Real-valued distributions

ixia.beta_variate

Link: Original section for random.betavariate

def beta_variate(alpha: Number, beta: Number) -> float

Beta distribution.

Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between $0$ and $1$.

ixia.binomial_variate

Link: Original section for random.binomialvariate

Binomial random variable.

Gives the number of successes for n independent trials with the probability of success in each trial being p.

Equivalent to sum(random() < p for _ in range(n)).

Returns an integer in the range $[0, n]$.

ixia.expo_variate

Link: Original section for random.expovariate

def expo_variate(lambda_: float = 1.0) -> float

Exponential distribution.

lambda_ is $1$ divided by the desired mean. It should be nonzero. Returned values are in range $[0, +\infty)$ for lambda_ > 0, and $(-\infty, 0]$ for lambda_ < 0.

ixia.gamma_variate

Link: Original section for random.gammavariate

def gamma_variate(alpha: Number, beta: Number) -> float

Gamma distribution.

Conditions on the parameters are alpha > 0 and beta > 0.

The probability distribution function is $$f(x)=\frac{x^{\alpha-1}\cdot e^{\frac{-x}{\beta}}}{\Gamma(\alpha)\cdot\beta^\alpha}$$

ixia.gauss

Link: Original section for random.gauss

def gauss(mu: Number, sigma: Number) -> float

Normal distribution, also called the Gaussian distribution.

mu is the mean, and sigma is the standard deviation. This is slightly faster than the ixia.normal_variate() function.

Multithreading Note
When two threads call this function simultaneously, it is possible that they will receive the same return value. This can be avoided in two ways: 1. Put locks around all calls 2. Use the slower, but thread-safe ixia.normal_variate() function instead.

ixia.log_norm_variate

Link: Original section for random.lognormvariate

def log_norm_variate(mu: Number, sigma: Number) -> float

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 $0$.

ixia.normal_variate

Link: Original section for random.normalvariate

def normal_variate(mu: Number, sigma: Number) -> float

Normal distribution.

mu is the mean, and sigma is the standard deviation.

ixia.pareto_variate

Link: Original section for random.paretovariate

def pareto_variate(alpha: Number) -> float

Pareto distribution.

alpha is the shape parameter.

ixia.random

Link: Original section for random.random

def random() -> float

Generates a random floating point number in the range $[0, 1)$.

ixia.triangular

Link: Original section for random.triangular

def triangular(
    low: float = 0.0,
    high: float = 1.0,
    mode: float | None = None
) -> float

Returns a random floating point number N such that low <= N <= high and with the specified mode between those bounds. The low and high bounds default to zero and one. The mode argument defaults to the midpoint between the bounds, giving a symmetric distribution.

ixia.uniform

Link: Original section for random.uniform

def uniform(a: Number, b: Number) -> float

Returns a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a.

The end-point value b may or may not be included in the range depending on floating-point rounding in the equation a + (b-a) * random().

ixia.von_mises_variate

Link: Original section for random.vonmisesvariate

def von_mises_variate(mu: Number, kappa: Number) -> float

mu is the mean angle, expressed in radians between $0$ and $\tau$, 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 $\tau$.

ixia.weibull_variate

Link: Original section for random.weibullvariate

def weibull_variate(alpha: Number, beta: Number) -> float

Weibull distribution.

alpha is the scale parameter and beta is the shape parameter.