How to use the chaco.api.ToolbarPlot function in chaco

tool_chooser = Enum(values="tool_list")
    ts_list = List()
    ts1_chooser = Enum(values="ts_list")
    ts2_chooser = Enum(values="ts_list")
    # Moving average window size (in number of observations)
    ma_window_size = Int(0) 
    # Analysis details
    ts_analysis_details = Str("No details available")
    
    # Data
    ts_data = Dict()
    arr_plot_data = Instance(ArrayPlotData, ())
    index_is_dates = Bool()

    # Plots
    ts_plot = Instance(ToolbarPlot)
    ts_analysis_plot = Instance(ToolbarPlot)

    def trait_view(self, view):
        """ Build the view. The local namespace is 
        """
        return View(
            VGroup(Item('data_file', style='simple', label="HDF file to load"), 
                   HSplit(Item('ts_plot', editor=ComponentEditor(size=(400, 600)), 
                               show_label=False),
                          VGroup(Item('tool_chooser', show_label = True, label="Choose tool"),
                                 Item('ts1_chooser', label="TS 1"),
                                 Item('ts2_chooser', label="TS 2",
                                      visible_when="tool_chooser in ['%s']" % CORRELATION),
                                 Item('ma_window_size', label="MA window size",
                                      visible_when="tool_chooser in ['%s']" % MA),
                                 Item('ts_analysis_plot', editor=ComponentEditor(size=(400, 600)),

def update_main_plot(self):
        """ Build main plot
        """
        self.ts_plot = ToolbarPlot(self.arr_plot_data)
        for i, k in enumerate([k for k in self.ts_data.keys() if k != "index"]):
            renderer = self.ts_plot.plot(("index", k), name = k, color = colors[i % len(colors)])[0]
        if self.index_is_dates:
            # Index was an array of datetime: overwrite the x axis
            self.ts_plot.x_axis = None
            x_axis = PlotAxis(self.ts_plot, orientation="bottom",
                              tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
            self.ts_plot.overlays.append(x_axis)
            self.ts_plot.x_grid.tick_generator = x_axis.tick_generator
            
        if self.data_file:
            self.ts_plot.title = ("Time series visualization from %s" 
                                  % (os.path.split(self.data_file)[1]))
        else:
            self.ts_plot.title = "Time series visualization"
        attach_tools(self.ts_plot)

ts_list = List()
    ts1_chooser = Enum(values="ts_list")
    ts2_chooser = Enum(values="ts_list")
    # Moving average window size (in number of observations)
    ma_window_size = Int(0) 
    # Analysis details
    ts_analysis_details = Str("No details available")
    
    # Data
    ts_data = Dict()
    arr_plot_data = Instance(ArrayPlotData, ())
    index_is_dates = Bool()

    # Plots
    ts_plot = Instance(ToolbarPlot)
    ts_analysis_plot = Instance(ToolbarPlot)

    def trait_view(self, view):
        """ Build the view. The local namespace is 
        """
        return View(
            VGroup(Item('data_file', style='simple', label="HDF file to load"), 
                   HSplit(Item('ts_plot', editor=ComponentEditor(size=(400, 600)), 
                               show_label=False),
                          VGroup(Item('tool_chooser', show_label = True, label="Choose tool"),
                                 Item('ts1_chooser', label="TS 1"),
                                 Item('ts2_chooser', label="TS 2",
                                      visible_when="tool_chooser in ['%s']" % CORRELATION),
                                 Item('ma_window_size', label="MA window size",
                                      visible_when="tool_chooser in ['%s']" % MA),
                                 Item('ts_analysis_plot', editor=ComponentEditor(size=(400, 600)), 
                                      show_label=False),

def _plot_default(self, toolbar=True, **pltkwds):
        ''' Draw bare plot, including main plotting area, toolbar, etc...
         either at initialization or global redo'''      
        
        if toolbar:
            self.plot=ToolbarPlot(self.plotdata, **pltkwds)
        else:
            self.plot=Plot(self.plotdata, **pltkwds)

        self.plot.title = self.title
        self.plot.padding = 50
        self.plot.legend.visible=False

        self.plot.tools.append(PanTool(self.plot))
        zoom=BetterSelectingZoom(component=self.plot, tool_mode="box", always_on=False)
        self.plot.overlays.append(zoom)

def update_main_plot(self):
        """ Build main plot
        """
        self.ts_plot = ToolbarPlot(self.arr_plot_data)
        for i, k in enumerate([k for k in self.ts_data.keys() if k != "index"]):
            self.ts_plot.plot(("index", k), name = k, color = colors[i % len(colors)])
        if self.index_is_dates:
            # Index was an array of datetime: overwrite the x axis
            self.ts_plot.x_axis = None
            x_axis = PlotAxis(self.ts_plot, orientation="bottom",
                              tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
            self.ts_plot.overlays.append(x_axis)
            self.ts_plot.x_grid.tick_generator = x_axis.tick_generator
            
        if self.data_file:
            self.ts_plot.title = "Time series visualization from %s" % self.data_file
        else:
            self.ts_plot.title = "Time series visualization"
        attach_tools(self.ts_plot)

