from unittest.mock import MagicMock, Mock, patch

import numpy as np
import pandas as pd
import pytest

from decline_analysis.utils.data_loader import scrape_ndic


class TestDataLoader:
    """Test suite for data loading functionality."""

    @patch("decline_analysis.utils.data_loader.requests.get")
    @patch("decline_analysis.utils.data_loader.xlrd.open_workbook")
    def test_basic_ndic_scraping(self, mock_xlrd, mock_requests):
        """Test basic NDIC data scraping functionality."""
        # Mock HTTP response
        mock_response = Mock()
        mock_response.content = b"fake_excel_content"
        mock_response.raise_for_status = Mock()
        mock_requests.return_value = mock_response

        # Mock Excel workbook
        mock_book = Mock()
        mock_book.datemode = 0
        mock_sheet = Mock()
        mock_sheet.nrows = 3
        mock_sheet.row_values.side_effect = [
            ["Date", "Well_Name", "Production"],  # Header
            ["2023-01-01", "Well_001", 1000],  # Data row 1
            ["2023-01-01", "Well_002", 800],  # Data row 2
        ]
        mock_sheet.cell_value.side_effect = [44927.0, 44927.0]  # Excel date values
        mock_sheet._cell_values = [[None], ["2023-01-01"], ["2023-01-01"]]
        mock_book.sheet_by_index.return_value = mock_sheet
        mock_xlrd.return_value = mock_book

        # Test scraping
        result = scrape_ndic(["2023-01"])

        # Verify result
        assert isinstance(result, pd.DataFrame)
        assert len(result) > 0
        assert "SourceMonth" in result.columns

        # Verify HTTP request was made
        mock_requests.assert_called_once()
        assert "2023-01.xlsx" in mock_requests.call_args[0][0]

    @patch("decline_analysis.utils.data_loader.requests.get")
    def test_ndic_scraping_network_error(self, mock_requests):
        """Test handling of network errors during NDIC scraping."""
        # Mock network error
        mock_requests.side_effect = Exception("Network error")

        # Should handle error gracefully
        result = scrape_ndic(["2023-01"])

        # Should return empty DataFrame or handle gracefully
        assert isinstance(result, pd.DataFrame)

    @patch("decline_analysis.utils.data_loader.requests.get")
    @patch("decline_analysis.utils.data_loader.xlrd.open_workbook")
    def test_ndic_scraping_multiple_months(self, mock_xlrd, mock_requests):
        """Test scraping multiple months of NDIC data."""
        # Mock HTTP response
        mock_response = Mock()
        mock_response.content = b"fake_excel_content"
        mock_response.raise_for_status = Mock()
        mock_requests.return_value = mock_response

        # Mock Excel workbook
        mock_book = Mock()
        mock_book.datemode = 0
        mock_sheet = Mock()
        mock_sheet.nrows = 2
        mock_sheet.row_values.side_effect = [
            ["Date", "Well_Name", "Production"],  # Header
            ["2023-01-01", "Well_001", 1000],  # Data row
        ]
        mock_sheet.cell_value.return_value = 44927.0  # Excel date value
        mock_sheet._cell_values = [[None], ["2023-01-01"]]
        mock_book.sheet_by_index.return_value = mock_sheet
        mock_xlrd.return_value = mock_book

        # Test multiple months
        months = ["2023-01", "2023-02", "2023-03"]
        result = scrape_ndic(months)

        # Should make multiple HTTP requests
        assert mock_requests.call_count == len(months)

        # Should combine data from all months
        assert isinstance(result, pd.DataFrame)
        if len(result) > 0:
            assert "SourceMonth" in result.columns

    def test_ndic_scraping_empty_months_list(self):
        """Test behavior with empty months list."""
        result = scrape_ndic([])

        # Should return empty DataFrame
        assert isinstance(result, pd.DataFrame)
        assert len(result) == 0

    @patch("decline_analysis.utils.data_loader.requests.get")
    def test_ndic_scraping_timeout(self, mock_requests):
        """Test handling of request timeouts."""
        # Mock timeout error
        import requests

        mock_requests.side_effect = requests.Timeout("Request timeout")

        result = scrape_ndic(["2023-01"])

        # Should handle timeout gracefully
        assert isinstance(result, pd.DataFrame)

    @patch("decline_analysis.utils.data_loader.requests.get")
    @patch("decline_analysis.utils.data_loader.xlrd.open_workbook")
    def test_ndic_scraping_invalid_excel(self, mock_xlrd, mock_requests):
        """Test handling of invalid Excel files."""
        # Mock HTTP response
        mock_response = Mock()
        mock_response.content = b"invalid_excel_content"
        mock_response.raise_for_status = Mock()
        mock_requests.return_value = mock_response

        # Mock Excel parsing error
        mock_xlrd.side_effect = Exception("Invalid Excel file")

        result = scrape_ndic(["2023-01"])

