iframe-proxy | Sunbelt Computer Software

History

201 lines (165 loc) · 7.63 KB

Raw

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

"""

======================================

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()

Sunbelt Computer Software

PL/B Language Development and Support

Navigation Menu

Uh oh!

Sunbelt Computer Software

PL/B Language Development and Support

Uh oh!

FilesExpand file tree

radar_chart.py

Latest commit

History

radar_chart.py

File metadata and controls