sim-lab 0.4.1

A set of classes for simulating various business-related scenarios for educational use

SimLab: Comprehensive Simulation Toolkit

SimLab is a Python package providing a versatile set of simulation tools for modeling complex systems across various domains. It offers a unified interface for different simulation paradigms, making it ideal for educational, research, and business applications.

Installation

To install SimLab with all features:

pip install sim-lab

For specific interfaces only:

# CLI only
pip install sim-lab[cli]

# Web interface
pip install sim-lab[web]

# TUI (terminal interface)
pip install sim-lab[tui]

Key Features

• Unified Interface: All simulators share a consistent API
• Registry System: Dynamic discovery and instantiation of simulation models
• Multiple Interfaces: CLI, TUI, Web, and Python API
• Visualization Tools: Built-in plotting and visualization capabilities
• Data Import/Export: Support for common data formats
• Parameter Validation: Comprehensive input validation
• Stochastic Processes: Support for random processes with seed control

Simulation Categories

SimLab includes a wide range of simulation types:

Basic Simulations

• Stock Market: Model stock price fluctuations with volatility, drift, and market events
• Resource Fluctuations: Simulate resource price dynamics with supply disruptions
• Product Popularity: Model product demand considering growth, marketing, and promotions

Discrete Event Simulations

• Discrete Event: General-purpose event-driven simulation engine
• Queueing: Model service systems with arrivals, queues, and servers

Statistical Simulations

• Monte Carlo: Sample random processes to estimate numerical results
• Markov Chain: Model stochastic processes with the Markov property

Agent-Based Simulation

• Model complex systems through interactions of autonomous agents

System Dynamics

• Model systems with stocks, flows, and feedback loops

Network Simulations

• Model processes on complex networks with different topologies

Ecological Simulations

• Predator-Prey: Model population dynamics using Lotka-Volterra equations

Domain-Specific Simulations

• Epidemiological: SIR/SEIR disease spread models
• Cellular Automaton: Grid-based models with local update rules
• Supply Chain: Model multi-tier supply chains with inventory management

Basic Usage

from sim_lab.core import SimulatorRegistry

# Create a simulation using the registry
sim = SimulatorRegistry.create(
    "StockMarket",
    start_price=100.0,
    days=252,
    volatility=0.02,
    drift=0.0005,
    random_seed=42
)

# Run the simulation
prices = sim.run_simulation()

# Visualize the results
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.plot(prices)
plt.title('Stock Price Simulation')
plt.xlabel('Trading Days')
plt.ylabel('Price ($)')
plt.grid(True)
plt.show()

Command Line Interface

# Run a stock market simulation
simlab stock-market run --start-price 100 --days 365 --volatility 0.02 --drift 0.001 --output prices.csv

# Get help for all commands
simlab --help

Terminal UI

# Launch the interactive terminal UI
simlab-tui

Web Interface

# Start the web server
simlab-web

# Then visit http://localhost:8000 in your browser

Educational Applications

SimLab is designed with education in mind, helping students:

• Understand complex systems through hands-on simulation
• Explore the impact of parameters on system dynamics
• Develop data analysis and visualization skills
• Apply theoretical concepts to practical scenarios
• Create and test hypotheses in a simulated environment

Documentation

For comprehensive documentation, visit:

• Getting Started Guide
• API Reference
• Simulation Catalog
• Developer Guide
• Teaching Guide

Development

SimLab uses modern Python development tools:

• uv for dependency management
• Ruff for linting and formatting
• pytest for testing
• MkDocs for documentation

To set up a development environment:

# Clone the repository
git clone https://github.com/teaching-repositories/sim-lab.git
cd sim-lab

# Run the setup script
./scripts/setup_dev.sh

# Or manually
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .[dev]

See DEVELOPMENT.md for more details.

Registry System

SimLab features a powerful registry system for dynamically discovering and instantiating simulators:

from sim_lab.core import SimulatorRegistry, BaseSimulation

# Register a custom simulator
@SimulatorRegistry.register("MySimulator")
class MyCustomSimulation(BaseSimulation):
    # Your implementation here
    pass

# List available simulators
simulators = SimulatorRegistry.list_simulators()
print(f"Available simulators: {simulators}")

# Create an instance
sim = SimulatorRegistry.create("MySimulator", days=100, random_seed=42)

For more information, see the Registry System documentation.

Contributing

We welcome contributions to the SimLab project! See the Contributing Guide for more details.

License

This project is licensed under the MIT License. See the LICENSE file for more details.