"""
Test to demonstrate how both cloud cover parameters work together.

This test shows the relationship between:
1. max_entire_image_cloud_cover (filters during STAC search)
2. max_cropped_area_cloud_cover (filters during data loading)
"""

import pytest
import geopandas as gpd
from shapely.geometry import Polygon
from unittest.mock import Mock, patch
import xarray as xr
import numpy as np
import pandas as pd


@pytest.fixture
def sample_geometry():
    """Create a sample geometry for testing"""
    polygon = Polygon([
        (-122.2751, 47.5469),
        (-121.9613, 47.5469),
        (-121.9613, 47.7458),
        (-122.2751, 47.7458),
        (-122.2751, 47.5469)
    ])
    return gpd.GeoDataFrame([1], geometry=[polygon], crs="EPSG:4326")


def test_both_parameters_work_together():
    """
    Test to demonstrate how both cloud cover parameters work together.
    """
    
    print("\n🔍 Testing Both Cloud Cover Parameters Working Together")
    print("=" * 70)
    
    # 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 combinations of both parameters
    test_configs = [
        # (entire_image, cropped_area, description, expected_items_after_search, expected_time_steps_after_loading)
        (5, 10, "Very restrictive (both low)", 1, 1),      # Only 0% cloud cover passes both filters
        (20, 30, "Moderate (both moderate)", 2, 2),        # 0%, 10% pass both filters
        (50, 40, "Mixed (high entire, moderate cropped)", 5, 4),  # 5 items pass search, 4 pass loading
        (100, 100, "Maximum (both high)", 10, 10),         # All items pass both filters
    ]
    
    print(f"{'Description':<35} {'Entire':<8} {'Cropped':<8} {'After Search':<12} {'After Loading':<12} {'Explanation'}")
    print("-" * 90)
    
    for entire_threshold, cropped_threshold, description, expected_search, expected_loading in test_configs:
        # Simulate STAC search filtering (entire_image_cloud_cover)
        items_after_search = [item for item in mock_items if item.properties['eo:cloud_cover'] < entire_threshold]
        search_count = len(items_after_search)
        
        # Simulate post-processing filtering (cropped_area_cloud_cover)
        items_after_loading = [item for item in items_after_search if item.properties['eo:cloud_cover'] < cropped_threshold]
        loading_count = len(items_after_loading)
        
        explanation = f"Search: <{entire_threshold}%, Loading: <{cropped_threshold}%"
        
        print(f"{description:<35} {entire_threshold:<8} {cropped_threshold:<8} {search_count:<12} {loading_count:<12} {explanation}")
        
        # Verify the filtering works correctly
        assert search_count == expected_search, f"Expected {expected_search} items after search, got {search_count}"
        assert loading_count == expected_loading, f"Expected {loading_count} items after loading, got {loading_count}"
    
    print("\n✅ Both parameters work together as expected!")


def test_parameter_interaction_scenarios():
    """
    Test different scenarios of parameter interaction.
    """
    
    print("\n🔍 Testing Parameter Interaction Scenarios")
    print("=" * 50)
    
    # Create mock items with various cloud cover values
    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 scenarios
    scenarios = [
        {
            'name': 'Scenario 1: Cropped more restrictive than entire',
            'entire': 50,
            'cropped': 20,
            'description': 'Entire image allows 50%, but cropped area only allows 20%'
        },
        {
            'name': 'Scenario 2: Entire more restrictive than cropped',
            'entire': 20,
            'cropped': 50,
            'description': 'Entire image only allows 20%, cropped area allows 50%'
        },
        {
            'name': 'Scenario 3: Both equally restrictive',
            'entire': 30,
            'cropped': 30,
            'description': 'Both parameters set to same threshold'
        },
        {
            'name': 'Scenario 4: Maximum data availability',
            'entire': 100,
            'cropped': 100,
            'description': 'Both parameters set to maximum'
        }
    ]
    
    for scenario in scenarios:
        print(f"\n📊 {scenario['name']}")
        print(f"   {scenario['description']}")
        
        # Apply entire image filtering (STAC search)
        items_after_search = [item for item in mock_items if item.properties['eo:cloud_cover'] < scenario['entire']]
        search_count = len(items_after_search)
        
        # Apply cropped area filtering (post-processing)
        items_after_loading = [item for item in items_after_search if item.properties['eo:cloud_cover'] < scenario['cropped']]
        loading_count = len(items_after_loading)
        
        print(f"   Entire image threshold: {scenario['entire']}% → {search_count} items pass search")
        print(f"   Cropped area threshold: {scenario['cropped']}% → {loading_count} items pass loading")
        
        # Show which items pass each filter
        search_cloud_covers = [item.properties['eo:cloud_cover'] for item in items_after_search]
        loading_cloud_covers = [item.properties['eo:cloud_cover'] for item in items_after_loading]
        
        print(f"   Cloud covers after search: {search_cloud_covers}")
        print(f"   Cloud covers after loading: {loading_cloud_covers}")
        
        # Determine which filter is more restrictive
        if search_count > loading_count:
            print(f"   → Cropped area filter is more restrictive")
        elif search_count < loading_count:
            print(f"   → Entire image filter is more restrictive")
        else:
            print(f"   → Both filters are equally restrictive")


def test_maximum_data_availability_both_parameters():
    """
    Test that setting both parameters to maximum returns the most data.
    """
    
    print("\n🔍 Testing Maximum Data Availability with Both Parameters")
    print("=" * 60)
    
