Almost everything I code is open source and goes to Github. Feel free to take a look. Below, a few selected repositories with code for astronomical high-contrast imaging:
The SODINN package is the consolidation and evolution of the framework proposed in Gomez Gonzalez et al. 2018. This is work in progress and is being developed “in the open” (see the repository on Github), as an exercise of open science. This framework for exoplanet detection in multidimensional (3d and 4d arrays) high-contrast imaging datacubes consists of 2 main components: a labeled data generation system and a discriminator, in the form of a deep neural network.
VIP is a Python package/library for angular, reference star and spectral differential imaging for exoplanet/disk detection through high-contrast imaging. Check the Github repository here and the documentation at readthedocs. VIP is available on PyPi:
pip install vip_hci
Datacubes, Jupyter tutorials and other materials related to VIP.
High-contrast Imaging Plotting library. The goal of this library is to be the “Swiss army” solution for plotting and visualizing multi-dimensional high-contrast imaging datacubes on Jupyter lab.
pip install hciplot
Code for the paper: “Supervised detection of exoplanets in high-contrast imaging sequences”, Gomez Gonzalez et al 2018. Developed in Python 2 but compatible with Python 3. This package enables the generation of labeled data (MLAR smaples) for training machine learning classifiers. It also contains a function for building and training the neural network model that succesfully exploits the 3 dimensions of the training samples (hybrid convolutional and recurrent network). Keras/Tensorflow were used for the implementing network. Finally, it also contains the code for generating the ROC curves (figures 7 and 8) comparing the supervised detection framework to standard model PSF subtraction techniques. Code on GitHub.
Python package dedicated to the planet orbit fitting using Markov chain Monte Carlo (MCMC) methods. GitHub repository.