mccd 0.0.1

builtins

Author: Tobias Liaudat Email: [e-mail] Year: 2020 A non-parametric Multi-CCD Point Spread Function modelling

Home-page: https://github.com/CosmoStat/mccd Author: Tobias Liaudat Author-email: [e-mail] License: MIT Description:

[](https://travis-ci.org/CosmoStat/mccd)

# MCCD PSF Modelling

Multi-CCD Point Spread Function Modelling.

— > Main contributor: <a href=”https://tobias-liaudat.github.io” target=”_blank” style=”text-decoration:none; color: #F08080”>Tobias Liaudat</a> > Email: <a href=”mailto:[e-mail]” style=”text-decoration:none; color: #F08080”>[e-mail]</a> > Documentation: <a href=”https://cosmostat.github.io/mccd/” target=”_blank” style=”text-decoration:none; color: #F08080”>https://cosmostat.github.io/mccd/</a> > Release: 08/10/2020 —

The non-parametric MCCD PSF modelling, or MCCD for short, is a Point Spread Function modelling pure python package. It is used to generate a PSF model based on stars observations in the field of view. Once trained, the MCCD PSF model can then recover the PSF at any position in the field of view.

## Contents

1. [Dependencies](#Dependencies) 1. [Installation](#Installation) 1. [Recomendations](#Recomendations)

## Dependencies

The following python packages should be installed with their specific dependencies:

• [numpy](https://github.com/numpy/numpy)

• [scipy](https://github.com/scipy/scipy)

• [astropy](https://github.com/astropy/astropy)

• [GalSim](https://github.com/GalSim-developers/GalSim)

• [ModOpt](https://github.com/CEA-COSMIC/ModOpt)

• [PySAP](https://github.com/CEA-COSMIC/pysap)

It is of utmost importance that the PySAP package is correctly installed as we will be using the wavelet transforms provided by it.

## Installation

After installing all the dependencies one can perform the MCCD package installation:

#### Locally `bash git clone https://github.com/CosmoStat/mccd.git cd mccd python setup.py install `

To verify that the PySAP package is correctly installed and that the MCCD package is accesing the needed wavelet transforms one can run: python setup.py test and check that all the tests are passed.

#### From Pypi `bash pip install mccd `

## Recomendations

A useful example notebook testing-simulated-data.ipynb can be found [here](https://github.com/CosmoStat/mccd/tree/master/notebooks).

Quick tutorial will be written soon as well as examples on how to run the MCCD PSF modelling on real images using as input SExtractor catalogs.

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