sde-sim-rs 0.3.0

Powerful and flexible stochastic differential equation (quasi) Monte-Carlo simulation library written in Rust with Python bindings
Documentation 
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use crate::filtration::Filtration;
use crate::rng::{Rng, pseudo::PseudoRng, sobol::SobolRng};
use crate::sim::simulate;
use ordered_float::OrderedFloat;
use polars::prelude::*;
use pyo3::prelude::*;
use pyo3_polars::PyDataFrame;
use std::collections::HashMap;

#[pyfunction]
#[pyo3(name = "simulate")]
pub fn simulate_py(
    processes_equations: Vec<String>,
    time_steps: Vec<f64>,
    scenarios: i32,
    initial_values: HashMap<String, f64>,
    rng_method: String,
    scheme: String,
) -> PyResult<PyDataFrame> {
    let time_steps_ordered: Vec<OrderedFloat<f64>> =
        time_steps.iter().copied().map(OrderedFloat).collect();

    let processes =
        crate::proc::util::parse_equations(&processes_equations, time_steps_ordered.clone())
            .map_err(|e| {
                PyErr::new::<pyo3::exceptions::PyValueError, _>(format!(
                    "Failed to parse process equations: {}",
                    e
                ))
            })?;

    let mut filtration = Filtration::new(
        time_steps_ordered.clone(),
        (1..=scenarios).collect(),
        processes,
        Some(initial_values),
    );

    let num_incrementors = crate::proc::util::num_incrementors();
    let mut rng: Box<dyn Rng> = if rng_method == "sobol" {
        Box::new(SobolRng::new(num_incrementors, time_steps_ordered.len()))
    } else {
        Box::new(PseudoRng::new(num_incrementors))
    };

    simulate(&mut filtration, &mut *rng, &scheme);

    let df: DataFrame = filtration.to_dataframe();
    Ok(PyDataFrame(df))
}

#[pymodule]
fn sde_sim_rs(m: &Bound<'_, PyModule>) -> PyResult<()> {
    m.add_function(wrap_pyfunction!(simulate_py, m)?)?;
    Ok(())
}