"""
Simple test to demonstrate how cloud cover filtering affects data availability.

This test focuses on the core cloud cover filtering logic without heavy dependencies.
"""

from unittest.mock import Mock, patch

import geopandas as gpd
import pytest
from shapely.geometry import Polygon

from environmental_risk_metrics.utils.planetary_computer import (
    get_planetary_computer_items,
)


@pytest.fixture
def sample_geometry():
    """Create a sample geometry for testing - a small area in Redmond, WA"""
    polygon = Polygon([
        (-122.2751, 47.5469),  # SW corner
        (-121.9613, 47.5469),  # SE corner
        (-121.9613, 47.7458),  # NE corner
        (-122.2751, 47.7458),  # NW corner
        (-122.2751, 47.5469)   # Close the polygon
    ])
    return gpd.GeoDataFrame([1], geometry=[polygon], crs="EPSG:4326")


def test_cloud_cover_parameter_behavior():
    """
    Test to verify that the cloud cover parameters work as expected.
    This test mocks the STAC search to demonstrate the filtering behavior.
    """
    
    # Mock STAC items with different cloud cover values
    mock_items = []
    for i in range(10):
        item = Mock()
        item.properties = {
            'eo:cloud_cover': i * 10,  # 0%, 10%, 20%, ..., 90%
            'datetime': f'2021-01-{i+1:02d}T10:00:00Z'
        }
        item.id = f'item_{i}'
        mock_items.append(item)
    
            # Test different cloud cover thresholds
        test_cases = [
            (5, "Very restrictive (<5%)", 1),    # Should only get 0% cloud cover
            (20, "Moderate (<20%)", 2),          # Should get 0%, 10%
            (50, "Permissive (<50%)", 5),        # Should get 0%, 10%, 20%, 30%, 40%
            (100, "Maximum (all)", 10),          # Should get all items
        ]
    
    print("\nCloud Cover Parameter Behavior Test:")
    print(f"{'Description':<20} {'Threshold':<10} {'Expected':<10} {'Explanation'}")
    print("-" * 60)
    
    for threshold, description, expected_count in test_cases:
        # Filter items based on cloud cover threshold
        filtered_items = [item for item in mock_items if item.properties['eo:cloud_cover'] < threshold]
        actual_count = len(filtered_items)
        
        print(f"{description:<20} {threshold:<10} {actual_count:<10} Items with <{threshold}% cloud cover")
        
        # Verify the filtering works correctly
        assert actual_count == expected_count, f"Expected {expected_count} items, got {actual_count} for threshold {threshold}"
        
        # Verify that all returned items have cloud cover below threshold
        for item in filtered_items:
            assert item.properties['eo:cloud_cover'] < threshold, f"Item {item.id} has {item.properties['eo:cloud_cover']}% cloud cover, above threshold {threshold}"


def test_cloud_cover_filtering_direction():
    """
    Test to verify that higher cloud cover thresholds return more data.
    """
    
    # Create mock items with varying cloud cover
    mock_items = []
    cloud_covers = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95]
    
    for i, cloud_cover in enumerate(cloud_covers):
        item = Mock()
        item.properties = {'eo:cloud_cover': cloud_cover}
        item.id = f'item_{i}'
        mock_items.append(item)
    
    # Test increasing thresholds
    thresholds = [5, 10, 20, 30, 50, 80, 100]
    results = []
    
    print("\nCloud Cover Filtering Direction Test:")
    print(f"{'Threshold':<10} {'Items Returned':<15} {'Cloud Covers Included'}")
    print("-" * 50)
    
    for threshold in thresholds:
        filtered_items = [item for item in mock_items if item.properties['eo:cloud_cover'] < threshold]
        count = len(filtered_items)
        cloud_covers_included = [item.properties['eo:cloud_cover'] for item in filtered_items]
        
        results.append((threshold, count, cloud_covers_included))
        
        print(f"{threshold:<10} {count:<15} {cloud_covers_included}")
    
    # Verify that higher thresholds return more data
    for i in range(1, len(results)):
        prev_threshold, prev_count, _ = results[i-1]
        curr_threshold, curr_count, _ = results[i]
        
        assert curr_count >= prev_count, f"Higher threshold {curr_threshold} should return at least as much data as {prev_threshold}"
        print(f"✓ Threshold {curr_threshold} ({curr_count} items) >= {prev_threshold} ({prev_count} items)")


def test_maximum_data_availability():
    """
    Test that setting maximum cloud cover values returns the most data possible.
    """
    
    # Create mock items with various cloud cover values
    mock_items = []
    for i in range(20):
        item = Mock()
        item.properties = {
            'eo:cloud_cover': i * 5,  # 0%, 5%, 10%, ..., 95%
            'datetime': f'2021-01-{i+1:02d}T10:00:00Z'
        }
        item.id = f'item_{i}'
        mock_items.append(item)
    
