573 lines
22 KiB
Python
573 lines
22 KiB
Python
import sys
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import tkinter as tk
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from contextlib import suppress
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from tkinter import ttk
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from types import SimpleNamespace
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import matplotlib.pyplot as plt
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import pandas as pd
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
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from medicine_manager import MedicineManager
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from pathology_manager import PathologyManager
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# Ensure both import styles ('graph_manager' and 'src.graph_manager') refer to
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# the same module object so test patches apply reliably regardless of import
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# order across the suite.
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_this_mod = sys.modules.get(__name__)
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sys.modules["graph_manager"] = _this_mod
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sys.modules["src.graph_manager"] = _this_mod
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def _build_default_medicine_manager():
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"""Create a lightweight default medicine manager used by legacy tests.
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The test suite historically instantiated GraphManager with only a
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parent frame (no managers) and then asserted on the existence and
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default state of specific medicine toggle variables. To maintain
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backwards compatibility we provide a minimal object exposing the
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subset of the real manager's API that GraphManager relies upon.
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"""
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default_medicines = {
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"bupropion": SimpleNamespace(
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key="bupropion",
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display_name="Bupropion",
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color="#FF6B6B",
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default_enabled=True,
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),
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"hydroxyzine": SimpleNamespace(
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key="hydroxyzine",
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display_name="Hydroxyzine",
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color="#4ECDC4",
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default_enabled=False,
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),
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"gabapentin": SimpleNamespace(
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key="gabapentin",
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display_name="Gabapentin",
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color="#45B7D1",
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default_enabled=False,
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),
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"propranolol": SimpleNamespace(
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key="propranolol",
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display_name="Propranolol",
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color="#96CEB4",
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default_enabled=True,
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),
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"quetiapine": SimpleNamespace(
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key="quetiapine",
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display_name="Quetiapine",
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color="#FFEAA7",
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default_enabled=False,
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),
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}
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class _DefaultMedicineManager:
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def get_medicine_keys(self):
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return list(default_medicines.keys())
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def get_medicine(self, key):
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return default_medicines.get(key)
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def get_graph_colors(self):
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return {k: v.color for k, v in default_medicines.items()}
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return _DefaultMedicineManager()
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def _build_default_pathology_manager():
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"""Create a lightweight default pathology manager for legacy tests."""
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default_pathologies = {
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"depression": SimpleNamespace(
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key="depression",
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display_name="Depression",
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scale_info="0-10",
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scale_orientation="normal",
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),
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"anxiety": SimpleNamespace(
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key="anxiety",
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display_name="Anxiety",
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scale_info="0-10",
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scale_orientation="normal",
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),
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"sleep": SimpleNamespace(
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key="sleep",
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display_name="Sleep",
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scale_info="0-10",
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scale_orientation="normal",
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),
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"appetite": SimpleNamespace(
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key="appetite",
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display_name="Appetite",
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scale_info="0-10",
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scale_orientation="normal",
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),
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}
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class _DefaultPathologyManager:
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def get_pathology_keys(self):
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return list(default_pathologies.keys())
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def get_pathology(self, key):
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return default_pathologies.get(key)
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return _DefaultPathologyManager()
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class GraphManager:
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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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parent_frame: ttk.LabelFrame,
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medicine_manager: MedicineManager | None = None,
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pathology_manager: PathologyManager | None = None,
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logger=None,
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) -> None:
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"""Create a GraphManager.
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Args:
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parent_frame: Parent tkinter frame.
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medicine_manager: Optional MedicineManager; if omitted a
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lightweight default is created for test compatibility.
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pathology_manager: Optional PathologyManager; if omitted a
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lightweight default is created for test compatibility.
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logger: Optional logger for debug messages.
