matplotlib 雷达图/蛛网图

```"""
======================================
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.
"""
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

"""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)#角度(弧度制)
def draw_poly_patch(self):
# rotate theta such that the first axis is at the top
verts = unit_poly_verts(theta + np.pi / 2)
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) #画圆
patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch}
if frame not in patch_dict:
raise ValueError('unknown value for `frame`: %s' % frame)
# use 1 line segment to connect specified points
RESOLUTION = 1
# define draw_frame method
draw_patch = patch_dict[frame]
def __init__(self, *args, **kwargs):
# rotate plot such that the first axis is at the top
self.set_theta_zero_location('N')
def fill(self, *args, **kwargs):
"""Override fill so that line is closed by default"""
closed = kwargs.pop('closed', True)
def plot(self, *args, **kwargs):
"""Override plot so that line is closed by default"""
for line in lines:
self._close_line(line)
def _close_line(self, line):#使曲线封闭，首尾相连
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 + np.pi / 2)
# 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}
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
data = example_data()
spoke_labels = data.pop(0)
fig, axes = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
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) ##本质是在极坐标下画封闭曲线
ax.fill(theta, d, facecolor=color, alpha=0.25)##填充
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()
```

```'''
matplotlib雷达图
'''
import numpy as np
import matplotlib.pyplot as plt

# 雷达图
n = len(labels)
# 转化为十分制！！！
if score in [5, 10, 100]:
data = data * 10/score
elif score == 1:
data = data * 10
angles = np.linspace(0, 2*np.pi, n, endpoint=False)
data = np.concatenate((data, [data[0]])) # 闭合
angles = np.concatenate((angles, [angles[0]])) # 闭合

fig = plt.figure()
# 画grid线（5条环形线）
for i in [2,4,6,8,10]:
ax.plot(angles, [i]*(n+1), 'k-',lw=0.5,) # 之所以 n +1，是因为要闭合！

# 填充底色
ax.fill(angles, [10]*(n+1), facecolor='lightgreen', alpha=0.2)
# 自己画grid线（n条半径线）
for i in range(n):
ax.plot([angles[i], angles[i]], [0, 10], 'b-',lw=0.5)

# 画线(数据)!
ax.set_theta_zero_location( "N")#从正上方开始(对称比较美@_@）
#ax.set_theta_zero_location(self, loc, offset=0.0), loc May be one of "N", "NW", "W", "SW", "S", "SE", "E", or "NE".
ax.plot(angles, data, 'b-', linewidth=2,marker = "o", markersize =5)
# 填充
ax.fill(angles, data, facecolor='r', alpha=1.0)

grid_angles = [ i* 360.0/n for i in range(n)]
ax.set_thetagrids(grid_angles, labels, fontproperties="SimHei") #grid_angles是角度制！！
ax.set_title("matplotlib雷达图", va='bottom', fontproperties="SimHei", fontsize =14,color="g")

ax.set_rlim(0,10)
# 下两行去掉所有默认的grid线
ax.spines['polar'].set_visible(False) # 去掉最外围的黑圈
ax.grid(False)                        # 去掉中间的黑圈

ax.set_yticks([])# 关闭径向数值刻度
plt.show()

if __name__ == '__main__':
labels = np.array(['水','火','风','云','雷','电']) #标签
data = np.array([95,60,80,60,70,88]) # 数据
score = 100 # 其可选的选项有1分制、5分制、10分制、100分制
# 画雷达图

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