This tutorial was generated from an Jupyter notebook. You can download the notebook here.
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.
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.
Before we get rolling with the Anaconda distribution, we have some considerations and installations to get out of the way first.
If you are using macOS, you should install XCode, if you haven't already. It's a large piece of software, taking up about 5GB on your hard drive, so make sure you have enough space. 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. After these are completed, you can close XCode.
Unless you have experience with Anaconda and know how to set up environments, if you have previously installed Anaconda with a version of Python other than 3.7, 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 is simple.
That's it! After you do that, you will have a functioning Python distribution.
After installing the Anaconda distribution, you should be able to launch the Anaconda Navigator. If you are using macOS, this is available in your
Applications menu. If you are using Windows, you can do this from the Start menu. Launch Anaconda Navigator.
We will be using JupyterLab throughout the course. You should see an option to launch JupyterLab. 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. 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.
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 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.
There are several additional installations you need to do. First, to configure JupyterLab, we need to install node.js.
conda install nodejs
We will also install watermark, which enables us to conveniently display version numbers of the software we are using. For this installation, we will use
pip. There are a few other packages from pip we will need, so we can go ahead and install those now.
pip install watermark black blackcellmagic bokeh-catplot bi1x
Finally, we need to configure JupyterLab to work with the plotting packages we will use.
jupyter labextension install --no-build @pyviz/jupyterlab_pyviz
You may also wish to install a spell-checker (this one isn't necessary).
jupyter labextension install --no-build @ijmbarr/jupyterlab_spellchecker
After installing all of these extensions, you can rebuild JupyterLab.
jupyter lab build
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.
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.
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!
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='Bi 1x', text_align='center') p.add_layout(text) bokeh.io.show(p)
%load_ext watermark %watermark -v -p numpy,bokeh,jupyterlab
CPython 3.7.7 IPython 7.13.0 numpy 1.18.1 bokeh 2.0.0 jupyterlab 1.2.6