dfreproject 0.0.1

A high-performance Python package for reprojecting astronomical images between different coordinate systems with support for SIP distortion correction.

dfreproject

A high-performance Python package for reprojecting astronomical images between different coordinate systems with support for SIP distortion correction.

The idea behind this package was to make a stripped down version of the reproject package by astropy in order to reduce computational time. We achieve approximately 40X faster computations with this package. Take a look at the demos for an example.

Features

• Fast reprojection of astronomical images between different WCS frames
• Support for Simple Imaging Polynomial (SIP) distortion correction
• GPU acceleration using PyTorch
• Multiple interpolation methods (nearest neighbor, bilinear, bicubic)
• Simple high-level API and detailed low-level control

Installation

Using PyPi

If you want to install using PyPi (which is certainly the easiest way), you can simply run

pip install dfreproject

Requirements

• Python 3.7+
• NumPy
• Astropy
• PyTorch
• Matplotlib
• cmcrameri

Installing from Source

For the latest development version, install directly from the GitHub repository:

git clone https://github.com/DragonflyTelescope/dfreproject.git
cd dfreproject
pip install -e .

For development installation with documentation dependencies:

pip install -e ".[docs]"

Quick Start

from astropy.io import fits
from astropy.wcs import WCS
from dfreproject import calculate_reprojection

# Load source and target images
source_hdu = fits.open('source_image.fits')[0]
target_hdu = fits.open('target_grid.fits')[0]
target_wcs = WCS(target_hdu.header)
# Perform dfreproject with bilinear interpolation
reprojected = calculate_reprojection(
    source_hdus=source_hdu,
    target_wcs=target_wcs,
    shape_out=target_hdu.data.shape,
    order='bilinear'
)

# Convert back to NumPy and save as FITS
reprojected_np = reprojected.cpu().numpy()
output_hdu = fits.PrimaryHDU(data=reprojected_np)
output_hdu.header.update(target_wcs.to_header())
output_hdu.writeto('reprojected_image.fits', overwrite=True)

The arguments for calculate_reprojection are the same as for the standard reprojection options in the reproject package such as reproject_interp, reproject_adaptive, or reproject_exact. Therefore, it can be directly swapped for one of these by simply importing it with from dfreproject import calculate_reprojection and then using calculate_reproject instead of reproject_interp. This comes with the caveat that the flux calculation most closely mimics that to reproject_interp.

Demos and Examples

A collection of example notebooks and scripts is available in the demos folder to help you get started:

• reprojection-comparison.ipynb - Simple example of reprojecting between two WCS frames and comparing the result of our implementation with the reproject package.
• Coordinate-Comparison.ipynb' - A step-by-step walkthrough of our coordinate transformations with a comparison to astropy.wcs`.

To run the demos:

cd demos
jupyter notebook

Documentation

Comprehensive documentation is available at https://dfreproject.readthedocs.io/

The documentation includes:

• API reference
• Mathematical details of the reprojection process
• Tutorials and examples
• Performance tips

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

Citation

If you use this package in your research, please cite: TBD

Acknowledgments

• Based on the FITS WCS standard and SIP convention
• Inspired by Astropy's reproject package
• Accelerated with PyTorch
• Documentation aided by Claude.ai