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PIC2, The Cumulative Distribution Function and Quantile Plot
Defination:
累积分布函数： The cumulative distribution function (CDF) does just that. The CDF at point x tells us what
fraction of events has occurred “to the left” of x. In other words, the CDF is the fraction of
all points xi with xi ≤ x.
http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.cumfreq.html#scipy.stats.cumfreq # -*- coding: utf-8 -*-"""Created on Thu Oct 23 20:29:33 2014@author: dell"""from scipy import statsimport matplotlib.pyplot as pltimport numpy as npimport randomif __name__ == ‘__main__‘: ax1 = plt.subplot(211) x = [random.gauss(0, 1) for i in range(1000)] #res = stats.probplot(x, plot=plt) mylist = stats.cumfreq(x, numbins = 100) ax1.plot(mylist[0]) ax2 = plt.subplot(212) mylist1 = stats.cumfreq(x, numbins = 100, defaultreallimits = [0,1]) ax2.plot(mylist1[0]) plt.show()
defaultreallimits 还是不是很明白是什么定义， 没太看懂。
分位图：A quantile plot is just the plot of a CDF in which the x and y axes have been switched.
概率图：probability plot 高斯分布求反函数 
这样会落到同一条直线。
http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.probplot.html
# -*- coding: utf-8 -*-"""Created on Thu Oct 23 20:06:03 2014@author: dell"""from scipy import statsimport matplotlib.pyplot as pltimport numpy as npimport randomif __name__ == ‘__main__‘: nsample = 100 np.random.seed(7654321) #A t distribution with small degrees of freedom: ax = plt.subplot(321) x = stats.t.rvs(3, size=nsample) res = stats.probplot(x, plot=plt) #A t distribution with larger degrees of freedom: ax2 = plt.subplot(322) x = stats.t.rvs(25, size=nsample) res = stats.probplot(x, plot=plt) #A mixture of two normal distributions with broadcasting: ax3 = plt.subplot(323) x = stats.norm.rvs(loc=[0,5], scale=[1,1.5], size=(nsample/2.,2)).ravel() res = stats.probplot(x, plot=plt) #A standard normal distribution: ax4 = plt.subplot(324) x = stats.norm.rvs(loc=0, scale=1, size=nsample) res = stats.probplot(x, plot=plt) # my test ax5 = plt.subplot(325) x = [random.gauss(0, 1) for i in range(100)] res = stats.probplot(x, plot=plt) #my test ax6 = plt.subplot(326) x = np.random.rand(100) * 100 res = stats.probplot(x, plot=plt) plt.show()
PIC2, The Cumulative Distribution Function and Quantile Plot
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