MCCD PSF Modelling

Multi-CCD Point Spread Function Modelling.

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Main contributor: Tobias Liaudat
Email: tobias.liaudat@cea.fr
Documentation: https://cosmostat.github.io/mccd/
Article: DOI - A&A
Current release: 19/10/2020

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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
2. Installation
3. Quick usage
4. Recommendations

Dependencies

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

• numpy
• scipy
• astropy
• GalSim
• ModOpt
• PySAP

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

Note: The GalSim package was removed from requirements.txt, it is expected to be installed (preferably with conda) before installing the MCCD package.

Installation

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

Locally

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 accessing the needed wavelet transforms one can run: python setup.py test and check that all the tests are passed.

From Pypi

pip install mccd

Quick usage

The easiest usage of the method is to go through the configuration file config_MCCD.ini using the helper classes found in auxiliary_fun.py (documentation). Description of the parameters can be found directly in the configuration file config_MCCD.ini. The MCCD method can handle SExtractor dataset as input catalogs given that they follow an appropriate naming convention.

The main MCCD model parameters are:

• LOC_MODEL: Indicating the type of local model to be used (MCCD-HYB, MCCD-RCA, or MCCD-POL),
• N_COMP_LOC: Indicating the number of eigenPSFs to use in the local model.
• D_COMP_GLOB: Indicating the maximum polynomial degree for the global model.

After setting up all the parameters from the configuration file there are three main functions, one to fit the model, one to validate the model and the last one to fit and then validate the model. The usage is as follows:

import mccd

config_file_path = 'path_to_config_file.ini'

run_mccd_instance = mccd.auxiliary_fun.RunMCCD(config_file_path,
                                               fits_table_pos=1)

run_mccd_instance.fit_MCCD_models()

For the validation one should replace the last line with:

run_mccd_instance.validate_MCCD_models()

Finally for the fit and validation one should change the last line to:

run_mccd_instance.run_MCCD()

All the output file will be saved on the directories specified on the configuration files.

PSF recovery

To recover PSFs from the model at specific positions test_pos from the CCD ccd_id one could use the following example:

import numpy as np
import mccd

config_file_path = 'path_to_config_file.ini'
mccd_model_path = 'path_to_fitted_mccd_model.npy'
test_pos = np.load(..)
ccd_id = np.load(..)
local_pos = True

mccd_instance = mccd.auxiliary_fun.RunMCCD(config_file_path,
                                           fits_table_pos=1)

rec_PSFs = mccd_instance.recover_MCCD_PSFs(mccd_model_path,
                                           positions=test_pos,
                                           ccd_id=ccd_id,
                                           local_pos=local_pos)

See the documentation of the recover_MCCD_PSFs() function for more information.

Recommendations

Some notebook examples can be found here.