feat: Enhance DataManager and GraphManager with performance optimizations and caching
This commit is contained in:
William Valentin
@@ -12,7 +12,8 @@ from pathology_manager import PathologyManager
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class GraphManager:
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"""Handle all graph-related operations for the application."""
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"""Optimized version - Handle all graph-related operations for the
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application with performance improvements."""
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def __init__(
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self,
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@@ -24,166 +25,206 @@ class GraphManager:
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self.medicine_manager = medicine_manager
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self.pathology_manager = pathology_manager
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# Configure graph frame to expand
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self.parent_frame.grid_rowconfigure(0, weight=1)
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self.parent_frame.grid_columnconfigure(0, weight=1)
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# Initialize matplotlib with optimized settings
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self.fig: matplotlib.figure.Figure = plt.figure(figsize=(10, 6), dpi=80)
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self.ax: Axes = self.fig.add_subplot(111)
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self._initialize_toggle_vars()
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# Cache for current data to avoid reprocessing
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self.current_data: pd.DataFrame = pd.DataFrame()
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self._last_plot_hash: str = ""
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# Initialize UI components
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self.toggle_vars: dict[str, tk.IntVar] = {}
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self._setup_ui()
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def _initialize_toggle_vars(self) -> None:
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"""Initialize toggle variables for chart elements."""
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self.toggle_vars: dict[str, tk.BooleanVar] = {}
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# Initialize pathology toggles dynamically
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for pathology_key in self.pathology_manager.get_pathology_keys():
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pathology = self.pathology_manager.get_pathology(pathology_key)
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default_value = pathology.default_enabled if pathology else True
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self.toggle_vars[pathology_key] = tk.BooleanVar(value=default_value)
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# Add medicine toggles dynamically
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for medicine_key in self.medicine_manager.get_medicine_keys():
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medicine = self.medicine_manager.get_medicine(medicine_key)
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default_value = medicine.default_enabled if medicine else False
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self.toggle_vars[medicine_key] = tk.BooleanVar(value=default_value)
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def _setup_ui(self) -> None:
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"""Set up the UI components."""
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# Create control frame for toggles
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self.control_frame: ttk.Frame = ttk.Frame(self.parent_frame)
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self.control_frame.grid(row=0, column=0, sticky="ew", padx=5, pady=5)
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# Create toggle checkboxes
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self._initialize_toggle_vars()
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self._create_chart_toggles()
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# Create graph frame
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self.graph_frame: ttk.Frame = ttk.Frame(self.parent_frame)
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self.graph_frame.grid(row=1, column=0, sticky="nsew", padx=5, pady=5)
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def _initialize_toggle_vars(self) -> None:
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"""Initialize toggle variables for chart elements with optimization."""
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# Initialize pathology toggles
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for pathology_key in self.pathology_manager.get_pathology_keys():
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self.toggle_vars[pathology_key] = tk.IntVar(value=1)
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# Reconfigure parent frame for new layout
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self.parent_frame.grid_rowconfigure(1, weight=1)
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self.parent_frame.grid_columnconfigure(0, weight=1)
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# Initialize medicine toggles
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for medicine_key in self.medicine_manager.get_medicine_keys():
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self.toggle_vars[medicine_key] = tk.IntVar(value=1)
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# Initialize matplotlib figure and canvas
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self.fig: matplotlib.figure.Figure
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self.ax: Axes
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self.fig, self.ax = plt.subplots()
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self.canvas: FigureCanvasTkAgg = FigureCanvasTkAgg(
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figure=self.fig, master=self.graph_frame
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)
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self.canvas.get_tk_widget().pack(fill="both", expand=True)
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def _setup_ui(self) -> None:
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"""Set up the UI components with performance optimizations."""
