1""" 2====================================== 3Radar chart (aka spider or star chart) 4====================================== 5 6This example creates a radar chart, also known as a spider or star chart [1]_. 7 8Although this example allows a frame of either 'circle' or 'polygon', polygon 9frames don't have proper gridlines (the lines are circles instead of polygons). 10It's possible to get a polygon grid by setting GRIDLINE_INTERPOLATION_STEPS in 11matplotlib.axis to the desired number of vertices, but the orientation of the 12polygon is not aligned with the radial axes. 13 14.. [1] http://en.wikipedia.org/wiki/Radar_chart 15""" 16 17import numpy as np 18 19import matplotlib.pyplot as plt 20from matplotlib.patches import Circle, RegularPolygon 21from matplotlib.path import Path 22from matplotlib.projections.polar import PolarAxes 23from matplotlib.projections import register_projection 24from matplotlib.spines import Spine 25from matplotlib.transforms import Affine2D 26 27 28def radar_factory(num_vars, frame='circle'): 29 """ 30 Create a radar chart with `num_vars` axes. 31 32 This function creates a RadarAxes projection and registers it. 33 34 Parameters 35 ---------- 36 num_vars : int 37 Number of variables for radar chart. 38 frame : {'circle', 'polygon'} 39 Shape of frame surrounding axes. 40 41 """ 42 # calculate evenly-spaced axis angles 43 theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False) 44 45 class RadarAxes(PolarAxes): 46 47 name = 'radar' 48 # use 1 line segment to connect specified points 49 RESOLUTION = 1 50 51 def __init__(self, *args, **kwargs): 52 super().__init__(*args, **kwargs) 53 # rotate plot such that the first axis is at the top 54 self.set_theta_zero_location('N') 55 56 def fill(self, *args, closed=True, **kwargs): 57 """Override fill so that line is closed by default""" 58 return super().fill(closed=closed, *args, **kwargs) 59 60 def plot(self, *args, **kwargs): 61 """Override plot so that line is closed by default""" 62 lines = super().plot(*args, **kwargs) 63 for line in lines: 64 self._close_line(line) 65 66 def _close_line(self, line): 67 x, y = line.get_data() 68 # FIXME: markers at x[0], y[0] get doubled-up 69 if x[0] != x[-1]: 70 x = np.append(x, x[0]) 71 y = np.append(y, y[0]) 72 line.set_data(x, y) 73 74 def set_varlabels(self, labels): 75 self.set_thetagrids(np.degrees(theta), labels) 76 77 def _gen_axes_patch(self): 78 # The Axes patch must be centered at (0.5, 0.5) and of radius 0.5 79 # in axes coordinates. 80 if frame == 'circle': 81 return Circle((0.5, 0.5), 0.5) 82 elif frame == 'polygon': 83 return RegularPolygon((0.5, 0.5), num_vars, 84 radius=.5, edgecolor="k") 85 else: 86 raise ValueError("Unknown value for 'frame': %s" % frame) 87 88 def _gen_axes_spines(self): 89 if frame == 'circle': 90 return super()._gen_axes_spines() 91 elif frame == 'polygon': 92 # spine_type must be 'left'/'right'/'top'/'bottom'/'circle'. 93 spine = Spine(axes=self, 94 spine_type='circle', 95 path=Path.unit_regular_polygon(num_vars)) 96 # unit_regular_polygon gives a polygon of radius 1 centered at 97 # (0, 0) but we want a polygon of radius 0.5 centered at (0.5, 98 # 0.5) in axes coordinates. 99 spine.set_transform(Affine2D().scale(.5).translate(.5, .5) 100 + self.transAxes) 101 return {'polar': spine} 102 else: 103 raise ValueError("Unknown value for 'frame': %s" % frame) 104 105 register_projection(RadarAxes) 106 return theta 107 108 109def example_data(): 110 # The following data is from the Denver Aerosol Sources and Health study. 111 # See doi:10.1016/j.atmosenv.2008.12.017 112 # 113 # The data are pollution source profile estimates for five modeled 114 # pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical 115 # species. The radar charts are experimented with here to see if we can 116 # nicely visualize how the modeled source profiles change across four 117 # scenarios: 118 # 1) No gas-phase species present, just seven particulate counts on 119 # Sulfate 120 # Nitrate 121 # Elemental Carbon (EC) 122 # Organic Carbon fraction 1 (OC) 123 # Organic Carbon fraction 2 (OC2) 124 # Organic Carbon fraction 3 (OC3) 125 # Pyrolized Organic Carbon (OP) 126 # 2)Inclusion of gas-phase specie carbon monoxide (CO) 127 # 3)Inclusion of gas-phase specie ozone (O3). 128 # 4)Inclusion of both gas-phase species is present... 129 data = [ 130 ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'], 131 ('Basecase', [ 132 [0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00], 133 [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00], 134 [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00], 135 [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00], 136 [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]), 137 ('With CO', [ 138 [0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00], 139 [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00], 140 [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00], 141 [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00], 142 [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]), 143 ('With O3', [ 144 [0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03], 145 [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00], 146 [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00], 147 [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95], 148 [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]), 149 ('CO & O3', [ 150 [0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01], 151 [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00], 152 [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00], 153 [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88], 154 [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]]) 155 ] 156 return data 157 158 159if __name__ == '__main__': 160 N = 9 161 theta = radar_factory(N, frame='polygon') 162 163 data = example_data() 164 spoke_labels = data.pop(0) 165 166 fig, axs = plt.subplots(figsize=(9, 9), nrows=2, ncols=2, 167 subplot_kw=dict(projection='radar')) 168 fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05) 169 170 colors = ['b', 'r', 'g', 'm', 'y'] 171 # Plot the four cases from the example data on separate axes 172 for ax, (title, case_data) in zip(axs.flat, data): 173 ax.set_rgrids([0.2, 0.4, 0.6, 0.8]) 174 ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1), 175 horizontalalignment='center', verticalalignment='center') 176 for d, color in zip(case_data, colors): 177 ax.plot(theta, d, color=color) 178 ax.fill(theta, d, facecolor=color, alpha=0.25) 179 ax.set_varlabels(spoke_labels) 180 181 # add legend relative to top-left plot 182 labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5') 183 legend = axs[0, 0].legend(labels, loc=(0.9, .95), 184 labelspacing=0.1, fontsize='small') 185 186 fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios', 187 horizontalalignment='center', color='black', weight='bold', 188 size='large') 189 190 plt.show() 191 192 193############################################################################# 194# 195# .. admonition:: References 196# 197# The use of the following functions, methods, classes and modules is shown 198# in this example: 199# 200# - `matplotlib.path` 201# - `matplotlib.path.Path` 202# - `matplotlib.spines` 203# - `matplotlib.spines.Spine` 204# - `matplotlib.projections` 205# - `matplotlib.projections.polar` 206# - `matplotlib.projections.polar.PolarAxes` 207# - `matplotlib.projections.register_projection` 208