Python-based PSF Homogenization kERnels production
Project description
- Documentation:
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
Warp (rotation + resampling) the PSF images (if necessary),
Filter images in Fourier space using a regularized Wiener filter,
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
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