🔔 Reminder Management System 🔔

This repository contains data.py, a Python module designed to manage reminders effectively. It handles the lifecycle of reminders—from creation to dismissal or renewal—and interacts with CSV files for persistent storage.

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Table of Contents

• Overview
• Features
• How it Works
  • Data Structures
  • Core Functions
• Usage
• File Structure
• Setting the Current Time (now)
• Author

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Overview

data.py provides the backend for a straightforward reminder system. It manages reminder data through three distinct CSV files: one for core reminder details, one for active schedules, and another for dismissed records. This approach ensures data integrity and a clear separation of concerns.

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Features

• Load Database: Reads reminder data from three CSV files (reminders.csv, active.csv, dismissed.csv) into memory.
• Categorize Reminders:
  • Active Reminders: Lists reminders currently due.
  • Past Reminders: Tracks reminders that were dismissed after becoming active.
  • Future Reminders: Shows reminders scheduled to activate later.
• Manage Reminders:
  • Set New Reminders: Create new reminders with a specified text and activation date.
  • Dismiss Reminders: Mark currently active reminders as dismissed.
  • Renew Reminders: Add new future activation times to existing reminders.
• Dump Database: Consolidates all reminder data (past, active, future) and writes it back to a single, structured CSV file.

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How it Works

The system manages reminder data using global Python lists, each tracking a specific aspect of the reminder's state.

Data Structures

• reminders_database: Stores core reminder details (reminder_id, reminder_text).
• reminders_active_database: Tracks when reminders are set to become active.
• reminders_dismissed_database: Records when reminders have been dismissed.

Core Functions

1. load_database(reminders_file, active_file, dismissed_file): Initializes data from CSVs.
2. get_active_reminders(): Returns reminders currently due based on now and dismissal status.
3. get_past_reminders(): Identifies reminders that were active and later dismissed.
4. get_future_reminders(): Finds reminders scheduled to activate after now.
5. set_reminder(reminder_text, active_from): Creates a new reminder and its initial active entry.
6. dismiss_reminder(reminder_id): Marks an active reminder as dismissed at now.
7. renew_reminder(reminder_id, active_from): Adds a new future activation time for an existing reminder.
8. dump_database(database_file): Consolidates and writes all reminders to a single CSV file, sorted by active date.

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Usage

To use this module, simply import data.py into your Python script and call its functions. Ensure your reminder CSV files are in the same directory or provide their full paths.

import data

# Load your reminder data
data.load_database('reminders.csv', 'active.csv', 'dismissed.csv')

# Get and print currently active reminders
active_reminders = data.get_active_reminders()
for rem in active_reminders:
    print(f"Active: ID {rem['reminder_id']}, Text: {rem['reminder_text']}")

# Set a new reminder
data.set_reminder("Pick up dry cleaning", "2025-06-01 10:00:00")

# Dump all reminders to a new CSV file
data.dump_database('all_reminders_output.csv')

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File Structure

For the system to operate, ensure these CSV files (even if empty with just headers) are alongside data.py:

.
├── data.py
├── reminders.csv
├── active.csv
└── dismissed.csv

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Setting the Current Time (now)

The now variable is a fixed timestamp within data.py:

now = datetime.datetime(2025, 4, 7, 10, 0, 0)

This acts as the reference point for categorizing reminders. You would change this value directly in data.py to simulate different "current" times for testing or specific scenarios.

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Author

• Suveer Dhawan - GitHub Profile