sde-sim-rs 0.5.1

Powerful and flexible stochastic differential equation (quasi) Monte-Carlo simulation library written in Rust with Python bindings
Documentation 
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use crate::rng::{BaseRng, StepCache};
use rand::{Rng as RandRng, SeedableRng};
use rand_chacha::ChaCha8Rng;

// --- Pseudo RNG ---

pub struct PseudoRng {
    last_step: Option<StepCache>,
    num_increments: usize,
    rng: ChaCha8Rng,
}

impl PseudoRng {
    pub fn new(seed: u64, num_increments: usize) -> Self {
        Self {
            last_step: None,
            num_increments,
            rng: ChaCha8Rng::seed_from_u64(seed),
        }
    }

    fn refresh_cache(&mut self, time_idx: usize) {
        let mut values = Vec::with_capacity(self.num_increments);
        for _ in 0..self.num_increments {
            values.push(self.rng.random::<f64>());
        }
        self.last_step = Some(StepCache {
            time_idx: Some(time_idx),
            values,
        });
    }
}

impl BaseRng for PseudoRng {
    fn sample(&mut self, time_idx: usize, increment_idx: usize) -> f64 {
        let is_cached = self
            .last_step
            .as_ref()
            .is_some_and(|c| c.time_idx == Some(time_idx));

        if !is_cached {
            self.refresh_cache(time_idx);
        }

        // CHANGE THIS: Don't return 0.0. Panic or return a clear error.
        // Returning 0.0 here kills the variance of your simulation silently.
        *self
            .last_step
            .as_ref()
            .unwrap()
            .values
            .get(increment_idx)
            .unwrap_or_else(|| {
                panic!(
                    "RNG Index {} out of bounds (max {})",
                    increment_idx, self.num_increments
                )
            })
    }
}