = self.plot.plot(('t', 'gyr_z'),
                               type='line',
                               color='auto',
                               name='Gyro Z')

        self.link = link
        self.link.add_callback(self.imu_raw_callback, SBP_MSG_IMU_RAW)
        self.link.add_callback(self.imu_aux_callback, SBP_MSG_IMU_AUX)

        self.python_console_cmds = {'track': self}

        self.update_scheduler = UpdateScheduler()
Exemplo n.º 60
0
class IMUView(HasTraits):
    python_console_cmds = Dict()
    plot = Instance(Plot)
    plot_data = Instance(ArrayPlotData)
    imu_temp = Float(0)
    imu_conf = Int(0)
    rms_acc_x = Float(0)
    rms_acc_y = Float(0)
    rms_acc_z = Float(0)

    traits_view = View(
        VGroup(
            Item(
                'plot',
                editor=ComponentEditor(bgcolor=(0.8, 0.8, 0.8)),
                show_label=False,
            ),
            HGroup(
                Item('imu_temp', format_str='%.2f C'),
                Item('imu_conf', format_str='0x%02X'),
                Item('rms_acc_x', format_str='%.2f g'),
                Item('rms_acc_y', format_str='%.2f g'),
                Item('rms_acc_z', format_str='%.2f g'),
            ),
        ))

    def imu_aux_callback(self, sbp_msg, **metadata):
        if sbp_msg.imu_type == 0:
            self.imu_temp = 23 + sbp_msg.temp / 2.**9
            self.imu_conf = sbp_msg.imu_conf
        else:
            print "IMU type %d not known" % sbp_msg.imu_type

    def imu_raw_callback(self, sbp_msg, **metadata):
        self.acc[:-1, :] = self.acc[1:, :]
        self.gyro[:-1, :] = self.gyro[1:, :]
        self.acc[-1] = (sbp_msg.acc_x, sbp_msg.acc_y, sbp_msg.acc_z)
        self.gyro[-1] = (sbp_msg.gyr_x, sbp_msg.gyr_y, sbp_msg.gyr_z)
        self.plot_data.set_data('acc_x', self.acc[:, 0])
        self.plot_data.set_data('acc_y', self.acc[:, 1])
        self.plot_data.set_data('acc_z', self.acc[:, 2])
        self.plot_data.set_data('gyr_x', self.gyro[:, 0])
        self.plot_data.set_data('gyr_y', self.gyro[:, 1])
        self.plot_data.set_data('gyr_z', self.gyro[:, 2])

        if self.imu_conf is not None:
            acc_range = self.imu_conf & 0xF
            sf = 2.**(acc_range + 1) / 2.**15
            self.rms_acc_x = sf * np.sqrt(np.mean(np.square(self.acc[:, 0])))
            self.rms_acc_y = sf * np.sqrt(np.mean(np.square(self.acc[:, 1])))
            self.rms_acc_z = sf * np.sqrt(np.mean(np.square(self.acc[:, 2])))

    def __init__(self, link):
        super(IMUView, self).__init__()

        self.acc = np.empty((NUM_POINTS, 3))
        self.gyro = np.empty((NUM_POINTS, 3))

        self.plot_data = ArrayPlotData(t=np.arange(NUM_POINTS),
                                       acc_x=[0.0],
                                       acc_y=[0.0],
                                       acc_z=[0.0],
                                       gyr_x=[0.0],
                                       gyr_y=[0.0],
                                       gyr_z=[0.0])

        self.plot = Plot(self.plot_data,
                         auto_colors=colours_list,
                         emphasized=True)
        self.plot.title = 'Raw IMU Data'
        self.plot.title_color = [0, 0, 0.43]
        self.ylim = self.plot.value_mapper.range
        self.ylim.low = -32768
        self.ylim.high = 32767
        #self.plot.value_range.bounds_func = lambda l, h, m, tb: (0, h * (1 + m))
        self.plot.value_axis.orientation = 'right'
        self.plot.value_axis.axis_line_visible = False
        self.plot.value_axis.title = 'LSB count'

        self.legend_visible = True
        self.plot.legend.visible = True
        self.plot.legend.align = 'll'
        self.plot.legend.line_spacing = 1
        self.plot.legend.font = 'modern 8'
        self.plot.legend.draw_layer = 'overlay'
        self.plot.legend.tools.append(
            LegendTool(self.plot.legend, drag_button="right"))

        acc_x = self.plot.plot(('t', 'acc_x'),
                               type='line',
                               color='auto',
                               name='Accn. X')
        acc_x = self.plot.plot(('t', 'acc_y'),
                               type='line',
                               color='auto',
                               name='Accn. Y')
        acc_x = self.plot.plot(('t', 'acc_z'),
                               type='line',
                               color='auto',
                               name='Accn. Z')
        acc_x = self.plot.plot(('t', 'gyr_x'),
                               type='line',
                               color='auto',
                               name='Gyro X')
        acc_x = self.plot.plot(('t', 'gyr_y'),
                               type='line',
                               color='auto',
                               name='Gyro Y')
        acc_x = self.plot.plot(('t', 'gyr_z'),
                               type='line',
                               color='auto',
                               name='Gyro Z')

        self.link = link
        self.link.add_callback(self.imu_raw_callback, SBP_MSG_IMU_RAW)
        self.link.add_callback(self.imu_aux_callback, SBP_MSG_IMU_AUX)
        self.python_console_cmds = {'track': self}