Add comprehensive tests for dose tracking functionality
- Implemented `test_dose_parsing_simple.py` to validate the dose parsing workflow. - Created `test_dose_save.py` to verify the saving functionality of dose tracking. - Added `test_dose_save_simple.py` for programmatic testing of dose saving without UI interaction. - Developed `test_final_workflow.py` to test the complete dose tracking workflow, ensuring doses are preserved during edits. - Enhanced `conftest.py` with a mock pathology manager for testing. - Updated `test_data_manager.py` to include pathology manager in DataManager tests and ensure compatibility with new features.
This commit is contained in:
William Valentin
+46
-87
@@ -5,33 +5,43 @@ import os
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import pandas as pd
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from medicine_manager import MedicineManager
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from pathology_manager import PathologyManager
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class DataManager:
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"""Handle all data operations for the application."""
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def __init__(
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self, filename: str, logger: logging.Logger, medicine_manager: MedicineManager
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self,
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filename: str,
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logger: logging.Logger,
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medicine_manager: MedicineManager,
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pathology_manager: PathologyManager,
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) -> None:
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self.filename: str = filename
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self.logger: logging.Logger = logger
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self.medicine_manager = medicine_manager
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self.pathology_manager = pathology_manager
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self._initialize_csv_file()
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def _get_csv_headers(self) -> list[str]:
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"""Get CSV headers based on current medicine configuration."""
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base_headers = ["date", "depression", "anxiety", "sleep", "appetite"]
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"""Get CSV headers based on current pathology and medicine configuration."""
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# Start with date
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headers = ["date"]
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# Add pathology headers
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for pathology_key in self.pathology_manager.get_pathology_keys():
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headers.append(pathology_key)
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# Add medicine headers
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medicine_headers = []
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for medicine_key in self.medicine_manager.get_medicine_keys():
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medicine_headers.extend([medicine_key, f"{medicine_key}_doses"])
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headers.extend([medicine_key, f"{medicine_key}_doses"])
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return base_headers + medicine_headers + ["note"]
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return headers + ["note"]
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def _initialize_csv_file(self) -> None:
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"""Create CSV file with headers if it doesn't exist."""
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if not os.path.exists(self.filename):
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"""Create CSV file with headers if it doesn't exist or is empty."""
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if not os.path.exists(self.filename) or os.path.getsize(self.filename) == 0:
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with open(self.filename, mode="w", newline="") as file:
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writer = csv.writer(file)
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writer.writerow(self._get_csv_headers())
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@@ -44,14 +54,11 @@ class DataManager:
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try:
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# Build dtype dictionary dynamically
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dtype_dict = {
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"depression": int,
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"anxiety": int,
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"sleep": int,
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"appetite": int,
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"date": str,
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"note": str,
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}
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dtype_dict = {"date": str, "note": str}
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# Add pathology types
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for pathology_key in self.pathology_manager.get_pathology_keys():
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dtype_dict[pathology_key] = int
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# Add medicine types
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for medicine_key in self.medicine_manager.get_medicine_keys():
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@@ -99,69 +106,24 @@ class DataManager:
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)
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return False
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# Find the row to update using original_date as a unique identifier
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# Handle both old format (10 columns) and new format (16 columns)
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if len(values) == 16:
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# New format with all dose columns including quetiapine
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df.loc[
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df["date"] == original_date,
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[
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"date",
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"depression",
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"anxiety",
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"sleep",
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"appetite",
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"bupropion",
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"bupropion_doses",
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"hydroxyzine",
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"hydroxyzine_doses",
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"gabapentin",
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"gabapentin_doses",
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"propranolol",
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"propranolol_doses",
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"quetiapine",
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"quetiapine_doses",
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"note",
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],
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] = values
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elif len(values) == 14:
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# Format without quetiapine
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df.loc[
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df["date"] == original_date,
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[
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"date",
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"depression",
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"anxiety",
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"sleep",
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"appetite",
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"bupropion",
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"bupropion_doses",
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"hydroxyzine",
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"hydroxyzine_doses",
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"gabapentin",
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"gabapentin_doses",
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"propranolol",
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"propranolol_doses",
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"note",
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],
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] = values
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else:
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# Old format - only update the user-editable columns
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df.loc[
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df["date"] == original_date,
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[
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"date",
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"depression",
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"anxiety",
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"sleep",
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"appetite",
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"bupropion",
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"hydroxyzine",
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"gabapentin",
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"propranolol",
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"note",
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],
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] = values
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# Get current CSV headers to match with values
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headers = self._get_csv_headers()
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# Ensure we have the right number of values
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if len(values) != len(headers):
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self.logger.warning(
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f"Value count mismatch: expected {len(headers)}, got {len(values)}"
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)
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# Pad with defaults if too few values
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while len(values) < len(headers):
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header = headers[len(values)]
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if header == "note" or header.endswith("_doses"):
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values.append("")
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else:
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values.append(0)
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# Update the row using column names
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df.loc[df["date"] == original_date, headers] = values
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df.to_csv(self.filename, index=False)
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return True
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except Exception as e:
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@@ -193,14 +155,11 @@ class DataManager:
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# Find or create entry for the given date
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if df.empty or date not in df["date"].values:
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# Create new entry for today with default values
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new_entry = {
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"date": date,
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"depression": 0,
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"anxiety": 0,
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"sleep": 0,
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"appetite": 0,
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"note": "",
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}
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new_entry = {"date": date, "note": ""}
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# Add pathology columns with default values
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for pathology_key in self.pathology_manager.get_pathology_keys():
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new_entry[pathology_key] = 0
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# Add medicine columns dynamically
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for medicine_key in self.medicine_manager.get_medicine_keys():
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