Metadata-Version: 2.1
Name: mccd
Version: 0.0.1
Summary: builtins 

 Author: Tobias Liaudat 
 Email: tobiasliaudat@gmail.com 
 Year: 2020 
 A non-parametric Multi-CCD Point Spread Function modelling 


Home-page: https://github.com/CosmoStat/mccd
Author: Tobias Liaudat
Author-email: tobiasliaudat@gmail.com
License: MIT
Description: 
        
        [![Build Status](https://travis-ci.org/CosmoStat/mccd.svg?branch=master)](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:tobias.liaudat@cea.fr" style="text-decoration:none; color: #F08080">tobias.liaudat@cea.fr</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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Description-Content-Type: text/markdown
