pypher 0.6.2

Python-based PSF Homogenization kERnels production

Documentation:

https://pypher.readthedocs.org/

Compute an homogenization kernel between two PSFs.

This code is well suited for PSF matching applications in both an astronomical or microscopy context.

It has been developed as part of the ESA Euclid mission and is currently being used for multi-band photometric studies of HST (visible) and Herschel (IR) data.

Features

1. Warp (rotation + resampling) the PSF images (if necessary),

2. Filter images in Fourier space using a regularized Wiener filter,

3. Produce a homogenization kernel.

Note: pypher needs the pixel scale information to be present in the FITS files. If not, use the provided addpixscl method to add this missing info.

Warning: This code does not

• interpolate NaN values (replaced by 0 instead),

• center PSF images,

• minimize the kernel size.

Installation

PyPHER works both with Python 2.7 and 3.3 or later and relies on numpy, scipy and pyfits (or astropy) libraries.

Option 1: Pip

$ pip install pypher

Option 2: from source

$ git clone https://git.ias.u-psud.fr/aboucaud/pypher.git
$ cd pypher
$ python setup.py install

Basic example

$ pypher psf_a.fits psf_b.fits kernel_a_to_b.fits -r 1.e-5

This will create the desired kernel kernel_a_to_b.fits and a short log kernel_a_to_b.log with information about the processing.

Acknowledging

If you make use of any product of this code in a scientific publication, please consider acknowledging the work by citing the following paper

Boucaud et al. (2016) “Convolution kernels for multi-wavelength imaging”

Note: A&A submitted