Revision: 7301
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=7301&view=rev
Author: jdh2358
Date: 2009-07-28 19:55:30 +0000 (Tue, 28 Jul 2009)
Log Message:
-----------
added Tony's radar chart demo
Added Paths:
-----------
trunk/matplotlib/examples/api/radar_chart.py
Added: trunk/matplotlib/examples/api/radar_chart.py
===================================================================
--- trunk/matplotlib/examples/api/radar_chart.py
(rev 0)
+++ trunk/matplotlib/examples/api/radar_chart.py 2009-07-28 19:55:30 UTC
(rev 7301)
@@ -0,0 +1,144 @@
+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()
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