Multi-Project Repository

This repository contains multiple Python projects for different purposes. Each project is designed to handle specific tasks related to data analysis, image processing, and documentation. I have used these self-built tools to boost my productivity and simplify my work with the Gemini Multimodality Project at Turing.

1. Chart Similarity Checker

A tool to analyze and identify similar images using deep learning techniques.

Directory Structure

Chart-Similarity-Checker/
├── image-sim-main/
│   ├── file/
│   │   ├── __pycache__/
│   │   ├── __init__.py
│   │   ├── check_image_similarities.py
│   │   └── img_plotter.py
│   ├── .gitignore
│   ├── main.py
│   ├── README.md
│   ├── output/
│   └── png/

Usage

1. Place your images in the png directory
2. Run the script:

python3 -m main

3. Similar images will be stored in the output directory

2. Unused Import / Variable Checker

A GUI tool to analyze Python code for unused imports and variables.

Features

• Detects unused imports using vulture
• Identifies unused columns in data dictionaries
• Tracks numpy function usage
• Provides GUI interface for results

Usage

python unused_imports_checker.py

3. Documentation Generator

A tool to generate documentation and metadata for Jupyter notebooks.

Features

• Creates metadata CSV files
• Creates data CSV files
• Generates one shot go script to paste the respectives links on the web tool we used.

Example Usage

# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

# (See provided script for full implementation)

4. NebulaNoteHarvester

A tool for processing and organizing Jupyter notebooks with Google Drive integration.

Features

• Downloads and processes Colab notebooks
• Extracts and saves visualizations (both static and Plotly)
• Generates comprehensive metadata
• Handles data dictionary extraction
• Supports multiple plot title detection methods
• File organization and Google Drive management

Key Components

• Authentication and Google Drive API integration
• Automatic file type detection
• Support for multiple visualization formats
• Metadata generation with detailed attributes
• Additional file dependency detection

Usage

# Initialize with Google Drive folders
image_folder_id = "your_image_folder_id"
data_folder_id = "your_data_folder_id"
metadata_folder_id = "your_metadata_folder_id"
drive_folder_id = "your_drive_folder_id"

# Process a Colab notebook
colab_link = "your_colab_link"
notebook_name = get_colab_name(colab_id)
notebook_file = download_notebook(colab_id)
analyze_notebook(notebook_file, colab_id)

Dependencies

Core Dependencies

• Python 3.x
• NumPy
• Pandas
• Torch
• PIL
• sklearn

Additional Dependencies

• tkinter (for GUI)
• vulture (for unused import detection)
• google-colab (for documentation generator)
• plotly (for interactive visualizations)
• googleapiclient
• base64

Installation

Clone the repository:

git clone https://github.com/Theternos/NebuSynth.git

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Contact

If anything is needed or if you have any queries, contact me at [email protected]