Chaco bar plots

Question

I'm really struggling to understand how to build chaco bar plots.

I've been poring over an online example and have reduced it down to the following simplified code:

import numpy as np
from traits.api import HasTraits, Instance
from traitsui.api import View, Item
from chaco.api import BarPlot, ArrayDataSource, DataRange1D, LinearMapper
from enable.api import ComponentEditor

class MyBarPlot(HasTraits):
    plot = Instance(BarPlot)

    traits_view = View(
        Item('plot',editor=ComponentEditor(), show_label=False))

    def _plot_default(self):
        idx = np.array([1, 2, 3, 4, 5])
        vals = np.array([2, 4, 7, 4, 3])

        index = ArrayDataSource(idx)
        index_range = DataRange1D(index, low=0.5, high=5.5)
        index_mapper = LinearMapper(range=index_range)

        value = ArrayDataSource(vals)
        value_range = DataRange1D(value, low=0)
        value_mapper = LinearMapper(range=value_range)

        plot = BarPlot(index=index, value=value,
                       value_mapper=value_mapper,
                       index_mapper=index_mapper)
        return plot

if __name__ == "__main__":
    myplot = MyBarPlot()
    myplot.configure_traits()

Needless to say my attempt didn't work. When I run this code (in an ipython notebook) all I get is a blank plot window, filled with black.

I suspect my error might have something to do with the 'valuemapper' entries as I don't really understand what these are for. I'd be grateful for any pointers on my code error.

More generally, this coding approach seems very complicated to me - is there a simpler way to make chaco bar plots?

score 3 · Accepted Answer · edited May 23 '17 at 11:44

3

Unfortunately, the bar_width trait of BarPlot doesn't try to be smart here. The default is a width of 10 in data space, which completely overwhelms the width of the plot. To fix this, you can adjust the width manually:

 plot = BarPlot(index=index, value=value,
                value_mapper=value_mapper,
                index_mapper=index_mapper,
                bar_width=0.8)

Note that this still won't give you a proper "bar plot" because you'll be missing the surrounding axes decorations and other components. The easiest way to accomplish that would be to use chaco's Plot object.

Here's an example that adds a BarPlot instance to a Plot:

https://github.com/enthought/chaco/blob/master/examples/demo/tornado.py

And here's an example that creates a bar plot using the plot convenience method in Plot:

https://github.com/enthought/chaco/blob/master/examples/demo/basic/bar_plot_stacked.py

Yes, "plot" is quite terribly overloaded (Chaco - Getting multiple data series to use the same axes and maps).

edited May 23 '17 at 11:44

Community

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answered Feb 05 '15 at 04:07

Tony S Yu

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Thanks very much for that response tsyu80. Yes, you were right, setting an appropriate value for bar_width made the plot bars distinguishable from each other, instead of them all being blocked out by one big fat bar (which is why my plot was all black). Thanks also for the examples. The second one with the plot convenience method seemed to offer the 'simplest' solution, though it still seems awfully complex compared to a matplotlib bar chart – dreme Feb 06 '15 at 01:31
Chaco is definitely more verbose if your goal is just to produce a static plot. That said, Chaco is focused more on embedding plots into applications. If you're creating an app with interactive plots, you'll probably find Chaco simpler than matplotlib. – Tony S Yu Feb 06 '15 at 03:46
Yes, good point. As I'm starting to get the hang of the whole traits/ui/chaco paradigm, I'm quite liking it. – dreme Feb 15 '15 at 22:18

Chaco bar plots

1 Answers1