MYTHRA: Mean Astrophysical Images with PYRA

MYTHRA is a Python code and extension of PYRA used to create a mean image from all the MiRA outputs generated by PYRA. PYRA (https://github.com/jdrevon/PYRA) is a Python wrapper designed to facilitate and optimize the use of MiRA (Multi-aperture Image Reconstruction Algorithm), developed by the JMMC. This tool enables efficient and random scanning of the parameter space for image reconstruction.

Requirements:

• MiRA
• Python 3.8+
• Linux/Ubuntu environment

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Overview

The purpose of MYTHRA is to create a mean image and the associated error maps.

How to Use

Main Script: after_reconstruction2_v2.py

The primary script contains variables you need to configure before running. Below is a list of key variables and their purposes:

Variable	Description
path_MiRA	Full path to the regularization directory where the PYRA outputs are stored.
path_data	Full path to the .fits file containing observations. If multiple .fits files exist, merge them before running the script (e.g., using Python or OIFits Explorer).
num_cores	Number of CPU cores dedicated to the routine.
bin_num	Number of bins used to model the l-curve trend for the script to spot the divergence point. Needs to be adjusted regarding the shape of the l-curve provided by PYRA and the number of image reconstructed (default for 1000 images and a clean l-curve: 10).
negative_offset	Purcentage used to shift the detected divergence point to a lower value in the logarithmic scale (default: 0.1)
window	Width of the window around the divergence point to select data. (default: 0.04). You can increase or decrease this value if you want to include more data. A plot with the data taken is saved inside path_MiRA.
chi2_boundaries	True or False, set a threshol constraint on the chi2 for the selection of the datasubset on the l-curve. This parameter is useful in case you do not have a clean l-curve with a lot of outlayers to make also a chi2 selection on the good data you want to keep.
chi2_boundaries_up	Superior threshold on the chi2 values that will define the best data subset.
chi2_boundaries_down	Inferior threshold on the chi2 values that will define the best data subset.
maxeval	Maximum number of evaluations for MiRA to stop if convergence is not achieved (default: 50). This parameter is used to operate a common resampling of the pixelsize for all the images using MiRA.
chi2_treshold	Threshold on the chi2 of the data-subset. In case the median chi2 of the datasubset selected from the l-curve is below the chi2_threshold, the routine use the function optimize_centring2.py to optimize the chi2 of the final image. The images are rejected if the image increases the chi2 of the mean image above the thresold chi2_mean_V2 for the chi2 on the squared visibilities and chi2_mean_CP for the closure phase.
chi2_mean_V2	Threshold on the visibility chi2 of the mean image used to select the outlayers from the image reconstructions subset in case the median chi2 of the datasubset is below chi2_threshold.
chi2_mean_CP	Threshold on the closure phase chi2 used to select the outlayers from the image reconstructions subset in case the median chi2 of the datasubset is below chi2_threshold.

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Running the Script

1. Configure the above variables in the script.
2. Run the script:

python3 after_reconstruction2_v2.py

Outputs

Results are stored directly in path_MiRA. The user is also able to check the results from the resampling by MiRA which are stored in the same main folder than the path_MiRA folder.