Overview

This full-stack application is designed to visualize voltage data from various sensors. It consists of a backend API to process and serve the data, and a frontend application to display the data in a user-friendly way.

Backend

• Data Processing:

• Cleaning: The backend processes the raw data from measurements.json to ensure data consistency and accuracy. Formatting: The data is formatted into a suitable structure for serving to the frontend.

API Endpoints:

• Sensor Data: Provides voltage data for a specific sensor, including timestamps and voltage values.
• Sensor List: Returns a list of available sensors.

Technology Stack:

Backend:

• Python
• RESTful API framework (FastAPI)

Frontend:

• Angular (latest version)
• Component library (e.g., Angular Material, PrimeNG)
• Charting library (e.g., eCharts or d3.js)

Features:

Chart View:

• Allows users to select a sensor from a dropdown list.
• Displays a line chart of the selected sensor's voltage data over time.
• Utilizes a charting library (e.g., eCharts or d3.js) for visualization.

About View:

• Provides information about the project, challenges faced, and implementation details. Technology Stack:

Getting Started

• Backend Setup:

• Clone the repository.
• Set up a Python environment (e.g., using virtualenv or venv).
• Install required dependencies (e.g., Flask, FastAPI, libraries for data processing and API creation).
• Run the backend server (instructions in ReadMe in Backend)

• Frontend Setup:
• Clone the repository.
• Install Node.js and npm.
• Install dependencies using npm install.
• Start the development server using ng serve (instructions in voltage-monitor frontend)

Usage:

• Access the Frontend: Open a web browser and navigate to the URL specified by the development server.

• Select a Sensor: Choose a sensor from the dropdown list on the Chart page. View Voltage Data: The selected sensor's voltage data will be displayed as a line chart. About Page: Navigate to the "About" page to learn more about the project. Challenges and Considerations:

• Data Quality: Ensure data integrity and handle potential missing or invalid data points.

• Performance Optimization: Optimize data processing and API response times for large datasets.

• User Experience: Provide a user-friendly interface with clear visualizations. Security: Implement appropriate security measures to protect sensitive data (if applicable).

Future Improvements:

• Real-time Data: Explore options for real-time data updates. Advanced Visualization: Implement more sophisticated chart types and interactive features.
• User Authentication and Authorization: Implement user authentication and authorization to control access to different features.

Error Handling and Logging: Implement robust error handling and logging mechanisms.

By following these guidelines and leveraging the provided technology stack, you can create a robust and efficient voltage monitoring application.