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Randish is a thread-safe random number generator library in Go, providing distinct random sequences with a unique seeding approach based on a mix of system and context-specific elements. It offers singleton and array-based pseudo-random number generation, ensuring high performance and randomness across different contexts and systems.

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Randish

Randish is a Go package that provides pseudo-random number generators which are unique and thread-safe.

Do not use this for cryptography! If you need a package for that purpose, please use the crypto/rand package instead.

Features

  • Rand(): Generates a new pseudo-random number generator that is initialized with a unique seed value.
  • RandS(): Singleton and thread-safe variant of the Rand() function. Returns a globally unique instance of a pseudo-random number generator.
  • RandSA(): Initializes an array of pseudo-random number generators and returns one from the array using pseudo-random selection.
  • Seed(): Function that generates a seed for pseudo-random number generation using various system and context-specific elements.

Getting Started

Prerequisites

  • Go 1.7 or later

Installation

  1. Download randish using the go get command:
    go get github.com/TechMDW/randish
    
  2. Import randish in your code:
    import "github.com/TechMDW/randish"
  3. Use the functions as needed.

Usage

Here is an example of how to use randish in your Go code:

package main

import (
    "github.com/TechMDW/randish"
)

func main() {
    // Generate a new random number generator
    //
    // Returns the *rand.Rand from "math/rand" package
    r := randish.Rand()

    // Generate a random number
    num := r.Int()

    println(num)
}

TODO

  • Write README
  • Version control

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Maintained by @TechMDW - contact@techmdw.com - Feel free to contact us if you have any questions.

About

Randish is a thread-safe random number generator library in Go, providing distinct random sequences with a unique seeding approach based on a mix of system and context-specific elements. It offers singleton and array-based pseudo-random number generation, ensuring high performance and randomness across different contexts and systems.

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