Improve comments. Clarify docs.

rhettinger · rhettinger · commit 311f4196284b · 2002-11-18T09:01:24.000Z
Replace "type(0)" with "int".
Replace "while 1" with "while True"
diff --git a/Doc/lib/librandom.tex b/Doc/lib/librandom.tex
@@ -182,20 +182,21 @@ \section{\module{random} ---
 \begin{funcdesc}{sample}{population, k}
   Return a \var{k} length list of unique elements chosen from the
   population sequence.  Used for random sampling without replacement.
+  \versionadded{2.3}
 
-  Returns a new list containing elements from the population.  The
-  list itself is in random order so that all sub-slices are also
-  random samples.  The original sequence is left undisturbed.
-
-  If the population has repeated elements, then each occurence is a
-  possible selection in the sample.
+  Returns a new list containing elements from the population while
+  leaving the original population unchanged.  The resulting list is
+  in selection order so that all sub-slices will also be valid random
+  samples.  This allows raffle winners (the sample) to be partitioned
+  into grand prize and second place winners (the subslices).
 
-  If indices are needed for a large population, use \function{xrange}
-  as an argument:  \code{sample(xrange(10000000), 60)}.
+  Members of the population need not be hashable or unique.  If the
+  population contains repeats, then each occurrence is a possible
+  selection in the sample.
 
-  Optional argument random is a 0-argument function returning a random
-  float in [0.0, 1.0); by default, the standard random.random.  			   
-  \versionadded{2.3}
+  To choose a sample from a range of integers, use \function{xrange}
+  as an argument.  This is especially fast and space efficient for
+  sampling from a large population:  \code{sample(xrange(10000000), 60)}.
 \end{funcdesc}
 
 
diff --git a/Lib/random.py b/Lib/random.py
@@ -239,7 +239,7 @@ def __whseed(self, x=0, y=0, z=0):
         These must be integers in the range [0, 256).
         """
 
-        if not type(x) == type(y) == type(z) == type(0):
+        if not type(x) == type(y) == type(z) == int:
             raise TypeError('seeds must be integers')
         if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256):
             raise ValueError('seeds must be in range(0, 256)')
@@ -407,8 +407,7 @@ def sample(self, population, k, random=None, int=int):
         # Previous selections are stored in dictionaries which provide
         # __contains__ for detecting repeat selections.  Discarding repeats
         # is efficient unless most of the population has already been chosen.
-        # So, tracking selections is useful when sample sizes are much
-        # smaller than the total population.
+        # So, tracking selections is fast only with small sample sizes.
 
         n = len(population)
         if not 0 <= k <= n:
@@ -417,19 +416,19 @@ def sample(self, population, k, random=None, int=int):
             random = self.random
         result = [None] * k
         if n < 6 * k:     # if n len list takes less space than a k len dict
-            pool = list(population)             # track potential selections
-            for i in xrange(k):
-                j = int(random() * (n-i))       # non-selected at [0,n-i)
-                result[i] = pool[j]             # save selected element
-                pool[j] = pool[n-i-1]           # non-selected to head of list
+            pool = list(population)
+            for i in xrange(k):         # invariant:  non-selected at [0,n-i)
+                j = int(random() * (n-i))
+                result[i] = pool[j]
+                pool[j] = pool[n-i-1]
         else:
-            selected = {}                       # track previous selections
+            selected = {}
             for i in xrange(k):
                 j = int(random() * n)
-                while j in selected:            # discard and replace repeats
+                while j in selected:
                     j = int(random() * n)
                 result[i] = selected[j] = population[j]
-        return result       # return selections in the order they were picked
+        return result
 
 ## -------------------- real-valued distributions  -------------------
 
@@ -455,7 +454,7 @@ def normalvariate(self, mu, sigma):
         # Math Software, 3, (1977), pp257-260.
 
         random = self.random
-        while 1:
+        while True:
             u1 = random()
             u2 = random()
             z = NV_MAGICCONST*(u1-0.5)/u2
@@ -548,7 +547,7 @@ def vonmisesvariate(self, mu, kappa):
         b = (a - _sqrt(2.0 * a))/(2.0 * kappa)
         r = (1.0 + b * b)/(2.0 * b)
 
-        while 1:
+        while True:
             u1 = random()
 
             z = _cos(_pi * u1)
@@ -595,7 +594,7 @@ def gammavariate(self, alpha, beta):
             bbb = alpha - LOG4
             ccc = alpha + ainv
 
-            while 1:
+            while True:
                 u1 = random()
                 u2 = random()
                 v = _log(u1/(1.0-u1))/ainv
@@ -616,7 +615,7 @@ def gammavariate(self, alpha, beta):
 
             # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
 
-            while 1:
+            while True:
                 u = random()
                 b = (_e + alpha)/_e
                 p = b*u
