NumPy

Miscellaneous Python tools (monorepo).

Python libraries and utilities for custom measurement and control devices.

Pyralysis is a Python library for radio interferometry: visibility simulation, gridding, optimization-based image reconstruction, and analysis pipelines with Dask-friendly scaling, documented APIs, and optional cluster (SLURM) workflows. GPL-3.0.

A procedural ambient sound synthesiser with a desktop GUI. All audio is generated in real-time — no samples or audio files required.

Materials on various sections in Computer Science (CS). 🎓

MCODAC is a Fortran library for the numerical evaluation of fiber composite damage. The library contains analysis methods specifically tailored to fiber composites, from micromechanical homogenization approaches to macroscopic fatigue models of orthotropic multilayer composites.

Explore and analyze data provided by Motivate, a bike share system provider for many major cities in the United States. This tool uncovers bike share usage patterns and compares system usage between Chicago, New York City, and Washington, DC. The challenge involves building a robust, interactive Command Line Interface (CLI) application capable of quickly filtering, crunching, and displaying descriptive statistics on large datasets.

Python creative coding workspace for automated video generation, animation experiments, procedural media, OpenCV, MoviePy, NumPy and FFmpeg.

Aston University DG1AID lab repository with AI and data science notes, Python notebooks, NumPy, Pandas, search algorithms and machine learning practice.

A practical, linear-algebra-first introduction to data science.

The python-native Latina Conservatory version of the SMS (Spectral Modeling Synthesis) tools developed by Xavier Serra in his Phd thesis in 1989.

PyVISA libraries for measurement and control devices.

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

Provides fractional delay filter design and evaluation routines for Numpy arrays

These Python scripts and batch files stand alone for One Shot Color (OSC) narrowband data captures, but they can also be used in conjunction with my Siril Seestar Narrowband Extraction Scripts to produce a SOO (Sulfur Oxygen Oxygen) master image file from narrowband astronomy data. The Align script is mandatory, but most users will also want the other scripts as well to complete the full SOO workflow. Both the Multiscale, and the SOO APOD scripts call a user adjustable parameter file that can be tweaked from object to object... Also included are two renaming scripts, and a way to archive your completed project with a SHA256 manifest. Clear skies!

These Python scripts and batch files stand alone for One Shot Color (OSC) narrowband data captures, but they can also be used in conjunction with my Siril Seestar Narrowband Extraction Scripts to produce a SHO (Hubble Pallet) master image file from narrowband astronomy data. The Align script is mandatory, but most users will also want the other scripts as well to complete the full SHO workflow. Both the Multiscale, and the SHO APOD scripts call a user adjustable parameter file that can be tweaked from object to object... Also included are two renaming scripts, and a way to archive your completed project with a SHA256 manifest. Clear skies!

These Python scripts and batch files stand alone for One Shot Color (OSC) narrowband data captures, but they can also be used in conjunction with my Siril Seestar Narrowband Extraction Scripts to produce a HOO (Hydrogen Oxygen Oxygen) master image file from narrowband astronomy data. The Align script is mandatory, but most users will also want the other scripts as well to complete the full HOO workflow. Both the Multiscale, and the HOO APOD scripts call a user adjustable parameter file that can be tweaked from object to object... Also included are two renaming scripts, and a way to archive your completed project with a SHA256 manifest. Clear skies!

Experimental implementations of mathematical functions focusing on HPC techniques

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)