D3

d3-random ​

Generate random numbers from various distributions. For seeded random number generation, see random.source and randomLcg.

randomUniform(min, max) ​

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d3.randomUniform(6) // generate numbers ≥0 and <6

Examples · Source · Returns a function for generating random numbers with a uniform distribution. The minimum allowed value of a returned number is min (inclusive), and the maximum is max (exclusive). If min is not specified, it defaults to 0; if max is not specified, it defaults to 1. For example:

randomInt(min, max) ​

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d3.randomInt(100) // generate integers ≥0 and <100

Examples · Source · Returns a function for generating random integers with a uniform distribution. The minimum allowed value of a returned number is ⌊min⌋ (inclusive), and the maximum is ⌊max - 1⌋ (inclusive). If min is not specified, it defaults to 0. For example:

randomNormal(mu, sigma) ​

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d3.randomNormal(0, 1) // mean of 0, and standard deviation of 1

Examples · Source · Returns a function for generating random numbers with a normal (Gaussian) distribution. The expected value of the generated numbers is mu, with the given standard deviation sigma. If mu is not specified, it defaults to 0; if sigma is not specified, it defaults to 1.

randomLogNormal(mu, sigma) ​

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d3.randomLogNormal(0, 1)

Examples · Source · Returns a function for generating random numbers with a log-normal distribution. The expected value of the random variable’s natural logarithm is mu, with the given standard deviation sigma. If mu is not specified, it defaults to 0; if sigma is not specified, it defaults to 1.

randomBates(n) ​

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d3.randomBates(3) // generates numbers between 0 and 1

Examples · Source · Returns a function for generating random numbers with a Bates distribution with n independent variables. The case of fractional n is handled as with d3.randomIrwinHall, and d3.randomBates(0) is equivalent to d3.randomUniform().

randomIrwinHall(n) ​

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d3.randomIrwinHall(3) // generates numbers between 0 and 3

Examples · Source · Returns a function for generating random numbers with an Irwin–Hall distribution with n independent variables. If the fractional part of n is non-zero, this is treated as adding d3.randomUniform() times that fractional part to the integral part.

randomExponential(lambda) ​

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d3.randomExponential(1 / 40)

Examples · Source · Returns a function for generating random numbers with an exponential distribution with the rate lambda; equivalent to time between events in a Poisson process with a mean of 1 / lambda. For example, randomExponential(1 / 40) generates random times between events where, on average, one event occurs every 40 units of time.

randomPareto(alpha) ​

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d3.randomPareto(6)

Examples · Source · Returns a function for generating random numbers with a Pareto distribution with the shape alpha. The value alpha must be a positive value.

randomBernoulli(p) ​

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d3.randomBernoulli(0.5)

Examples · Source · Returns a function for generating either 1 or 0 according to a Bernoulli distribution with 1 being returned with success probability p and 0 with failure probability q = 1 - p. The value p is in the range [0, 1].

randomGeometric(p) ​

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d3.randomGeometric(0.1)

Examples · Source · Returns a function for generating numbers with a geometric distribution with success probability p. The value p is in the range [0, 1].

randomBinomial(n, p) ​

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d3.randomBinomial(40, 0.5)

Examples · Source · Returns a function for generating random numbers with a binomial distribution with n the number of trials and p the probability of success in each trial. The value n is greater or equal to 0, and the value p is in the range [0, 1].

randomGamma(k, theta) ​

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d3.randomGamma(2, 1)

Examples · Source · Returns a function for generating random numbers with a gamma distribution with k the shape parameter and theta the scale parameter. The value k must be a positive value; if theta is not specified, it defaults to 1.

randomBeta(alpha, beta) ​

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d3.randomBeta(3, 1.5)

Examples · Source · Returns a function for generating random numbers with a beta distribution with alpha and beta shape parameters, which must both be positive.

randomWeibull(k, a, b) ​

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d3.randomWeibull(10)

Examples · Source · Returns a function for generating random numbers with one of the generalized extreme value distributions, depending on k:

In all three cases, a is the location parameter and b is the scale parameter. If a is not specified, it defaults to 0; if b is not specified, it defaults to 1.

randomCauchy(a, b) ​

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d3.randomCauchy(0, 1) // above, clipped to [-5, 5] because “fat tails”

Examples · Source · Returns a function for generating random numbers with a Cauchy distribution. a and b have the same meanings and default values as in d3.randomWeibull.

randomLogistic(a, b) ​

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d3.randomLogistic(0, 1)

Examples · Source · Returns a function for generating random numbers with a logistic distribution. a and b have the same meanings and default values as in d3.randomWeibull.

randomPoisson(lambda) ​

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d3.randomPoisson(400)

Examples · Source · Returns a function for generating random numbers with a Poisson distribution with mean lambda.

random.source(source) ​

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const seed = 0.44871573888282423; // any number in [0, 1)
const random = d3.randomNormal.source(d3.randomLcg(seed))(0, 1);
random(); // -0.6253955998897069

Examples · Returns the same type of function for generating random numbers but where the given random number generator source is used as the source of randomness instead of Math.random. The given random number generator must implement the same interface as Math.random and only return values in the range [0, 1). This is useful when a seeded random number generator is preferable to Math.random.

randomLcg(seed) ​

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d3.randomLcg(42)

Examples · Source · Returns a linear congruential generator; this function can be called repeatedly to obtain pseudorandom values well-distributed on the interval [0,1) and with a long period (up to 1 billion numbers), similar to Math.random. A seed can be specified as a real number in the interval [0,1) or as any integer. In the latter case, only the lower 32 bits are considered. Two generators instanced with the same seed generate the same sequence, allowing to create reproducible pseudo-random experiments. If the seed is not specified, one is chosen using Math.random.