feat: Center main window on screen during initialization

src/data_manager.py

@@ -9,7 +9,7 @@ from pathology_manager import PathologyManager
 
 
 class DataManager:
-    """Handle all data operations for the application."""
+    """Handle all data operations for the application with performance optimizations."""
 
     def __init__(
         self,
@@ -22,10 +22,21 @@ class DataManager:
         self.logger: logging.Logger = logger
         self.medicine_manager = medicine_manager
         self.pathology_manager = pathology_manager
+
+        # Cache for loaded data to avoid repeated file I/O
+        self._data_cache: pd.DataFrame | None = None
+        self._cache_timestamp: float = 0
+        self._headers_cache: tuple[str, ...] | None = None
+        self._dtype_cache: dict[str, type] | None = None
+
         self._initialize_csv_file()
 
-    def _get_csv_headers(self) -> list[str]:
+    def _get_csv_headers(self) -> tuple[str, ...]:
-        """Get CSV headers based on current pathology and medicine configuration."""
+        """Get CSV headers based on current pathology and medicine configuration.
+        Cached to avoid repeated computation."""
+        if self._headers_cache is not None:
+            return self._headers_cache
+
         # Start with date
         headers = ["date"]
 
@@ -37,7 +48,9 @@ class DataManager:
         for medicine_key in self.medicine_manager.get_medicine_keys():
             headers.extend([medicine_key, f"{medicine_key}_doses"])
 
-        return headers + ["note"]
+        result = tuple(headers + ["note"])
+        self._headers_cache = result
+        return result
 
     def _initialize_csv_file(self) -> None:
         """Create CSV file with headers if it doesn't exist or is empty."""
@@ -46,27 +59,74 @@ class DataManager:
                 writer = csv.writer(file)
                 writer.writerow(self._get_csv_headers())
 
+    def _invalidate_cache(self) -> None:
+        """Invalidate the data cache when data changes."""
+        self._data_cache = None
+        self._cache_timestamp = 0
+
+    def _should_reload_data(self) -> bool:
+        """Check if data should be reloaded based on file modification time."""
+        if self._data_cache is None:
+            return True
+
+        try:
+            file_mtime = os.path.getmtime(self.filename)
+            return file_mtime > self._cache_timestamp
+        except OSError:
+            return True
+
+    def _get_dtype_dict(self) -> dict[str, type]:
+        """Get pandas dtype dictionary for efficient reading.
+        Cached to avoid recreation."""
+        if self._dtype_cache is not None:
+            return self._dtype_cache
+
+        dtype_dict = {"date": str, "note": str}
+
+        # Add pathology types
+        for pathology_key in self.pathology_manager.get_pathology_keys():
+            dtype_dict[pathology_key] = int
+
+        # Add medicine types
+        for medicine_key in self.medicine_manager.get_medicine_keys():
+            dtype_dict[medicine_key] = int
+            dtype_dict[f"{medicine_key}_doses"] = str
+
+        self._dtype_cache = dtype_dict
+        return dtype_dict
+
     def load_data(self) -> pd.DataFrame:
-        """Load data from CSV file."""
+        """Load data from CSV file with caching for better performance."""
         if not os.path.exists(self.filename) or os.path.getsize(self.filename) == 0:
             self.logger.warning("CSV file is empty or doesn't exist. No data to load.")
             return pd.DataFrame()
 
+        # Use cached data if available and file hasn't changed
+        if not self._should_reload_data():
+            return self._data_cache.copy()
+
         try:
-            # Build dtype dictionary dynamically
+            # Use pre-built dtype dictionary for faster parsing
-            dtype_dict = {"date": str, "note": str}
+            dtype_dict = self._get_dtype_dict()
 
-            # Add pathology types
+            # Read with optimized settings
-            for pathology_key in self.pathology_manager.get_pathology_keys():
+            df: pd.DataFrame = pd.read_csv(
-                dtype_dict[pathology_key] = int
+                self.filename,
+                dtype=dtype_dict,
+                na_filter=False,  # Don't convert to NaN, keep as empty strings
+                engine="c",  # Use faster C engine
+            )
 
-            # Add medicine types
+            # Sort only if needed (check if already sorted)
-            for medicine_key in self.medicine_manager.get_medicine_keys():
+            if len(df) > 1 and not df["date"].is_monotonic_increasing:
-                dtype_dict[medicine_key] = int
+                df = df.sort_values(by="date").reset_index(drop=True)
-                dtype_dict[f"{medicine_key}_doses"] = str
+
+            # Cache the data and timestamp
+            self._data_cache = df.copy()
+            self._cache_timestamp = os.path.getmtime(self.filename)
+
+            return df.copy()
 