    # Test with maximum cloud cover tolerance
    max_threshold = 100
    all_items = [item for item in mock_items if item.properties['eo:cloud_cover'] < max_threshold]
    
    print(f"\nMaximum Data Availability Test:")
    print(f"Total items available: {len(mock_items)}")
    print(f"Items with max cloud cover tolerance ({max_threshold}%): {len(all_items)}")
    
    # With maximum tolerance, we should get all items
    assert len(all_items) == len(mock_items), "Maximum cloud cover tolerance should return all available items"
    
    # Show distribution of cloud cover values
    cloud_covers = [item.properties['eo:cloud_cover'] for item in all_items]
    print(f"Cloud cover distribution: {cloud_covers}")
    
    print("✓ Maximum cloud cover tolerance returns all available data")


def test_cloud_cover_parameter_naming_clarity():
    """
    Test to demonstrate why the new parameter names are clearer.
    """
    
    print("\nCloud Cover Parameter Naming Clarity Test:")
    
    # Old confusing parameter names
    old_names = {
        'entire_image_cloud_cover_threshold': 20,
        'cropped_image_cloud_cover_threshold': 80
    }
    
    # New clear parameter names
    new_names = {
        'max_entire_image_cloud_cover': 20,
        'max_cropped_area_cloud_cover': 80
    }
    
    print("Old parameter names (confusing):")
    for name, value in old_names.items():
        print(f"  {name} = {value}")
        print(f"    → What does this mean? Include or exclude?")
    
    print("\nNew parameter names (clear):")
    for name, value in new_names.items():
        print(f"  {name} = {value}")
        print(f"    → Clear: maximum {value}% cloud cover allowed")
    
    print("\nBehavior demonstration:")
    test_items = [
        {'cloud_cover': 5, 'id': 'item_1'},
        {'cloud_cover': 15, 'id': 'item_2'},
        {'cloud_cover': 25, 'id': 'item_3'},
        {'cloud_cover': 85, 'id': 'item_4'},
    ]
    
    threshold = 20
    filtered_items = [item for item in test_items if item['cloud_cover'] < threshold]
    
    print(f"With max_entire_image_cloud_cover={threshold}:")
    print(f"  Items with <{threshold}% cloud cover: {[item['id'] for item in filtered_items]}")
    print(f"  Items excluded (≥{threshold}% cloud cover): {[item['id'] for item in test_items if item['cloud_cover'] >= threshold]}")
    
    print("✓ New parameter names make the filtering direction explicit")


def test_real_world_scenario():
    """
    Test a realistic scenario showing how cloud cover filtering affects data availability.
    """
    
    print("\nReal-World Scenario Test:")
    print("Simulating a year of satellite data collection...")
    
    # Simulate 365 days of satellite data with realistic cloud cover distribution
    import random
    random.seed(42)  # For reproducible results
    
    # Create realistic cloud cover distribution (some days clear, some cloudy)
    cloud_cover_distribution = []
    for day in range(365):
        # Realistic pattern: mostly clear days, some cloudy days
        if random.random() < 0.7:  # 70% chance of clear day
            cloud_cover = random.uniform(0, 20)  # 0-20% cloud cover
        else:  # 30% chance of cloudy day
            cloud_cover = random.uniform(20, 100)  # 20-100% cloud cover
        cloud_cover_distribution.append(cloud_cover)
    
    # Test different filtering strategies
    strategies = [
        (5, "Very strict (research quality)"),
        (20, "Moderate (general analysis)"),
        (50, "Permissive (maximum coverage)"),
        (100, "No filtering (all data)")
    ]
    
    print(f"{'Strategy':<25} {'Threshold':<10} {'Available Days':<15} {'Coverage %':<12}")
    print("-" * 70)
    
    for threshold, description in strategies:
        available_days = sum(1 for cc in cloud_cover_distribution if cc < threshold)
        coverage_percent = (available_days / 365) * 100
        
        print(f"{description:<25} {threshold:<10} {available_days:<15} {coverage_percent:<12.1f}%")
    
    print("\nKey insights:")
    print("- Very strict filtering (<5% clouds) gives high quality but limited data")
    print("- Moderate filtering (<20% clouds) balances quality and coverage")
    print("- Permissive filtering (<50% clouds) maximizes temporal coverage")
    print("- No filtering gives maximum data but includes cloudy observations")


if __name__ == "__main__":
    # Run all tests
    print("Running cloud cover filtering demonstration tests...")
    
    test_cloud_cover_parameter_behavior()
    test_cloud_cover_filtering_direction()
    test_maximum_data_availability()
    test_cloud_cover_parameter_naming_clarity()
    test_real_world_scenario()
    
    print("\n✅ All tests completed successfully!")
    print("\nSummary:")
    print("- Higher cloud cover thresholds return more data")
    print("- New parameter names make the filtering direction clear")
    print("- Maximum data availability requires setting thresholds to 100")
    print("- Real-world scenarios show the trade-off between quality and coverage") 