RandomNumberGenerator

class pyarts3.arts.RandomNumberGenerator(*args, **kwargs)

Random number generator interface. Create an instance of this class and call the distribution methods to get a function that generates random numbers following the specified distribution.

Overview

Method	`binomial_distribution()`	Generate random numbers following a binomial distribution
Method	`cauchy_distribution()`	Generate random numbers following a cauchy distribution
Method	`chi_squared_distribution()`	Generate random numbers following a chi-squared distribution
Method	`exponential_distribution()`	Generate random numbers following an exponential distribution
Method	`fisher_f_distribution()`	Generate random numbers following a fisher f distribution
Method	`gamma_distribution()`	Generate random numbers following a gamma distribution
Method	`geometric_distribution()`	Generate random numbers following a geometric distribution
Method	`lognormal_distribution()`	Generate random numbers following a lognormal distribution
Method	`normal_distribution()`	Generate random numbers following a normal distribution
Method	`poisson_distribution()`	Generate random numbers following a poisson distribution
Method	`student_t_distribution()`	Generate random numbers following a student t distribution
Method	`uniform_int_distribution()`	Generate random numbers following a uniform distribution
Method	`uniform_real_distribution()`	Generate random numbers following a uniform distribution
Method	`weibull_distribution()`	Generate random numbers following a weibull distribution
Operator	`__eq__()`	Return self==value.
Operator	`__format__()`	Default object formatter.
Operator	`__ge__()`	Return self>=value.
Operator	`__gt__()`	Return self>value.
Operator	`__hash__()`	Return hash(self).
Operator	`__init__()`	Overloaded function.
Operator	`__le__()`	Return self<=value.
Operator	`__lt__()`	Return self<value.
Operator	`__ne__()`	Return self!=value.
Operator	`__repr__()`	Return repr(self).
Operator	`__str__()`	Return str(self).

Constructors

__init__(self, time: pyarts3.arts.Time = 2026-02-24 13:31:15.888388879) → None

__init__(self, seed: int) → None

__init__(self) → None

__init__(self, arg: pyarts3.arts.RandomNumberGenerator) → None

Overloaded function.

__init__(self, time: pyarts3.arts.Time = 2026-02-24 13:31:15.888388879) -> None

Initialize the RNG with the time seed (at startup by default)

__init__(self, seed: int) -> None

Initialize the RNG with an integer seed

__init__(self) -> None
__init__(self, arg: pyarts3.arts.RandomNumberGenerator) -> None

Methods

binomial_distribution(self, n: int = 1, p: float = 0.5) → collections.abc.Callable[[], int]: Generate random numbers following a binomial distribution

cauchy_distribution(self, location: float = 0.0, scale: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a cauchy distribution

chi_squared_distribution(self, n: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a chi-squared distribution

exponential_distribution(self, lambda: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following an exponential distribution

fisher_f_distribution(self, d1: float = 1.0, d2: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a fisher f distribution

gamma_distribution(self, alpha: float = 1.0, beta: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a gamma distribution

geometric_distribution(self, p: float = 0.5) → collections.abc.Callable[[], int]: Generate random numbers following a geometric distribution

lognormal_distribution(self, mean: float = 0.0, stddev: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a lognormal distribution

normal_distribution(self, mean: float = 0.0, stddev: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a normal distribution

poisson_distribution(self, mean: float = 1.0) → collections.abc.Callable[[], int]: Generate random numbers following a poisson distribution

student_t_distribution(self, n: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a student t distribution

uniform_int_distribution(self, lower_bound: int = 0, upper_bound: int = 10) → collections.abc.Callable[[], int]: Generate random numbers following a uniform distribution

uniform_real_distribution(self, lower_bound: float = 0.0, upper_bound: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a uniform distribution

weibull_distribution(self, a: float = 1.0, b: float = 1.0) → collections.abc.Callable[[], float]: Generate random numbers following a weibull distribution

Operators

__eq__(value, /): Return self==value.

__format__(format_spec, /)

Default object formatter.

Return str(self) if format_spec is empty. Raise TypeError otherwise.

__ge__(value, /): Return self>=value.

__gt__(value, /): Return self>value.

__hash__(): Return hash(self).

__init__(self, time: pyarts3.arts.Time = 2026-02-24 13:31:15.888388879) → None