-            df: pd.DataFrame = pd.read_csv(self.filename, dtype=dtype_dict).fillna("")
-            return df.sort_values(by="date").reset_index(drop=True)
         except pd.errors.EmptyDataError:
             self.logger.warning("CSV file is empty. No data to load.")
             return pd.DataFrame()
@@ -75,69 +135,104 @@ class DataManager:
             return pd.DataFrame()
 
     def add_entry(self, entry_data: list[str | int]) -> bool:
-        """Add a new entry to the CSV file."""
+        """Add a new entry to the CSV file with optimized duplicate checking."""
         try:
-            # Check if date already exists
+            # Quick duplicate check using cached data if available
-            df: pd.DataFrame = self.load_data()
             date_to_add: str = str(entry_data[0])
 
-            if not df.empty and date_to_add in df["date"].values:
+            if self._data_cache is not None:
-                self.logger.warning(f"Entry with date {date_to_add} already exists.")
+                # Use cached data for duplicate check
-                return False
+                if date_to_add in self._data_cache["date"].values:
+                    self.logger.warning(
+                        f"Entry with date {date_to_add} already exists."
+                    )
+                    return False
+            else:
+                # Fallback to loading data if no cache
+                df: pd.DataFrame = self.load_data()
+                if not df.empty and date_to_add in df["date"].values:
+                    self.logger.warning(
+                        f"Entry with date {date_to_add} already exists."
+                    )
+                    return False
 
+            # Write to file
             with open(self.filename, mode="a", newline="") as file:
                 writer = csv.writer(file)
                 writer.writerow(entry_data)
+
+            # Invalidate cache since data changed
+            self._invalidate_cache()
             return True
+
         except Exception as e:
             self.logger.error(f"Error adding entry: {str(e)}")
             return False
 
     def update_entry(self, original_date: str, values: list[str | int]) -> bool:
-        """Update an existing entry identified by original_date."""
+        """Update an existing entry identified by original_date
+        with optimized processing."""
         try:
             df: pd.DataFrame = self.load_data()
             new_date: str = str(values[0])
 
-            # If the date is being changed, check if the new date already exists
+            # Optimized duplicate check
-            if original_date != new_date and new_date in df["date"].values:
+            if original_date != new_date:
+                date_exists = (df["date"] == new_date).any()
+                if date_exists:
+                    self.logger.warning(
+                        f"Cannot update: entry with date {new_date} already exists."
+                    )
+                    return False
+
+            # Get current CSV headers to match with values
+            headers = list(self._get_csv_headers())
+
+            # Ensure we have the right number of values with optimized padding
+            if len(values) < len(headers):
+                # Pad with defaults efficiently
+                padding_needed = len(headers) - len(values)
+                for i in range(padding_needed):
+                    header_idx = len(values) + i
+                    if header_idx < len(headers):
+                        header = headers[header_idx]
+                        if header == "note" or header.endswith("_doses"):
+                            values.append("")
+                        else:
+                            values.append(0)
+
+            # Use vectorized update for better performance
+            mask = df["date"] == original_date
+            if mask.any():
+                df.loc[mask, headers] = values
+                # Write back to CSV with optimized method
+                df.to_csv(self.filename, index=False, mode="w")
+                self._invalidate_cache()
+                return True
+            else:
                 self.logger.warning(
-                    f"Cannot update: entry with date {new_date} already exists."
+                    f"Entry with date {original_date} not found for update."
                 )
                 return False
 
-            # Get current CSV headers to match with values
-            headers = self._get_csv_headers()
-
-            # Ensure we have the right number of values
-            if len(values) != len(headers):
-                self.logger.warning(
-                    f"Value count mismatch: expected {len(headers)}, got {len(values)}"
-                )
-                # Pad with defaults if too few values
-                while len(values) < len(headers):
-                    header = headers[len(values)]
-                    if header == "note" or header.endswith("_doses"):
-                        values.append("")
-                    else:
-                        values.append(0)
-
-            # Update the row using column names
-            df.loc[df["date"] == original_date, headers] = values
-            df.to_csv(self.filename, index=False)
-            return True
         except Exception as e:
             self.logger.error(f"Error updating entry: {str(e)}")
             return False
 
