.. _api-radar_chart:

api example code: radar_chart.py
================================



.. plot:: /tmp/buildd/matplotlib-0.99.3/doc/mpl_examples/api/radar_chart.py

::

    import numpy as np
    
    import matplotlib.pyplot as plt
    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."""
        # calculate evenly-spaced axis angles
        theta = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars)
        # rotate theta such that the first axis is at the top
        theta += np.pi/2
    
        def draw_poly_frame(self, x0, y0, r):
            # TODO: use transforms to convert (x, y) to (r, theta)
            verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]
            return plt.Polygon(verts, closed=True, edgecolor='k')
    
        def draw_circle_frame(self, x0, y0, r):
            return plt.Circle((x0, y0), r)
    
        frame_dict = {'polygon': draw_poly_frame, 'circle': draw_circle_frame}
        if frame not in frame_dict:
            raise ValueError, 'unknown value for `frame`: %s' % frame
    
        class RadarAxes(PolarAxes):
            """Class for creating a radar chart (a.k.a. a spider or star chart)
    
            http://en.wikipedia.org/wiki/Radar_chart
            """
            name = 'radar'
            # use 1 line segment to connect specified points
            RESOLUTION = 1
            # define draw_frame method
            draw_frame = frame_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(theta * 180/np.pi, labels)
    
            def _gen_axes_patch(self):
                x0, y0 = (0.5, 0.5)
                r = 0.5
                return self.draw_frame(x0, y0, r)
    
        register_projection(RadarAxes)
        return theta
    
    
    if __name__ == '__main__':
        #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 speciesis present...
        N = 9
        theta = radar_factory(N)
        spoke_labels = ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO',
                        'O3']
        f1_base = [0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00]
        f1_CO =   [0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00]
        f1_O3 =   [0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03]
        f1_both = [0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01]
    
        f2_base = [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00]
        f2_CO =   [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00]
        f2_O3 =   [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00]
        f2_both = [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00]
    
        f3_base = [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00]
        f3_CO =   [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00]
        f3_O3 =   [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00]
        f3_both = [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00]
    
        f4_base = [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00]
        f4_CO =   [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00]
        f4_O3 =   [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95]
        f4_both = [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88]
    
        f5_base = [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]
        f5_CO =   [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]
        f5_O3 =   [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]
        f5_both = [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]
    
        fig = plt.figure(figsize=(9,9))
        # adjust spacing around the subplots
        fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
        title_list = ['Basecase', 'With CO', 'With O3', 'CO & O3']
        data = {'Basecase': [f1_base, f2_base, f3_base, f4_base, f5_base],
                'With CO': [f1_CO, f2_CO, f3_CO, f4_CO, f5_CO],
                'With O3': [f1_O3, f2_O3, f3_O3, f4_O3, f5_O3],
                'CO & O3': [f1_both, f2_both, f3_both, f4_both, f5_both]}
        colors = ['b', 'r', 'g', 'm', 'y']
        # chemicals range from 0 to 1
        radial_grid = [0.2, 0.4, 0.6, 0.8]
        # If you don't care about the order, you can loop over data_dict.items()
        for n, title in enumerate(title_list):
            ax = fig.add_subplot(2, 2, n+1, projection='radar')
            plt.rgrids(radial_grid)
            ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
                         horizontalalignment='center', verticalalignment='center')
            for d, color in zip(data[title], 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
        plt.subplot(2,2,1)
        labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
        legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)
        plt.setp(legend.get_texts(), fontsize='small')
        plt.figtext(0.5, 0.965,  '5-Factor Solution Profiles Across Four Scenarios',
                   ha='center', color='black', weight='bold', size='large')
        plt.show()
    

Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)