FeatherPy 0.0.1

A python package to combine radio as

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