ois 0.2

Optimal Image Subtraction

Optimal Image Subtraction (OIS)

OIS is a Python package to perform optimal image subtraction on astronomical images. It also has a companion command-line program written entirely in C.

OIS 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.

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Installation

To install the Python module:

$ pip install ois

To instal and run the C command-line program, download this repo to your local machine and execute:

$ git clone https://github.com/toros-astro/ois.git
$ cd ois
$ make ois
$ ./ois --help

The C command-line program is somewhat limited in functionality compared to the Python module. Please see the documentation for more information.

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Minimal usage example

>>> from ois import optimal_system
>>> diff = optimal_system(image, image_ref)[0]

Check the documentation for a full tutorial.

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Other Parameters:

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.

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Author: Martin Beroiz

[e-mail]