Welcome to Hazelbean Documentation¶
Hazelbean is a powerful Python library that transforms complex geospatial workflows into organized, reproducible, and efficient analysis pipelines. Whether you're a researcher, student, or professional working with geospatial data, Hazelbean provides the tools and structure to make your work more productive and reliable.
🎯 What Makes Hazelbean Special?¶
🏗️ Intelligent Project Organization¶
Hazelbean's ProjectFlow automatically creates and manages organized directory structures, making complex geospatial projects maintainable and shareable.
🔍 Smart Data Discovery¶
The intelligent get_path() system finds your data across multiple directories, cloud storage, and data repositories without hardcoded paths.
⚡ Efficient Processing¶
Optimized raster processing with memory-efficient operations, parallel processing support, and performance tracking.
📚 Educational Focus¶
Comprehensive learning resources with progressive tutorials, extensive test documentation, and real-world examples.
🚀 Quick Start Options¶
🎓 New to Hazelbean?
Perfect for: First-time users, students, guided learning
Time: ~65 minutes of hands-on practice
Start Learning Journey →🔬 Technical Deep Dive
Perfect for: Developers, maintainers, advanced users
Focus: Implementation details and patterns
Explore Test Docs →📊 Performance & Metrics
Perfect for: Quality assessment, system monitoring
Content: Test results, benchmarks, system health
View Reports →🔧 Site Information
Perfect for: Understanding documentation system
Content: Site maintenance, contribution guidelines
GitHub Repository →🌟 Featured Content¶
📖 Educational Journey¶
Our progressive learning system takes you from basic concepts to advanced geospatial analysis:
graph LR
A[🚀 Setup & Installation] --> B[📁 Project Organization]
B --> C[🔍 Data Discovery]
C --> D[⚙️ Processing & Analysis]
D --> E[📊 Results & Export]
style A fill:#e3f2fd,stroke:#1976d2
style B fill:#e8f5e8,stroke:#388e3c
style C fill:#fff3e0,stroke:#f57c00
style D fill:#f3e5f5,stroke:#7b1fa2
style E fill:#e0f2f1,stroke:#00796bPerfect for:
- 🎓 Students learning geospatial concepts
- 🔬 Researchers building analysis workflows
- 👩💻 Developers understanding Hazelbean patterns
- 📊 Analysts creating reproducible processes
🧪 Comprehensive Test Documentation¶
Explore over 50+ test cases that demonstrate real-world usage patterns:
| Category | Focus | Test Count | Coverage |
|---|---|---|---|
| Unit Tests | Individual functions | 9 modules | Core functionality |
| Integration Tests | Workflow testing | 4 modules | End-to-end processes |
| Performance Tests | Benchmarks & optimization | 3 modules | Efficiency tracking |
| System Tests | Complete system validation | 2 modules | Smoke testing |
📈 Live System Metrics¶
Metrics updated automatically from latest test runs
🎯 User-Focused Navigation¶
🌱 For Beginners¶
- Start Here: Educational Overview - Complete learning roadmap
- Step 1: Project Setup - Your first Hazelbean project
- Step 2: Data Loading - Intelligent data discovery
🔬 For Researchers & Analysts¶
- Integration Examples - Real-world workflow patterns
- Performance Benchmarks - Optimization and efficiency
- System Reports - Quality metrics and monitoring
👩💻 For Developers & Contributors¶
- Unit Test Patterns - Individual function testing
- Architecture Overview - System design
- Site Maintenance - Documentation system
🔍 Powerful Search & Discovery¶
This documentation site features intelligent search across all content types:
- 🔍 Full-text search across tutorials, tests, and documentation
- 🏷️ Tagged content for easy category filtering
- 📱 Mobile-responsive design for on-the-go reference
- 🌓 Light/dark themes for comfortable reading
- 📋 Code copy buttons for easy example usage
Search Tips:
- Use specific function names (e.g., "get_path")
- Search by concept (e.g., "raster processing")
- Filter by test category (e.g., "unit test")
- Look for error patterns (e.g., "file not found")
⚡ Quick Reference¶
Essential Hazelbean Patterns¶
import hazelbean as hb
# 1. Initialize organized project
p = hb.ProjectFlow('my_analysis')
# 2. Intelligent data discovery
raster_path = p.get_path('land_cover.tif')
# 3. Efficient processing
result = hb.arrayframe_to_array(raster_path)
# 4. Organized output
output_path = p.get_path('processed_result.tif', 'output')
Common Use Cases¶
| Task | Starting Point | Documentation |
|---|---|---|
| Learn Hazelbean | Educational Journey | Progressive tutorials |
| Process Rasters | Step 3: Processing | Array operations |
| Organize Projects | Step 1: Setup | ProjectFlow patterns |
| Find Test Examples | Test Categories | Implementation patterns |
| Check System Health | Reports | Metrics and monitoring |
| Contribute to Project | GitHub Repository | Development guide |
🎉 Ready to Get Started?¶
The most effective way to learn Hazelbean is through hands-on practice with our carefully designed tutorial progression.
"Organized workflows, intelligent data discovery, efficient processing"
Auto-generated from tests • Educational content • Live metrics
2025-01-17