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Random variable generators. integers  uniform within range sequences  pick random element pick random sample generate random permutation distributions on the real line:  uniform normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull distributions on the circle (angles 0 to 2pi)  circular uniform von Mises General notes on the underlying Mersenne Twister core generator: * The period is 2**199371. * It is one of the most extensively tested generators in existence. * Without a direct way to compute N steps forward, the semantics of jumpahead(n) are weakened to simply jump to another distant state and rely on the large period to avoid overlapping sequences. * The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe.


Random Random number generator base class used by bound module functions. 

WichmannHill  
SystemRandom Alternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows). 








x in the interval [0, 1). 




































None 


x 



NV_MAGICCONST = 1.71552776992


TWOPI = 6.28318530718


LOG4 = 1.38629436112


SG_MAGICCONST = 2.50407739678


BPF = 53


RECIP_BPF = 1.11022302463e16


_inst = Random()

Imports: _warn, _MethodType, _BuiltinMethodType, _log, _exp, _pi, _e, _ceil, _sqrt, _acos, _cos, _sin, _urandom, _hexlify, _random

Initialize internal state from hashable object. None or no argument seeds from current time or from an operating system specific randomness source if available. If a is not None or an int or long, hash(a) is used instead. 
Choose a random item from range(start, stop[, step]). This fixes the problem with randint() which includes the endpoint; in Python this is usually not what you want. Do not supply the 'int', 'default', and 'maxwidth' arguments. 
Chooses k unique random elements from a population sequence. 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 subslices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices). 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. To choose a sample in a range of integers, use xrange as an argument. This is especially fast and space efficient for sampling from a large population: sample(xrange(10000000), 60) 
x, random=random.random > shuffle list x in place; return None. Optional arg random is a 0argument function returning a random float in [0.0, 1.0); by default, the standard random.random. 
Normal distribution. mu is the mean, and sigma is the standard deviation. 
Log normal distribution. 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. 
Exponential distribution. 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. 
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. 
Gamma distribution. Not the gamma function! Conditions on the parameters are alpha > 0 and beta > 0. 
Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function. Not threadsafe without a lock around calls. 
Beta distribution. Conditions on the parameters are alpha > 1 and beta} > 1. Returned values range between 0 and 1. 
Pareto distribution. alpha is the shape parameter. 
Weibull distribution. alpha is the scale parameter and beta is the shape parameter. 
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