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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<h1>api example code: radar_chart.py<a class="headerlink" href="#api-example-code-radar-chart-py" title="Permalink to this headline">¶</a></h1>

(<a class="reference external" href="../../mpl_examples/api/radar_chart.py">Source code</a>, <a class="reference external" href="../../mpl_examples/api/radar_chart.png">png</a>, <a class="reference external" href="../../mpl_examples/api/radar_chart.hires.png">hires.png</a>, <a class="reference external" href="../../mpl_examples/api/radar_chart.pdf">pdf</a>)

</div>

<div class="highlight-python"><div class="highlight"><pre>"""

Example of creating a radar chart (a.k.a. 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 = 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_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(theta * 180/np.pi, 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 speciesis present...

data = {

'column names':

['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('column names')

fig = plt.figure(figsize=(9, 9))

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 n, title in enumerate(data.keys()):

ax = fig.add_subplot(2, 2, n+1, projection='radar')

plt.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(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()

</pre></div>

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Keywords: python, matplotlib, pylab, example, codex (see <a class="reference internal" href="../../faq/howto_faq.html#how-to-search-examples">Search examples</a>)

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