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Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms.

We are proud to introduce version 5 of CAMPARI. We have added a number of new features, most notably a Python interface for interpreting user-supplied code (with the help of ForPy), a novel trajectory storage standard (with the help of libpqxx/PostgreSQL), and a module for performing transition path theory. Naturally, CAMPARI continues to provide the reference implementation of the ABSINTH force field paradigm and implicit solvation model.

Several python libraries implement discrete wavelet transforms. However, none of them, or at least none that I know, is aimed at scientific use. This library aims at filling this gap, in particular considering discrete wavelet transform as described by Percival and Walden. This module started as translation of the wmtsa Matlab toolbox (http://www.atmos.washington.edu/~wmtsa/), so most naming conventions and most of the code structure follows their choices.