🌌 Astronomical Visualization

Tagline: Python-based astronomical image visualization and analysis using FITS files, NumPy, and Matplotlib.

GitHub About Section (suggestion):

Explore, process, and visualize astronomical data using Python and Jupyter. Includes FITS image processing, pixel-level analysis, and data-driven star visualization.

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🚀 Overview

This project focuses on processing, analyzing, and visualizing astronomical data — including FITS (Flexible Image Transport System) files and star catalogs — to create meaningful visual representations of celestial objects.

Through a series of Jupyter notebooks, the project demonstrates how to:

• Load and explore astronomical image data.
• Perform image preprocessing and pixel-level transformations.
• Clean, filter, and analyze star datasets.
• Visualize star patterns, intensity maps, and image features.

This repository serves as a foundation for researchers, students, and enthusiasts interested in scientific visualization, data cleaning, and space data analysis.

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🧠 Key Features

✅ Data Cleaning and Preparation

• Handles raw star catalog data.
• Cleans missing or noisy entries to create a usable dataset (cleaned_star_data.csv).

✅ Image Processing

• Works with astronomical .FITS image files (e.g., Test_FITS_file.fits).
• Reads, normalizes, and enhances celestial image data.
• Demonstrates per-pixel operations and visualization.

✅ Visualization

• Generates plots of star data and intensity maps.
• Builds visualizations using Matplotlib and Plotly.
• Explores patterns, brightness, and object distribution.

✅ Exploratory Notebooks

• Modular Jupyter notebooks for step-by-step experimentation:
  • Basics.ipynb: Foundational concepts and initial exploration.
  • Image_Processing.ipynb: Image enhancement and FITS file manipulation.
  • Image_pixel.ipynb: Pixel-level image analysis.
  • Visualize.ipynb: Final visualizations and plots.
  • star_plots/: Additional plots and utilities for star mapping.

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🧩 Repository Structure

Astronomical_Visualization/
│
├── star_plots/                  # Visualization scripts and outputs
├── Basics.ipynb                 # Introductory exploration notebook
├── Image_Processing.ipynb       # FITS image preprocessing & enhancement
├── Image_pixel.ipynb            # Pixel-level data manipulations
├── Visualize.ipynb              # Final visualization and plotting
├── cleaned_star_data.csv        # Processed dataset of stars
├── Test_FITS_file.fits          # Sample astronomical image
├── Test7_FITS_file.fits         # Additional FITS sample
├── skv1001259399386.png         # Example output image
└── .vscode/                     # Editor configuration

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🧰 Tech Stack

Category	Tools Used
Language	Python
Environment	Jupyter Notebook
Data Formats	CSV, FITS
Visualization Libraries	Matplotlib, Plotly
Image Processing	NumPy, SciPy, Astropy
Data Handling	Pandas

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🪐 Sample Workflow

1. Load astronomical FITS image

   from astropy.io import fits
   image = fits.getdata("Test_FITS_file.fits")

2. Visualize the image

   import matplotlib.pyplot as plt
   plt.imshow(image, cmap="plasma")
   plt.title("Astronomical Image Visualization")
   plt.colorbar()
   plt.show()

3. Analyze pixel intensity distribution

   import numpy as np
   plt.hist(image.ravel(), bins=256, color='cyan')
   plt.title("Pixel Intensity Distribution")
   plt.show()

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📊 Example Outputs

Visualization	Description
🌠 Star Map	Plots the position and brightness of stars from the cleaned dataset.
🌌 Intensity Heatmap	Displays energy distribution across astronomical images.
🪞 Pixel-Level Visualization	Highlights brightness clusters and noise patterns in FITS data.

(See Visualize.ipynb for full examples.)

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🧭 Future Improvements

• Add machine learning–based star classification (using CNNs).
• Integrate interactive 3D visualization using Plotly or Bokeh.
• Wrap into a Flask/Dash app for live exploration.
• Add detailed documentation and code comments.
• Publish on Kaggle or HuggingFace Datasets.

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💡 How to Run Locally

1. Clone the repository:

   git clone https://github.com/Shubham91999/Astronomical_Visualization.git
   cd Astronomical_Visualization

2. Install dependencies:

   pip install -r requirements.txt

3. Launch Jupyter:

   jupyter notebook

4. Open and run any of the notebooks sequentially.

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🧑‍🚀 Author

Shubham Kulkarni
ML Software Engineer @ PACCAR | Arizona State University
🌍 LinkedIn • GitHub

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🪙 License

This project is released under the MIT License — feel free to use and modify it for learning or research.

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⭐ If you find this useful, consider giving the repository a star to support open-source science! 🌟