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.

tests/conftest.py

@@ -89,3 +89,33 @@ def sample_dose_data():
         'nan_value': 'nan',  # Should be 0
         'no_units': '2025-07-28 18:59:45:10|2025-07-28 19:34:19:5',  # Should sum to 15
     }
+
+
+@pytest.fixture
+def legend_test_dataframe():
+    """DataFrame specifically designed for testing legend functionality."""
+    return pd.DataFrame({
+        'date': ['2024-01-01', '2024-01-02', '2024-01-03'],
+        'depression': [3, 2, 4],
+        'anxiety': [2, 3, 1],
+        'sleep': [4, 3, 5],
+        'appetite': [3, 4, 2],
+        # Medicine with consistent doses for average testing
+        'bupropion': [1, 1, 1],
+        'bupropion_doses': ['2024-01-01 08:00:00:100mg',
+                           '2024-01-02 08:00:00:200mg',
+                           '2024-01-03 08:00:00:150mg'],  # Average: 150mg
+        # Medicine with varying doses
+        'propranolol': [1, 1, 0],
+        'propranolol_doses': ['2024-01-01 12:00:00:10mg',
+                             '2024-01-02 12:00:00:20mg',
+                             ''],  # Average: 15mg (10+20)/2
+        # Medicines without dose data
+        'hydroxyzine': [0, 0, 0],
+        'hydroxyzine_doses': ['', '', ''],
+        'gabapentin': [0, 0, 0],
+        'gabapentin_doses': ['', '', ''],
+        'quetiapine': [0, 0, 0],
+        'quetiapine_doses': ['', '', ''],
+        'note': ['Test note 1', 'Test note 2', 'Test note 3']
+    })

tests/test_graph_manager.py

@@ -433,6 +433,329 @@ class TestGraphManager:
                 assert mock_ax.clear.call_count >= 2
                 assert mock_canvas.draw.call_count >= 2
 
+    def test_enhanced_legend_functionality(self, parent_frame):
+        """Test that the enhanced legend displays correctly with medicine data."""
+        df_with_doses = pd.DataFrame({
+            'date': ['2024-01-01', '2024-01-02'],
+            'depression': [3, 2],
+            'anxiety': [2, 3],
+            'sleep': [4, 3],
+            'appetite': [3, 4],
+            'bupropion': [1, 1],
+            'bupropion_doses': ['2024-01-01 08:00:00:150mg', '2024-01-02 08:00:00:200mg'],
+            'hydroxyzine': [0, 0],
+            'hydroxyzine_doses': ['', ''],
+            'gabapentin': [0, 0],
+            'gabapentin_doses': ['', ''],
+            'propranolol': [1, 1],
+            'propranolol_doses': ['2024-01-01 12:00:00:10mg', '2024-01-02 12:00:00:15mg'],
+            'quetiapine': [0, 0],
+            'quetiapine_doses': ['', ''],
+        })
+
+        with patch('matplotlib.pyplot.subplots') as mock_subplots:
+            mock_fig = Mock()
+            mock_ax = Mock()
+            mock_ax.get_legend_handles_labels.return_value = ([], [])
+            mock_subplots.return_value = (mock_fig, mock_ax)
+
+            with patch('graph_manager.FigureCanvasTkAgg') as mock_canvas_class:
+                mock_canvas = Mock()
+                mock_canvas_class.return_value = mock_canvas
+
+                gm = GraphManager(parent_frame)
+
+                # Enable some medicine toggles
+                gm.toggle_vars["bupropion"].set(True)
+                gm.toggle_vars["propranolol"].set(True)
+                gm.toggle_vars["hydroxyzine"].set(True)  # No dose data
+
+                gm.update_graph(df_with_doses)
+
+                # Verify that legend is called with enhanced parameters
+                mock_ax.legend.assert_called()
+                legend_call = mock_ax.legend.call_args
+
+                # Check that enhanced legend parameters are used
+                assert 'ncol' in legend_call.kwargs
+                assert legend_call.kwargs['ncol'] == 2
+                assert 'fontsize' in legend_call.kwargs
+                assert legend_call.kwargs['fontsize'] == 'small'
+                assert 'frameon' in legend_call.kwargs
+                assert legend_call.kwargs['frameon'] is True
+
+    def test_legend_with_medicines_without_data(self, parent_frame):
+        """Test that medicines without dose data are properly tracked in legend."""
