🌌 JWST Image Processing Pipeline

A comprehensive Python toolkit for downloading, processing, and visualizing REAL James Webb Space Telescope (JWST) data using Astropy and other scientific Python libraries. Successfully tested with actual JWST observations!

✨ Features

✅ REAL Data Download: Download actual JWST data from MAST using astroquery
✅ Image Processing: Complete pipeline for calibrating, denoising, and enhancing JWST images
✅ Multi-Filter Support: Process and combine multiple filter images
✅ Source Detection: Automatically detect and catalog astronomical sources
✅ Beautiful Visualizations: Create publication-quality plots and RGB composites
✅ Multiple Instruments: Support for NIRCam, MIRI, NIRSpec, and NIRISS
✅ Tested with Real Data: Successfully processed NIRISS observations (2048×2048 pixels)

🚀 Quick Start

GitHub Repository

git clone https://github.com/yourusername/jwst-image-processing.git
cd jwst-image-processing

Installation

Clone or download this repository

git clone <repository-url>
cd jwst-image-processing

Install dependencies
```
pip install -r requirements.txt
```
Download and process REAL JWST data
```
python find_jwst_data.py
```
Run the example with synthetic data
```
python jwst_demo.py
```

Basic Usage

Download and Process Data for a Specific Target

python jwst_main.py --target "NGC 3132" --instrument NIRCam --max-files 5

Download Only (No Processing)

python jwst_main.py --target "M51" --download-only

Process Existing Data

python jwst_main.py --process-only

Create Visualizations Only

python jwst_main.py --visualize-only

📁 Project Structure

jwst-image-processing/
├── jwst_main.py              # Main pipeline script
├── jwst_data_downloader.py   # Data downloading from MAST
├── jwst_image_processor.py   # Image processing functions
├── jwst_visualizer.py        # Visualization tools
├── find_jwst_data.py         # REAL data finder & downloader
├── download_real_jwst_*.py   # Multiple real data downloaders
├── jwst_demo.py              # Synthetic data demo
├── jwst_real_data_demo.py    # Realistic data demo
├── requirements.txt          # Python dependencies
├── README.md                 # This file
└── jwst_data/               # Data directory (created automatically)
    ├── raw/                 # REAL JWST FITS files
    ├── processed/           # Processed data
    └── visualizations/      # Generated plots

🎉 Real Data Success!

✅ Successfully downloaded and processed REAL JWST data!

Downloaded: 3 real NIRISS observations (2048×2048 pixels each)
Instrument: NIRISS (Near-Infrared Imager and Slitless Spectrograph)
Filter: CLEAR (broadband near-infrared)
Program: 01063 (JWST Early Release Science)
Data Source: MAST archive via astroquery
Processing: Complete pipeline from raw FITS to enhanced visualizations

The project has been tested and verified with actual telescope data from space!

🔧 Detailed Usage

1. Real Data Download

The find_jwst_data.py script downloads real JWST data from MAST:

from find_jwst_data import find_jwst_data

# Download real JWST data
downloaded_files = find_jwst_data()

2. Legacy Data Download

The JWSTDataDownloader class handles downloading data from MAST:

from jwst_data_downloader import JWSTDataDownloader

# Initialize downloader
downloader = JWSTDataDownloader()

# Search for observations
observations = downloader.search_observations(
    target="NGC 3132",
    instrument="NIRCam",
    max_records=10
)

# Download data
obs_id = observations[0]['obsid']
files = downloader.download_observation(obs_id, max_files=5)

2. Image Processing

The JWSTImageProcessor class handles all image processing:

from jwst_image_processor import JWSTImageProcessor

# Initialize processor
processor = JWSTImageProcessor()

# Process a single image
result = processor.process_single_image(
    "path/to/image.fits",
    enhance_method='log',
    denoise_method='gaussian'
)

# Process multiple filters
filepaths = ["file1.fits", "file2.fits", "file3.fits"]
processed_filters = processor.process_multiple_filters(filepaths)

3. Visualization

The JWSTVisualizer class creates beautiful plots:

from jwst_visualizer import JWSTVisualizer

# Initialize visualizer
visualizer = JWSTVisualizer()

# Plot single image
visualizer.plot_single_image(
    data,
    title="My JWST Image",
    filter_name="F444W",
    stretch='asinh',
    colormap='hubble'
)

# Create RGB composite
visualizer.plot_rgb_composite(
    rgb_data,
    title="RGB Composite",
    filters_used=["F444W", "F277W", "F090W"]
)

🎨 Visualization Options

Colormaps

hubble: Hubble Space Telescope style
infrared: Infrared optimized
cosmic: Cosmic color scheme
viridis, plasma, inferno, magma: Standard scientific colormaps