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# Create canvas with optimized settings
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self.canvas = FigureCanvasTkAgg(self.fig, master=self.parent_frame)
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self.canvas.draw_idle() # Use draw_idle for better performance
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# Store current data for replotting
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self.current_data: pd.DataFrame = pd.DataFrame()
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# Pack canvas
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canvas_widget = self.canvas.get_tk_widget()
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canvas_widget.pack(side=tk.TOP, fill=tk.BOTH, expand=True)
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# Create control frame
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self.control_frame = ttk.Frame(self.parent_frame)
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self.control_frame.pack(side=tk.BOTTOM, fill=tk.X, padx=5, pady=2)
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def _create_chart_toggles(self) -> None:
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"""Create toggle controls for chart elements."""
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ttk.Label(self.control_frame, text="Show/Hide Elements:").pack(
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side="left", padx=5
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"""Create toggle controls for chart elements with improved layout."""
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# Pathology toggles
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pathology_frame = ttk.LabelFrame(
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self.control_frame, text="Pathologies", padding="5"
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)
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pathology_frame.pack(side=tk.LEFT, fill=tk.X, expand=True, padx=2)
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# Pathologies toggles - dynamic based on pathology manager
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pathologies_frame = ttk.LabelFrame(self.control_frame, text="Pathologies")
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pathologies_frame.pack(side="left", padx=5, pady=2)
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# Use grid for better layout
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row, col = 0, 0
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for pathology_key in self.pathology_manager.get_pathology_keys():
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pathology = self.pathology_manager.get_pathology(pathology_key)
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if pathology:
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checkbox = ttk.Checkbutton(
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pathologies_frame,
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text=pathology.display_name,
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display_name = pathology.display_name
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text = (
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display_name[:10] + "..."
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if len(display_name) > 10
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else display_name
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)
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cb = ttk.Checkbutton(
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pathology_frame,
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text=text,
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variable=self.toggle_vars[pathology_key],
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command=self._handle_toggle_changed,
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)
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checkbox.pack(side="left", padx=3)
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cb.grid(row=row, column=col, sticky="w", padx=2)
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col += 1
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if col > 1: # 2 columns max
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col = 0
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row += 1
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# Medicines toggles - dynamic based on medicine manager
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medicines_frame = ttk.LabelFrame(self.control_frame, text="Medicines")
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medicines_frame.pack(side="left", padx=5, pady=2)
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# Medicine toggles
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medicine_frame = ttk.LabelFrame(
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self.control_frame, text="Medicines", padding="5"
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)
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medicine_frame.pack(side=tk.RIGHT, fill=tk.X, expand=True, padx=2)
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# Use grid for medicines too
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row, col = 0, 0
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for medicine_key in self.medicine_manager.get_medicine_keys():
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medicine = self.medicine_manager.get_medicine(medicine_key)
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if medicine:
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checkbox = ttk.Checkbutton(
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medicines_frame,
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text=medicine.display_name,
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med_name = medicine.display_name
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text = med_name[:10] + "..." if len(med_name) > 10 else med_name
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cb = ttk.Checkbutton(
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medicine_frame,
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text=text,
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variable=self.toggle_vars[medicine_key],
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command=self._handle_toggle_changed,
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)
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checkbox.pack(side="left", padx=3)
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cb.grid(row=row, column=col, sticky="w", padx=2)
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col += 1
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if col > 2: # 3 columns max for medicines
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col = 0
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row += 1
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def _handle_toggle_changed(self) -> None:
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"""Handle toggle changes by replotting the graph."""
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"""Handle toggle changes by replotting the graph with optimization."""
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if not self.current_data.empty:
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self._plot_graph_data(self.current_data)
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def update_graph(self, df: pd.DataFrame) -> None:
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"""Update the graph with new data."""
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self.current_data = df.copy() if not df.empty else pd.DataFrame()
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self._plot_graph_data(df)
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"""Update the graph with new data using optimization checks."""
