stochas 2.2.3

stochas is a Python framework built to handle the complexity of Monte Carlo simulations, parametric studies, and probabilistic modeling.

stochas: Smart Data Orchestration

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.

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Installation

Install the package via your preferred manager:

uv add stochas

or with pip:

pip install stochas

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Core Features

• 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.
• Unit System: Declare SI, IPS, FPS, or a custom unit system once and tag distributions with unit=u.inch, sampling paths convert values automatically so your input data stays in familiar units while the simulation receives physically consistent numbers.

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Why use stochas?

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: NamedValue objects 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.
• Automatic Unit Conversion: Attach a UnitDescriptor to any distribution or design variable and let StochasBase multiply the conversion factor at sampling time (without manual scaling in generator code).
• Serialized Registries: Automatically track exactly which "rules" (Distributions) and "results" (NamedValues) were used in every trial for easy export to JSON or databases.