1.对数图
便于观察图形之间变化的情况
from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(1,100)
y =[5** i for i in x]
fig,al = plt.subplots()
al.set_yscale('log')
al.plot(x,y,color='gold')
al.grid(True)
plt.show()
2.频谱图
主要显示信号强度随着时间变化而变化
import wave
import matplotlib.pyplot as plt
import numpy as np
import os
f = wave.open('音频文件本地地址','rb')
pa = f.getparams()
nchannels, sampwidth, framerate, nframes = pa[:4]
st= f.readframes(nframes)
wd = np.fromstring(st,dtype=np.int16)
wd = wd*2.0/(max(abs(wd)))
wd = np.reshape(wd,[nframes,nchannels]).T
f.close()
plt.specgram(wd[0],Fs = framerate, scale_by_freq = False, sides = 'default')
plt.ylabel('Frequency(Hz)')
plt.xlabel('Time(s)')
plt.show()
3.矢量场流线图
用来观察磁场运动和力场流动的情况
import numpy as np
import matplotlib.pyplot as plt
whe = 10
y, x = np.mgrid[-whe:whe:500j, -whe:whe:500j]
un = -1 - x**5 + y
vt = 1 + x - y**5
speed = np.sqrt(un*un + vt*vt)
fig, ax = plt.subplots()
ax.streamplot(x, y, un, vt, density=[2, 1])
ax.set_title('Varying Density')
plt.show()
4.互相关图
用来观察两个不同数据之间是否有互相关联的匹配情况
import matplotlib.pyplot as plt
import numpy as np
x1=[0.1,0.2,0.3,0.5,0.6]
y1=[0.1,0.3,0.1050,0.111,0.1155]
fig = plt.figure()
fug = fig.add_subplot(211)
fug.xcorr(x1,y1, usevlines=True, maxlags=4, normed=True, lw=20)
fug.grid(True)
fug.axhline(0, color='blue', lw=20)
ax2 = fig.add_subplot(212, sharex=fug)
ax2.acorr(x1,usevlines=True, normed=True, maxlags=4, lw=20)
ax2.grid(True)
ax2.axhline(0, color='gold', lw=20)
plt.show()
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