Surf Data Analysis Project

Overview

This project extracts, processes, and analyzes surf conditions data from various surf spots around the world. The system extracts data from surf forecast graphs, processes the information, and creates a comprehensive database of surf conditions including wave heights, cleanliness ratings, and suitability for different skill levels.

Features

• Automated OCR-based extraction of surf consistency data from images
• Wave height analysis and classification
• Surf conditions categorization (clean, blown out, too small)
• Skill level suitability calculation (beginner, intermediate, advanced)
• Data validation and completion checking
• Regional surf analytics

Structure

• src/extractors/ - Contains modules for data extraction
• src/processors/ - Data cleaning and processing utilities
• src/analysis/ - Analytics and calculation modules
• src/utils/ - Helper functions and utilities
• data/ - Placeholder for processed data (not included in repo)

Technical Details

This project utilizes:

• Python for data processing and analysis
• OCR (Optical Character Recognition) via Tesseract
• Pandas for data manipulation and analysis
• PIL (Python Imaging Library) for image processing
• RegEx for pattern matching and text extraction
• SQLite for data storage

Getting Started

Prerequisites

• Python 3.8+
• Tesseract OCR
• Required Python packages (see requirements.txt)

Installation

# Clone the repository
git clone https://github.com/yourusername/surf-data-analysis.git
cd surf-data-analysis

# Install Tesseract OCR (Ubuntu/Debian)
apt-get install -y tesseract-ocr

# Install Python dependencies
pip install -r requirements.txt

Usage

1. Configure your spot locations in a CSV file
2. Run the data extractor to process surf condition images
3. Analyze the results with the provided analytics tools

# Example usage
from src.extractors.gif_extractor import extract_surf_data_from_url
from src.processors.data_processor import process_surf_data

# Extract data for a specific spot and month
data = extract_surf_data_from_url(url, spot_name, month)

# Process the extracted data
processed_data = process_surf_data(data)

Future Improvements

• Web interface for data visualization
• Predictive models for surf forecasting
• API for accessing processed data
• Mobile application integration

License

MIT License