Nim's standard random number generator.
Its implementation is based on the xoroshiro128+ (xor/rotate/shift/rotate) library.
 More information: http://xoroshiro.di.unimi.it/
 C implementation: http://xoroshiro.di.unimi.it/xoroshiro128plus.c
Do not use this module for cryptographic purposes!
Basic usage
To get started, here are some examples:
import random # Call randomize() once to initialize the default random number generator # If this is not called, the same results will occur every time these # examples are run randomize() # Pick a number between 0 and 100 let num = rand(100) echo num # Roll a sixsided die let roll = rand(1..6) echo roll # Pick a marble from a bag let marbles = ["red", "blue", "green", "yellow", "purple"] let pick = sample(marbles) echo pick # Shuffle some cards var cards = ["Ace", "King", "Queen", "Jack", "Ten"] shuffle(cards) echo cards
These examples all use the default random number generator. The Rand type represents the state of a random number generator. For convenience, this module contains a default Rand state that corresponds to the default random number generator. Most procs in this module which do not take in a Rand parameter, including those called in the above examples, use the default generator. Those procs are not threadsafe.
Note that the default generator always starts in the same state. The randomize proc can be called to initialize the default generator with a seed based on the current time, and it only needs to be called once before the first usage of procs from this module. If randomize is not called, then the default generator will always produce the same results.
Generators that are independent of the default one can be created with the initRand proc.
Again, it is important to remember that this module must not be used for cryptographic applications.
See also
 math module for basic math routines
 mersenne module for the Mersenne Twister random number generator
 stats module for statistical analysis
 list of cryptographic and hashing modules in the standard library
Types
Rand = object a0, a1: Ui

State of a random number generator.
Create a new Rand state using the initRand proc.
The module contains a default Rand state for convenience. It corresponds to the default random number generator's state. The default Rand state always starts with the same values, but the randomize proc can be used to seed the default generator with a value based on the current time.
Many procs have two variations: one that takes in a Rand parameter and another that uses the default generator. The procs that use the default generator are not threadsafe!
Source Edit
Procs
proc next(r: var Rand): uint64 {...}{.raises: [], tags: [].}

Computes a random uint64 number using the given state.
See also:
 rand proc that returns an integer between zero and a given upper bound
 rand proc that returns a float
 rand proc that accepts a slice
 rand proc that accepts an integer or range type
 skipRandomNumbers proc
Examples:
var r = initRand(2019) doAssert r.next() == 138744656611299'u64 doAssert r.next() == 979810537855049344'u64 doAssert r.next() == 3628232584225300704'u64
Source Edit proc skipRandomNumbers(s: var Rand) {...}{.raises: [], tags: [].}

The jump function for the generator.
This proc is equivalent to 2^64 calls to next, and it can be used to generate 2^64 nonoverlapping subsequences for parallel computations.
When multiple threads are generating random numbers, each thread must own the Rand state it is using so that the thread can safely obtain random numbers. However, if each thread creates its own Rand state, the subsequences of random numbers that each thread generates may overlap, even if the provided seeds are unique. This is more likely to happen as the number of threads and amount of random numbers generated increases.
If many threads will generate random numbers concurrently, it is better to create a single Rand state and pass it to each thread. After passing the Rand state to a thread, call this proc before passing it to the next one. By using the Rand state this way, the subsequences of random numbers generated in each thread will never overlap as long as no thread generates more than 2^64 random numbers.
The following example below demonstrates this pattern:
# Compile this example with threads:on import random import threadpool const spawns = 4 const numbers = 100000 proc randomSum(rand: Rand): int = var r = rand for i in 1..numbers: result += rand(1..10) var r = initRand(2019) var vals: array[spawns, FlowVar[int]] for val in vals.mitems: val = spawn(randomSum(r)) r.skipRandomNumbers() for val in vals: echo ^val
See also:
Source Edit proc random(max: int): int {...}{.gcsafe, locks: 0, deprecated: "Deprecated since v0.18.0; use \'rand\' instead", raises: [], tags: [].}
 Source Edit
proc random(max: float): float {...}{.gcsafe, locks: 0, deprecated: "Deprecated since v0.18.0; use \'rand\' instead", raises: [], tags: [].}
 Source Edit
proc random[T](x: HSlice[T, T]): T {...}{.deprecated: "Deprecated since v0.18.0; use \'rand\' instead".}
 Source Edit
proc random[T](a: openArray[T]): T {...}{.deprecated: "Deprecated since v0.18.0; use \'sample\' instead".}
 Source Edit
proc rand(r: var Rand; max: Natural): int {...}{.gcsafe, locks: 0, raises: [], tags: [].}

