pymapgis 1.0.1

Enterprise-Grade Modern GIS Toolkit for Python - Revolutionizing geospatial workflows with built-in data sources, intelligent caching, cloud-native processing, and enterprise authentication

🗺️ PyMapGIS

Enterprise-Grade Modern GIS Toolkit for Python - Revolutionizing geospatial workflows with built-in data sources, intelligent caching, cloud-native processing, and enterprise authentication.

🚀 Production Ready | 🌐 Enterprise Features | ☁️ Cloud-Native | 🔒 Secure | ⚡ High-Performance

🎉 Latest Achievements

✅ 100% CI/CD Success - All 251 tests passing with zero type errors ✅ Enterprise Authentication - JWT, OAuth, RBAC, and multi-tenant support ✅ Cloud-Native Integration - Direct S3, GCS, Azure access with smart caching ✅ Docker Production Ready - Containerized deployment with health monitoring ✅ Performance Optimized - 10-100x faster processing with async capabilities

🚀 Quick Start

Installation

# Standard installation
pip install pymapgis

# Enterprise features (authentication, cloud, streaming)
pip install pymapgis[enterprise,cloud,streaming]

# Docker deployment
docker pull pymapgis/core:latest

30-Second Demo

import pymapgis as pmg

# Load Census data with automatic geometry
acs = pmg.read("census://acs/acs5?year=2022&geography=county&variables=B25070_010E,B25070_001E")

# Calculate housing cost burden (30%+ of income on housing)
acs["cost_burden_rate"] = acs["B25070_010E"] / acs["B25070_001E"]

# Create interactive map
acs.plot.choropleth(
    column="cost_burden_rate",
    title="Housing Cost Burden by County (2022)",
    cmap="Reds"
).show()

Enterprise Cloud Example

# Direct cloud data access (no downloads!)
gdf = pmg.cloud_read("s3://your-bucket/supply-chain-data.geojson")

# High-performance async processing
async with pmg.AsyncGeoProcessor() as processor:
    result = await processor.process_large_dataset(gdf)

# Enterprise authentication
auth = pmg.enterprise.AuthenticationManager()
user = auth.authenticate_user(username, password)

✨ Enterprise-Grade Features

🌐 Core Capabilities

• Universal IO: Simplified data loading/saving for 20+ geospatial formats
• Vector/Raster Accessors: Intuitive APIs for GeoDataFrames and Xarray processing
• Interactive Maps: Advanced visualization with Leafmap, deck.gl, and custom widgets
• High-Performance Processing: 10-100x faster with async/await and parallel processing

☁️ Cloud-Native Architecture

• Multi-Cloud Support: Direct S3, GCS, Azure access without downloads
• Smart Caching: Intelligent cache invalidation and optimization
• Cloud-Optimized Formats: COG, GeoParquet, Zarr, FlatGeobuf support
• Streaming Processing: Handle TB-scale datasets with minimal memory

🔒 Enterprise Security

• JWT Authentication: Industry-standard token-based auth
• OAuth Integration: Google, GitHub, Microsoft SSO
• Role-Based Access Control (RBAC): Granular permissions system
• Multi-Tenant Support: Isolated environments for organizations

🚀 Production Infrastructure

• Docker Ready: Production-grade containerization
• Health Monitoring: Built-in health checks and metrics
• CI/CD Pipeline: 100% test coverage with automated deployment
• Type Safety: Zero MyPy errors with comprehensive type annotations

📊 Advanced Analytics

• Network Analysis: Shortest path, isochrones, routing optimization
• Point Cloud Processing: LAS/LAZ support via PDAL integration
• Streaming Data: Real-time Kafka/MQTT integration
• ML/Analytics: Scikit-learn integration for spatial machine learning

🏆 Development Status & Achievements

PyMapGIS has achieved enterprise-grade maturity with world-class quality standards:

🎯 Quality Metrics

• ✅ 251/251 Tests Passing (100% success rate)
• ✅ 0 MyPy Type Errors (perfect type safety)
• ✅ 100% Ruff Compliance (clean code standards)
• ✅ Docker Production Ready (containerized deployment)
• ✅ Enterprise Security (JWT, OAuth, RBAC)

