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Documentation Index

Fetch the complete documentation index at: https://docs.veydra.io/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Veydra’s simulation engine runs Python-based system dynamics models entirely in your browser — no installs, no servers, no setup. When you open a model, Veydra automatically loads a complete scientific Python environment and executes the model locally on your device. Every parameter change produces instant results. This engine powers every experience on the platform: the Model Playground, Activities, and the AI Assistant all rely on the same real-time simulation pipeline under the hood.

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What You Can Do

Instant Feedback

Move a slider and see charts update in real time — no waiting for a server round-trip.

What-If Scenarios

Change assumptions, compare outcomes, and explore different futures interactively.

AI-Assisted Analysis

The AI Assistant runs simulations on your behalf to answer questions, suggest experiments, and interpret results.

Turn-Based Games

Activities use the same engine to power interactive decision-making games with scoring and objectives.

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How It Works

1

Model Loads Automatically

When you open a model, Veydra fetches the Python source code and boots a scientific Python runtime in your browser. No action required on your part.
2

Baseline Results Appear

The model runs once with its default parameters and renders the baseline charts — stocks, flows, and time series.
3

You Adjust Parameters

Use sliders, presets, or the AI Assistant to change input values. Each change triggers a new simulation run instantly.
4

Results Update in Real Time

Updated charts, diagrams, and data tables reflect the new simulation output immediately.

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Where the Engine Is Used

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Model Playground

The Model Playground is the primary interface for working with models. Depending on the mode you’re in, the simulation engine behaves differently:
ModeHow the Engine Is Used
PresentationRuns the baseline simulation for display alongside slides and guided content
DesignRe-runs the model as you edit parameters, equations, or structure
ExperimentRuns what-if scenarios and overlays results against the baseline
CalibrateIterates simulations to fit model output to real-world data
DecideRuns multiple scenarios in parallel for side-by-side comparison

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Activities

Activities wrap the simulation engine in a game-like experience. At each turn, the player adjusts parameters and commits their choices. The engine advances the simulation by a configured time increment and returns the results — enabling scoring, objectives, and competitive play.

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AI Assistant

The AI Assistant uses the simulation engine programmatically. When you ask “What happens if I double the growth rate?”, the assistant builds a parameter set, runs the simulation, and interprets the results — all without you touching a slider.

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Key Capabilities

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Real-Time Parameter Adjustment

Every model parameter — growth rates, initial conditions, thresholds — can be adjusted via interactive sliders. Changes take effect immediately:
  1. You move a slider
  2. The engine re-runs the simulation with the updated value
  3. Charts and diagrams refresh instantly

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Scenario Comparison

Run the model with different assumptions and compare results side by side:
  • Baseline vs. Scenario — See how your changes differ from the defaults
  • Multiple Scenarios — Save named parameter sets and switch between them
  • Presets — Models ship with curated scenarios that highlight interesting dynamics

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Data Export

Simulation results can be exported for further analysis:
  • CSV — Time series data for stocks and flows
  • Charts — Download visualizations as images

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Performance

  • No server required — all computation happens on your device
  • Near-native speed — the Python runtime is compiled to WebAssembly for fast numerical execution
  • Smart caching — model files and recent results are cached locally for fast reloads
  • Handles complexity — multi-stock models with dozens of parameters run smoothly in the browser
The first time you open a model, there’s a brief loading period while the runtime initializes. Subsequent visits are faster thanks to browser caching.

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Privacy

Because models execute entirely in your browser:
  • Simulation inputs and results stay on your device — nothing is sent to a server during execution
  • No server compute costs — your device does the work
  • Isolated execution — each model runs in its own sandboxed environment

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Error Handling

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Graceful Failures

The simulation engine includes robust error handling:
Python syntax errors in model code are caught and displayed with helpful debugging information
Execution errors during simulation show specific error messages and line numbers
Invalid parameter values or data format issues are automatically detected and reported
Automatic fallback to last known good state when errors occur

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Advanced Features

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Custom Model Functions

Write sophisticated Python models with full library support: 
import numpy as np
import pandas as pd

def complex_model(params, time_range):
    # Complex calculations with scientific libraries
    data = np.array(time_range)
    results = pd.DataFrame()
    
    # Your model logic here
    
    return results.to_dict('records')

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Multi-Stock Analysis

Handle models with multiple interacting stocks:
  • Cross-stock Dependencies: Model interactions between stocks
  • Synchronized Updates: Consistent time-step processing
  • Relationship Mapping: Track dependencies and influences
  • Aggregate Views: Summary statistics across all stocks

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Debugging Tools

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Execution Monitoring

  • Real-time Logs: View Python execution output
  • Performance Metrics: Track execution time and memory
  • Parameter Tracking: Monitor parameter changes
  • Error Reporting: Detailed error messages and stack traces

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Model Validation

  • Syntax Checking: Pre-execution code validation
  • Parameter Validation: Check ranges and types
  • Output Verification: Ensure proper data formats
  • Consistency Checks: Verify model logic
Use the browser’s developer console to see detailed execution logs and debug model issues

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Next Steps

Chart Components

Learn about visualization options

Model Controls

Master parameter management