Returns a random integer in the range 0..max using the given state.
See also:
 rand proc that returns an integer using the default random number generator
 rand proc that returns a float
 rand proc that accepts a slice
 rand proc that accepts an integer or range type
Examples:
var r = initRand(123) doAssert r.rand(100) == 0 doAssert r.rand(100) == 96 doAssert r.rand(100) == 66
Source Edit proc rand(max: int): int {...}{.gcsafe, locks: 0, raises: [], tags: [].}

Returns a random integer in the range 0..max.
If randomize has not been called, the sequence of random numbers returned from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 rand proc that returns an integer using a provided state
 rand proc that returns a float
 rand proc that accepts a slice
 rand proc that accepts an integer or range type
Examples:
randomize(123) doAssert rand(100) == 0 doAssert rand(100) == 96 doAssert rand(100) == 66
Source Edit proc rand(r: var Rand; max: range[0.0 .. high(float)]): float {...}{.gcsafe, locks: 0, raises: [], tags: [].}

Returns a random floating point number in the range 0.0..max using the given state.
See also:
 rand proc that returns a float using the default random number generator
 rand proc that returns an integer
 rand proc that accepts a slice
 rand proc that accepts an integer or range type
Examples:
var r = initRand(234) let f = r.rand(1.0) ## f = 8.717181376738381e07
Source Edit proc rand(max: float): float {...}{.gcsafe, locks: 0, raises: [], tags: [].}

Returns a random floating point number in the range 0.0..max.
If randomize has not been called, the sequence of random numbers returned from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 rand proc that returns a float using a provided state
 rand proc that returns an integer
 rand proc that accepts a slice
 rand proc that accepts an integer or range type
Examples:
randomize(234) let f = rand(1.0) ## f = 8.717181376738381e07
Source Edit proc rand[T: Ordinal or SomeFloat](r: var Rand; x: HSlice[T, T]): T

For a slice a..b, returns a value in the range a..b using the given state.
Allowed types for T are integers, floats, and enums without holes.
See also:
 rand proc that accepts a slice and uses the default random number generator
 rand proc that returns an integer
 rand proc that returns a float
 rand proc that accepts an integer or range type
Examples:
var r = initRand(345) doAssert r.rand(1 .. 6) == 4 doAssert r.rand(1 .. 6) == 4 doAssert r.rand(1 .. 6) == 6 let f = r.rand(1.0 .. 1.0) ## f = 0.8741183448756229
Source Edit proc rand[T: Ordinal or SomeFloat](x: HSlice[T, T]): T

For a slice a..b, returns a value in the range a..b.
Allowed types for T are integers, floats, and enums without holes.
If randomize has not been called, the sequence of random numbers returned from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 rand proc that accepts a slice and uses a provided state
 rand proc that returns an integer
 rand proc that returns a floating point number
 rand proc that accepts an integer or range type
Examples:
randomize(345) doAssert rand(1 .. 6) == 4 doAssert rand(1 .. 6) == 4 doAssert rand(1 .. 6) == 6
Source Edit proc rand[T](r: var Rand; a: openArray[T]): T {...}{. deprecated: "Deprecated since v0.20.0; use \'sample\' instead".}
 Source Edit
proc rand[T: SomeInteger](t: typedesc[T]): T

Returns a random integer in the range low(T)..high(T).
If randomize has not been called, the sequence of random numbers returned from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 rand proc that returns an integer
 rand proc that returns a floating point number
 rand proc that accepts a slice
Examples:
randomize(567) doAssert rand(int8) == 55 doAssert rand(int8) == 42 doAssert rand(int8) == 43 doAssert rand(uint32) == 578980729'u32 doAssert rand(uint32) == 4052940463'u32 doAssert rand(uint32) == 2163872389'u32 doAssert rand(range[1 .. 16]) == 11 doAssert rand(range[1 .. 16]) == 4 doAssert rand(range[1 .. 16]) == 16
Source Edit proc rand[T](a: openArray[T]): T {...}{.deprecated: "Deprecated since v0.20.0; use \'sample\' instead".}
 Source Edit
proc sample[T](r: var Rand; s: set[T]): T

Returns a random element from the set s using the given state.
See also:
 sample proc that uses the default random number generator
 sample proc for openarrays
 sample proc that uses a cumulative distribution function
Examples:
var r = initRand(987) let s = {1, 3, 5, 7, 9} doAssert r.sample(s) == 5 doAssert r.sample(s) == 7 doAssert r.sample(s) == 1
Source Edit proc sample[T](s: set[T]): T

Returns a random element from the set s.
If randomize has not been called, the order of outcomes from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 sample proc that uses a provided state
 sample proc for openarrays
 sample proc that uses a cumulative distribution function
Examples:
randomize(987) let s = {1, 3, 5, 7, 9} doAssert sample(s) == 5 doAssert sample(s) == 7 doAssert sample(s) == 1
Source Edit proc sample[T](r: var Rand; a: openArray[T]): T

