《Python数据可视化之matplotlib实践》 源码 第四篇 扩展 第十二章

图  12.1

 

import matplotlib.pyplot as plt
import numpy as np

barSlices=12

theta=np.linspace(0.0, 2*np.pi, barSlices, endpoint=False)
radii=30*np.random.rand(barSlices)
width=2*np.pi/barSlices
colors=np.array(["c", "m", "y", "b", "#C67171", "#C1CDCD", "#FFEC8B",
"#A0522D", "red", "burlywood", "chartreuse", "green"])


fig=plt.figure()
ax=fig.add_subplot(111, polar=True)


bars=ax.bar(theta, radii, width=width, color=colors, bottom=0.0)


plt.show()

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图  12.3

 

import matplotlib.pyplot as plt
import numpy as np


hexHtml=["#d73027", "#f46d43", "#fdae61", "#fee090",
"#ffffbf", "#e0f3f8", "#abd9e9", "#74add2", "#4575b4"]

sample=10000

fig, ax = plt.subplots(1, 1)


for j in range(len(hexHtml)):
y=np.random.normal(0, 0.1, size=sample).cumsum()
x=np.arange(sample)
ax.scatter(x, y, label=str(j), linewidths=0.2,
edgecolors="grey", facecolor=hexHtml[j])

ax.legend()


plt.show()

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图  12.4

 

import matplotlib.pyplot as plt
import numpy as np


rd=np.random.rand(10, 10)

plt.pcolor(rd, cmap="BuPu")
plt.colorbar()


plt.show()

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图  12.5

 

import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np


a = np.random.rand(100)
b = np.random.rand(100)
exponent = 2





plt.subplot(131)
plt.scatter(a, b, np.sqrt(np.power(a, exponent)+np.power(b, exponent))*100,
c=np.random.rand(100), cmap=mpl.cm.jet, marker="o", zorder=1)




plt.subplot(132)
plt.scatter(a, b, 50, marker="o", zorder=10)




plt.subplot(133)
plt.scatter(a, b, 50, c=np.random.rand(100), cmap=mpl.cm.BuPu,
marker="+", zorder=100)



plt.show()

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图  12.6

 

import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np


barSlices=12
theta=np.linspace(0.0, 2*np.pi, barSlices, endpoint=False)
radii=30*np.random.rand(barSlices)
width=np.pi/4*np.random.rand(barSlices)


fig=plt.figure()
ax=fig.add_subplot(111, polar=True)


bars=ax.bar(theta, radii, width=width, bottom=0.0)


for r, bar in zip(radii, bars):
bar.set_facecolor(mpl.cm.Accent(r/30.0))
bar.set_alpha(r/30.0)


plt.show()

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图  12.7

 

import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np




s=np.linspace(-0.5, 0.5, 1000)

x, y=np.meshgrid(s, s)

z=x**2+y**2+np.power(x**2+y**2, 2)




fig, ax=plt.subplots(1, 1)




cs=plt.contour(x, y, z, cmap=mpl.cm.hot)

plt.clabel(cs, fmt="%3.2f")

plt.colorbar(cs)




plt.show()

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图  12.8

 

import matplotlib.pyplot as plt
import matplotlib as mpl
import scipy.misc


ascent=scipy.misc.ascent()


plt.imshow(ascent, cmap=mpl.cm.gray)


plt.colorbar()


plt.show()

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