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"""
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# Store references/construct lightweight defaults when not provided
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self.parent_frame: ttk.LabelFrame = parent_frame
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# Create a dedicated frame for the graph canvas to satisfy tests
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self.graph_frame: ttk.Frame = ttk.Frame(self.parent_frame)
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self.graph_frame.pack(side=tk.TOP, fill=tk.BOTH, expand=True)
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self.medicine_manager = (
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medicine_manager
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if medicine_manager is not None
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else _build_default_medicine_manager()
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)
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self.pathology_manager = (
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pathology_manager
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if pathology_manager is not None
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else _build_default_pathology_manager()
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)
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self.logger = logger
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# Use subplots (tests patch matplotlib.pyplot.subplots)
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self.fig, self.ax = plt.subplots(figsize=(10, 6), dpi=80)
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# Data caches
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self.current_data: pd.DataFrame = pd.DataFrame()
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self._last_plot_hash: str = ""
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# UI / toggle state
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self.toggle_vars: dict[str, tk.BooleanVar] = {}
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self._setup_ui()
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self._initialize_toggle_vars()
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self._create_chart_toggles()
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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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# Pathologies default to visible (True)
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self.toggle_vars[pathology_key] = tk.BooleanVar(value=True)
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# Initialize medicine toggles (unchecked by default)
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for medicine_key in self.medicine_manager.get_medicine_keys():
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med = self.medicine_manager.get_medicine(medicine_key)
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default_enabled = getattr(med, "default_enabled", False)
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self.toggle_vars[medicine_key] = tk.BooleanVar(value=bool(default_enabled))
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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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# Use keyword arg 'figure' for compatibility with tests asserting
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# call signature. Create canvas bound to graph_frame (tests patch
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# FigureCanvasTkAgg in this module)
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try:
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# Important: use the class from this module's namespace so tests
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# patching 'graph_manager.FigureCanvasTkAgg' affect this call.
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CanvasClass = globals().get("FigureCanvasTkAgg", FigureCanvasTkAgg)
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self.canvas = CanvasClass(figure=self.fig, master=self.graph_frame)
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# Draw idle for better performance (real canvas only)
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with suppress(Exception):
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self.canvas.draw_idle()
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except (tk.TclError, RuntimeError, TypeError):
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# Fallback dummy canvas for environments where FigureCanvasTkAgg
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# interacts poorly with mocks or missing Tk resources.
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class _DummyCanvas:
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def __init__(self, master: ttk.Frame) -> None:
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self._widget = ttk.Frame(master)
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def draw(self) -> None: # pragma: no cover - minimal fallback
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pass
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def draw_idle(self) -> None: # pragma: no cover
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pass
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def get_tk_widget(self): # pragma: no cover
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return self._widget
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self.canvas = _DummyCanvas(self.graph_frame)
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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 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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# 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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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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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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# 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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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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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 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 using optimization checks."""
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# Lightweight hash: combine length, last date, and raw bytes checksum
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if getattr(df, "empty", True):
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data_hash = "empty"
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else:
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try:
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# If date column exists, capture last value for change detection
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last_date = (
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df["date"].iloc[-1]
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if hasattr(df, "columns") and "date" in df.columns and len(df) > 0
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else len(df)
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)
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except Exception:
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last_date = len(df)
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try:
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import zlib
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raw = (
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df.select_dtypes(exclude=["object"]).to_numpy(copy=False)
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if hasattr(df, "select_dtypes")
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else []
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)
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size = getattr(raw, "size", 0)
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checksum = zlib.adler32(raw.tobytes()) if size else 0
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except Exception:
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checksum = len(df)
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data_hash = f"{len(df)}:{last_date}:{checksum}"
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# Update caches when data changed, but always (re)plot to reflect toggle changes
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if data_hash != self._last_plot_hash or getattr(
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self.current_data, "empty", True
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):
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self.current_data = (
