OIS is a package to perform optimal image subtraction on astronomical images.
It offers different methods to subtract images:
- Modulated multi-Gaussian kernel (as described in Alard&Lupton (1998))
- Delta basis kernel (as described in Bramich (2010))
- Adaptive Delta Basis kernel (as described in Miller (2008))
Each method can (optionally) simultaneously fit and remove common background.
You can find a Jupyter notebook example with the main features at http://toros-astro.github.io/ois/
$ pip install ois
Or from this distribution:
$ python setup.py install
>>> import ois
>>> diff = ois.optimal_system(image, image_ref)[0]
Check the documentation for a full tutorial.
kernelshape: shape of the kernel to use. Must be of odd size.
bkgdegree: degree of the polynomial to fit the background. To turn off background fitting set this to None.
method: One of the following strings
-
Bramich: A Delta basis for the kernel (all pixels fit independently). Default method. -
AdaptiveBramich: Same as Bramich, but with a polynomial variation across the image. It needs the parameter poly_degree, which is the polynomial degree of the variation. -
Alard-Lupton: A modulated multi-Gaussian kernel. It needs the gausslist keyword. gausslist is a list of dictionaries containing data of the gaussians used in the decomposition of the kernel. Dictionary keywords are: center, sx, sy, modPolyDeg
Extra parameters are passed to the individual methods.
poly_degree: needed only for AdaptiveBramich. It is the degree
of the polynomial for the kernel spatial variation.
gausslist: needed only for Alard-Lupton. A list of dictionaries with info for the modulated multi-Gaussian. Dictionary keys are:
- center: a (row, column) tuple for the center of the Gaussian. Default: kernel center.
- modPolyDeg: the degree of the modulating polynomial. Default: 2
- sx: sigma in x direction. Default: 2.
- sy: sigma in y direction. Deafult: 2.
Author: Martin Beroiz