Draw samples from a Logistic distribution.
Samples are drawn from a Logistic distribution with specified parameters, loc (location or mean, also median), and scale (>0).
Parameters:  loc : float scale : float > 0. size : {tuple, int}


Returns:  samples : {ndarray, scalar}

See also
Notes
The probability density for the Logistic distribution is
where = location and = scale.
The Logistic distribution is used in Extreme Value problems where it can act as a mixture of Gumbel distributions, in Epidemiology, and by the World Chess Federation (FIDE) where it is used in the Elo ranking system, assuming the performance of each player is a logistically distributed random variable.
References
[R93]  Reiss, R.D. and Thomas M. (2001), Statistical Analysis of Extreme Values, from Insurance, Finance, Hydrology and Other Fields, Birkhauser Verlag, Basel, pp 132133. 
[R94]  Weisstein, Eric W. “Logistic Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/LogisticDistribution.html 
[R95]  Wikipedia, “Logisticdistribution”, http://en.wikipedia.org/wiki/Logisticdistribution 
Examples
Draw samples from the distribution:
>>> loc, scale = 10, 1
>>> s = np.random.logistic(loc, scale, 10000)
>>> count, bins, ignored = plt.hist(s, bins=50)
# plot against distribution
>>> def logist(x, loc, scale):
... return exp((locx)/scale)/(scale*(1+exp((locx)/scale))**2)
>>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\
... logist(bins, loc, scale).max())
>>> plt.show()