GeoBotv1 Examples

This directory contains example scripts demonstrating the capabilities of GeoBotv1.

Examples Overview

01_basic_usage.py

Basic introduction to GeoBotv1 core components:

Creating geopolitical scenarios
Building causal graphs
Running Monte Carlo simulations
Bayesian belief updating
Uncertainty quantification

Run it:

python 01_basic_usage.py

02_data_ingestion.py

Demonstrates data ingestion capabilities:

PDF document processing
Web scraping and article extraction
News aggregation from multiple sources
Intelligence extraction from documents
Entity and keyword extraction

Run it:

python 02_data_ingestion.py

Note: For full functionality, install optional dependencies:

pip install pypdf pdfplumber beautifulsoup4 newspaper3k trafilatura feedparser

03_intervention_simulation.py

Advanced intervention and counterfactual analysis:

Policy intervention simulation
Comparing multiple policy options
Finding optimal interventions
Counterfactual reasoning ("what if" scenarios)
Causal effect estimation

Run it:

python 03_intervention_simulation.py

04_advanced_features.py

Research-grade advanced mathematical features:

Sequential Monte Carlo (particle filtering) for nonlinear state estimation
Stochastic Differential Equations (Euler-Maruyama, Milstein, Jump-Diffusion)
Gradient-based Optimal Transport with Kantorovich duality
Entropic OT with Sinkhorn algorithm
Structured event extraction from intelligence text
Event database with temporal normalization

Run it:

python 04_advanced_features.py

Note: Some features require additional dependencies:

pip install torch  # For advanced features

Additional Resources

Creating Custom Scenarios

from geobot.core.scenario import Scenario
import numpy as np

scenario = Scenario(
    name="custom_scenario",
    features={
        'tension': np.array([0.7]),
        'stability': np.array([0.4]),
    },
    probability=1.0
)

Building Causal Models

from geobot.models.causal_graph import CausalGraph

graph = CausalGraph(name="my_model")
graph.add_node('cause')
graph.add_node('effect')
graph.add_edge('cause', 'effect', strength=0.8)

Monte Carlo Simulation

from geobot.simulation.monte_carlo import MonteCarloEngine, SimulationConfig

config = SimulationConfig(n_simulations=1000, time_horizon=100)
engine = MonteCarloEngine(config)

Web Scraping

from geobot.data_ingestion.web_scraper import ArticleExtractor

extractor = ArticleExtractor()
article = extractor.extract_article('https://example.com/article')
print(article['title'])
print(article['text'])

PDF Processing

from geobot.data_ingestion.pdf_reader import PDFProcessor

processor = PDFProcessor()
result = processor.extract_intelligence('report.pdf')
print(f"Risk Level: {result['intelligence']['risk_level']}")

Need Help?

Check the main README.md in the project root
Review the module documentation in each package
Examine the source code for detailed implementation

Contributing

Have an interesting use case? Create a new example script and submit a PR!