Repository of computing methods with specific applications to physics and data analysis. Here you can find notes and codes about:
- basics of Git, Packaging, Continuous Integration and Documentation.
- basics of several programming languages, such as Python, Fortran, C and C++.
- basics of Python packages for Scientific Computation, such as Numpy, Matplotlib and Scipy.
- basics of Machine Learning with Keras and Tensorflow
- introduction to Parallel Computing with GPU and Google Colab.
- introduction to the ROOT framework for Data Analysis.
- introduction to the Qiskit framework for Quantum Computing.
- some LaTex models and introduction to Manim.
Notes and examples come from different sources, but most are from the "Computing Methods for Experimental Physics and Data Analysis" course at the University of Pisa. All sources are cited in the documentation and in the README file of each directory.
Looke at the link for a complete example of a ML package for Physics: EM
Get started to GitHub, Colab and ML with the following Jupyter notebook: INTRO
Here are some of the plots generated from the programs:
- Logistic map and Lyapunov exponent:
- Standard map and Torus breaking:
- Hopfield model evolving from initial condition to a stored pattern:
