Basic Processing Operations - Hazelbean Tutorial

Basic Processing Operations

Learning Time: 15 minutes
Prerequisites: Data Loading and File Discovery
Source File: examples/step_3_basic_processing.py

Overview

Perform basic raster transformations and processing operations

🎯 Learning Objectives

This tutorial will help you understand:

  • Raster Operations: Core concepts and practical application

  • Transformations: Core concepts and practical application

  • Coordinate Systems: Core concepts and practical application

πŸ’» Hands-on Learning: Each code block is executable - run them step-by-step to build understanding progressively.

Key Concepts

  • Raster Operations: Core spatial data processing functions for analysis and transformation

  • Transformations: Important concept for effective Hazelbean workflows

  • Coordinate Systems: Important concept for effective Hazelbean workflows

Tutorial

πŸš€ Getting Started
  1. Make sure you have the Hazelbean environment activated
  2. Run each code block in order to see the functionality in action
  3. Try modifying parameters to experiment with different results
  4. Refer back to the Key Concepts if you need clarification

πŸ“ Working Directory: This tutorial assumes you’re running from the project root directory.

Complete Example

The following code demonstrates the complete workflow for this step:

"""
Demonstrates basic raster processing operations.
"""

# Initialize project (builds on previous steps)
p = hb.ProjectFlow('hazelbean_tutorial')

print("=== Hazelbean Basic Processing Demo ===")
print()

# Try to find sample data for processing
try:
    input_path = p.get_path('tests/ee_r264_ids_900sec.tif')
    if hb.path_exists(input_path):
        has_sample_data = True
        print(f"βœ“ Using sample raster: {os.path.basename(input_path)}")
    else:
        raise FileNotFoundError("Path found but file doesn't exist")
except:
    has_sample_data = False
    print("βœ— Sample data not found, creating synthetic raster...")

if has_sample_data:
    # Working with real data
    print()
    print("=== Basic Raster Information ===")
    info = hb.get_raster_info_hb(input_path)
    print(f"Original size: {info['raster_size']}")
    print(f"Pixel size: {info['pixel_size']}")

    # Create output path in intermediate directory
    processed_path = os.path.join(p.intermediate_dir, 'processed_raster.tif')

    print()
    print("=== Resampling Raster ===")
    # Resample to a different resolution (make pixels 2x larger)
    target_pixel_size = (info['pixel_size'][0] * 2, info['pixel_size'][1] * 2)

    try:
        hb.warp_raster(
            input_path,
            target_pixel_size,
            processed_path,
            resample_method='nearest'
        )

        # Check the result
        new_info = hb.get_raster_info_hb(processed_path)
        print(f"βœ“ Resampled raster created")
        print(f"New size: {new_info['raster_size']}")
        print(f"New pixel size: {new_info['pixel_size']}")

    except Exception as e:
        print(f"βœ— Resampling failed: {e}")
        print("Continuing with array operations...")

    # Load and process array data
    print()
    print("=== Array-based Processing ===")
    array = hb.as_array(input_path)

else:
    # Create synthetic data for demonstration
    print()
    print("=== Creating Synthetic Data ===")
    array = np.random.rand(50, 50) * 100
    processed_path = os.path.join(p.intermediate_dir, 'synthetic_processed.tif')

    print(f"Created {array.shape} array with values 0-100")

# Mathematical operations on arrays
print()
print("=== Mathematical Operations ===")
print(f"Original - Min: {np.nanmin(array):.2f}, Max: {np.nanmax(array):.2f}")

# Apply some transformations
# Scale values
scaled_array = array * 2.0
print(f"Scaled x2 - Min: {np.nanmin(scaled_array):.2f}, Max: {np.nanmax(scaled_array):.2f}")

# Apply threshold
threshold_array = np.where(array > np.nanmean(array), 1, 0)
unique_values = np.unique(threshold_array)
print(f"Threshold (mean={np.nanmean(array):.2f}) - Unique values: {unique_values}")

# Calculate statistics
print()
print("=== Statistical Summary ===")
print(f"Mean: {np.nanmean(array):.2f}")
print(f"Standard deviation: {np.nanstd(array):.2f}")
print(f"Non-zero pixels: {np.count_nonzero(array)}")
print(f"Total pixels: {array.size}")

print()
print("πŸŽ‰ Basic processing complete!")
print("Next: Run step_4_analysis.py to learn spatial analysis workflows")

Step-by-Step Breakdown

Expected Output

When you run this tutorial, you should see output similar to:

Tutorial execution completed successfully.

Learning Tips

πŸ“š Try This Next

  • Modify the example parameters to see how results change

  • Combine concepts from this step with previous tutorials

  • Experiment with your own data using the same patterns

  • Continue to Spatial Analysis for the next learning step

πŸ” Troubleshooting: If you encounter issues, check that your environment is properly activated and all required data files are accessible.