SimLab Simulations¶

SimLab offers a comprehensive collection of simulation tools for modeling complex systems across different domains. All simulators follow a consistent interface pattern, are statically typed, and provide robust validation and error handling.

Simulation Categories¶

Our simulations are organized into the following categories:

Basic Simulations¶

Stock Market Simulation: Model stock price fluctuations with factors like volatility, drift, and market events
Resource Fluctuations Simulation: Simulate resource price dynamics with supply disruptions
Product Popularity Simulation: Model product demand considering growth, marketing, and promotions
Modelling Market Dynamics: Theory behind our market dynamics models

Discrete Event Simulations¶

Discrete Event Simulation: General-purpose event-driven simulation engine
Queueing Simulation: Model service systems with arrivals, queues, and servers (coming soon)

Statistical Simulations¶

Monte Carlo Simulation: Sample random processes to estimate numerical results (coming soon)
Markov Chain Simulation: Model stochastic processes with the Markov property (coming soon)

Agent-Based Simulations¶

Agent-Based Simulation: Model complex systems through interactions of autonomous agents

System Dynamics¶

System Dynamics Simulation: Model systems with stocks, flows, and feedback loops (coming soon)

Network Simulations¶

Network Simulation: Model processes on complex networks with different topologies

Ecological Simulations¶

Predator-Prey Simulation: Model population dynamics using Lotka-Volterra equations (coming soon)

Domain-Specific Simulations¶

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

Common Features¶

All SimLab simulators share these common features:

Consistent Interface: All simulators inherit from BaseSimulation and provide a consistent API
Registry System: Dynamic discovery and instantiation of simulation models
Parameter Validation: Comprehensive input validation and error handling
Visualization Support: Integration with common plotting libraries
Stochastic Processes: Support for random processes with seed control for reproducibility
Extensibility: Easy to extend with custom behavior

Getting Started¶

To use any simulation in SimLab, follow this general pattern:

from sim_lab.core import SimulatorRegistry

# Method 1: Create using the registry
sim = SimulatorRegistry.create(
    "SimulatorName",
    param1=value1,
    param2=value2
)

# Method 2: Create directly
from sim_lab.core import SpecificSimulation

sim = SpecificSimulation(
    param1=value1,
    param2=value2
)

# Run the simulation
results = sim.run_simulation()

# Analyze results
# (Each simulator provides specific methods for analysis)

Check the documentation for each specific simulator to learn about its parameters, methods, and examples.