Whitespace normalized.
diff --git a/Lib/random.py b/Lib/random.py
index 424a905..6debaf3 100644
--- a/Lib/random.py
+++ b/Lib/random.py
@@ -117,7 +117,7 @@
Class Random can also be subclassed if you want to use a different basic
generator of your own devising: in that case, override the following
methods: random(), seed(), getstate(), setstate() and jumpahead().
-
+
"""
VERSION = 1 # used by getstate/setstate
@@ -374,7 +374,7 @@
"""Normal distribution.
mu is the mean, and sigma is the standard deviation.
-
+
"""
# mu = mean, sigma = standard deviation
@@ -401,7 +401,7 @@
If you take the natural logarithm of this distribution, you'll get a
normal distribution with mean mu and standard deviation sigma.
mu can have any value, and sigma must be greater than zero.
-
+
"""
return _exp(self.normalvariate(mu, sigma))
@@ -417,7 +417,7 @@
Deprecated in version 2.3. Use:
(mean + arc * (Random.random() - 0.5)) % Math.pi
-
+
"""
# mean: mean angle (in radians between 0 and pi)
# arc: range of distribution (in radians between 0 and pi)
@@ -436,7 +436,7 @@
lambd is 1.0 divided by the desired mean. (The parameter would be
called "lambda", but that is a reserved word in Python.) Returned
values range from 0 to positive infinity.
-
+
"""
# lambd: rate lambd = 1/mean
# ('lambda' is a Python reserved word)
@@ -451,12 +451,12 @@
def vonmisesvariate(self, mu, kappa):
"""Circular data distribution.
-
+
mu is the mean angle, expressed in radians between 0 and 2*pi, and
kappa is the concentration parameter, which must be greater than or
equal to zero. If kappa is equal to zero, this distribution reduces
to a uniform random angle over the range 0 to 2*pi.
-
+
"""
# mu: mean angle (in radians between 0 and 2*pi)
# kappa: concentration parameter kappa (>= 0)
@@ -590,7 +590,7 @@
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls.
-
+
"""
# When x and y are two variables from [0, 1), uniformly
@@ -641,9 +641,9 @@
Conditions on the parameters are alpha > -1 and beta} > -1.
Returned values range between 0 and 1.
-
+
"""
-
+
# 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.)
@@ -667,7 +667,7 @@
"""Weibull distribution.
alpha is the scale parameter and beta is the shape parameter.
-
+
"""
# Jain, pg. 499; bug fix courtesy Bill Arms