Installation (Python)

The stochastic_rs Python package wraps the Rust crates via PyO3. At v2.0 the surface covers 210 entries — 198 PyO3 classes plus 12 free functions across distributions, stochastic processes, pricers, calibrators, copulas, and stats.

From PyPI (pre-built wheels)

pip install stochastic-rs

Wheels are published for:

• CPython 3.10 / 3.11 / 3.12 / 3.13
• Linux (manylinux x86_64, aarch64), macOS (universal2), Windows (x86_64)

If your platform / ABI combination is missing, pip will fall back to building from source — that needs a Rust toolchain.

Build from source

You need the Rust toolchain (rustup), Python ≥ 3.10, and maturin.

git clone https://github.com/dancixx/stochastic-rs.git
cd stochastic-rs

python -m venv .venv
source .venv/bin/activate

pip install maturin
maturin develop --release --manifest-path stochastic-rs-py/Cargo.toml

The manifest-path flag is required because the Python crate lives in a sub-folder of the workspace. After this completes, import stochastic_rs will use the just-built shared library.

Verify

import stochastic_rs as srs
import numpy as np

p = srs.Ou(theta=2.0, mu=0.0, sigma=1.0, n=1000, x0=0.0, t=1.0)
path = p.sample()
assert isinstance(path, np.ndarray)
assert path.shape == (1000,)
print("OK", path.mean(), path.std())

If this prints two finite numbers, you are good. Continue with the Quickstart.

NumPy interop

All sample() calls return numpy.ndarray. Bulk samplers (sample_par(m, n)) return (m, n)-shaped arrays. The dtype is float64 by default; pass dtype="f32" for float32 paths where the underlying sampler supports it (most distributions and processes do).