# Test Updates for Enhanced Legend Feature

## Overview
Updated test suite to cover the new enhanced legend functionality that displays individual medicines with average dosages and tracks medicines without dose data.

## New Test Methods Added

### 1. `test_enhanced_legend_functionality`
**Purpose**: Tests that the enhanced legend displays correctly with medicine dose data.

**What it tests**:
- Legend is called with enhanced formatting parameters (ncol=2, fontsize='small', etc.)
- Medicine toggles are properly handled
- Legend configuration parameters are correctly applied

**Key assertions**:
- `mock_ax.legend.assert_called()`
- Verifies `ncol=2`, `fontsize='small'`, `frameon=True` parameters

### 2. `test_legend_with_medicines_without_data`
**Purpose**: Tests that medicines without dose data are properly tracked and displayed in legend info.

**What it tests**:
- Medicines with dose data vs. medicines without dose data
- Additional legend entries for "Tracked (no doses)" information
- Proper handling of mixed data scenarios

**Key assertions**:
- Legend has more labels than original when medicines without data are present
- `mock_ax.legend.assert_called()`

### 3. `test_average_dose_calculation_in_legend`
**Purpose**: Tests that average doses are correctly calculated and used in legend labels.

**What it tests**:
- Dose calculation accuracy for varying dose amounts
- Average calculation logic for medicines with multiple daily entries
- Proper dose processing and bar plotting

**Key assertions**:
- Direct dose calculation verification: `assert bup_avg == 100.0`
- Bar plotting verification: `mock_ax.bar.assert_called()`

### 4. `test_legend_positioning_and_styling`
**Purpose**: Tests that all legend styling parameters are correctly applied.

**What it tests**:
- Complete set of legend parameters (loc, bbox_to_anchor, ncol, fontsize, frameon, fancybox, shadow, framealpha)
- Parameter value accuracy
- Consistent application of styling

**Key assertions**:
```python
expected_params = {
    'loc': 'upper left',
    'bbox_to_anchor': (0, 1),
    'ncol': 2,
    'fontsize': 'small',
    'frameon': True,
    'fancybox': True,
    'shadow': True,
    'framealpha': 0.9
}
```

### 5. `test_medicine_tracking_lists`
**Purpose**: Tests that medicines are correctly categorized into medicines_with_data and medicines_without_data lists.

**What it tests**:
- Proper categorization of medicines based on dose data availability
- Toggle state handling for different medicine states
- Mixed scenarios with some medicines having data and others not

**Key assertions**:
- `mock_ax.bar.assert_called()` for medicines with data
- `mock_ax.legend.assert_called()` for legend creation

### 6. `test_legend_dummy_handle_creation`
**Purpose**: Tests that dummy handles are created for medicines without dose data in legend.

**What it tests**:
- Rectangle dummy handle creation for text-only legend entries
- Proper import and usage of matplotlib.patches.Rectangle
- Integration of dummy handles with existing legend system

**Key assertions**:
- `mock_rectangle.assert_called()` when medicines without data are present

### 7. `test_empty_dataframe_legend_handling`
**Purpose**: Tests that legend is handled correctly with empty DataFrame scenarios.

**What it tests**:
- No legend creation when no data is present
- Proper graph clearing and canvas redrawing
- Edge case handling

**Key assertions**:
- `mock_ax.legend.assert_not_called()` for empty data
- `mock_ax.clear.assert_called()` and `mock_canvas.draw.assert_called()`

## Test Data Enhancements

### Enhanced Sample DataFrames
Tests now use more comprehensive DataFrames that include:
- **Realistic dose data**: Multiple dose entries with varying amounts
- **Mixed scenarios**: Some medicines with data, others without
- **Average calculation data**: Varying doses across multiple days for accurate average testing
- **Edge cases**: Empty dose strings, missing data scenarios

### Example Test Data Structure:
```python
df_with_varying_doses = pd.DataFrame({
    'bupropion_doses': ['100mg', '200mg', '150mg'],  # Avg: 150mg
    'propranolol_doses': ['10mg', '20mg', ''],       # Avg: 15mg
    'hydroxyzine_doses': ['', '', ''],               # No data
})
```

## Mock Enhancements

### Legend-Specific Mocks:
- **`mock_ax.get_legend_handles_labels`**: Returns mock handles and labels
- **`matplotlib.patches.Rectangle`**: Mocked for dummy handle creation
- **Enhanced legend parameter verification**: Detailed parameter checking

### Integration Testing:
- Tests work with existing matplotlib mocking structure
- Compatible with existing GraphManager test patterns
- Maintains isolation between test methods

## Coverage Areas

### Legend Functionality:
✅ **Enhanced formatting**: Multi-column, styling, positioning
✅ **Medicine tracking**: With/without data categorization
✅ **Average calculations**: Accurate dose averaging in labels
✅ **Dummy handles**: Text-only legend entries
✅ **Parameter validation**: All styling parameters verified

### Edge Cases:
✅ **Empty DataFrames**: No legend creation
✅ **Mixed data scenarios**: Some medicines with/without data
✅ **Toggle combinations**: Various medicine toggle states
✅ **Import handling**: Matplotlib patches import testing

### Integration:
✅ **Existing functionality**: Compatible with previous tests
✅ **Mock consistency**: Uses established mocking patterns
✅ **Error handling**: Graceful handling of edge cases

## Running the Tests

```bash
# Run all graph manager tests
.venv/bin/python -m pytest tests/test_graph_manager.py -v

# Run only legend-related tests
.venv/bin/python -m pytest tests/test_graph_manager.py -k "legend" -v

# Run with coverage
.venv/bin/python -m pytest tests/test_graph_manager.py --cov=src.graph_manager --cov-report=html
```

## Benefits

### Test Quality:
- **Comprehensive coverage** of new legend functionality
- **Edge case testing** for robust error handling
- **Integration testing** with existing graph functionality

### Maintenance:
- **Clear test names** indicating specific functionality
- **Isolated test methods** for easy debugging
- **Consistent patterns** following existing test structure

The updated tests ensure that the enhanced legend functionality is thoroughly validated while maintaining compatibility with existing GraphManager features.