        # Should handle Excel parsing error gracefully
        assert isinstance(result, pd.DataFrame)

    @patch("decline_analysis.utils.data_loader.Path.mkdir")
    @patch("decline_analysis.utils.data_loader.requests.get")
    @patch("decline_analysis.utils.data_loader.xlrd.open_workbook")
    def test_ndic_output_directory_creation(self, mock_xlrd, mock_requests, mock_mkdir):
        """Test that output directory is created."""
        # Mock successful scraping
        mock_response = Mock()
        mock_response.content = b"fake_excel_content"
        mock_response.raise_for_status = Mock()
        mock_requests.return_value = mock_response

        mock_book = Mock()
        mock_book.datemode = 0
        mock_sheet = Mock()
        mock_sheet.nrows = 1
        mock_sheet.row_values.return_value = ["Date", "Well_Name", "Production"]
        mock_book.sheet_by_index.return_value = mock_sheet
        mock_xlrd.return_value = mock_book

        # Test with custom output directory
        scrape_ndic(["2023-01"], output_dir="custom_dir")

        # Should create output directory
        mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)


class TestDataLoaderIntegration:
    """Test integration with main DCA API."""

    @patch("decline_analysis.utils.data_loader.scrape_ndic")
    def test_load_ndic_data_api(self, mock_scrape):
        """Test NDIC data loading through main API."""
        from decline_analysis.dca import load_ndic_data

        # Mock successful scraping
        mock_data = pd.DataFrame(
            {
                "Date": ["2023-01-01", "2023-01-01"],
                "Well_Name": ["Well_001", "Well_002"],
                "Production": [1000, 800],
                "SourceMonth": ["2023-01", "2023-01"],
            }
        )
        mock_scrape.return_value = mock_data

        # Test API function
        result = load_ndic_data(["2023-01"])

        assert isinstance(result, pd.DataFrame)
        # The mock should return the mock_data, so check that it was called correctly
        mock_scrape.assert_called_once_with(["2023-01"], "ndic_raw")
        # Result should match what the mock returned
        assert len(result) == len(mock_data)

    def test_data_loader_realistic_workflow(self):
        """Test realistic data loading workflow (mocked)."""
        # This would be a realistic test if we had actual NDIC data
        # For now, we'll test the workflow with mock data

        # Simulate loaded production data
        mock_production_data = pd.DataFrame(
            {
                "Date": pd.date_range("2023-01-01", periods=12, freq="MS"),
                "Well_Name": ["Well_001"] * 12,
                "Oil_Production": [
                    1000,
                    950,
                    900,
                    850,
                    800,
                    750,
                    700,
                    650,
                    600,
                    550,
                    500,
                    450,
                ],
                "Gas_Production": [
                    5000,
                    4750,
                    4500,
                    4250,
                    4000,
                    3750,
                    3500,
                    3250,
                    3000,
                    2750,
                    2500,
                    2250,
                ],
            }
        )

        # Test data processing workflow
        well_data = mock_production_data[
            mock_production_data["Well_Name"] == "Well_001"
        ]

        # Create time series for decline analysis
        oil_series = pd.Series(
            well_data["Oil_Production"].values, index=well_data["Date"]
        )

        # Basic validation
        assert len(oil_series) == 12
        assert oil_series.index.freq is not None or len(oil_series.index) == 12
        assert all(oil_series > 0)  # All production should be positive


class TestDataLoaderErrorHandling:
    """Test error handling and edge cases."""

    def test_invalid_month_format(self):
        """Test handling of invalid month formats."""
        # These should be handled gracefully
        invalid_months = ["invalid", "2023-13", "2023-00", ""]

        for month in invalid_months:
            result = scrape_ndic([month])
            assert isinstance(result, pd.DataFrame)

    @patch("decline_analysis.utils.data_loader.requests.get")
    def test_http_error_handling(self, mock_requests):
        """Test handling of HTTP errors."""
        import requests

        # Test different HTTP errors
        errors = [
            requests.HTTPError("404 Not Found"),
            requests.ConnectionError("Connection failed"),
            requests.RequestException("General request error"),
        ]

        for error in errors:
            mock_requests.side_effect = error
            result = scrape_ndic(["2023-01"])
            assert isinstance(result, pd.DataFrame)

    @patch("decline_analysis.utils.data_loader.requests.get")
    @patch("decline_analysis.utils.data_loader.xlrd.open_workbook")
    def test_date_parsing_errors(self, mock_xlrd, mock_requests):
        """Test handling of date parsing errors."""
        # Mock HTTP response
        mock_response = Mock()
        mock_response.content = b"fake_excel_content"
        mock_response.raise_for_status = Mock()
        mock_requests.return_value = mock_response

        # Mock Excel workbook with problematic dates
        mock_book = Mock()
        mock_book.datemode = 0
        mock_sheet = Mock()
        mock_sheet.nrows = 2
        mock_sheet.row_values.side_effect = [
            ["Date", "Well_Name", "Production"],
            ["invalid_date", "Well_001", 1000],
        ]
        mock_sheet.cell_value.side_effect = Exception("Date parsing error")
        mock_sheet._cell_values = [[None], [""]]  # Will be set to empty string
        mock_book.sheet_by_index.return_value = mock_sheet
        mock_xlrd.return_value = mock_book

        result = scrape_ndic(["2023-01"])

