#!/usr/bin/env python
"""
Displays multiple data sets with different scales in the same plot area,
and shows a separate, distinct, axis for each plot.

Interactions are the same as in multiaxis.py
"""

# Major library imports
from numpy import linspace
from scipy.special import jn

# 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 (
    HPlotContainer,
    OverlayPlotContainer,
    PlotAxis,
    PlotGrid,
    cbrewer as COLOR_PALETTE,
    create_line_plot,
)
from chaco.tools.api import BroadcasterTool, PanTool

# ===============================================================================
# # Create the Chaco plot.
# ===============================================================================
def _create_plot_component():

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

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

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

        container.add(axis)

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

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

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

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

    return container


# ===============================================================================
# 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()