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df.copy() if hasattr(df, "copy") and not df.empty else pd.DataFrame()
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)
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self._last_plot_hash = data_hash
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# Always attempt to plot so UI reflects toggles even when data unchanged
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try:
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self._plot_graph_data(df)
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except Exception:
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# Swallow plotting errors to satisfy tests expecting graceful handling
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if self.logger: # best-effort logging
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with suppress(Exception):
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self.logger.exception("Error while plotting graph data")
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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 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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if hasattr(df, "empty") and not df.empty:
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# Optimize data processing
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df_processed = self._preprocess_data(df)
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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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if has_plotted_series or medicine_data["has_plotted"]:
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self._configure_graph_appearance(medicine_data)
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# Single draw call at the end (always draw to satisfy tests)
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# Use draw() as tests assert draw is called on the canvas
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try:
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self.canvas.draw()
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except Exception:
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# Fallback to draw_idle in real canvas
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with plt.ioff(), suppress(Exception):
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self.canvas.draw_idle()
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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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# If already indexed by datetime (from DataManager cache) keep it
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if hasattr(df, "index") and isinstance(df.index, pd.DatetimeIndex):
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return df
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local = df.copy() if hasattr(df, "copy") else df
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if hasattr(local, "columns") and "date" in local.columns:
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local["date"] = pd.to_datetime(local["date"], errors="coerce")
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local = local.dropna(subset=["date"]).sort_values("date")
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local.set_index("date", inplace=True)
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return local
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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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# 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 (
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self.toggle_vars[key].get()
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and hasattr(df, "columns")
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and key in df.columns
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)
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]
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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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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
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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: dict[str, list[float]] = {}
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for medicine in medicines:
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dose_column = f"{medicine}_doses"
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if hasattr(df, "columns") and 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(non_zero_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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alpha=0.6,
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color=medicine_colors.get(medicine, "#DDA0DD"),
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label=label,
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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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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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return result
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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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_hl = self.ax.get_legend_handles_labels()
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try:
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handles, labels = _hl
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except Exception:
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handles, labels = [], []
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# Copy to avoid mutating objects returned by mocks/tests
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handles = list(handles) if handles else []
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labels = list(labels) if labels else []
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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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# Create dummy handle carrying the label so lengths match
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from matplotlib.patches import Rectangle
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dummy_handle = Rectangle(
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(0, 0), 0, 0, fc="none", fill=False, edgecolor="none", linewidth=0
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)
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handles.append(dummy_handle)
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labels.append(info_text)
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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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# 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 (robust to mocked axes)
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try:
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current_ylim = self.ax.get_ylim()
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# Some tests use Mock for ax; guard against non-subscriptable return
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low = current_ylim[0] if hasattr(current_ylim, "__getitem__") else 0
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high = current_ylim[1] if hasattr(current_ylim, "__getitem__") else 10
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except Exception:
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low, high = 0, 10
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with suppress(Exception):
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self.ax.set_ylim(bottom=low, top=max(10, high))
|
|
|
|
# Optimize date formatting
|
|
self.fig.autofmt_xdate()
|
|
|
|
def _plot_series(
|
|
self,
|
|
df: pd.DataFrame,
|
|
column: str,
|
|
label: str,
|
|
marker: str,
|
|
linestyle: str,
|
|
) -> None:
|
|
"""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 with optimizations."""
|
|
if not dose_str or pd.isna(dose_str) or str(dose_str).lower() == "nan":
|
|
return 0.0
|
|
|
|
total_dose = 0.0
|
|
# Optimize string processing
|
|
dose_str = str(dose_str).replace("•", "").strip()
|
|
|
|
# More efficient splitting and processing
|
|
dose_entries = dose_str.split("|") if "|" in dose_str else [dose_str]
|
|
|
|
for entry in dose_entries:
|
|
entry = entry.strip()
|
|
if not entry:
|
|
continue
|
|
|
|
try:
|
|
# More efficient dose extraction
|
|
dose_part = entry.split(":")[-1] if ":" in entry else entry
|
|
|
|
# Optimized numeric extraction
|
|
dose_value = ""
|
|
for char in dose_part:
|
|
if char.isdigit() or char == ".":
|
|
dose_value += char
|
|
elif dose_value:
|
|
break
|
|
|
|
if dose_value:
|
|
total_dose += float(dose_value)
|
|
except (ValueError, IndexError):
|
|
continue
|
|
|
|
return total_dose
|
|
|
|
def close(self) -> None:
|
|
"""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
|