        # Should handle date parsing errors gracefully
        assert isinstance(result, pd.DataFrame)

    def test_memory_limitations(self):
        """Test behavior with large month lists (simulated)."""
        # Test with many months (would be memory intensive in real scenario)
        many_months = (
            [f"2020-{i:02d}" for i in range(1, 13)]
            + [f"2021-{i:02d}" for i in range(1, 13)]
            + [f"2022-{i:02d}" for i in range(1, 13)]
        )

        # This should not crash (though will fail due to mocking)
        try:
            result = scrape_ndic(many_months)
            assert isinstance(result, pd.DataFrame)
        except Exception:
            # Expected to fail in test environment, but shouldn't crash
            pass


class TestDataLoaderUtilities:
    """Test utility functions and helpers."""

    def test_data_quality_assessment(self):
        """Test data quality assessment functionality."""
        # Create sample data with quality issues
        sample_data = pd.DataFrame(
            {
                "Date": ["2023-01-01", None, "2023-03-01", "2023-04-01"],
                "Well_Name": ["Well_001", "Well_002", "Well_003", "Well_004"],
                "Production": [1000, 800, -50, None],  # Negative and missing values
                "SourceMonth": ["2023-01"] * 4,
            }
        )

        # Basic quality checks
        missing_dates = sample_data["Date"].isna().sum()
        missing_production = sample_data["Production"].isna().sum()
        negative_production = (sample_data["Production"] < 0).sum()

        assert missing_dates == 1
        assert missing_production == 1
        assert negative_production == 1

    def test_data_cleaning_workflow(self):
        """Test typical data cleaning workflow."""
        # Create messy data
        messy_data = pd.DataFrame(
            {
                "Date": ["2023-01-01", "2023-02-01", None, "2023-04-01"],
                "Well_Name": ["Well_001", "Well_002", "Well_003", "Well_004"],
                "Oil_Production": [1000, 800, 600, None],
                "Gas_Production": [5000, 4000, 3000, 2000],
                "Water_Production": [100, 200, 300, 400],
            }
        )

        # Clean data
        cleaned_data = messy_data.dropna(subset=["Date", "Oil_Production"])

        # Should remove rows with missing critical data
        assert len(cleaned_data) == 2
        assert not cleaned_data["Date"].isna().any()
        assert not cleaned_data["Oil_Production"].isna().any()

    def test_data_aggregation(self):
        """Test data aggregation functionality."""
        # Create sample well data
        well_data = pd.DataFrame(
            {
                "Date": pd.date_range("2023-01-01", periods=12, freq="MS"),
                "Well_Name": ["Well_001"] * 6 + ["Well_002"] * 6,
                "Oil_Production": [
                    1000,
                    950,
                    900,
                    850,
                    800,
                    750,
                    800,
                    760,
                    720,
                    680,
                    640,
                    600,
                ],
            }
        )

        # Aggregate by well
        well_summary = well_data.groupby("Well_Name").agg(
            {"Oil_Production": ["sum", "mean", "count"], "Date": ["min", "max"]}
        )

        assert len(well_summary) == 2  # Two wells
        assert well_summary.loc["Well_001", ("Oil_Production", "count")] == 6
        assert well_summary.loc["Well_002", ("Oil_Production", "count")] == 6


class TestDataLoaderPerformance:
    """Test performance characteristics."""

    def test_large_dataset_handling(self):
        """Test handling of large datasets (simulated)."""
        # Simulate large dataset
        large_data = pd.DataFrame(
            {
                "Date": pd.date_range("2020-01-01", periods=1000, freq="D"),
                "Well_Name": [f"Well_{i:03d}" for i in range(1000)],
                "Production": np.random.randint(100, 2000, 1000),
            }
        )

        # Test basic operations on large dataset
        assert len(large_data) == 1000
        assert large_data["Production"].sum() > 0

        # Test groupby operation (common in data processing)
        monthly_summary = large_data.groupby(large_data["Date"].dt.to_period("M"))[
            "Production"
        ].sum()

        assert len(monthly_summary) > 0

    @patch("decline_analysis.utils.data_loader.requests.get")
    def test_concurrent_requests_simulation(self, mock_requests):
        """Test simulation of concurrent request handling."""
        # Mock delayed response to simulate network latency
        mock_response = Mock()
        mock_response.content = b"fake_excel_content"
        mock_response.raise_for_status = Mock()

        import time

        def slow_response(*args, **kwargs):
            time.sleep(0.1)  # Simulate network delay
            return mock_response

        mock_requests.side_effect = slow_response

        # Test multiple months (would benefit from concurrent processing)
        months = ["2023-01", "2023-02", "2023-03"]

        start_time = time.time()
        result = scrape_ndic(months)
        end_time = time.time()

        # Should complete in reasonable time
        assert end_time - start_time < 5.0  # 5 seconds max
        assert isinstance(result, pd.DataFrame)
