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是否可以将 chaco 代码转换为等效的 matplotlib 代码？

例如，我想将此chaco 代码转换为等效的 matplotlib 代码。我是处理 matplot 库的新手。因此，任何形式的帮助都将受到高度赞赏。

代码片段

""" Tornado plot example from Brennan Williams """
from enable.api import Component, ComponentEditor
from traits.api import HasTraits, Instance
from traitsui.api import Item, View

# Chaco imports
from chaco.api import ArrayDataSource, BarPlot, DataRange1D, LabelAxis, \
                  LinearMapper, OverlayPlotContainer, PlotAxis
from chaco.example_support import COLOR_PALETTE

class PlotExample(HasTraits):
    plot = Instance(Component)

    traits_view = View(Item('plot', editor=ComponentEditor(), show_label=False),
                resizable=True, title="Tornado Plot",
                width=800, height=600
                )

    def _plot_default(self):
        container = OverlayPlotContainer(bgcolor = "white")
        plots = self._make_curves()
        for plot in plots:
        plot.padding = 60
        container.add(plot)

        bottom_axis = PlotAxis(plot, orientation='bottom')

        label_list=['var a', 'var b', 'var c', 'var d', 'var e', 'var f',
                'var g', 'var h', 'var i']
        vertical_axis = LabelAxis(plot, orientation='left',
                            title='Categories',
                            positions = range(1, 10),
                            labels=label_list)
        vertical2_axis = LabelAxis(plot, orientation='right',
                               positions = range(1, 10),
                               labels=label_list)

        plot.underlays.append(vertical_axis)
        plot.underlays.append(vertical2_axis)
        plot.underlays.append(bottom_axis)

        return container

    def _get_points(self):
        index = linspace(pi/4, 3*pi/2, 9)
        data = sin(index) + 2
        return (range(1, 10), data)

    def _make_curves(self):
        (index_points, value_points) = self._get_points()
        size = len(index_points)

        middle_value=2500000.0
        mid_values=middle_value*ones(size)
        low_values=mid_values-10000.0*value_points
        high_values=mid_values+20000.0*value_points

        idx = ArrayDataSource(index_points)
        vals = ArrayDataSource(low_values, sort_order="none")

        idx2 = ArrayDataSource(index_points)
        vals2 = ArrayDataSource(high_values, sort_order="none")

        starting_vals = ArrayDataSource(mid_values, sort_order="none")

        # Create the index range
        index_range = DataRange1D(idx, low=0.5, high=9.5)
        index_mapper = LinearMapper(range=index_range)

        # Create the value range
        value_range = DataRange1D(vals, vals2, low_setting='auto',
                              high_setting='auto', tight_bounds=False)
        value_mapper = LinearMapper(range=value_range,tight_bounds=False)

        # Create the plot
        plot1 = BarPlot(index=idx, value=vals,
                    value_mapper=value_mapper,
                    index_mapper=index_mapper,
                    starting_value=starting_vals,
                    line_color='black',
                    orientation='v',
                    fill_color=tuple(COLOR_PALETTE[6]),
                    bar_width=0.8, antialias=False)

        plot2 = BarPlot(index=idx2, value=vals2,
                    value_mapper=value_mapper,
                    index_mapper=index_mapper,
                    starting_value=starting_vals,
                    line_color='black',
                    orientation='v',
                    fill_color=tuple(COLOR_PALETTE[1]),
                    bar_width=0.8, antialias=False)

        return [plot1, plot2]


demo = PlotExample()

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

我想生成一个如下所示的龙卷风图。

龙卷风图

4

1 回答 1

3

在 matplotlib 邮件列表中有一个关于龙卷风图的旧线程。有两个不同的示例和几个指向带有龙卷风和相应来源的画廊的链接。你可以在这里找到线程

于 2013-06-27T14:22:04.100 回答