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📓 Jupyter Notebook (Interactive Exploration & Experimentation)

The MIRROR platform includes a fully integrated Jupyter Notebook environment inside Docker — allowing you to explore, validate, and prototype workflows with live access to your PostgreSQL database, point cloud data, and ingestion tools.

✅ Key Capabilities

🔌 Pre-connected to PostgreSQL

  • The notebook is preconfigured to connect to your platform's PostgreSQL instance.
  • You can run live SQL queries to inspect data, test joins, and validate ingestion.

🛠️ Data Science & Analysis

  • Leverage Python tools like pandas, geopandas, matplotlib, and psycopg2 for:
  • Building spatial analytics
  • Validating geometry data
  • Creating charts and summaries from ingested content

Folder Structure

All notebooks and data can be placed inside the shared volume:

./db/notebooks/
  ├── sample_data.laz
  ├── example_pipeline.ipynb
  └── test_query.ipynb

This folder is mounted inside the Jupyter container and persists across runs.

🚀 Accessing Jupyter

Once your Docker setup is running:

👉 Open http://localhost:8888

No token is required — you’ll land directly in the browser interface.

🧪 Example Use Cases

  • Test .laz ingestion pipelines with custom PDAL configurations
  • Query and inspect glossary, projects, or GIS tables with SQL
  • Analyze ingestion failures by checking raw data
  • Prototype logic before hardcoding into backend pipelines

🧠 Why It Matters

The Jupyter environment makes the MIRROR platform:

  • 🌐 More transparent for technical and semi-technical users
  • 🧪 Perfect for experimentation without rebuilding containers
  • 🔬 Ideal for exploration, troubleshooting, and education