Chaco - Getting multiple data series to use the same axes and maps - enthought

Question

I am trying to plot several collections of data on a single plot.
Each dataset can be represented as an x-series (index) and several y-series (values). The ranges of x and y data series may be different in each data set. I want to have several of these data sets display on one plot. However, when I simply add the second plot object to the first (see below) it makes a second axis for it that is nested inside the plot.
I want both plots to share the same axis and for the axis bounds to be updated to fit all the data. What is the best way to achieve this? I am struggling to find topics on this in the documentation.
Thanks for your help. The code below highlights my problem.
# Major library imports
from numpy import linspace
from scipy.special import jn
from chaco.example_support import COLOR_PALETTE
# Enthought library imports
from enable.api import Component, ComponentEditor
from traits.api import HasTraits, Instance
from traitsui.api import Item, Group, View
# Chaco imports
from chaco.api import ArrayPlotData, Plot
from chaco.tools.api import BroadcasterTool, PanTool, ZoomTool
from chaco.api import create_line_plot, add_default_axes
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
x2 =linspace(-5.0, 10.0, 100)
pd = ArrayPlotData(index = x)
for i in range(5):
pd.set_data("y" + str(i), jn(i,x))
#slightly different plot data
pd2 = ArrayPlotData(index = x2)
for i in range(5):
pd2.set_data("y" + str(i), 2*jn(i,x2))
# Create some line plots of some of the data
plot1 = Plot(pd)
plot1.plot(("index", "y0", "y1", "y2"), name="j_n, n<3", color="red")
# Tweak some of the plot properties
plot1.title = "My First Line Plot"
plot1.padding = 50
plot1.padding_top = 75
plot1.legend.visible = True
plot2 = Plot(pd2)
plot2.plot(("index", "y0", "y1"), name="j_n, n<3", color="green")
plot1.add(plot2)
# Attach some tools to the plot
broadcaster = BroadcasterTool()
broadcaster.tools.append(PanTool(plot1))
broadcaster.tools.append(PanTool(plot2))
for c in (plot1, plot2):
zoom = ZoomTool(component=c, tool_mode="box", always_on=False)
broadcaster.tools.append(zoom)
plot1.tools.append(broadcaster)
return plot1
# Attributes to use for the plot view.
size=(900,500)
title="Multi-Y plot"
# # Demo class that is used by the demo.py application.
#===============================================================================
class Demo(HasTraits):
plot = Instance(Component)
traits_view = View(
Group(
Item('plot', editor=ComponentEditor(size=size),
show_label=False),
orientation = "vertical"),
resizable=True, title=title,
width=size[0], height=size[1]
)
def _plot_default(self):
return _create_plot_component()
demo = Demo()
if __name__ == "__main__":
demo.configure_traits()

Answer 1

One of the warts in Chaco (and indeed many plotting libraries) is the overloading of terms---especially the word "plot".
You're creating two different (capital-"P") Plots, but (I believe) you really only want one. Plot is the container that holds all of your individual line ... umm ... plots. The Plot.plot method returns a list of LinePlot instances (this "plot" is also called a "renderer" sometimes). That renderer is what you want to add to your (capital-"P") Plot container. The plot method actually creates the LinePlot instance and adds it to the Plot container for you. (Yup, that's three different uses of "plot": The container, the renderer, and the method on the container that adds/returns the renderer.)
Here's a simpler version of _create_plot_component that does roughly what you want. Note that only a single (capital-"P") Plot container is created.
def _create_plot_component():
# Create some x-y data series to plot
x = linspace(-2.0, 10.0, 100)
x2 =linspace(-5.0, 10.0, 100)
pd = ArrayPlotData(x=x, x2=x2)
for i in range(3):
pd.set_data("y" + str(i), jn(i,x))
# slightly different plot data
for i in range(3, 5):
pd.set_data("y" + str(i), 2*jn(i,x2))
# Create some line plots of some of the data
canvas = Plot(pd)
canvas.plot(("x", "y0", "y1", "y2"), name="plot 1", color="red")
canvas.plot(("x2", "y3", "y4"), name="plot 2", color="green")
return canvas
Edit: An earlier response fixed the issue with a two-line modification, but it wasn't the ideal way to solve the problem.