+        df_with_partial_doses = pd.DataFrame({
+            'date': ['2024-01-01'],
+            'depression': [3],
+            'anxiety': [2],
+            'sleep': [4],
+            'appetite': [3],
+            'bupropion': [1],
+            'bupropion_doses': ['2024-01-01 08:00:00:150mg'],
+            'hydroxyzine': [0],
+            'hydroxyzine_doses': [''],  # No dose data
+            'gabapentin': [0],
+            'gabapentin_doses': [''],  # No dose data
+            'propranolol': [0],
+            'propranolol_doses': [''],
+            'quetiapine': [0],
+            'quetiapine_doses': [''],
+        })
+
+        with patch('matplotlib.pyplot.subplots') as mock_subplots:
+            mock_fig = Mock()
+            mock_ax = Mock()
+
+            # Mock the legend handles and labels
+            original_handles = [Mock()]
+            original_labels = ['Bupropion (avg: 150.0mg)']
+            mock_ax.get_legend_handles_labels.return_value = (original_handles, original_labels)
+
+            mock_subplots.return_value = (mock_fig, mock_ax)
+
+            with patch('graph_manager.FigureCanvasTkAgg') as mock_canvas_class:
+                mock_canvas = Mock()
+                mock_canvas_class.return_value = mock_canvas
+
+                gm = GraphManager(parent_frame)
+
+                # Enable medicines with and without data
+                gm.toggle_vars["bupropion"].set(True)  # Has data
+                gm.toggle_vars["hydroxyzine"].set(True)  # No data
+                gm.toggle_vars["gabapentin"].set(True)  # No data
+
+                gm.update_graph(df_with_partial_doses)
+
+                # Verify legend was called
+                mock_ax.legend.assert_called()
+
+                # Check that the legend call includes additional handles/labels
+                legend_call = mock_ax.legend.call_args
+                handles, labels = legend_call.args[:2]
+
+                # Should have more labels than just the original ones
+                assert len(labels) > len(original_labels)
+
+    def test_average_dose_calculation_in_legend(self, parent_frame):
+        """Test that average doses are correctly calculated and displayed in legend."""
+        df_with_varying_doses = pd.DataFrame({
+            'date': ['2024-01-01', '2024-01-02', '2024-01-03'],
+            'depression': [3, 2, 4],
+            'anxiety': [2, 3, 1],
+            'sleep': [4, 3, 5],
+            'appetite': [3, 4, 2],
+            'bupropion': [1, 1, 1],
+            'bupropion_doses': ['2024-01-01 08:00:00:100mg',
+                               '2024-01-02 08:00:00:200mg',
+                               '2024-01-03 08:00:00:150mg'],  # Average should be 150mg
+            'propranolol': [1, 1, 0],
+            'propranolol_doses': ['2024-01-01 12:00:00:10mg',
+                                 '2024-01-02 12:00:00:20mg',
+                                 ''],  # Average should be 15mg
+            'hydroxyzine': [0, 0, 0],
+            'hydroxyzine_doses': ['', '', ''],
+            'gabapentin': [0, 0, 0],
+            'gabapentin_doses': ['', '', ''],
+            'quetiapine': [0, 0, 0],
+            'quetiapine_doses': ['', '', ''],
+        })
+
+        with patch('matplotlib.pyplot.subplots') as mock_subplots:
+            mock_fig = Mock()
+            mock_ax = Mock()
+            mock_subplots.return_value = (mock_fig, mock_ax)
+
+            with patch('graph_manager.FigureCanvasTkAgg') as mock_canvas_class:
+                mock_canvas = Mock()
+                mock_canvas_class.return_value = mock_canvas
+
+                gm = GraphManager(parent_frame)
+
+                # Test the average calculation directly
+                bup_avg = gm._calculate_daily_dose('2024-01-01 08:00:00:100mg')
+                assert bup_avg == 100.0
+
+                prop_avg = gm._calculate_daily_dose('2024-01-01 12:00:00:10mg')
+                assert prop_avg == 10.0
+
+                # Test with full data
+                gm.toggle_vars["bupropion"].set(True)
+                gm.toggle_vars["propranolol"].set(True)
+                gm.update_graph(df_with_varying_doses)
+
+                # Verify that bars were plotted (indicating dose data was processed)
+                mock_ax.bar.assert_called()
+
+    def test_legend_positioning_and_styling(self, parent_frame):
+        """Test that legend positioning and styling parameters are correctly applied."""
