# d3-random Generate random numbers from various distributions. ## Installing If you use NPM, `npm install d3-random`. Otherwise, download the [latest release](https://github.com/d3/d3-random/releases/latest). You can also load directly from [d3js.org](https://d3js.org), either as a [standalone library](https://d3js.org/d3-random.v1.min.js) or as part of [D3 4.0](https://github.com/d3/d3). AMD, CommonJS, and vanilla environments are supported. In vanilla, a `d3` global is exported: ```html ``` [Try d3-random in your browser.](https://runkit.com/npm/d3-random) ## API Reference # d3.randomUniform([min, ][max]) [<>](https://github.com/d3/d3-random/blob/master/src/uniform.js "Source") Returns a function for generating random numbers with a [uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribution_\(continuous\)). The minimum allowed value of a returned number is *min*, and the maximum is *max*. If *min* is not specified, it defaults to 0; if *max* is not specified, it defaults to 1. For example: ```js d3.randomUniform(6)(); // Returns a number greater than or equal to 0 and less than 6. d3.randomUniform(1, 5)(); // Returns a number greater than or equal to 1 and less than 5. ``` Note that you can also use the built-in [Math.random](https://developer.mozilla.org/en-US/docs/JavaScript/Reference/Global_Objects/Math/random) to generate uniform distributions directly. For example, to generate a random integer between 0 and 99 (inclusive), you can say `Math.random() * 100 | 0`. # d3.randomNormal([mu][, sigma]) [<>](https://github.com/d3/d3-random/blob/master/src/normal.js "Source") Returns a function for generating random numbers with a [normal (Gaussian) distribution](https://en.wikipedia.org/wiki/Normal_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. # d3.randomLogNormal([mu][, sigma]) [<>](https://github.com/d3/d3-random/blob/master/src/logNormal.js "Source") Returns a function for generating random numbers with a [log-normal distribution](https://en.wikipedia.org/wiki/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. # d3.randomBates(n) [<>](https://github.com/d3/d3-random/blob/master/src/bates.js "Source") Returns a function for generating random numbers with a [Bates distribution](https://en.wikipedia.org/wiki/Bates_distribution) with *n* independent variables. # d3.randomIrwinHall(n) [<>](https://github.com/d3/d3-random/blob/master/src/irwinHall.js "Source") Returns a function for generating random numbers with an [Irwin–Hall distribution](https://en.wikipedia.org/wiki/Irwin–Hall_distribution) with *n* independent variables. # d3.randomExponential(lambda) [<>](https://github.com/d3/d3-random/blob/master/src/exponential.js "Source") Returns a function for generating random numbers with an [exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution) with the rate *lambda*; equivalent to time between events in a [Poisson process](https://en.wikipedia.org/wiki/Poisson_point_process) with a mean of 1 / *lambda*. For example, exponential(1/40) generates random times between events where, on average, one event occurs every 40 units of time. # random.source(source) 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. For example: ```js var d3 = require("d3-random"), seedrandom = require("seedrandom"), random = d3.randomNormal.source(seedrandom("a22ebc7c488a3a47"))(0, 1); random(); // 0.9744193494813501 ```