how to use scipy.stats.kstest/basic questions about Kolmogorov–Smirnov test
Question: The help link is
<http://docs.scipy.org/doc/scipy-0.7.x/reference/generated/scipy.stats.kstest.html>
I can compute the ks-test value now,but I do not understand it. The code is as
below.

    
    
    from scipy import stats
    import numpy as np
    sample =np.loadtxt('mydata',delimiter=",",usecols=(2,),unpack=True)
    print stats.kstest(sample, 'poisson', args=(1,))
    

Q1  
If the reference distribution is constant,what word can replace 'poisson'
above?  
Q2  
what is the meaning of `args=(1,)`?  
Q3  
If anybody is interested in ks-test,here is the wiki link.  
<http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test>  
Can we write our own python code to practice? We can get _max(D)_ easily,but
how to get _Pr(k <=x)_ in the link? What is the relation between _max(D)_ and
_Pr(k <=x)_?


Answer: Q2: look at this, I have a array called `x1`

    
    
    >>> stats.kstest(x1, 'norm')
    (0.50018855199491585, 0.0)
    >>> stats.kstest(x1, stats.norm.cdf)
    (0.50018855199491585, 0.0)
    >>> stats.kstest(x1, stats.norm.cdf, args=(0,))
    (0.50018855199491585, 0.0)
    >>> stats.kstest(x1, stats.norm.cdf, args=(2,))
    (0.84134903906580316, 0.0)
    >>> stats.kstest(x1, 'norm', args=(2,))
    (0.84134903906580316, 0.0)
    

If you pass the name of distribution, i.e., `'norm'`, what actually get passed
to `kstest` is the standard distribution `cdf`. By standard, it means for
normal distribution having mean==0 and sigma=1. If you don't want the standard
`cdf`, you can pass additional parameters to `cdf` using `args=()`. In this
case I only passed the mean. That is, we testing the difference between `x1`
and a normal distribution with mean==2 and sigma=1.

Q3: The short answer is, yes. But, why reinventing the wheel? If you want to
know how it is implemented, just check the source code. It is in
`your_package_folder\scipy\stats\stats.py`, line 3292.

