Sep 5, 2022 · Error propagation and statistical analysis for Monte Carlo simulations in lattice QCD and statistical mechanics using autograd. Python package to construct free energy profiles from biased molecular simulation data using both log-likelihood maximization and self-consistent iteration approaches.

Aug 26, 2024 · This post contains many Python programs to be taught in statistical mechanics course of several universities.

The tutorial includes classical concepts from statistical physics (partition function, virial expansion, ...), and the homework session will show that the equiprobability principle might be more subtle than expected.

This document describes Python implementations of concepts from statistical mechanics and probability. It includes programs for plotting random numbers, simulating statistical ensembles like coin tosses and dice throws, calculating probability distributions like the binomial distribution, performing transformations between distributions, using ...

This collection of scripts implements two popular and fundamental statistical physics models with Python 3. As a side objective, it aims to introduce the students to few basic Python Object concepts such as classes, inheritance, iterators, properties, and decorators, and the most important data structures i.e. list and dictionary.

Introduction to Statistical Mechanics in python 3.x, using jupyter notebooks. To follow these lectures, you need a modern installation of python, together with jupyter, numpy, matplotlib and some other standard python libraries.

Numpy, powerful numerical array objects, and routines to manipulate them. Scipy, high-level data processing routines. Matplotlib, plotting and visualization. Pandas, data analysis/management. Mayavi, 3-D visualization. Work in a Jupyter/IPython notebook. Write code in cells, analyze, plot, etc. Everything stored in .ipynb file.

Huang - Statistical Mechanics. Part B and C of Kerson Huang’s Statistical Mechanics cover equilibrium stat mech and phase transitions (and other special topics) respectively. Practice problems can be found at the end of each chapter. Tutorial #3. Notes on the 2-state system example can be found here. Tutorial #6

What makes python special for STEM fields is its vast ecosystem of scientific libraries (numpy, pandas, scipy, matplotlib). This section will serve as a quick crash course of python programming language and some of its most essential scientific libraries.

They are designed to provide a deeper insight into the physics of many-particle systems than would be possible through analytic calculations alone. This Appendix provides sample programs to support your own programming. There are definite advantages to …