Shared Resources

README.md

@@ -2,6 +2,8 @@
 [![Build](https://github.com/primitivefinance/aika/actions/workflows/build.yaml/badge.svg)](https://github.com/primitivefinance/aika/actions/workflows/build.yaml)
 [![Rust](https://github.com/primitivefinance/aika/actions/workflows/rust.yaml/badge.svg)](https://github.com/primitivefinance/aika/actions/workflows/rust.yaml)
 
-Discrete event simulator built in Rust 🦀 . Designed to have a similar syntax to SimPy with a particular focus on configurability for complex simulation environments.
+Discrete event simulation manager built in Rust 🦀 . Aika is designed to have a similar base syntax to [SimPy](https://gitlab.com/team-simpy/simpy), however, has the addition of a `Manager` to improve focus on large, repetitive, data intensive simulations.
 
 This simulator utilizes `generators`, currently an expiremental feature of Rust nightly version 1.71.0. 
+
+![](/assets/aika-clock.png)

aika/src/distribution.rs

@@ -2,7 +2,7 @@
 //! Distributions must enforce a sampling of only positive real numbers, as this describes a time delta moving forward.
 
 use rand::Rng;
-use rand_distr::{Gamma as GammaDistribution, Poisson as PoissonDistribution};
+use rand_distr::{Gamma as GammaDistribution, Poisson as PoissonDistribution, LogNormal as LogNormalDistribution};
 
 /// The `Distribution` trait allows for the creation of custom distributions to be used in the `ProcessExecution::Stochastic` variant.
 pub trait Distribution {
@@ -47,3 +47,22 @@ impl Distribution for Gamma {
         rng.sample(self.distribution)
     }
 }
+
+/// The `LogNormal` struct implements the `Distribution` trait for the LogNormal distribution.
+pub struct LogNormal {
+    pub distribution: LogNormalDistribution<f64>,
+}
+
+impl LogNormal {
+    pub fn new(mean: f64, std_dev: f64) -> LogNormal {
+        Self {
+            distribution: LogNormalDistribution::new(mean, std_dev).unwrap(),
+        }
+    }
+}
+
+impl Distribution for LogNormal {
+    fn sample(&self, rng: &mut rand::rngs::StdRng) -> f64 {
+        rng.sample(self.distribution)
+    }
+}