Returns a random element from a using the given state.
See also:
 sample proc that uses the default random number generator
 sample proc that uses a cumulative distribution function
 sample proc for sets
Examples:
let marbles = ["red", "blue", "green", "yellow", "purple"] var r = initRand(456) doAssert r.sample(marbles) == "blue" doAssert r.sample(marbles) == "yellow" doAssert r.sample(marbles) == "red"
Source Edit proc sample[T](a: openArray[T]): T

Returns a random element from a.
If randomize has not been called, the order of outcomes from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 sample proc that uses a provided state
 sample proc that uses a cumulative distribution function
 sample proc for sets
Examples:
let marbles = ["red", "blue", "green", "yellow", "purple"] randomize(456) doAssert sample(marbles) == "blue" doAssert sample(marbles) == "yellow" doAssert sample(marbles) == "red"
Source Edit proc sample[T, U](r: var Rand; a: openArray[T]; cdf: openArray[U]): T

Returns an element from a using a cumulative distribution function (CDF) and the given state.
The cdf argument does not have to be normalized, and it could contain any type of elements that can be converted to a float. It must be the same length as a. Each element in cdf should be greater than or equal to the previous element.
The outcome of the cumsum proc and the return value of the cumsummed proc, which are both in the math module, can be used as the cdf argument.
See also:
 sample proc that also utilizes a CDF but uses the default random number generator
 sample proc that does not use a CDF
 sample proc for sets
Examples:
from math import cumsummed let marbles = ["red", "blue", "green", "yellow", "purple"] let count = [1, 6, 8, 3, 4] let cdf = count.cumsummed var r = initRand(789) doAssert r.sample(marbles, cdf) == "red" doAssert r.sample(marbles, cdf) == "green" doAssert r.sample(marbles, cdf) == "blue"
Source Edit proc sample[T, U](a: openArray[T]; cdf: openArray[U]): T

Returns an element from a using a cumulative distribution function (CDF).
This proc works similarly to sample[T, U](Rand, openArray[T], openArray[U]). See that proc's documentation for more details.
If randomize has not been called, the order of outcomes from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 sample proc that also utilizes a CDF but uses a provided state
 sample proc that does not use a CDF
 sample proc for sets
Examples:
from math import cumsummed let marbles = ["red", "blue", "green", "yellow", "purple"] let count = [1, 6, 8, 3, 4] let cdf = count.cumsummed randomize(789) doAssert sample(marbles, cdf) == "red" doAssert sample(marbles, cdf) == "green" doAssert sample(marbles, cdf) == "blue"
Source Edit proc initRand(seed: int64): Rand {...}{.raises: [], tags: [].}

Initializes a new Rand state using the given seed.
seed must not be zero. Providing a specific seed will produce the same results for that seed each time.
The resulting state is independent of the default random number generator's state.
See also:
 randomize proc that accepts a seed for the default random number generator
 randomize proc that initializes the default random number generator using the current time
Examples:
from times import getTime, toUnix, nanosecond var r1 = initRand(123) let now = getTime() var r2 = initRand(now.toUnix * 1000000000 + now.nanosecond)
Source Edit proc randomize(seed: int64) {...}{.gcsafe, locks: 0, raises: [], tags: [].}

Initializes the default random number generator with the given seed.
seed must not be zero. Providing a specific seed will produce the same results for that seed each time.
See also:
 initRand proc
 randomize proc that uses the current time instead
Examples:
from times import getTime, toUnix, nanosecond randomize(123) let now = getTime() randomize(now.toUnix * 1000000000 + now.nanosecond)
Source Edit proc shuffle[T](r: var Rand; x: var openArray[T])

Shuffles a sequence of elements inplace using the given state.
See also:
 shuffle proc that uses the default random number generator
Examples:
var cards = ["Ace", "King", "Queen", "Jack", "Ten"] var r = initRand(678) r.shuffle(cards) doAssert cards == ["King", "Ace", "Queen", "Ten", "Jack"]
Source Edit proc shuffle[T](x: var openArray[T])

Shuffles a sequence of elements inplace.
If randomize has not been called, the order of outcomes from this proc will always be the same.
This proc uses the default random number generator. Thus, it is not threadsafe.
See also:
 shuffle proc that uses a provided state
Examples:
var cards = ["Ace", "King", "Queen", "Jack", "Ten"] randomize(678) shuffle(cards) doAssert cards == ["King", "Ace", "Queen", "Ten", "Jack"]
Source Edit proc randomize() {...}{.gcsafe, locks: 0, raises: [], tags: [TimeEffect].}

Initializes the default random number generator with a value based on the current time.
This proc only needs to be called once, and it should be called before the first usage of procs from this module that use the default random number generator.
Note: Does not work for NimScript.
See also:
 randomize proc that accepts a seed
 initRand proc