Meteorological Soaring Analysis (MSA)

A Python-based desktop application that analyzes and visualizes historical meteorological data alongside cross-country soaring flight logs. By applying data science techniques to flight records and Open-Meteo API data, this tool helps glider pilots identify the exact weather patterns that lead to optimal soaring conditions.

---

Tech Stack

• GUI Framework: PyQt6
• Data Science & Math: Pandas, NumPy, SciPy
• Data Visualization: Matplotlib
• Database & APIs: SQLite3, Open-Meteo REST API

---

Key Features

• Statistical Analysis: Performs regression analysis to find lines of best fit, calculating statistical measures like R² and RMSE.
• Data Normalization: Normalizes dissimilar datasets (e.g., weather metrics vs. flight performance) for accurate comparative analysis.
• Interactive Visualization: Generates customizable graphs for temperature, humidity, wind speed, and flight data.
• Custom Filtering: Filter analysis by specific regions, date ranges, and meteorological conditions.
• Export Functionality: Export generated plots as image files for flight planning and sharing.

---

Domain Context: Bridging Data and Aviation

Cross-country gliding relies entirely on natural atmospheric forces—specifically thermals (rising columns of warm air)—to stay airborne and cover large distances. Predicting these conditions requires interpreting complex meteorological variables.

This software solves a real problem in the competitive gliding community: bridging the gap between raw meteorological data and practical, actionable insights. By mathematically correlating historical UK flight databases with weather data, it determines the ingredients of a successful soaring day, allowing pilots to plan safer, faster, and more efficient flights.

---

Installation & Setup

1. Prerequisites

Ensure you have Python 3.x installed on your machine.

2. Clone and Install Dependencies

git clone [https://github.com/your-username/msa-software.git](https://github.com/your-username/msa-software.git)
cd msa-software
pip install PyQt6 matplotlib numpy pandas scipy openmeteo_requests requests_cache retry_requests

3. Run the Application

python entry_point.py

---

Usage Guide

Upon launching, the Main Menu provides access to the core modules:

• View Data: Select regions, date ranges, and conditions (Line A & optional Line B) to plot historical weather against flight data.
• Run Analysis: Automatically perform regression analysis on selected conditions and plot the lines of best fit.
• Export: Save any generated graph directly to your local machine as an image file.

---

Project Architecture

• entry_point.py — The main application launcher.
• Main_Menu.py, View_Data.py, Analyse_Data.py — Core UI window controllers.
• Custom_UI_Elements.py, Drop_Down_Options.py — Reusable PyQt6 interface components.
• Maths_Functions.py — Core logic for regression and statistical optimization.
• MSA_Utils.py — Utility functions for Open-Meteo API requests and data validation.
• lookup_tables.py — Maps UI labels to SQLite database fields.
• MSA2.db — Local SQLite database containing historical flight records.