Stretch Functions

linear: Linear scaling
log: Logarithmic scaling
sqrt: Square root scaling
asinh: Arcsinh scaling (recommended for most cases)

Plot Types

Single Image: Individual filter images
Processing Pipeline: Shows original → calibrated → denoised → enhanced
Source Detection: Overlays detected sources on images
Multi-Filter: Grid of all available filters
RGB Composite: Color composite from multiple filters
Publication Plot: High-quality combined visualization

🔬 Supported JWST Instruments

NIRCam (Near-Infrared Camera)

Filters: F090W, F150W, F200W, F277W, F356W, F444W
Wavelength: 0.6-5.0 μm
Best for: Galaxy structure, star formation, exoplanets

MIRI (Mid-Infrared Instrument)

Filters: F560W, F770W, F1000W, F1130W, F1280W, F1500W, F1800W, F2100W, F2550W
Wavelength: 5-28 μm
Best for: Dust, molecular clouds, evolved stars

NIRSpec (Near-Infrared Spectrograph)

Modes: Prism, G140M, G235M, G395M
Wavelength: 0.6-5.3 μm
Best for: Spectroscopy, galaxy evolution

NIRISS (Near-Infrared Imager and Slitless Spectrograph)

Filters: F115W, F150W, F200W, F277W, F356W, F444W
Wavelength: 0.8-5.0 μm
Best for: Wide-field imaging, exoplanet transit spectroscopy

🌟 Example Targets

Famous JWST Targets

NGC 3132: Southern Ring Nebula
M51: Whirlpool Galaxy
NGC 3324: Cosmic Cliffs in Carina Nebula
Stephan's Quintet: Galaxy group
WASP-96b: Exoplanet atmosphere
SMACS 0723: Deep field galaxy cluster

How to Find More Targets

Visit MAST Portal
Search for your target of interest
Filter by JWST observations
Use the observation ID in the pipeline

🛠️ Advanced Usage

Custom Processing Pipeline

# Custom processing with specific parameters
processor = JWSTImageProcessor()

# Load and calibrate
data, header, wcs = processor.load_fits_file("image.fits")
calibrated = processor.basic_calibration(data, header)

# Custom enhancement
enhanced = processor.enhance_contrast(calibrated, method='histogram')

# Custom denoising
denoised = processor.denoise_image(enhanced, method='tv')

# Detect sources with custom threshold
catalog = processor.detect_sources(denoised, threshold=5.0)

Batch Processing

# Process multiple observations
targets = ["NGC 3132", "M51", "NGC 3324"]
instruments = ["NIRCam", "MIRI"]

for target in targets:
    for instrument in instruments:
        # Download and process
        observations = downloader.search_observations(target, instrument)
        if len(observations) > 0:
            obs_id = observations[0]['obsid']
            files = downloader.download_observation(obs_id)
            # Process files...

📊 Output Files

The pipeline generates several types of output:

Data Files

Raw FITS: Downloaded from MAST
Processed FITS: Calibrated and enhanced images
Source Catalogs: Detected sources with properties

Visualization Files

Single Images: Individual filter plots
Processing Pipeline: Step-by-step processing visualization
Source Detection: Images with overlaid sources
Multi-Filter: Grid of all filters
RGB Composite: Color composite images
Publication Plot: High-quality combined visualization

🔍 Troubleshooting

Common Issues

No data found for target
- Try different target names or coordinates
- Check if JWST has observed your target
- Use the sample data option
Download errors
- Check internet connection
- Verify target name spelling
- Try reducing max_files parameter
Processing errors
- Ensure FITS files are valid
- Check file permissions
- Verify all dependencies are installed
Visualization issues
- Check matplotlib backend
- Ensure output directory exists
- Verify data is not empty

Getting Help

🤝 Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

Astropy: Core astronomical Python library
MAST: Mikulski Archive for Space Telescopes
JWST Team: For the incredible telescope and data
Photutils: Source detection and photometry
Matplotlib: Visualization capabilities

Happy exploring the cosmos! 🌌✨

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
jwst_data/visualizations		jwst_data/visualizations
.gitignore		.gitignore
README.md		README.md
create_sample_visualization.py		create_sample_visualization.py
find_jwst_data.py		find_jwst_data.py
jwst_data_downloader.py		jwst_data_downloader.py
jwst_demo.py		jwst_demo.py
jwst_image_processor.py		jwst_image_processor.py
jwst_main.py		jwst_main.py
jwst_real_data_demo.py		jwst_real_data_demo.py
jwst_visualizer.py		jwst_visualizer.py
project_summary.py		project_summary.py
requirements.txt		requirements.txt
setup_github.sh		setup_github.sh

Sarvesh2304/JWST_image_processing

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