Using git to obtain tutorial material

While you can manually download all necessary code and data files from our gitHub repository, we strongly recommend to clone this repository with git to obtain a local, sychronised copy of all material. Assuming you have a working git installation, you can do this by executing the following command in the terminal:

git clone --depth 1 https://github.com/IngoScholtes/csh2018-tutorial

The option --depth 1 ensures that you only get the latest version, ignoring the history of the repository. If you don’t have git installed already, here you can find information on how to set up git.

In the code directory of the repository, you will find the code files that we will work through during the tutorial. These files contain explanations, as well as python code that we will explain during the live coding sessions. As we make changes to these files, you can sync your local copy with the tutor. Just execute the terminal command

git pull

to receive the latest updates. If you are using Visual Studio Code (see below) this is even easier: Just click the sync symbol in the status bar to update the current sample solution shown on the tutor’s screen!

Installing python 3.X

To complete the hands-on exercises, you will need a working python 3.x environment running on an operating system of your choice. For Windows, MacOS, and Linux users we recommend Anaconda 5.2 distribution, an OpenSource python 3.6 distribution that comes pre-configured for data science and machine learning tasks.

The only additional package that you may need for this tutorial is the package markdown. We use it to produce nicely formatted output with the python skeleton files. You can just install it by typing:

pip install markdown

Installing Visual Studio Code

To complete the exercises, we recommend using the development environment Visual Studio Code, a platform-independent Open Source code editor available for Windows, MacOS, and Linux. Just download the installation file and run the setup. Once the installation has completed, run Visual Studio Code either by clicking the icon or by typing code in the terminal.

To conveniently work with python and jupyter notebooks in Visual Studio Code, we recommend two extensions, which you can install free of charge directly from Visual Studio Code’s integrated extension manager. We will need the official Python extension, which adds python code editing, debugging, and linting functionality. We further need the Jupyter extension, which provides a convenient interface to the jupyter notebook server automatically installed by Anaconda 5.2.

To install these two extensions, click the “module” icon in the bottom of the left menu bar or press Ctrl+Shift+X. This will bring up the Extensions window. Type python and click the top-most search result Python 2018.7.1. In the window on the right, click install. Repeat this procedure with the jupyter extension, i.e. search for jupyter, click the top-most result Jupyter 1.1.4 by user Don Jayamanne and install the extension. A restart of Visual Studio Code completes the installation.

Once the installation is finished, open Visual Studio Code, click File -> Open Folder and navigate to your local copy of the cloned github repository. In the Explorer panel (the files symbol in the left bar) you can then find the notebook files that you need to complete the tutorial.

Conveniently, Visual Studio Code comes with integrated support for git. This means you can fetch the current, growing sample solution simply by navigating to the Source Control panel (the fork symbol in the left bar). In the … menu extension you just have to click Pull.

Setting up pathpy

pathpy is pure python code and is available under an OpenSource license. It has no platform-specific dependencies and thus work on all platforms. It depends on numpy and scipy which come preinstalled in the Anaconda 5.2 environment. Assuming that you have python 3.x environment, the latest version of pathpy can be installed via the python package index pypi. Just open a terminal window and run the command:

pip install pathpy2

Unfortunately, the pypi name pathpy has been name-squatted after we had released pathpy to the pypi test server. While we are working with the pypi administrators to resolve this issue, we have to use pypi name pathpy2 instead. So make sure that you install the pypi package pathpy2 rather than the empty (spam) package pathpy.

Verifying your environment

Now that we have installed all necessary tools and packages, let us verify that our environment is set up properly.

For this, you can either create a new file in Visual Studio Code and copy the following code:

#%%
import pathpy as pp
paths = pp.Paths()
paths.add_path('a,b,c')
#%%
print(paths)

Or you can open your local copy of the tutorial repository in Visual Studio Code as described above. In the code directory, you will find the file 0_test_environment.py, which contains the lines above.

If the python extension of Visual Studio Code has been installed properly, you should see the python code properly highlighted and colored. If the jupyter extension has been set up properly, two code lenses Run cell will appear above the #%% tags. These tags mark the start of a cell in a jupyter notebook that we can execute directly fron within Visual Studio Code.

Click the top-most Run cell code lens. A menu will appear, asking you whether to start a new notebook or whether to select an existing jupyter notebook server. Select Start a new Notebook and wait for the status line Python 3 Kernel (idle) to appear in Visual Studio Code’s status bar. Now click the second Run cell code lens. A new window should pop up that shows the output of your code, in our example a list frequencies of paths of different lengths.

If you see this output, all is set up properly, and you are all set to complete the first two sessions of the hands-on tutorial.