Doing statistical error propagation with Python

uncertainty.py

 import uncertainties as unc  
 import uncertainties.unumpy as unumpy  
 import numpy  
 import nemmen  
   
 # Defines x and y  
 x=numpy.linspace(0,10,50)  
 y=numpy.linspace(15,20,50)  
   
 # Defines the error arrays, values follow a normal distribution  
 # (method random_normal defined in http://astropython.blogspot.com/2012/04/how-to-generate-array-of-random-numbers.html)  
 errx=nemmen.random_normal(0.1,0.2,50);     errx=numpy.abs(errx)  
 erry=nemmen.random_normal(0.3,0.2,50);     erry=numpy.abs(erry)  
   
 # Defines special arrays holding the values *and* errors  
 x=unumpy.uarray(( x, errx ))  
 y=unumpy.uarray(( y, erry ))  
   
 """  
 Now any operation that you carry on xerr and yerr will   
 automatically propagate the associated errors, as long  
 as you use the methods provided with uncertainties.unumpy  
 instead of using the numpy methods.  
   
 Let's for instance define z as   
 z = log10(x+y**2)  
 and estimate errz.  
 """  
 z=unumpy.log10(x+y**2)  
   
 # Print the propagated error errz  
 errz=unumpy.std_devs(z)  
 print errz