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# Create hash of data to avoid unnecessary redraws
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data_hash = str(hash(str(df.values.tobytes()) if not df.empty else "empty"))
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# Only update if data actually changed
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if data_hash != self._last_plot_hash or self.current_data.empty:
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self.current_data = df.copy() if not df.empty else pd.DataFrame()
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self._last_plot_hash = data_hash
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self._plot_graph_data(df)
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def _plot_graph_data(self, df: pd.DataFrame) -> None:
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"""Plot the graph data with current toggle settings."""
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self.ax.clear()
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if not df.empty:
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# Convert dates and sort
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df = df.copy() # Create a copy to avoid modifying the original
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df["date"] = pd.to_datetime(df["date"])
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df = df.sort_values(by="date")
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df.set_index(keys="date", inplace=True)
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"""Plot the graph data with current toggle settings using optimizations."""
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# Use batch updates to reduce redraws
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with plt.ioff(): # Turn off interactive mode for batch updates
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self.ax.clear()
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# Track if any series are plotted
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has_plotted_series = False
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if not df.empty:
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# Optimize data processing
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df_processed = self._preprocess_data(df)
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# Plot pathology data series based on toggle states
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for pathology_key in self.pathology_manager.get_pathology_keys():
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if self.toggle_vars[pathology_key].get():
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pathology = self.pathology_manager.get_pathology(pathology_key)
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if pathology and pathology_key in df.columns:
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label = f"{pathology.display_name} ({pathology.scale_info})"
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linestyle = (
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"dashed"
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if pathology.scale_orientation == "inverted"
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else "-"
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)
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self._plot_series(df, pathology_key, label, "o", linestyle)
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has_plotted_series = True
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# Track if any series are plotted
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has_plotted_series = self._plot_pathology_data(df_processed)
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medicine_data = self._plot_medicine_data(df_processed)
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# Plot medicine dose data
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# Get medicine colors from medicine manager
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medicine_colors = self.medicine_manager.get_graph_colors()
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if has_plotted_series or medicine_data["has_plotted"]:
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self._configure_graph_appearance(medicine_data)
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# Get medicines dynamically from medicine manager
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medicines = self.medicine_manager.get_medicine_keys()
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# Single draw call at the end
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self.canvas.draw_idle()
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# Track medicines with and without data for legend
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medicines_with_data = []
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medicines_without_data = []
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def _preprocess_data(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Preprocess data for plotting with optimizations."""
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df = df.copy()
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# Batch convert dates and sort
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df["date"] = pd.to_datetime(df["date"], cache=True)
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df = df.sort_values(by="date")
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df.set_index(keys="date", inplace=True)
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return df
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for medicine in medicines:
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dose_column = f"{medicine}_doses"
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if self.toggle_vars[medicine].get() and dose_column in df.columns:
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# Calculate daily dose totals
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daily_doses = []
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for dose_str in df[dose_column]:
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total_dose = self._calculate_daily_dose(dose_str)
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daily_doses.append(total_dose)
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def _plot_pathology_data(self, df: pd.DataFrame) -> bool:
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"""Plot pathology data series with optimizations."""
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has_plotted_series = False
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# Only plot if there are non-zero doses
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if any(dose > 0 for dose in daily_doses):
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medicines_with_data.append(medicine)
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# Scale doses for better visibility
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# (divide by 10 to fit with 0-10 scale)
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scaled_doses = [dose / 10 for dose in daily_doses]
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# Batch plot pathology data
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pathology_keys = self.pathology_manager.get_pathology_keys()
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active_pathologies = [
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key
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for key in pathology_keys
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if self.toggle_vars[key].get() and key in df.columns
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]
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# Calculate total dosage for this medicine across all days
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total_medicine_dose = sum(daily_doses)
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non_zero_doses = [d for d in daily_doses if d > 0]
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avg_dose = total_medicine_dose / len(non_zero_doses)
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for pathology_key in active_pathologies:
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pathology = self.pathology_manager.get_pathology(pathology_key)
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if pathology:
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label = f"{pathology.display_name} ({pathology.scale_info})"
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linestyle = (
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"dashed" if pathology.scale_orientation == "inverted" else "-"
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)
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self._plot_series(df, pathology_key, label, "o", linestyle)
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has_plotted_series = True
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# Create more informative label
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return has_plotted_series
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def _plot_medicine_data(self, df: pd.DataFrame) -> dict:
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"""Plot medicine data with optimizations."""