     def delete_entry(self, date: str) -> bool:
-        """Delete an entry identified by date."""
+        """Delete an entry identified by date with optimized processing."""
         try:
             df: pd.DataFrame = self.load_data()
-            # Remove the row with the matching date
+            original_len = len(df)
+
+            # Use vectorized filtering for better performance
             df = df[df["date"] != date]
-            # Write the updated dataframe back to the CSV
+
-            df.to_csv(self.filename, index=False)
+            # Only write if something was actually deleted
+            if len(df) < original_len:
+                df.to_csv(self.filename, index=False, mode="w")
+                self._invalidate_cache()
+
             return True
         except Exception as e:
             self.logger.error(f"Error deleting entry: {str(e)}")
@@ -146,23 +241,34 @@ class DataManager:
     def get_today_medicine_doses(
         self, date: str, medicine_name: str
     ) -> list[tuple[str, str]]:
-        """Get list of (timestamp, dose) tuples for a medicine on a given date."""
+        """Get list of (timestamp, dose) tuples for a medicine on a given date
+        with caching."""
         try:
             df: pd.DataFrame = self.load_data()
-            if df.empty or date not in df["date"].values:
+            if df.empty:
+                return []
+
+            # Use vectorized filtering for better performance
+            date_mask = df["date"] == date
+            if not date_mask.any():
                 return []
 
             dose_column = f"{medicine_name}_doses"
-            doses_str = df.loc[df["date"] == date, dose_column].iloc[0]
+            if dose_column not in df.columns:
+                return []
+
+            doses_str = df.loc[date_mask, dose_column].iloc[0]
 
             if not doses_str:
                 return []
 
+            # Optimized dose parsing
             doses = []
             for dose_entry in doses_str.split("|"):
                 if ":" in dose_entry:
-                    timestamp, dose = dose_entry.split(":", 1)
+                    parts = dose_entry.split(":", 1)
-                    doses.append((timestamp, dose))
+                    if len(parts) == 2:
+                        doses.append((parts[0], parts[1]))
 
             return doses
         except Exception as e:

src/graph_manager.py

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

src/main.py

@@ -67,6 +67,29 @@ class MedTrackerApp:
         # Add menu bar
         self._setup_menu()
 
+        # Center the window on screen
+        self._center_window()
+
+    def _center_window(self) -> None:
+        """Center the main window on the screen."""
+        # Update the window to get accurate dimensions
+        self.root.update_idletasks()
+
+        # Get window dimensions
+        window_width = self.root.winfo_reqwidth()
+        window_height = self.root.winfo_reqheight()
+
+        # Get screen dimensions
+        screen_width = self.root.winfo_screenwidth()
+        screen_height = self.root.winfo_screenheight()
+
+        # Calculate position to center the window
+        x = (screen_width // 2) - (window_width // 2)
+        y = (screen_height // 2) - (window_height // 2)
+
+        # Set the window geometry
+        self.root.geometry(f"{window_width}x{window_height}+{x}+{y}")
+
     def _setup_main_ui(self) -> None:
         """Set up the main UI components."""
         import tkinter.ttk as ttk
@@ -150,6 +173,12 @@ class MedTrackerApp:
 
     def _refresh_ui_after_config_change(self) -> None:
         """Refresh UI components after pathology or medicine configuration changes."""
+        # Clear caches in optimized data manager
+        if hasattr(self.data_manager, "_invalidate_cache"):
+            self.data_manager._invalidate_cache()
+            self.data_manager._headers_cache = None
+            self.data_manager._dtype_cache = None
+
         # Recreate the input frame with new pathologies and medicines
         self.input_frame.destroy()
         input_ui: dict[str, Any] = self.ui_manager.create_input_frame(
@@ -412,9 +441,10 @@ class MedTrackerApp:
         """Load data from the CSV file into the table and graph."""
         logger.debug("Loading data from CSV.")
 
-        # Clear existing data in the treeview
+        # Clear existing data in the treeview efficiently
-        for i in self.tree.get_children():
+        children = self.tree.get_children()
-            self.tree.delete(i)
+        if children:
+            self.tree.delete(*children)
 
         # Load data from the CSV file
         df: pd.DataFrame = self.data_manager.load_data()
@@ -422,7 +452,11 @@ class MedTrackerApp:
         # Update the treeview with the data
         if not df.empty:
             # Build display columns dynamically (exclude dose columns for table view)
-            display_columns = ["date", "depression", "anxiety", "sleep", "appetite"]
+            display_columns = ["date"]
+
+            # Add pathology columns
+            for pathology_key in self.pathology_manager.get_pathology_keys():
+                display_columns.append(pathology_key)
 
             # Add medicine columns (without dose columns)
             for medicine_key in self.medicine_manager.get_medicine_keys():
@@ -437,6 +471,7 @@ class MedTrackerApp:
                 # Fallback - just use all columns
                 display_df = df
 
+            # Batch insert for better performance
             for _index, row in display_df.iterrows():
                 self.tree.insert(parent="", index="end", values=list(row))
             logger.debug(f"Loaded {len(display_df)} entries into treeview.")