⚠️ This is a legacy version of the repository, no longer updated since Sept 2021. If you are taking the Intropduzione alla Fisica Statistica course at the University of Milano during the 2021-2022 academic year or beyond, please use the official repository with the latest updates.

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. The simplest way to install all these packages without interfeering with your current python installation is the Anaconda distribution. Choose python 3.x and your OS, download, install, and you should be good to go.

Alternatively, if you cannot install jupyter on your computer, you can use the mybinder online environment, which is basically an online version of the repository. Notice that the code will not run on your computer, and that you will loose your work if you close the browser window. To launch the mybinder page for the course, click here!

After completing a notebook, remember to download it to your local computer!

These instructions should work for linux & mac users. Windows users might not be able to execute the which command, and might need to install the git command beforehand. In case of technical difficulties, please use the binder online environment.

Open a terminal and cd to a directory of your choice

$ cd Documents

Check that you have correctly installed Anaconda's python.

$ which python
/home/username/anaconda3/bin/python

Clone this repository

$ git clone https://github.com/fontclos/stat-mech-python-course.git

A new folder called stat-mech-python-course will be created. Enter it and start jupyter by typing jupyter lab

$ cd stat-mech-python-course
$ jupyter lab

A browser window/tab pointing to localhost:8888 will open automatically. Open the notebooks folder, then open the first notebook by double-clicking 1-Generating-Random-Numbers.ipynb. You are ready to go!

Being able to re-use someone else's code is as important as being able to write your own. You are not supposed to figure out everything by yourself, so googling how to X in python is just fine. In addition, some useful resources are:

• Ask questions

  • Stackoverflow

• Official documentation sites

  • Numpy documentation
  • Scipy documentation
  • Jupyter Lab documentation

• Tutorials

  • Data Flair Python Tutorials
  • Numpy Crash Course

• Windows users

  • Pre-compiled binaries