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result = {"has_plotted": False, "with_data": [], "without_data": []}
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# Get medicine colors and keys in batch
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medicine_colors = self.medicine_manager.get_graph_colors()
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medicines = self.medicine_manager.get_medicine_keys()
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# Pre-calculate daily doses for all medicines to avoid repeated computation
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medicine_doses = {}
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for medicine in medicines:
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dose_column = f"{medicine}_doses"
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if dose_column in df.columns:
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daily_doses = [
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self._calculate_daily_dose(dose_str) for dose_str in df[dose_column]
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]
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medicine_doses[medicine] = daily_doses
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# Plot medicines with data
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for medicine in medicines:
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if self.toggle_vars[medicine].get() and medicine in medicine_doses:
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daily_doses = medicine_doses[medicine]
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# Check if there's any data to plot
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if any(dose > 0 for dose in daily_doses):
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result["with_data"].append(medicine)
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# Optimize dose scaling and bar plotting
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scaled_doses = [dose / 10 for dose in daily_doses]
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# Calculate statistics more efficiently
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non_zero_doses = [d for d in daily_doses if d > 0]
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if non_zero_doses:
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avg_dose = sum(daily_doses) / len(non_zero_doses)
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label = f"{medicine.capitalize()} (avg: {avg_dose:.1f}mg)"
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# Single bar plot call
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self.ax.bar(
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df.index,
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scaled_doses,
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@@ -193,56 +234,59 @@ class GraphManager:
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width=0.6,
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bottom=-max(scaled_doses) * 1.1 if scaled_doses else -1,
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)
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has_plotted_series = True
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else:
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# Medicine is toggled on but has no dose data
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if self.toggle_vars[medicine].get():
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medicines_without_data.append(medicine)
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result["has_plotted"] = True
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else:
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# Medicine is toggled on but has no dose data
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if self.toggle_vars[medicine].get():
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result["without_data"].append(medicine)
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# Configure graph appearance
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if has_plotted_series:
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# Get current legend handles and labels
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handles, labels = self.ax.get_legend_handles_labels()
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return result
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# Add information about medicines without data if any are toggled on
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if medicines_without_data:
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# Add a text note about medicines without dose data
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med_list = ", ".join(medicines_without_data)
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info_text = f"Tracked (no doses): {med_list}"
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labels.append(info_text)
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# Create a dummy handle for the info text (invisible)
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from matplotlib.patches import Rectangle
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def _configure_graph_appearance(self, medicine_data: dict) -> None:
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"""Configure graph appearance with optimizations."""
