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-\section{Standard Module \module{random}}
-\label{module-random}
-\stmodindex{random}
-
-This module implements pseudo-random number generators for various
-distributions: on the real line, there are functions to compute normal
-or Gaussian, lognormal, negative exponential, gamma, and beta
-distributions.  For generating distribution of angles, the circular
-uniform and von Mises distributions are available.
-
-The module exports the following functions, which are exactly
-equivalent to those in the \module{whrandom} module:
-\function{choice()}, \function{randint()}, \function{random()} and
-\function{uniform()}.  See the documentation for the \module{whrandom}
-module for these functions.
-
-The following functions specific to the \module{random} module are also
-defined, and all return real values.  Function parameters are named
-after the corresponding variables in the distribution's equation, as
-used in common mathematical practice; most of these equations can be
-found in any statistics text.
-
-\begin{funcdesc}{betavariate}{alpha, beta}
-Beta distribution.  Conditions on the parameters are
-\code{\var{alpha} >- 1} and \code{\var{beta} > -1}.
-Returned values will range between 0 and 1.
-\end{funcdesc}
-
-\begin{funcdesc}{cunifvariate}{mean, arc}
-Circular uniform distribution.  \var{mean} is the mean angle, and
-\var{arc} is the range of the distribution, centered around the mean
-angle.  Both values must be expressed in radians, and can range
-between 0 and pi.  Returned values will range between
-\code{\var{mean} - \var{arc}/2} and \code{\var{mean} + \var{arc}/2}.
-\end{funcdesc}
-
-\begin{funcdesc}{expovariate}{lambd}
-Exponential distribution.  \var{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 will range from 0 to
-positive infinity.
-\end{funcdesc}
-
-\begin{funcdesc}{gamma}{alpha, beta}
-Gamma distribution.  (\emph{Not} the gamma function!)  Conditions on
-the parameters are \code{\var{alpha} > -1} and \code{\var{beta} > 0}.
-\end{funcdesc}
-
-\begin{funcdesc}{gauss}{mu, sigma}
-Gaussian distribution.  \var{mu} is the mean, and \var{sigma} is the
-standard deviation.  This is slightly faster than the
-\function{normalvariate()} function defined below.
-\end{funcdesc}
-
-\begin{funcdesc}{lognormvariate}{mu, sigma}
-Log normal distribution.  If you take the natural logarithm of this
-distribution, you'll get a normal distribution with mean \var{mu} and
-standard deviation \var{sigma}.  \var{mu} can have any value, and \var{sigma}
-must be greater than zero.  
-\end{funcdesc}
-
-\begin{funcdesc}{normalvariate}{mu, sigma}
-Normal distribution.  \var{mu} is the mean, and \var{sigma} is the
-standard deviation.
-\end{funcdesc}
-
-\begin{funcdesc}{vonmisesvariate}{mu, kappa}
-\var{mu} is the mean angle, expressed in radians between 0 and 2*pi,
-and \var{kappa} is the concentration parameter, which must be greater
-than or equal to zero.  If \var{kappa} is equal to zero, this
-distribution reduces to a uniform random angle over the range 0 to
-2*pi.
-\end{funcdesc}
-
-\begin{funcdesc}{paretovariate}{alpha}
-Pareto distribution.  \var{alpha} is the shape parameter.
-\end{funcdesc}
-
-\begin{funcdesc}{weibullvariate}{alpha, beta}
-Weibull distribution.  \var{alpha} is the scale parameter and
-\var{beta} is the shape parameter.
-\end{funcdesc}
-
-\begin{seealso}
-\seemodule{whrandom}{the standard Python random number generator}
-\end{seealso}