Minor code clean-ups (GH-20838)

Lib/random.py

@@ -39,7 +39,7 @@ General notes on the underlying Mersenne Twister core generator:
 
 from warnings import warn as _warn
 from math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil
-from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
+from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin, tau as TWOPI
 from os import urandom as _urandom
 from _collections_abc import Set as _Set, Sequence as _Sequence
 from itertools import accumulate as _accumulate, repeat as _repeat
@@ -54,15 +54,34 @@ except ImportError:
     from hashlib import sha512 as _sha512
 
 
-__all__ = ["Random","seed","random","uniform","randint","choice","sample",
+__all__ = [
-           "randrange","shuffle","normalvariate","lognormvariate",
+    "Random",
-           "expovariate","vonmisesvariate","gammavariate","triangular",
+    "SystemRandom",
-           "gauss","betavariate","paretovariate","weibullvariate",
+    "betavariate",
-           "getstate","setstate", "getrandbits", "choices",
+    "choice",
-           "SystemRandom"]
+    "choices",
+    "expovariate",
+    "gammavariate",
+    "gauss",
+    "getrandbits",
+    "getstate",
+    "lognormvariate",
+    "normalvariate",
+    "paretovariate",
+    "randint",
+    "random",
+    "randrange",
+    "sample",
+    "seed",
+    "setstate",
+    "shuffle",
+    "triangular",
+    "uniform",
+    "vonmisesvariate",
+    "weibullvariate",
+]
 
 NV_MAGICCONST = 4 * _exp(-0.5) / _sqrt(2.0)
-TWOPI = 2.0*_pi
 LOG4 = _log(4.0)
 SG_MAGICCONST = 1.0 + _log(4.5)
 BPF = 53        # Number of bits in a float
@@ -75,6 +94,7 @@ RECIP_BPF = 2**-BPF
 
 import _random
 
+
 class Random(_random.Random):
     """Random number generator base class used by bound module functions.
 
@@ -303,7 +323,8 @@ class Random(_random.Random):
 
     def choice(self, seq):
         """Choose a random element from a non-empty sequence."""
-        return seq[self._randbelow(len(seq))] # raises IndexError if seq is empty
+        # raises IndexError if seq is empty
+        return seq[self._randbelow(len(seq))]
 
     def shuffle(self, x, random=None):
         """Shuffle list x in place, and return None.
@@ -412,9 +433,10 @@ class Random(_random.Random):
         if k > 5:
             setsize += 4 ** _ceil(_log(k * 3, 4))  # table size for big sets
         if n <= setsize:
-            # An n-length list is smaller than a k-length set
+            # An n-length list is smaller than a k-length set.
+            # Invariant:  non-selected at pool[0 : n-i]
             pool = list(population)
-            for i in range(k):         # invariant:  non-selected at [0,n-i)
+            for i in range(k):
                 j = randbelow(n - i)
                 result[i] = pool[j]
                 pool[j] = pool[n - i - 1]  # move non-selected item into vacancy
@@ -502,7 +524,7 @@ class Random(_random.Random):
         # Math Software, 3, (1977), pp257-260.
 
         random = self.random
-        while 1:
+        while True:
             u1 = random()
             u2 = 1.0 - random()
             z = NV_MAGICCONST * (u1 - 0.5) / u2
@@ -571,7 +593,7 @@ class Random(_random.Random):
         s = 0.5 / kappa
         r = s + _sqrt(1.0 + s * s)
 
-        while 1:
+        while True:
             u1 = random()
             z = _cos(_pi * u1)
 
@@ -625,7 +647,7 @@ class Random(_random.Random):
 
             while 1:
                 u1 = random()
-                if not 1e-7 < u1 < .9999999:
+                if not 1e-7 < u1 < 0.9999999:
                     continue
                 u2 = 1.0 - random()
                 v = _log(u1 / (1.0 - u1)) / ainv
@@ -639,11 +661,10 @@ class Random(_random.Random):
             # expovariate(1/beta)
             return -_log(1.0 - random()) * beta
 
-        else:   # alpha is between 0 and 1 (exclusive)
+        else:
-
+            # alpha is between 0 and 1 (exclusive)
             # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
-
+            while True:
-            while 1:
                 u = random()
                 b = (_e + alpha) / _e
                 p = b * u
@@ -725,10 +746,9 @@ class Random(_random.Random):
         # This version due to Janne Sinkkonen, and matches all the std
         # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
         y = self.gammavariate(alpha, 1.0)
-        if y == 0:
+        if y:
-            return 0.0
-        else:
             return y / (y + self.gammavariate(beta, 1.0))
+        return 0.0
 
     ## -------------------- Pareto --------------------
 
@@ -752,6 +772,7 @@ class Random(_random.Random):
         u = 1.0 - self.random()
         return alpha * (-_log(u)) ** (1.0 / beta)
 
+
 ## --------------- Operating System Random Source  ------------------
 
 class SystemRandom(Random):
@@ -789,6 +810,7 @@ class SystemRandom(Random):
         raise NotImplementedError('System entropy source does not have state.')
     getstate = setstate = _notimplemented
 
+
 ## -------------------- test program --------------------
 
 def _test_generator(n, func, args):
@@ -809,8 +831,7 @@ def _test_generator(n, func, args):
     print(round(t1 - t0, 3), 'sec,', end=' ')
     avg = total / n
     stddev = _sqrt(sqsum / n - avg * avg)
-    print('avg %g, stddev %g, min %g, max %g\n' % \
+    print('avg %g, stddev %g, min %g, max %g\n' % (avg, stddev, smallest, largest))
-              (avg, stddev, smallest, largest))
 
 
 def _test(N=2000):