Phys 1600/2600 has it's own dedicated Jupyter Hub server which can be accessed at https://phys1600.jupyter.brown.edu. All you need is a web browser and a Brown login and you can start programming in Python on the hub!
You should be able to use the hub for all assignments and lecture workbooks in PHYS1600/2600. I recommend that you also install Python on your local machine following the instructions below.
Please create your own local clone of this repo on your Jupyter Hub, local machine, or both. The second option is best. Using git, we can access and work on our projects from many places at once.
Instructions for cloning a repo can be found here:
https://help.github.com/articles/cloning-a-repository/
You can also find more helpfull GitHub and Jupyter Hub information on the Brown CCV Jupyter hub Documentation:
https://docs.ccv.brown.edu/jupyterhub/
Later we will fork GitHub repo's and sync upstream, so that you can work on your own copy and maintain up-to-date material without loosing any of your own work.
Python 3.8 will be used in PHYS 1600. If you plan on running code on your local machine, then you must install python. A number of packages are required In addition to the base python installation. In particular, we will make extensive use of the numpy and matplotlib packages included as part of the SciPy library. We also require a few tools to help us work efficiently using python, including the ipython shell, an interactive python interpreter; Jupyter notebooks, an interactive python environment tailored for scientific computing; and a text editor.
Luckily, most of these packages and tools are included as part of the Anaconda python distribution, which is a great python distribution for scientific computation. To install, go to https://www.anaconda.com/download and download the installer for the Python 3.8 version appropriate to your operating system (either Windows, macOS, or Linux).
The easiest way to test your python installation is to open the Jupyter notebook application. You may need to search for it. When you open Jupyter, the interface will pop up as a tab in your web browser and a terminal will open in the background. Don’t worry about the terminal, just let it do its thing.
To close Jupyter program, close the tab on your browser, then go to the terminal, which is open, and press ctrl+c and then close the terminal.
There are many ways to run python programs. Jupyter notebooks is one method that is particularly well suited to scientific analysis, but depending on the problem or your personal preferences another method may be better. We will explore other ways of running python programs in class. You are free to use whatever method you like best for assignments and projects in this course. Python is a tool to help you solve problems and get work done, there is not one right way to interact with it.
Anconda contains a powerful package and environment management tool called Conda. Later in the course we may need to install an additional Python package or two using Conda. This tool is accessed from the command line. If you are not comfortable with the command line yet, don’t worry, almost everything you need is contained in the initial Anaconda installation. The use of the Conda tool will be demonstrated in lectures when it is required. More information on Conda can be found here: https://conda.io/docs/
A text editor is also required to edit Python programs. The default notepad or textEdit applications included with Windows and MacOs respectively are not sufficient. Luckily, there are many good options, a few are listed below:
A nice cross-platform option is Sublime text editor, find it here: https://www.sublimetext.com/
Notepad++ is another good and free option for windows, find it here: https://notepad-plus-plus.org/
Atom for MacOS: https://atom.io/