PyXPlot Users' Guide
PyXPlot Users' Guide : Reference manual : List of in-built functions : The random module

2.8 The random module

The random module contains function for generating random samples from probability distributions:

random.binomial($p,n$)
The random.binomial($p,n$) function returns a random sample from a binomial distribution with $n$ independent trials and a success probability $p$. $n$ must be a real positive dimensionless integer. $p$ must be a dimensionless number in the range $0\leq p\leq 1$.

random.chisq($\nu $)
The random.chisq($\nu $) function returns a random sample from a $\chi $-squared distribution with $\nu $ degrees of freedom, where $\nu $ must be a real positive dimensionless integer.

random.gaussian($\sigma $)
The random.gaussian($\sigma $) function returns a random sample from a Gaussian (normal) distribution of standard deviation $\sigma $ and centred upon zero. $\sigma $ must be real, but may have any physical units. The returned random sample shares the physical units of $\sigma $.

random.lognormal($\zeta ,\sigma $)
The random.lognormal($\zeta ,\sigma $) function returns a random sample from the log normal distribution centred on $\zeta $, and of width $\sigma $. $\sigma $ must be a real positive dimensionless number. $\zeta $ must be real, but may have any physical units. The returned random sample shares the physical units of $\zeta $.

random.poisson($n$)
The random.poisson($n$) function returns a random integer from a Poisson distribution with mean $n$, where $n$ must be a real positive dimensionless number.

random.random()
The random.random() function returns a random real number between 0 and 1.

random.tdist($\nu $)
The random.tdist($\nu $) function returns a random sample from a $t$-distribution with $\nu $ degrees of freedom, where $\nu $ must be a real positive dimensionless integer.

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