Lesson 0: Configuring your computer

In this lesson, you will set up a Python computing environment for scientific computing. There are two main ways people set up Python for scientific computing on their own machine.

By downloading and installing package by package with tools like apt-get, pip, etc.
By downloading and installing a Python distribution that contains binaries of many of the scientific packages needed. One widely used distribution is from Anaconda.

In this class, we will use Anaconda, with its associated package manager, conda. It has become the de facto package manager/distribution for scientific use.

macOS users: Install XCode

If you are using macOS, you should install XCode, if you haven’t already. It’s a large piece of software, taking up about 35 GB on your hard drive, so make sure you have enough space. Please install this ahead of the workshop, as it takes a long time to install. You can install it through the App Store.

After installing it, you need to open the program. Be sure to do that, for example by clicking on the XCode icon in your Applications folder. Upon opening XCode, it may perform more installations. You can let it go ahead and do this, and then close XCode.

Windows users: Install Git and Chrome or Firefox

We will be using JupyterLab in the workshop. It is browser-based, and Chrome, Firefox, and Safari are supported. Microsoft Edge is not. Therefore, if you are a Windows user, you need to be sure you have either Chrome of Firefox installed.

Git is installed on Macs with XCode. For Windows users, you need to install Git. You can do this by following the instructions here.

Uninstalling Anaconda

If you have previously installed Anaconda with a version of Python other than 3.9, you need to uninstall it, removing it completely from your computer. You can find instructions on how to do that from the official uninstallation documentation.

Downloading and installing Anaconda

Downloading and installing Anaconda is simple.

Go to the Anaconda homepage and download the graphical installer.
Install Anaconda with Python 3.9.
You may be prompted for your email address, which you should provide. If you are at a university, you may want to use your university email address because educational users can get some of the non-free goodies in Anaconda.
Follow the on-screen instructions for installation. While doing so, be sure that Anaconda is installed in your home directory, not in root.

That’s it! After you do that, you will have a functioning Python distribution.

Installing node.js

node.js is a platform that enables you to run JavaScript outside of the browser. We will not use it directly, but it needs to be installed for some of the more sophisticated JupyterLab functionality. Install node.js by following the instructions here.

Launching JupyterLab and a terminal

You can alternatively launch JupyterLab via the Anaconda Navigator or via your operating system’s terminal program (Terminal on macOS and PowerShell on Windows). If you wish to launch using the latter, skip to the next section.

If you’re using macOS, Anaconda Navigator will be available in your Applications menu. If you are using Windows, you can launch Anaconda Navigator from the Start menu.

We will be using JupyterLab throughout the workshop. You should see an option to launch JupyterLab from within Anaconda Navigator. When you do that, a new browser window or tab will open with JupyterLab running. Within the JupyterLab window, you will have the option to launch a notebook, a console, a terminal, or a text editor. We will use all of these during the workshop. For the updating and installation of necessary packages, click on Terminal to launch a terminal. You will get a terminal window (probably black) with a bash prompt. We refer to this text interface in the terminal as the command line.

Launching JupyterLab from the command line

While launching JupyterLab from the Anaconda Navigator is fine, I generally prefer to launch it from the command line on my own machine. If you are on a Mac, open the Terminal program. You can do this hitting Command + space bar and searching for “terminal.” Using Windows, you should launch PowerShell. You can do this by hitting Windows + R and typing powershell in the text box.

Once you have a terminal or PowerShell window open, you will have a prompt. At the prompt, type

jupyter lab

and you will have an instance of JupyterLab running in your browser. If you want to specify the browser, you can, for example, type

jupyter lab --browser=firefox

on the command line.

It is up to you if you want to launch JupyterLab from the Anaconda Navigator or command line.

The conda package manager

conda is a package manager for keeping all of your packages up-to-date. It has plenty of functionality beyond our basic usage in class, which you can learn more about by reading the docs. We will primarily be using conda to install and update packages.

conda works from the command line. Now that you know how to get a command line prompt, you can start using conda. The first thing we’ll do is update conda itself. Enter the following on the command line.

conda update conda

You can press y to continue. You should do this once more, again entering

conda update conda

on the commmand line.

Next, we will update the packages that came with the Anaconda distribution. To do this, enter the following on the command line:

conda update --all

If anything is out of date, you will be prompted to perform the updates, and press y to continue. (If everything is up to date, you will just see a list of all the installed packages.) They may even be some downgrades. This happens when there are package conflicts where one package requires an earlier version of another. conda is very smart and figures all of this out for you, so you can almost always say “yes” (or “y”) to conda when it prompts you.

Installations

There are several additional installations of Python packages you need to do for the workshop. Many of these packages are available through conda. First, we need to install jupyter_bokeh, which allows Bokeh plots to be displayed withing Jupyter notebooks. Do the following on the command line.

conda install -c bokeh jupyter_bokeh

Now, we can proceed with the rest of our installations. Colorcet is a useful package for color schemes.

conda install colorcet

There are a few other packages from pip we will need for the workshop, so we can go ahead and install those now.

pip install iqplot multiprocess bebi103 watermark blackcellmagic jupyterlab-spellchecker

You should close your JupyterLab session and terminate Anaconda Navigator after you have completed the build. Relaunch Anaconda Navigator and launch a fresh JupyterLab instance. As before, after JupyterLab launches, launch a new terminal window so that you can proceed with setting up Git.

Data sets

You should make sure you have all of the data sets we will use in the workshop downloaded to your machine. You can download all of the data sets from this link. To match the notes for the workshop, I advise the following directory structure.

pol-stats-workshop/
    data/
    lessons/
    exercises/

That way, when you are working on a lesson or exercise, the path to the data directory is always ../data/.

Checking your distribution

We’ll now run a quick test to make sure things are working properly. = We will make a quick plot that requires some of the scientific libraries we will use in the workshop.

Use the JupyterLab launcher (you can get a new launcher by clicking on the + icon on the left pane of your JupyterLab window) to launch a notebook. In the first cell (the box next to the [ ]: prompt), paste the code below. To run the code, press Shift+Enter while the cursor is active inside the cell. You should see a plot that looks like the one below. If you do, you have a functioning Python environment for scientific computing!

[2]:

import numpy as np
import bokeh.plotting
import bokeh.io

bokeh.io.output_notebook()

# Generate plotting values
t = np.linspace(0, 2 * np.pi, 200)
x = 16 * np.sin(t) ** 3
y = 13 * np.cos(t) - 5 * np.cos(2 * t) - 2 * np.cos(3 * t) - np.cos(4 * t)

p = bokeh.plotting.figure(height=250, width=275)
p.line(x, y, color="red", line_width=3)
text = bokeh.models.Label(x=0, y=0, text="Physics of Life", text_align="center")
p.add_layout(text)

bokeh.io.show(p)

Loading BokehJS ...

Computing environment

[2]:

%load_ext watermark
%watermark -v -p numpy,bokeh,jupyterlab

Python implementation: CPython
Python version       : 3.9.12
IPython version      : 8.3.0

numpy     : 1.21.5
bokeh     : 2.4.2
jupyterlab: 3.3.2