Add core architecture patterns and GIS/weather components from AIOS-Public
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collab/plans/gis-weather-plan.md
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collab/plans/gis-weather-plan.md
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# GIS and Weather Data Processing Container Plan
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## Overview
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This document outlines the plan for creating Docker containers to handle GIS data processing and weather data analysis. These containers will be used exclusively in CTO mode for R&D and data analysis tasks, with integration to documentation workflows and MinIO for data output.
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## Requirements
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### GIS Data Processing
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- Support for Shapefiles and other GIS formats
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- Self-hosted GIS stack (not Google Maps or other commercial services)
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- Integration with tools like GDAL, Tippecanoe, DuckDB
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- Heavy use of PostGIS database
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- Parquet format support for efficient data storage
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- Based on reference workflows from:
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- https://tech.marksblogg.com/american-solar-farms.html
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- https://tech.marksblogg.com/canadas-odb-buildings.html
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- https://tech.marksblogg.com/ornl-fema-buildings.html
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### Weather Data Processing
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- GRIB data format processing
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- NOAA and European weather APIs integration
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- Bulk data download via HTTP/FTP
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- Balloon path prediction system (to be forked/modified)
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### Shared Requirements
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- Python-based with appropriate libraries (GeoPandas, DuckDB, etc.)
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- R support for statistical analysis
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- Jupyter notebook integration for experimentation
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- MinIO bucket integration for data output
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- Optional but enabled GPU support for performance
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- All visualization types (command-line, web, desktop)
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- Flexible ETL capabilities for both GIS/Weather and business workflows
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## Proposed Container Structure
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### RCEO-AIOS-Public-Tools-GIS-Base
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- Foundation container with core GIS libraries
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- Python + geospatial stack (GDAL, GEOS, PROJ, DuckDB, Tippecanoe)
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- R with spatial packages
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- PostGIS client tools
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- Parquet support
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- File format support (Shapefiles, GeoJSON, etc.)
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### RCEO-AIOS-Public-Tools-GIS-Processing
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- Extends GIS-Base with advanced processing tools
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- Jupyter with GIS extensions
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- Specialized ETL libraries
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- Performance optimization tools
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### RCEO-AIOS-Public-Tools-Weather-Base
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- Foundation container with weather data libraries
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- GRIB format support (cfgrib)
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- NOAA and European API integration tools
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- Bulk download utilities (HTTP/FTP)
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### RCEO-AIOS-Public-Tools-Weather-Analysis
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- Extends Weather-Base with advanced analysis tools
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- Balloon path prediction tools
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- Forecasting libraries
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- Time series analysis
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### RCEO-AIOS-Public-Tools-GIS-Weather-Fusion (Optional)
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- Combined container for integrated GIS + Weather analysis
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- For balloon path prediction using weather data
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- High-resource container for intensive tasks
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## Technology Stack
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### GIS Libraries
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- GDAL/OGR for format translation and processing
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- GEOS for geometric operations
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- PROJ for coordinate transformations
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- PostGIS for spatial database operations
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- DuckDB for efficient data processing with spatial extensions
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- Tippecanoe for tile generation
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- Shapely for Python geometric operations
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- GeoPandas for Python geospatial data handling
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- Rasterio for raster processing in Python
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- Leaflet/Mapbox for web visualization
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### Data Storage & Processing
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- DuckDB with spatial extensions
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- Parquet format support
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- MinIO client tools for data output
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- PostgreSQL client for connecting to external databases
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### Weather Libraries
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- xarray for multi-dimensional data in Python
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- cfgrib for GRIB format handling
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- MetPy for meteorological calculations
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- Climate Data Operators (CDO) for climate data processing
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- R packages: raster, rgdal, ncdf4, rasterVis
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### Visualization
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- Folium for interactive maps
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- Plotly for time series visualization
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- Matplotlib/Seaborn for statistical plots
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- R visualization packages
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- Command-line visualization tools
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### ETL and Workflow Tools
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- Apache Airflow (optional in advanced containers)
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- Prefect or similar workflow orchestrators
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- DuckDB for ETL operations
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- Pandas/Dask for large data processing
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## Container Deployment Strategy
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### Workstation Prototyping
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- Lighter containers for development and testing
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- Optional GPU support
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- MinIO client for data output testing
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### Production Servers
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- Full-featured containers with all processing capabilities
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- GPU-enabled variants where applicable
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- Optimized for large RAM/CPU/disk requirements
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## Security & User Management
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- Follow same non-root user pattern as documentation containers
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- UID/GID mapping for file permissions
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- Minimal necessary privileges
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- Proper container isolation
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- Secure access to MinIO buckets
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## Integration with Existing Stack
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- Compatible with existing user management approach
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- Can be orchestrated with documentation containers when needed
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- Follow same naming conventions
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- Use same wrapper script patterns
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- Separate from documentation containers but can work together in CTO mode
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## Implementation Phases
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### Phase 1: Base GIS Container
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- Create GIS-Base with GDAL, DuckDB, PostGIS client tools
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- Implement Parquet and Shapefile support
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- Test with sample datasets from reference posts
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- Validate MinIO integration
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### Phase 2: Weather Base Container
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- Create Weather-Base with GRIB support
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- Integrate NOAA and European API tools
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- Implement bulk download capabilities
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- Test with weather data sources
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### Phase 3: Processing Containers
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- Create GIS-Processing container with ETL tools
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- Create Weather-Analysis container with prediction tools
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- Add visualization and Jupyter support
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- Implement optional GPU support
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### Phase 4: Optional Fusion Container
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- Combined container for balloon path prediction
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- Integration of GIS and weather data
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- High-complexity, high-resource usage
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## Data Flow Architecture
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- ETL workflows for processing public datasets
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- Output to MinIO buckets for business use
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- Integration with documentation tools for CTO mode workflows
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- Support for both GIS/Weather ETL (CTO) and business ETL (COO)
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## Next Steps
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1. Review and approve this enhanced plan
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2. Begin Phase 1 implementation
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3. Test with sample data from reference workflows
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4. Iterate based on findings
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## Risks & Considerations
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- Large container sizes due to GIS libraries and dependencies
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- Complex dependency management, especially with DuckDB and PostGIS
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- Computational resource requirements, especially for large datasets
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- GPU support implementation complexity
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- Bulk data download and processing performance
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