python matplotlib画图.docx

python matplotlib画图Matplotlib.pyplot是用来画图的方法，类似于matlab中plot命令，用法基本相同。一.最基本的：例如：In 1: import matplotlib.pyplot as pltIn 2: plt.plot(1,2,3)Out2: In 3: plt.ylabel(some numbers)Out3: In 4: plt.show()结果如图1从图中可以看到，如果我们给plot()参数是一个list或者array，那么画图默认是作为Y轴来显示，x轴是自动生成的数值范围。其实plot可以带一些参数，和matlab类似。如：plt.plot(1,2,3,1,4,9)则会按(1,1),(2,4),(3,9)来划线。当然和matlab类似，我们也可以指定线的类型和颜色，如果默认，则为b-,即蓝色的实线（如上图）。 import matplotlib.pyplot as plt plt.plot(1,2,3,4, 1,4,9,16, ro) plt.axis(0, 6, 0, 20)0, 6, 0, 20 plt.show()结果如图2：ro代表线形为红色圈。plt.axis(0, 6, 0, 20)是指定xy坐标的起始范围，它的参数是列表xmin, xmax, ymin, ymax。二，统一图上画多条曲线下面看看如何在同一张图画多条曲线，我们用numpy生成的array import numpy as np t = np.arange(0.,5.,0.2) tarray( 0. ,0.2,0.4,0.6,0.8,1. ,1.2,1.4,1.6,1.8,2. , 2.2,2.4,2.6,2.8,3. ,3.2,3.4,3.6,3.8,4. ,4.2, 4.4,4.6,4.8) plt.plot(t,t,r-,t,t*2,bs,t,t*3,g) plt.show()结果如图3：对于线的属性，我们也可以如下设定：lines = plt.plot(x1, y1, x2, y2)# use keyword argsplt.setp(lines, color=r, linewidth=2.0)# or matlab style string value pairsplt.setp(lines, color, r, linewidth, 2.0)三，subplot命令Matplotlib也有和matlab中一样的subplot命令 import numpy as np import matplotlib.pyplot as plt def f(t):return np.exp(-t)*np.cos(2*np.pi*t) t1 = np.arange(0.0,5.0,0.1) t2 = np.arange(0.0,5.0,0.02) plt.figure(1) plt.subplot(211) plt.plot(t1,f(t1),bo,t2,f(t2),k), plt.subplot(212) plt.plot(t2,np.cos(2*np.pi*t2),r-) plt.show()结果如图4：当然，也可以用figure来画多个图。import matplotlib.pyplot as pltplt.figure(1) # the first figureplt.subplot(211) # the first subplot in the first figureplt.plot(1,2,3)plt.subplot(212) # the second subplot in the first figureplt.plot(4,5,6)plt.figure(2) # a second figureplt.plot(4,5,6) # creates a subplot(111) by defaultplt.figure(1) # figure 1 current; subplot(212) still currentplt.subplot(211) # make subplot(211) in figure1 currentplt.title(Easy as 1,2,3) # subplot 211 title四，在图片上标上text。如：import numpy as npimport matplotlib.pyplot as pltmu,sigma = 100,15x = mu + sigma*np.random.randn(10000)# the histogram of the datan,bins,patches = plt.hist(x,50,normed=1,facecolor=g,alpha=0.75)plt.xlabel(Smarts)plt.ylabel(Probability)plt.title(Histogram of IQ)plt.text(60,.025,r$mu=100, sigma=15