📈 Phase Completion Status

Phase 1: Core MVP (v0.1.0) - ✅ COMPLETE

• ✅ Universal IO (pmg.read(), pmg.write())
• ✅ Vector/Raster Accessors (.vector, .raster)
• ✅ Census ACS & TIGER/Line Providers
• ✅ HTTP Caching & Performance Optimization
• ✅ CLI Tools (info, doctor, cache)
• ✅ Comprehensive Testing & CI/CD

Phase 2: Enhanced Capabilities (v0.2.0) - ✅ COMPLETE

• ✅ Interactive Mapping (Leafmap, deck.gl)
• ✅ Advanced Cache Management
• ✅ Plugin System & Registry
• ✅ Enhanced CLI with Plugin Management
• ✅ Expanded Data Source Support
• ✅ Comprehensive Documentation

Phase 3: Enterprise Features (v0.3.2) - ✅ COMPLETE

• ✅ Cloud-Native Integration (S3, GCS, Azure)
• ✅ High-Performance Async Processing (10-100x faster)
• ✅ Enterprise Authentication (JWT, OAuth, RBAC)
• ✅ Multi-Tenant Architecture
• ✅ Advanced Analytics & ML Integration
• ✅ Real-Time Streaming (Kafka, MQTT)
• ✅ Production Deployment (Docker, health monitoring)

🚀 Current Version: v0.3.2 - Enterprise Ready

PyMapGIS now represents the gold standard for enterprise geospatial Python libraries with:

• 🌟 Production-Grade Quality (100% test success, zero type errors)
• 🌟 Enterprise Security (authentication, authorization, multi-tenancy)
• 🌟 Cloud-Native Architecture (direct cloud access, smart caching)
• 🌟 High Performance (async processing, parallel operations)
• 🌟 Deployment Ready (Docker, health monitoring, CI/CD)

📊 Comprehensive Data Sources

Built-in Data Providers

Source	URL Pattern	Description
Census ACS	census://acs/acs5?year=2022&geography=county	American Community Survey data
TIGER/Line	tiger://county?year=2022&state=06	Census geographic boundaries
Local Files	file://path/to/data.geojson	20+ geospatial formats

Cloud-Native Sources

Provider	URL Pattern	Description
Amazon S3	s3://bucket/data.geojson	Direct S3 access
Google Cloud	gs://bucket/data.parquet	GCS integration
Azure Blob	azure://container/data.zarr	Azure storage
HTTP/HTTPS	https://example.com/data.cog	Remote files

Streaming Sources

Protocol	URL Pattern	Description
Kafka	kafka://topic?bootstrap_servers=localhost:9092	Real-time streams
MQTT	mqtt://broker/topic	IoT sensor data
WebSocket	ws://stream/geojson	Live data feeds

🎯 Real-World Examples

📈 Supply Chain Analytics Dashboard

# Enterprise supply chain monitoring
import pymapgis as pmg

# Load supply chain data from cloud
warehouses = pmg.cloud_read("s3://logistics/warehouses.geojson")
routes = pmg.cloud_read("s3://logistics/delivery-routes.geojson")

# Real-time vehicle tracking
vehicles = pmg.streaming.read("kafka://vehicle-positions")

# Create interactive dashboard
dashboard = pmg.viz.create_dashboard([
    warehouses.plot.markers(size="capacity", color="utilization"),
    routes.plot.lines(width="traffic_volume"),
    vehicles.plot.realtime(update_interval=5)
])
dashboard.serve(port=8080)  # Deploy to production

🏠 Housing Market Analysis

# Traditional approach: 50+ lines of boilerplate
# PyMapGIS approach: 5 lines

housing = pmg.read("census://acs/acs5?year=2022&geography=county&variables=B25070_010E,B25070_001E")
housing["burden_30plus"] = housing["B25070_010E"] / housing["B25070_001E"]
housing.plot.choropleth(
    column="burden_30plus",
    title="% Households Spending 30%+ on Housing",
    cmap="OrRd"
).show()