    # 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 different configurations
    configs = [
        (10, 10, "Both restrictive"),
        (50, 50, "Both moderate"),
        (100, 50, "Entire permissive, cropped moderate"),
        (50, 100, "Entire moderate, cropped permissive"),
        (100, 100, "Both maximum")
    ]
    
    print(f"{'Configuration':<35} {'Entire':<8} {'Cropped':<8} {'Final Items':<12}")
    print("-" * 70)
    
    for description, entire_threshold, cropped_threshold in configs:
        # Apply both filters
        items_after_search = [item for item in mock_items if item.properties['eo:cloud_cover'] < entire_threshold]
        items_after_loading = [item for item in items_after_search if item.properties['eo:cloud_cover'] < cropped_threshold]
        final_count = len(items_after_loading)
        
        print(f"{description:<35} {entire_threshold:<8} {cropped_threshold:<8} {final_count:<12}")
    
    # Verify maximum configuration returns all data
    max_items = [item for item in mock_items if item.properties['eo:cloud_cover'] < 100]
    max_after_loading = [item for item in max_items if item.properties['eo:cloud_cover'] < 100]
    
    print(f"\n✅ Maximum configuration (100, 100) returns {len(max_after_loading)} items")
    print(f"   This equals all available data: {len(mock_items)} items")


def test_real_world_usage_patterns():
    """
    Test realistic usage patterns for different applications.
    """
    
    print("\n🔍 Testing Real-World Usage Patterns")
    print("=" * 50)
    
    # Simulate 365 days of satellite data
    import random
    random.seed(42)
    
    # Create realistic cloud cover distribution
    cloud_cover_distribution = []
    for day in range(365):
        if random.random() < 0.7:  # 70% clear days
            cloud_cover = random.uniform(0, 20)
        else:  # 30% cloudy days
            cloud_cover = random.uniform(20, 100)
        cloud_cover_distribution.append(cloud_cover)
    
    # Define usage patterns
    patterns = [
        {
            'name': 'Research Quality',
            'entire': 5,
            'cropped': 10,
            'description': 'High quality, limited data'
        },
        {
            'name': 'General Analysis',
            'entire': 20,
            'cropped': 30,
            'description': 'Balanced quality and coverage'
        },
        {
            'name': 'Maximum Coverage',
            'entire': 50,
            'cropped': 80,
            'description': 'Maximum temporal coverage'
        },
        {
            'name': 'No Filtering',
            'entire': 100,
            'cropped': 100,
            'description': 'All data, post-process later'
        }
    ]
    
    print(f"{'Pattern':<20} {'Entire':<8} {'Cropped':<8} {'Available Days':<15} {'Coverage %':<12}")
    print("-" * 70)
    
    for pattern in patterns:
        # Apply entire image filtering
        days_after_search = sum(1 for cc in cloud_cover_distribution if cc < pattern['entire'])
        
        # Apply cropped area filtering
        days_after_loading = sum(1 for cc in cloud_cover_distribution if cc < pattern['cropped'])
        
        # The more restrictive filter determines final availability
        final_days = min(days_after_search, days_after_loading)
        coverage_percent = (final_days / 365) * 100
        
        print(f"{pattern['name']:<20} {pattern['entire']:<8} {pattern['cropped']:<8} {final_days:<15} {coverage_percent:<12.1f}%")
    
    print(f"\n🎯 Key Insights:")
    print(f"  • The more restrictive parameter determines final data availability")
    print(f"  • Research quality patterns give high quality but limited coverage")
    print(f"  • Maximum coverage patterns prioritize temporal coverage")
    print(f"  • No filtering gives maximum flexibility for post-processing")


def test_parameter_naming_clarity_both():
    """
    Test to demonstrate why the new parameter names are clearer for both parameters.
    """
    
    print("\n🔍 Testing Parameter Naming Clarity for Both Parameters")
    print("=" * 60)
    
    # Old confusing parameter names
    old_config = {
        'entire_image_cloud_cover_threshold': 20,
        'cropped_image_cloud_cover_threshold': 80
    }
    
    # New clear parameter names
    new_config = {
        'max_entire_image_cloud_cover': 20,
        'max_cropped_area_cloud_cover': 80
    }
    
    print("Old parameter names (confusing):")
    for name, value in old_config.items():
        print(f"  {name} = {value}")
        print(f"    → What does this mean? Include or exclude?")
    
    print("\nNew parameter names (clear):")
    for name, value in new_config.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'},
    ]
    
    entire_threshold = 20
    cropped_threshold = 80
    
    # Apply both filters
    after_entire = [item for item in test_items if item['cloud_cover'] < entire_threshold]
    after_cropped = [item for item in after_entire if item['cloud_cover'] < cropped_threshold]
    
    print(f"With max_entire_image_cloud_cover={entire_threshold} and max_cropped_area_cloud_cover={cropped_threshold}:")
    print(f"  Items after entire image filter: {[item['id'] for item in after_entire]}")
    print(f"  Items after cropped area filter: {[item['id'] for item in after_cropped]}")
    print(f"  Items excluded by entire image filter: {[item['id'] for item in test_items if item['cloud_cover'] >= entire_threshold]}")
    print(f"  Items excluded by cropped area filter: {[item['id'] for item in after_entire if item['cloud_cover'] >= cropped_threshold]}")
    
    print("✓ New parameter names make both filtering directions explicit")


if __name__ == "__main__":
    # Run all tests
    print("Running comprehensive cloud cover parameter tests...")
    
    test_both_parameters_work_together()
    test_parameter_interaction_scenarios()
    test_maximum_data_availability_both_parameters()
    test_real_world_usage_patterns()
    test_parameter_naming_clarity_both()
    
    print("\n✅ All comprehensive tests completed successfully!")
    print("\nSummary:")
    print("- Both parameters work together to filter data")
    print("- The more restrictive parameter determines final availability")
    print("- Maximum data requires setting both parameters to 100")
    print("- New parameter names make the filtering behavior clear")
    print("- Real-world patterns show different trade-offs between quality and coverage") 