"""
Scatterplot with range-selectable data points

Draws a colormapped scatterplot of random data.

In addition to normal zooming and panning on the plot, the user can select
a range of data values by right-dragging in the color bar.

Left-click in the color bar to cancel the range selection.
"""

# Major library imports
from numpy import exp, sort
from numpy.random import random

# Enthought library imports
from enable.api import Component, ComponentEditor, Window
from traits.api import HasTraits, Instance
from traitsui.api import Item, VGroup, View, Label

# Chaco imports
from chaco.api import (
    ArrayPlotData,
    ColorBar,
    ColormappedSelectionOverlay,
    HPlotContainer,
    viridis,
    LinearMapper,
    Plot,
)
from chaco.tools.api import (
    PanTool,
    ZoomTool,
    RangeSelection,
    RangeSelectionOverlay,
)

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

    # Create some data
    numpts = 1000
    x = sort(random(numpts))
    y = random(numpts)
    color = exp(-(x ** 2 + y ** 2))

    # Create a plot data obect and give it this data
    pd = ArrayPlotData()
    pd.set_data("index", x)
    pd.set_data("value", y)
    pd.set_data("color", color)

    # Create the plot
    plot = Plot(pd)
    plot.plot(
        ("index", "value", "color"),
        type="cmap_scatter",
        name="my_plot",
        color_mapper=viridis,
        marker="square",
        fill_alpha=0.5,
        marker_size=6,
        outline_color="black",
        border_visible=True,
        bgcolor="white",
    )

    # Tweak some of the plot properties
    plot.title = "Colormapped Scatter Plot with Range-selectable Data Points"
    plot.padding = 50
    plot.x_grid.visible = False
    plot.y_grid.visible = False
    plot.x_axis.font = "modern 16"
    plot.y_axis.font = "modern 16"

    # Right now, some of the tools are a little invasive, and we need the
    # actual ColomappedScatterPlot object to give to them
    cmap_renderer = plot.plots["my_plot"][0]

    # Attach some tools to the plot
    plot.tools.append(PanTool(plot, constrain_key="shift"))
    zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
    plot.overlays.append(zoom)
    selection = ColormappedSelectionOverlay(
        cmap_renderer, fade_alpha=0.35, selection_type="mask"
    )
    cmap_renderer.overlays.append(selection)

    # Create the colorbar, handing in the appropriate range and colormap
    colorbar = create_colorbar(plot.color_mapper)
    colorbar.plot = cmap_renderer
    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)
    container.add(plot)
    container.add(colorbar)
    container.bgcolor = "lightgray"
    return container


def create_colorbar(colormap):
    colorbar = ColorBar(
        index_mapper=LinearMapper(range=colormap.range),
        color_mapper=colormap,
        orientation="v",
        resizable="v",
        width=30,
        padding=20,
    )
    colorbar.tools.append(RangeSelection(component=colorbar))
    colorbar.overlays.append(
        RangeSelectionOverlay(
            component=colorbar,
            border_color="white",
            alpha=0.8,
            fill_color="lightgray",
        )
    )
    return colorbar


# ===============================================================================
# Attributes to use for the plot view.
size = (650, 650)
title = "Colormapped scatter plot"

# ===============================================================================
# # Demo class that is used by the demo.py application.
# ===============================================================================
class Demo(HasTraits):
    plot = Instance(Component)

    traits_view = View(
        VGroup(
            Label("Right-drag on colorbar to select data range"),
            Item("plot", editor=ComponentEditor(size=size), show_label=False),
        ),
        resizable=True,
        title=title,
    )

    def _plot_default(self):
        return _create_plot_component()


demo = Demo()

if __name__ == "__main__":
    demo.configure_traits()