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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import sde_sim_rs
import numpy as np
import plotly.express as px
import polars as pl

print(dir(sde_sim_rs))


def main():
    df: pl.DataFrame = sde_sim_rs.simulate(
        processes_equations=[
            "dX1 = ( sin(t) ) * dt",
            "dX2 = (0.01 * X1) * dW1",
            "dX3 = (0.005 * X3) * dt + (0.01 * X3) * dW2 + (0.1 * X2 * X3) * dJ1(0.01)",
        ],
        time_steps=list(np.arange(0.0, 100.0, 0.1)),
        scenarios=1000,
        initial_values={"X1": 0.0, "X2": 1.0, "X3": 100.0},
        rng_method="pseudo",
        scheme="euler",
    )
    print(df)
    for i in range(1, 4):
        fig = px.line(
            df.filter(pl.col("process_name") == f"X{i}"),
            x="time",
            y="value",
            color="scenario",
            line_dash="process_name",
            title="Simulated SDE Process",
        )
        fig.show()


if __name__ == "__main__":
    main()