Core Concepts

This section explains the mental model behind the public cimba.sim API. It is for model builders who want to understand what a Cimba Python model is, how simulated time moves, and how an experiment turns many trial runs into data.

The examples use a small clinic or service desk model: people arrive, wait for service, use a limited service capacity, and produce outputs for analysis. The snippets are intentionally partial. For complete runnable programs, continue with the tutorial.

• Models, Trials, and env
  • The trial record
  • Field roles
  • A useful rule of thumb
• Processes and Simulated Time
  • Simulated time, not wall-clock time
  • Blocking is the modeling language
  • Process copies
  • Process code should stay focused
• Shared Entities
  • Choosing an entity
  • Queues and resources
  • Pools and variable capacity
  • Conditions and datasets
• Experiments and Results
  • Trial-local entities
  • The measurement window
  • Collectors
  • Running and reading results
  • Summarizing across replications
  • What belongs outside the model

Where this fits

Read these pages before the tutorial if you want the vocabulary first. Read the tutorial first if you prefer to learn by building a complete model and then come back here when a concept needs a firmer shape.

Use the API reference for the full API overview once the core modeling picture is clear.