feat: Implement dose calculation fix and enhance legend feature

- Fixed dose calculation logic in `_calculate_daily_dose` to correctly parse timestamps with multiple colons.
- Added comprehensive test cases for various dose formats and edge cases in `test_dose_calculation.py`.
- Enhanced graph legend to display individual medicines with average dosages and track medicines without dose data.
- Updated legend styling and positioning for better readability and organization.
- Created new tests for enhanced legend functionality, including handling of medicines with and without data.
- Improved mocking for matplotlib components in tests to prevent TypeErrors.

LEGEND_TEST_UPDATES.md

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+# 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.