[LNT] Python 3 support: get rid of apply builtin function

[thopre]

Summary

Replace calls to deprecated python2-specific apply builtin by direct
function calls with explicit argument list unpacking. This was produced
by running futurize's stage1 lib2to3.fixes.fix_apply.

[hubert.reinterpretcast]

LGTM.

lnt/trunk/lnt/external/stats/stats.py

def __init__(self, *tuples):
             for t in types:
                 if t in self._dispatch:
                     raise ValueError("can't have two dispatches on " + str(t))
                 self._dispatch[t] = func
 
     def __call__(self, arg1, *args, **kw):
         if type(arg1) not in self._dispatch:
             raise TypeError("don't know how to dispatch %s arguments" % type(arg1))
-        return apply(self._dispatch[type(arg1)], (arg1,) + args, kw)
+        return self._dispatch[type(arg1)](*((arg1,) + args), **kw)
 
 
 ##########################################################################
 ########################   LIST-BASED FUNCTIONS   ########################
 ##########################################################################
 
 ### Define these regardless
 

 
 Usage:   lfriedmanchisquare(*args)
 Returns: chi-square statistic, associated p-value
 """
     k = len(args)
     if k < 3:
         raise ValueError('Less than 3 levels.  Friedman test not appropriate.')
     n = len(args[0])
-    data = apply(pstat.abut,tuple(args))
+    data = pstat.abut(*tuple(args))
     for i in range(len(data)):
         data[i] = rankdata(data[i])
     ssbn = 0
     for i in range(k):
         ssbn = ssbn + sum(args[i])**2
     chisq = 12.0 / (k*n*(k+1)) * ssbn - 3*n*(k+1)
     return chisq, chisqprob(chisq,k-1)
 

 
 Usage:   afriedmanchisquare(*args)   args are separate arrays for 2+ conditions
 Returns: chi-square statistic, associated p-value
 """
     k = len(args)
     if k < 3:
         raise ValueError('\nLess than 3 levels.  Friedman test not appropriate.\n')
     n = len(args[0])
-    data = apply(pstat.aabut,args)
+    data = pstat.aabut(*args)
     data = data.astype(N.float_)
     for i in range(len(data)):
         data[i] = arankdata(data[i])
     ssbn = asum(asum(args,1)**2)
     chisq = 12.0 / (k*n*(k+1)) * ssbn - 3*n*(k+1)
     return chisq, achisqprob(chisq,k-1)