stochas is a Python framework built to handle the complexity of Monte Carlo simulations, parametric studies, and probabilistic modeling.
It provides a robust bridge between abstract statistical rules and concrete simulation data, ensuring your experiments are repeatable, traceable, and easy to manage.
Install the package via your preferred manager:
uv add stochasor with pip:
pip install stochas- Salted Seeding: Combines Global Seeds, Parameter Names, and Trial Numbers for unique but deterministic draws.
- Numeric Mixins: Use your data containers directly in math operations (
container * 5.0) without manually extracting values. - Nominal Support: Easily toggle between "Perfect World" (Trial 0) and "Probabilistic World" (Monte Carlo) results.
- Pydantic Foundation: Every component is a Pydantic model, providing out-of-the-box validation and effortless JSON serialization.
Managing hundreds of simulation trials can quickly become a mess of manual seeds and inconsistent data. stochas solves this by providing:
- Repeatable Randomness: Our "Salted Seed" logic ensures that any specific trial can be perfectly recreated, even years later, by tying randomness to simple to set and store values.
- Smart Containers:
NamedValueobjects behave like numbers or arrays but protect your data from accidental overwrites using a state-machine logic. - Physics-Ready Distributions: A wide range of built-in distributions (Normal, Truncated Normal, Log-Normal, etc.) that handle their own random number generators internally.
- Serialized Registries: Automatically track exactly which "rules" (
Distributions) and "results" (NamedValues) were used in every trial for easy export to JSON or databases.