Offline Weather API Response Simulator & Analyzer

by Anish K M

Project Overview

This project is designed to simulate an API interaction for weather data analysis using a local JSON file. It includes a basic classification system that categorizes temperatures into "Hot", "Warm", or "Cold" based on predefined thresholds. The entire process is handled offline, making it a practical tool for understanding how to work with simulated API data without requiring internet access.

Features

• Offline Simulation: No internet connection is needed; the tool works entirely offline by using locally stored JSON data.
• Temperature Classification: Classifies temperatures into categories such as "Hot", "Warm", or "Cold" using simple logic.
• Simple and Lightweight: Minimalistic code, focusing on basic functionality and easy understanding.
• Flexible File Path: Easily configurable file path to load JSON data from any location on your system.

Steps

1. Create Local JSON Data

   • Prepare a JSON file containing simulated weather data. This file acts as if it's the response from a real weather API.
   • Example of the JSON format:

     [
         {"city": "New York", "temperature": 30, "humidity": 80},
         {"city": "London", "temperature": 15, "humidity": 65},
         {"city": "Tokyo", "temperature": 25, "humidity": 70},
         {"city": "Delhi", "temperature": 40, "humidity": 20}
     ]

2. Load Weather Data

   • Use the provided Python script to read the JSON file. This simulates fetching data from an API.

3. Classify Temperature

   • Implement a basic classification logic to categorize temperatures:
     • "Hot" for temperatures 30°C and above.
     • "Warm" for temperatures between 20°C and 30°C.
     • "Cold" for temperatures below 20°C.

4. Display Results

   • The script outputs each city’s temperature and its classification to the console.

How to Use

1. Clone the Repository

   • Download or clone the repository to your local machine:

     git clone https://github.com/yourusername/weather-analyzer.git
     cd weather-analyzer

2. Create the JSON File

   • Create a file named test.json in the specified directory with the example data provided above.

3. Update File Path in Script

   • Ensure that the file_path variable in source.py is set to the location of your test.json file. Example path:

     file_path = r'C:\Users\saral\Kong\test.json'

4. Run the Python Script

   • Execute the Python script to load and analyze the data:

     python source.py

5. View Results

   • The script will print the classification of each city's temperature to the console.

Outputs

• Temperature Classification: Console output showing each city's temperature and its classification.

  • Example output:

    Analyzing Weather Data:
    City: New York
    Temperature: 30°C - Condition: Hot
    ----------------------------------------
    City: London
    Temperature: 15°C - Condition: Cold
    ----------------------------------------
    City: Tokyo
    Temperature: 25°C - Condition: Warm
    ----------------------------------------
    City: Delhi
    Temperature: 40°C - Condition: Hot
    ----------------------------------------
    

• Local JSON File: Contains the simulated API data used for analysis.

Applications

• Educational Tool: Helps understand how to process and analyze JSON data offline, which can be useful for learning purposes and basic data handling skills.
• Prototype Development: Serves as a starting point for developing more advanced data analysis tools and applications that do not rely on real-time internet connections.
• Offline Data Testing: Allows developers to test their data processing and classification logic without needing to connect to live APIs, useful for environments with limited internet access.

Project Insights

This project demonstrates how to simulate API interactions and perform basic data classification offline. It provides a simple yet effective example of handling local data and classifying it using basic logic. This approach can be expanded to include more complex data and classifications as needed.

Note

This project is a prototype meant for educational purposes and simulates real-world API data interactions. The classification logic is basic and can be enhanced for more sophisticated analyses.

Links

• Project Code on GitHub
• JSON Data Example: test.json