+        df_simple = pd.DataFrame({
+            'date': ['2024-01-01'],
+            'depression': [3],
+            'anxiety': [2],
+            'sleep': [4],
+            'appetite': [3],
+            'bupropion': [1],
+            'bupropion_doses': ['2024-01-01 08:00:00:150mg'],
+            'hydroxyzine': [0],
+            'hydroxyzine_doses': [''],
+            'gabapentin': [0],
+            'gabapentin_doses': [''],
+            'propranolol': [0],
+            'propranolol_doses': [''],
+            'quetiapine': [0],
+            'quetiapine_doses': [''],
+        })
+
+        with patch('matplotlib.pyplot.subplots') as mock_subplots:
+            mock_fig = Mock()
+            mock_ax = Mock()
+            mock_ax.get_legend_handles_labels.return_value = ([Mock()], ['Test Label'])
+            mock_subplots.return_value = (mock_fig, mock_ax)
+
+            with patch('graph_manager.FigureCanvasTkAgg') as mock_canvas_class:
+                mock_canvas = Mock()
+                mock_canvas_class.return_value = mock_canvas
+
+                gm = GraphManager(parent_frame)
+                gm.update_graph(df_simple)
+
+                # Verify legend styling parameters
+                mock_ax.legend.assert_called()
+                legend_call = mock_ax.legend.call_args
+
+                expected_params = {
+                    'loc': 'upper left',
+                    'bbox_to_anchor': (0, 1),
+                    'ncol': 2,
+                    'fontsize': 'small',
+                    'frameon': True,
+                    'fancybox': True,
+                    'shadow': True,
+                    'framealpha': 0.9
+                }
+
+                for param, expected_value in expected_params.items():
+                    assert param in legend_call.kwargs
+                    assert legend_call.kwargs[param] == expected_value
+
+    def test_medicine_tracking_lists(self, parent_frame):
+        """Test that medicines are correctly categorized into with_data and without_data lists."""
+        df_mixed_data = pd.DataFrame({
+            'date': ['2024-01-01', '2024-01-02'],
+            'depression': [3, 2],
+            'anxiety': [2, 3],
+            'sleep': [4, 3],
+            'appetite': [3, 4],
+            # Medicines with data
+            'bupropion': [1, 1],
+            'bupropion_doses': ['2024-01-01 08:00:00:150mg', '2024-01-02 08:00:00:200mg'],
+            'propranolol': [1, 1],
+            'propranolol_doses': ['2024-01-01 12:00:00:10mg', '2024-01-02 12:00:00:15mg'],
+            # Medicines without data (but toggled on)
+            'hydroxyzine': [0, 0],
+            'hydroxyzine_doses': ['', ''],
+            'gabapentin': [0, 0],
+            'gabapentin_doses': ['', ''],
+            'quetiapine': [0, 0],
+            'quetiapine_doses': ['', ''],
+        })
+
+        with patch('matplotlib.pyplot.subplots') as mock_subplots:
+            mock_fig = Mock()
+            mock_ax = Mock()
+            mock_ax.get_legend_handles_labels.return_value = ([], [])
+            mock_subplots.return_value = (mock_fig, mock_ax)
+
+            with patch('graph_manager.FigureCanvasTkAgg') as mock_canvas_class:
+                mock_canvas = Mock()
+                mock_canvas_class.return_value = mock_canvas
+
+                gm = GraphManager(parent_frame)
+
+                # Enable all medicines
+                gm.toggle_vars["bupropion"].set(True)      # Has data
+                gm.toggle_vars["propranolol"].set(True)    # Has data
+                gm.toggle_vars["hydroxyzine"].set(True)    # No data
+                gm.toggle_vars["gabapentin"].set(True)     # No data
+                gm.toggle_vars["quetiapine"].set(False)    # Disabled
+
+                gm.update_graph(df_mixed_data)
+
+                # Verify that the method was called and plotting occurred
+                mock_ax.bar.assert_called()  # Should be called for medicines with data
+                mock_ax.legend.assert_called()  # Legend should be created
+
+    def test_legend_dummy_handle_creation(self, parent_frame):
+        """Test that dummy handles are created for medicines without data."""