# Show legend
    plot.legend.visible = True
    plot.legend.align = "lr"
    # Legend Highlighter: allows to click on the line in the legend to show that one
    highlight_tool = LegendHighlighter(plot.legend)
    plot.tools.append(highlight_tool)

class GSODDataPlotterView(HasTraits):
    """ Application of the zoom tool to the GSOD plotting tool.
    Load a HDF file containing one or more timeseries and plot the entire data inside.
    The zoom tool allows to explore a subset of it. The legend allows to (de)select some
    timeseries.
    """
    data_file = File()
    ts_data = Dict()
    ts_plot = Instance(ToolbarPlot)

    traits_view = View(
            VGroup(Item('data_file', style = 'simple', label="HDF file to load"), 
                   Item('ts_plot', editor=ComponentEditor(size=(800, 600)), 
                        show_label=False),), 
            title='Chaco Plot with file loader and legend highlighter',
            width=900, height=800, resizable=True)

    def __init__(self, pandas_list = [], array_dict = {}, *args, **kw):
        """ If a (list of) pandas or a dict of arrays is passed, load them up. 
        """
        ts_data = {}
        super(GSODDataPlotterView, self).__init__(*args, **kw)
        if not isinstance(pandas_list, list):
            pandas_list = [pandas_list]
        if pandas_list:

def __init__(self, depth, data_series, **kw):
        super(MyPlot, self).__init__(**kw)

        plot_data = ArrayPlotData(index=depth)
        plot_data.set_data('data_series', data_series)
        self.plot = ToolbarPlot(plot_data, orientation='v', origin='top left')
        line = self.plot.plot(('index', 'data_series'))[0]

        line_inspector = LineInspector(component=line, write_metadata=True)
        line.tools.append(line_inspector)
        line.overlays.append(line_inspector)

import numpy
from chaco.api import ToolbarPlot, ArrayPlotData
from chaco.tools.api import LineInspector
from enable.component_editor import ComponentEditor
from traits.api import HasTraits, Instance
from traitsui.api import UItem, View

class MyPlot(HasTraits):
    """ Plot where depth is the index such that the plot is vertical
        and the origin is the upper left
    """
    plot = Instance(ToolbarPlot)

    traits_view = View(UItem('plot', editor=ComponentEditor()),
                       width=600, height=600, resizable=True
                      )

    def __init__(self, depth, data_series, **kw):
        super(MyPlot, self).__init__(**kw)

        plot_data = ArrayPlotData(index=depth)
        plot_data.set_data('data_series', data_series)
        self.plot = ToolbarPlot(plot_data, orientation='v', origin='top left')
        line = self.plot.plot(('index', 'data_series'))[0]

        line_inspector = LineInspector(component=line, write_metadata=True)
        line.tools.append(line_inspector)
        line.overlays.append(line_inspector)

def update_analysis_plot(self):
        """ Build analysis plot
        """
        self.ts_analysis_plot = ToolbarPlot(self.arr_plot_data)
        if self.tool_chooser == CORRELATION:
            self.corr_renderer = self.ts_analysis_plot.plot((self.ts1_chooser, 
                            self.ts2_chooser), type = "scatter", color = "blue")[0]
            self.ts_analysis_plot.title = "%s plotted against %s" % (self.ts1_chooser, self.ts2_chooser)
            self.ts_analysis_plot.index_axis.title = self.ts1_chooser
            self.ts_analysis_plot.value_axis.title = self.ts2_chooser
        elif self.tool_chooser == MA and self.ma_window_size > 0:
            ts1_ma = pandas.rolling_mean(self.arr_plot_data.get_data(self.ts1_chooser),
                                         self.ma_window_size)
            self.arr_plot_data.set_data("ts1_ma", ts1_ma)
            self.ts_analysis_plot.plot(("index", self.ts1_chooser), type = "scatter", color = "blue")
            self.ts_analysis_plot.plot(("index", "ts1_ma"), type = "line", color = "blue")

def _ts_data_changed(self):
        """ Dataset has changed: update the plot.
        ENH: add the possibility to pass a dict to ArrayPlotData.
        """
        print "data changed: updating the plot..."
        arr_data = ArrayPlotData()
        for k,v in self.ts_data.items():
            arr_data.set_data(k,v)
        self.ts_plot = ToolbarPlot(arr_data)
        for i, k in enumerate([k for k in self.ts_data.keys() if k != "index"]):
            self.ts_plot.plot(("index", k), name = k, color = colors[i % len(colors)])
        if self.index_is_dates:
            # Index was an array of datetime: overwrite the x axis
            self.ts_plot.x_axis = None
            x_axis = PlotAxis(self.ts_plot, orientation="bottom",
                              tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem()))
            self.ts_plot.overlays.append(x_axis)
            self.ts_plot.x_grid.tick_generator = x_axis.tick_generator
            
        if self.data_file:
            self.ts_plot.title = "Time series visualization from %s" % self.data_file
        else:
            self.ts_plot.title = "Time series visualization"
        attach_tools(self.ts_plot)