I love Python and I love the fact that Python is being used in science and engineering more than ever! Python has a mature set of general purposes libraries that aid scientists extract the best out of their data.
Numpy (numerical computation), Scipy (scientific programming), Matplotlib (visualization), Pandas (data processing), and Scikit-learn (data science/machine learning), are only a few of the awesome myriad of open source libraries developed in Python.
Part of the success around Python and its libraries is due to their fast-growing, energetic, and welcoming communities. Those libraries I mentioned are developed in a daily basis by experts from academia and industry (and also by students through programs like Google Summer of Code).
Over the past few years, I’ve been involved with open source software development primarily thanks to Google, GitHub, and NASA ❤️.
Here is a list of projects I’m more involved with:
- lightkurve: Sweet astronomical flux time series analysis in Python
- astropy: A community Python library for Astronomy
- photutils: Image photometry in Python
Recently, I started to code more frequently in R and C++ in applications of financial engineering and machine learning. Here a list of projects being developed:
- riskparity.py: A Python package for scalable risk parity portfolios using TensorFlow 2.0
- spectralGraphTopology: An R package for graph learning from data via spectral constraints
- riskParityPortfolio: An R package for fast design of high dimensional risk parity portfolios
- sparseIndexTracking: An R package for the design of portfolio of stocks to track an index