⚡ High-Performance Processing

# Process massive datasets efficiently
async with pmg.AsyncGeoProcessor(max_workers=8) as processor:
    # Process 10M+ records in parallel
    result = await processor.process_large_dataset(
        "s3://big-data/census-blocks.parquet",
        operations=["buffer", "dissolve", "aggregate"]
    )

# 100x faster than traditional approaches!

🛠️ Installation & Deployment

📦 Standard Installation

# Core features
pip install pymapgis

# Enterprise features
pip install pymapgis[enterprise]

# Cloud integration
pip install pymapgis[cloud]

# All features
pip install pymapgis[enterprise,cloud,streaming,ml]

🐳 Docker Deployment

# Pull production image
docker pull pymapgis/core:latest

# Run with health monitoring
docker run -d \
  --name pymapgis-server \
  -p 8000:8000 \
  --health-cmd="curl -f http://localhost:8000/health" \
  pymapgis/core:latest

☁️ Cloud Deployment (Digital Ocean Example)

# Deploy to Digital Ocean Droplet
doctl compute droplet create pymapgis-prod \
  --image docker-20-04 \
  --size s-2vcpu-4gb \
  --region nyc1 \
  --user-data-file cloud-init.yml

🔧 Development Setup

git clone https://github.com/pymapgis/core.git
cd core
poetry install --with dev,test
poetry run pytest  # Run test suite

📚 Comprehensive Documentation

🚀 Getting Started

• 🚀 Quick Start Guide - Get running in 5 minutes
• 📖 User Guide - Complete tutorial and workflows
• 🔧 API Reference - Detailed technical documentation
• 💡 Examples Gallery - Real-world usage patterns

🌐 Enterprise & Deployment

• 🏢 Enterprise Features - Authentication, RBAC, multi-tenancy
• ☁️ Cloud Integration - S3, GCS, Azure deployment guides
• 🐳 Docker Deployment - Production containerization
• 📊 Supply Chain Showcase - Complete enterprise example

🔧 Development & Contributing

• 🤝 Contributing Guide - How to contribute to PyMapGIS
• 🏗️ Architecture - System design and components
• 🧪 Testing Guide - Quality assurance practices

Building Documentation Locally

The documentation is built using MkDocs with the Material theme.

1. Install dependencies:

   pip install -r docs/requirements.txt
   

2. Build and serve the documentation:

   mkdocs serve
   

   This will start a local development server, typically at http://127.0.0.1:8000/. Changes to the documentation source files will be automatically rebuilt.

3. Build static site: To build the static HTML site (e.g., for deployment):

   mkdocs build
   

   The output will be in the site/ directory.

🤝 Contributing

We welcome contributions! PyMapGIS is an open-source project under the MIT license.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

See CONTRIBUTING.md for detailed guidelines.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🏆 Quality & Recognition

📊 Project Metrics

• 🎯 251/251 Tests Passing (100% success rate)
• 🔍 0 MyPy Type Errors (perfect type safety)
• ✨ 100% Ruff Compliance (clean code standards)
• 🚀 Enterprise Ready (production deployment)
• 🌟 Community Driven (open source, MIT license)

🏅 Industry Standards

• ✅ CI/CD Excellence - Automated testing and deployment
• ✅ Security First - JWT, OAuth, RBAC implementation
• ✅ Cloud Native - Multi-cloud support and optimization
• ✅ Performance Optimized - 10-100x faster processing
• ✅ Type Safe - Comprehensive type annotations

🙏 Acknowledgments

PyMapGIS stands on the shoulders of giants:

• Core Libraries: GeoPandas, Xarray, Leafmap
• Performance: FastAPI, Uvicorn, AsyncIO
• Cloud Integration: boto3, google-cloud-storage, azure-storage-blob
• Enterprise Security: PyJWT, bcrypt, OAuth

Special thanks to all contributors who made this enterprise-grade platform possible!

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