"""
Basic tests for Distilled data stream reduction system.

These tests will validate the core functionality once implementation is complete.
"""

import unittest
from unittest.mock import Mock, patch
from distilled import DistilledProcessor, DataPoint, NumericGrader, CategoricalGrader


class TestDataPoint(unittest.TestCase):
    """Test the DataPoint class."""
    
    def test_datapoint_creation(self):
        """Test basic DataPoint creation."""
        data = {"value": 42}
        timestamp = 1234567890.0
        
        point = DataPoint(data=data, timestamp=timestamp)
        
        self.assertEqual(point.data, data)
        self.assertEqual(point.timestamp, timestamp)
        self.assertIsNone(point.vector_values)
        self.assertFalse(point.sent_previously)
    
    def test_datapoint_with_vector_values(self):
        """Test DataPoint with vector values."""
        data = {"value": 42}
        timestamp = 1234567890.0
        vector_values = {"age": 25.0, "category": "A"}
        
        point = DataPoint(
            data=data, 
            timestamp=timestamp, 
            vector_values=vector_values
        )
        
        self.assertEqual(point.vector_values, vector_values)


class TestGradingFunctions(unittest.TestCase):
    """Test grading function classes."""
    
    def test_numeric_grader_creation(self):
        """Test NumericGrader creation."""
        extractor = lambda x: float(x["value"])
        grader = NumericGrader("test_numeric", extractor)
        
        self.assertEqual(grader.name, "test_numeric")
        self.assertEqual(grader.extractor_func, extractor)
        self.assertFalse(grader.is_categorical())
    
    def test_categorical_grader_creation(self):
        """Test CategoricalGrader creation."""
        categories = ["A", "B", "C"]
        classifier = lambda x: x["category"]
        grader = CategoricalGrader("test_categorical", categories, classifier)
        
        self.assertEqual(grader.name, "test_categorical")
        self.assertEqual(grader.categories, categories)
        self.assertEqual(grader.classifier_func, classifier)
        self.assertTrue(grader.is_categorical())
        self.assertEqual(grader.get_categories(), categories)


class TestDistilledProcessor(unittest.TestCase):
    """Test the main DistilledProcessor class."""
    
    def setUp(self):
        """Set up test fixtures."""
        self.sample_grading_functions = {
            "numeric_var": NumericGrader(
                "numeric_var", 
                lambda x: float(x.get("value", 0))
            ),
            "categorical_var": CategoricalGrader(
                "categorical_var",
                ["A", "B", "C"],
                lambda x: x.get("category", "A")
            )
        }
    
    def test_processor_initialization(self):
        """Test DistilledProcessor initialization."""
        processor = DistilledProcessor(
            grading_functions=self.sample_grading_functions,
            time_horizon_seconds=3600,
            reduction_percentage=0.1,
            batch_size=100
        )
        
        self.assertEqual(processor.grading_functions, self.sample_grading_functions)
        self.assertEqual(processor.time_horizon_seconds, 3600)
        self.assertEqual(processor.reduction_percentage, 0.1)
        self.assertEqual(processor.batch_size, 100)
        self.assertIsNotNone(processor.historical_data)
        self.assertIsNotNone(processor.sent_data)
    
    def test_processor_default_parameters(self):
        """Test DistilledProcessor with default parameters."""
        processor = DistilledProcessor(
            grading_functions=self.sample_grading_functions
        )
        
        self.assertEqual(processor.time_horizon_seconds, 3600)
        self.assertEqual(processor.reduction_percentage, 0.1)
        self.assertEqual(processor.batch_size, 100)
    
    @unittest.skip("Implementation not complete")
    def test_process_batch(self):
        """Test processing a batch of data points."""
        processor = DistilledProcessor(
            grading_functions=self.sample_grading_functions,
            reduction_percentage=0.5  # 50% for easier testing
        )
        
        # Sample data
        sample_data = [
            {"value": i, "category": "A" if i % 2 == 0 else "B"}
            for i in range(10)
        ]
        
        result = processor.process_batch(sample_data)
        
        # Should return approximately 50% of input (5 points)
        self.assertLessEqual(len(result), len(sample_data))
        self.assertGreaterEqual(len(result), 1)
        
        # All results should be DataPoint objects
        for point in result:
            self.assertIsInstance(point, DataPoint)
    
    @unittest.skip("Implementation not complete")
    def test_proportional_representation(self):
        """Test that proportional representation is maintained."""
        processor = DistilledProcessor(
            grading_functions=self.sample_grading_functions,
            reduction_percentage=0.1
        )
        
        # Create biased sample data (70% A, 30% B categories)
        sample_data = []
        for i in range(100):
            category = "A" if i < 70 else "B"
            sample_data.append({"value": i, "category": category})
        
        result = processor.process_batch(sample_data)
        
        # Check that proportions are approximately maintained
        a_count = sum(1 for point in result if point.vector_values["categorical_var"] == "A")
        b_count = len(result) - a_count
        
        a_proportion = a_count / len(result)
        # Should be approximately 70% (within reasonable tolerance)
        self.assertGreater(a_proportion, 0.6)
        self.assertLess(a_proportion, 0.8)


class TestIntegration(unittest.TestCase):
    """Integration tests for the complete system."""
    
    @unittest.skip("Implementation not complete")
    def test_end_to_end_processing(self):
        """Test complete end-to-end data processing."""
        # This test will validate the entire pipeline once implemented
        pass
    
    @unittest.skip("Implementation not complete") 
    def test_time_horizon_management(self):
        """Test that time horizon management works correctly."""
        # This test will validate FIFO queue management once implemented
        pass
    
    @unittest.skip("Implementation not complete")
    def test_vector_analysis_accuracy(self):
        """Test that vector analysis produces accurate results."""
        # This test will validate the core selection algorithm once implemented
        pass


if __name__ == "__main__":
    unittest.main() 