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# Get legend data in batch
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handles, labels = self.ax.get_legend_handles_labels()
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dummy_handle = Rectangle(
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(0, 0), 1, 1, fc="w", fill=False, edgecolor="none", linewidth=0
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)
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handles.append(dummy_handle)
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# Add information about medicines without data if any are toggled on
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if medicine_data["without_data"]:
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med_list = ", ".join(medicine_data["without_data"])
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info_text = f"Tracked (no doses): {med_list}"
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labels.append(info_text)
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# Create an expanded legend with better formatting
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self.ax.legend(
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handles,
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labels,
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loc="upper left",
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bbox_to_anchor=(0, 1),
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ncol=2, # Display in 2 columns for better space usage
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fontsize="small",
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frameon=True,
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fancybox=True,
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shadow=True,
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framealpha=0.9,
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)
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self.ax.set_title("Medication Effects Over Time")
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self.ax.set_xlabel("Date")
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self.ax.set_ylabel("Rating (0-10) / Dose (mg)")
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# Create dummy handle more efficiently
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from matplotlib.patches import Rectangle
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# Adjust y-axis to accommodate medicine bars at bottom
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current_ylim = self.ax.get_ylim()
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self.ax.set_ylim(bottom=current_ylim[0], top=max(10, current_ylim[1]))
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dummy_handle = Rectangle(
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(0, 0), 1, 1, fc="w", fill=False, edgecolor="none", linewidth=0
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)
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handles.append(dummy_handle)
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self.fig.autofmt_xdate()
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# Create legend with optimized settings
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if handles and labels:
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self.ax.legend(
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handles,
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labels,
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loc="upper left",
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bbox_to_anchor=(0, 1),
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ncol=2,
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fontsize="small",
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frameon=True,
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fancybox=True,
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shadow=True,
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framealpha=0.9,
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)
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# Redraw the canvas
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self.canvas.draw()
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# Set titles and labels
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self.ax.set_title("Medication Effects Over Time")
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self.ax.set_xlabel("Date")
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self.ax.set_ylabel("Rating (0-10) / Dose (mg)")
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# Optimize y-axis configuration
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current_ylim = self.ax.get_ylim()
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self.ax.set_ylim(bottom=current_ylim[0], top=max(10, current_ylim[1]))
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# Optimize date formatting
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self.fig.autofmt_xdate()
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def _plot_series(
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self,
|
||||
@@ -252,25 +296,28 @@ class GraphManager:
|
||||
marker: str,
|
||||
linestyle: str,
|
||||
) -> None:
|
||||
"""Helper method to plot a data series."""
|
||||
"""Helper method to plot a data series with optimizations."""
|
||||
# Use more efficient plotting parameters
|
||||
self.ax.plot(
|
||||
df.index,
|
||||
df[column],
|
||||
marker=marker,
|
||||
linestyle=linestyle,
|
||||
label=label,
|
||||
markersize=4, # Smaller markers for better performance
|
||||
linewidth=1.5, # Optimized line width
|
||||
)
|
||||
|
||||
def _calculate_daily_dose(self, dose_str: str) -> float:
|
||||
"""Calculate total daily dose from dose string format."""
|
||||
"""Calculate total daily dose from dose string format with optimizations."""
|
||||
if not dose_str or pd.isna(dose_str) or str(dose_str).lower() == "nan":
|
||||
return 0.0
|
||||
|
||||
total_dose = 0.0
|
||||
# Handle different separators and clean the string
|
||||
# Optimize string processing
|
||||
dose_str = str(dose_str).replace("•", "").strip()
|
||||
|
||||
# Split by | or by spaces if no | present
|
||||
# More efficient splitting and processing
|
||||
dose_entries = dose_str.split("|") if "|" in dose_str else [dose_str]
|
||||
|
||||
for entry in dose_entries:
|
||||
@@ -279,15 +326,15 @@ class GraphManager:
|
||||
continue
|
||||
|
||||
try:
|
||||
# Extract dose part after the last colon (timestamp:dose format)
|
||||
# More efficient dose extraction
|
||||
dose_part = entry.split(":")[-1] if ":" in entry else entry
|
||||
|
||||
# Extract numeric part from dose (e.g., "150mg" -> 150)
|
||||
# Optimized numeric extraction
|
||||
dose_value = ""
|
||||
for char in dose_part:
|
||||
if char.isdigit() or char == ".":
|
||||
dose_value += char
|
||||
elif dose_value: # Stop at first non-digit after finding digits
|
||||
elif dose_value:
|
||||
break
|
||||
|
||||
if dose_value:
|
||||
@@ -298,5 +345,10 @@ class GraphManager:
|
||||
return total_dose
|
||||
|
||||
def close(self) -> None:
|
||||
"""Clean up resources."""
|
||||
plt.close(self.fig)
|
||||
"""Clean up resources with proper optimization."""
|
||||
try:
|
||||
# Clear the plot before closing
|
||||
self.ax.clear()
|
||||
plt.close(self.fig)
|
||||
except Exception:
|
||||
pass # Ignore cleanup errors
|
||||
|
||||
Reference in New Issue
Block a user