+        df_no_dose_data = pd.DataFrame({
+            'date': ['2024-01-01'],
+            'depression': [3],
+            'anxiety': [2],
+            'sleep': [4],
+            'appetite': [3],
+            'bupropion': [0],
+            'bupropion_doses': [''],
+            'hydroxyzine': [0],
+            'hydroxyzine_doses': [''],
+            'gabapentin': [0],
+            'gabapentin_doses': [''],
+            'propranolol': [0],
+            'propranolol_doses': [''],
+            'quetiapine': [0],
+            'quetiapine_doses': [''],
+        })
+
+        with patch('matplotlib.pyplot.subplots') as mock_subplots:
+            mock_fig = Mock()
+            mock_ax = Mock()
+            mock_ax.get_legend_handles_labels.return_value = ([Mock()], ['Depression'])
+            mock_subplots.return_value = (mock_fig, mock_ax)
+
+            with patch('graph_manager.FigureCanvasTkAgg') as mock_canvas_class:
+                mock_canvas = Mock()
+                mock_canvas_class.return_value = mock_canvas
+
+                # Mock Rectangle import for dummy handle creation
+                with patch('matplotlib.patches.Rectangle') as mock_rectangle:
+                    mock_dummy_handle = Mock()
+                    mock_rectangle.return_value = mock_dummy_handle
+
+                    gm = GraphManager(parent_frame)
+
+                    # Enable some medicines without data
+                    gm.toggle_vars["hydroxyzine"].set(True)
+                    gm.toggle_vars["gabapentin"].set(True)
+
+                    gm.update_graph(df_no_dose_data)
+
+                    # If there are medicines without data, Rectangle should be called
+                    # to create dummy handles
+                    if gm.toggle_vars["hydroxyzine"].get() or gm.toggle_vars["gabapentin"].get():
+                        mock_rectangle.assert_called()
+
+    def test_empty_dataframe_legend_handling(self, parent_frame):
+        """Test that legend is handled correctly with empty DataFrame."""
+        empty_df = pd.DataFrame()
+
+        with patch('matplotlib.pyplot.subplots') as mock_subplots:
+            mock_fig = Mock()
+            mock_ax = Mock()
+            mock_subplots.return_value = (mock_fig, mock_ax)
+
+            with patch('graph_manager.FigureCanvasTkAgg') as mock_canvas_class:
+                mock_canvas = Mock()
+                mock_canvas_class.return_value = mock_canvas
+
+                gm = GraphManager(parent_frame)
+                gm.update_graph(empty_df)
+
+                # With empty data, legend should not be called
+                mock_ax.legend.assert_not_called()
+                mock_ax.clear.assert_called()
+                mock_canvas.draw.assert_called()
+
     def test_dose_calculation_comprehensive(self, parent_frame, sample_dose_data):
         """Test dose calculation with comprehensive test cases."""
         gm = GraphManager(parent_frame)