Description:

Exploring how to best create truncated distributions (e.g., values constrained to [0,100])



In [1]:

    
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [12]:

    
import scipy.stats as stats
import matplotlib.pyplot as plt
import numpy as np
import numpy.random as rnd

# distribution truncated to: 
trunc_start = -1
trunc_end = 2

#plot the original distribution
xrng=np.arange(-10,10,.1)
yrng=stats.logistic.pdf(xrng)
plt.plot(xrng,yrng)

#plot the truncated distribution
nrm=stats.logistic.cdf(trunc_end)-stats.logistic.cdf(trunc_start)
xrng=np.arange(trunc_start,trunc_end,.01)
yrng=stats.logistic.pdf(xrng)/nrm
plt.plot(xrng,yrng)

#sample using the inverse cdf
yr=rnd.rand(100000)*(nrm)+stats.logistic.cdf(trunc_start)
xr=stats.logistic.ppf(yr)
plt.hist(xr,normed=True)

plt.show()