FeatherPy

Combine images in the Fourier domain.

Installation

This package is written in pure Python, with all dependencies (including the Python version) specified in the pyproject.toml.

PyPI (stable):

pip install featherpy

GitHub (latest):

pip install git+https://github.com/AlecThomson/FeatherPy

Usage

Command line:

$ featherpy -h
usage: featherpy [-h] [-c FEATHER_CENTRE] [-s FEATHER_SIGMA] [-u OUTER_UV_CUT] [-lu LOW_RES_UNIT] [-hu HIGH_RES_UNIT] [-p] [-o] low_res_file high_res_file output_file frequency

Feather two FITS files

positional arguments:
  low_res_file          Low resolution FITS file
  high_res_file         High resolution FITS file
  output_file           Output feathered FITS file
  frequency             Frequency of the data in Hz

options:
  -h, --help            show this help message and exit
  -c FEATHER_CENTRE, --feather-centre FEATHER_CENTRE
                        UV centre of the feathering function in meters (default: 0)
  -s FEATHER_SIGMA, --feather-sigma FEATHER_SIGMA
                        UV width of the feathering function in meters (default: 1)
  -u OUTER_UV_CUT, --outer-uv-cut OUTER_UV_CUT
                        Outer UV cut in meters (default: None)
  -lu LOW_RES_UNIT, --low-res-unit LOW_RES_UNIT
                        Unit of the low resolution data. Will try to read from BUNIT if not provided (default: None)
  -hu HIGH_RES_UNIT, --high-res-unit HIGH_RES_UNIT
                        Unit of the high resolution data. Will try to read from BUNIT if not provided (default: None)
  -p, --do-feather-plot
                        Make a plot of the feathering (default: False)
  -o, --overwrite       Overwrite the output file if it exists (default: False)

Python API:

from featherpy.core import feather_from_fits

See the API docs for further detail.

Algorithm

Here I follow the technique outlined by Weiss et al. 2001. For further reading the see the CASA Feather documentation and references.

Two input images are required a 'low resolution' image and a 'high resolution' image. I assume the 'low resolution' is sensitive to all spatial scales, as is typical for a single-dish radio image, and that the 'high resolution' image is missing short spacing scales.

Weighting between the two images is done in the $uv$-plane in unites of metres. I assume that:

• There is sufficient $uv$ overlap between the two images
• The images are measured at the same frequency and with the same bandwidth
• The images can be converted to units of Jy/sr

I weight between in the images in the $uv$-plane using a logistic (sigmoid) function. You must specify both the mid-point of the overlap in $uv$ space as well as the 1$\sigma$ overlap (which I approximately convert to the right parameter of the logictic function).

Steps:

• Regrid low resolution image to match high resolution
• Convert low and high resolution images to Jy/sr
• FFT the low-resolution image
• Optionally apply an outer $uv$ cut to the low resolution FFT
• Deconvolve the low-resolution FFT
• FFT the high-resolution image
• Calculate weights in the $uv$-plane
• Combine image FFTs as a weighted sum
• FFT back to the image domain
• Convert feathered image to Jy/beam