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# Radar chart (aka spider or star chart)

This example creates a radar chart, also known as a spider or star chart [1].

Although this example allows a frame of either ‘circle’ or ‘polygon’, polygon frames don’t have proper gridlines (the lines are circles instead of polygons). It’s possible to get a polygon grid by setting GRIDLINE_INTERPOLATION_STEPS in matplotlib.axis to the desired number of vertices, but the orientation of the polygon is not aligned with the radial axes.

[1] | http://en.wikipedia.org/wiki/Radar_chart

    import numpy as np

    import matplotlib.pyplot as plt
    from matplotlib.path import Path
    from matplotlib.spines import Spine
    from matplotlib.projections.polar import PolarAxes
    from matplotlib.projections import register_projection

    def radar_factory(num_vars, frame='circle'):
        """Create a radar chart with `num_vars` axes.

        This function creates a RadarAxes projection and registers it.

        Parameters
        ----------
        num_vars : int
            Number of variables for radar chart.
        frame : {'circle' | 'polygon'}
            Shape of frame surrounding axes.

        """
        # calculate evenly-spaced axis angles
        theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)
        # rotate theta such that the first axis is at the top
        theta += np.pi/2

        def draw_poly_patch(self):
            verts = unit_poly_verts(theta)
            return plt.Polygon(verts, closed=True, edgecolor='k')

        def draw_circle_patch(self):
            # unit circle centered on (0.5, 0.5)
            return plt.Circle((0.5, 0.5), 0.5)

        patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}
        if frame not in patch_dict:
            raise ValueError('unknown value for `frame`: %s' % frame)

        class RadarAxes(PolarAxes):

            name = 'radar'
            # use 1 line segment to connect specified points
            RESOLUTION = 1
            # define draw_frame method
            draw_patch = patch_dict[frame]

            def fill(self, *args, **kwargs):
                """Override fill so that line is closed by default"""
                closed = kwargs.pop('closed', True)
                return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)

            def plot(self, *args, **kwargs):
                """Override plot so that line is closed by default"""
                lines = super(RadarAxes, self).plot(*args, **kwargs)
                for line in lines:
                    self._close_line(line)

            def _close_line(self, line):
                x, y = line.get_data()
                # FIXME: markers at x[0], y[0] get doubled-up
                if x[0] != x[-1]:
                    x = np.concatenate((x, [x[0]]))
                    y = np.concatenate((y, [y[0]]))
                    line.set_data(x, y)

            def set_varlabels(self, labels):
                self.set_thetagrids(np.degrees(theta), labels)

            def _gen_axes_patch(self):
                return self.draw_patch()

            def _gen_axes_spines(self):
                if frame == 'circle':
                    return PolarAxes._gen_axes_spines(self)
                # The following is a hack to get the spines (i.e. the axes frame)
                # to draw correctly for a polygon frame.

                # spine_type must be 'left', 'right', 'top', 'bottom', or `circle`.
                spine_type = 'circle'
                verts = unit_poly_verts(theta)
                # close off polygon by repeating first vertex
                verts.append(verts[0])
                path = Path(verts)

                spine = Spine(self, spine_type, path)
                spine.set_transform(self.transAxes)
                return {'polar': spine}

        register_projection(RadarAxes)
        return theta

    def unit_poly_verts(theta):
        """Return vertices of polygon for subplot axes.

        This polygon is circumscribed by a unit circle centered at (0.5, 0.5)
        """
        x0, y0, r = [0.5] * 3
        verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]
        return verts

    def example_data():
        # The following data is from the Denver Aerosol Sources and Health study.
        # See  doi:10.1016/j.atmosenv.2008.12.017
        #
        # The data are pollution source profile estimates for five modeled
        # pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical
        # species. The radar charts are experimented with here to see if we can
        # nicely visualize how the modeled source profiles change across four
        # scenarios:
        #  1) No gas-phase species present, just seven particulate counts on
        #     Sulfate
        #     Nitrate
        #     Elemental Carbon (EC)
        #     Organic Carbon fraction 1 (OC)
        #     Organic Carbon fraction 2 (OC2)
        #     Organic Carbon fraction 3 (OC3)
        #     Pyrolized Organic Carbon (OP)
        #  2)Inclusion of gas-phase specie carbon monoxide (CO)
        #  3)Inclusion of gas-phase specie ozone (O3).
        #  4)Inclusion of both gas-phase species is present...
        data = [
            ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
            ('Basecase', [
                [0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],
                [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],
                [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],
                [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],
                [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]),
            ('With CO', [
                [0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],
                [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],
                [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],
                [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],
                [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]),
            ('With O3', [
                [0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],
                [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],
                [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],
                [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],
                [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]),
            ('CO & O3', [
                [0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],
                [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],
                [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],
                [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],
                [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]])
        ]
        return data

    if __name__ == '__main__':
        N = 9
        theta = radar_factory(N, frame='polygon')

        data = example_data()
        spoke_labels = data.pop(0)

        fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
                                 subplot_kw=dict(projection='radar'))
        fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)

        colors = ['b', 'r', 'g', 'm', 'y']
        # Plot the four cases from the example data on separate axes
        for ax, (title, case_data) in zip(axes.flatten(), data):
            ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
            ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
                         horizontalalignment='center', verticalalignment='center')
            for d, color in zip(case_data, colors):
                ax.plot(theta, d, color=color)
                ax.fill(theta, d, facecolor=color, alpha=0.25)
            ax.set_varlabels(spoke_labels)

        # add legend relative to top-left plot
        ax = axes[0, 0]
        labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
        legend = ax.legend(labels, loc=(0.9, .95),
                           labelspacing=0.1, fontsize='small')

        fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
                 horizontalalignment='center', color='black', weight='bold',
                 size='large')

        plt.show()

Total running time of the script: ( 0 minutes 0.153 seconds)

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