Example Code

Last updated on 2026-06-19 | Edit this page

Overview

Questions

  • What example code will we use for the lesson?
  • How do we set up the Python package prerequisites to run the code?

Objectives

  • Obtain the example code used for this lesson
  • Create a virtual environment to hold the Python packages needed to run the code
  • Open and run the example code within VSCode

Obtaining Example Code

For this lesson we’ll be using some example code available on GitHub, which we’ll clone onto our machines using the Bash shell. So firstly open a Bash shell (via Git Bash in Windows or Terminal on a Mac). Then, on the command line, navigate to where you’d like the example code to reside, and use Git to clone it. For example, to clone the repository in our home directory, and change our directory to the repository contents:

BASH 

cd
git clone https://github.com/Southampton-RSG-Training/ai-tools-example
cd ai-tools-example

Setting up a Virtual Environment

The existing code needs the NumPy and Matplotlib packages in order to run. Let’s now create a Python virtual environment and install them. Make sure you’re in the root directory of the repository, then type

BASH 

python3 -m venv venv

Once created, we can activate it so it’s the one in use:

BASH 

[Linux] source venv/bin/activate
[Mac] source venv/bin/activate
[Windows] source venv/Scripts/activate

Once activated, install the needed packages:

BASH 

python3 -m pip install numpy matplotlib

Examining the Repository

Next, let’s take a look at the contents of the repository by opening the repository directory within VSCode. After starting VSCode, you can do this in a couple of ways, either:

  1. Select the Source control icon from the middle of the icons on the left navigation bar. You should see an Open Folder option, so select that.
  2. Select the File option from the top menu bar, and select Open Folder....

In either case, you should then be able to use the file browser to locate the directory with the files you just extracted, and then select Open. Note that we’re looking for the folder that contains the files, not a specific file.

You may be presented with a window asking whether you trust the authors of this code. In general, it’s a good idea to be at least a little wary, since you’re obtaining code from the internet, so be sure to check your sources!

We also need to configure the Python extension within this workspace to use the Python contained with the virtual environment we created earlier. VSCode has a sophisticated method to access it’s inner functionality known as the Command Palette, which we’ll use to address this.

  1. Select View and Command Palette from the VSCode menu
  2. Begin typing Python: Select Interpreter, and then select it when it appears
  3. A list of available Python installations should appear. Look for and select the one that says ./venv/bin/python (our virtual environment)

Once selected, the default Python interpreter for VSCode will be configured.

So far within VSCode we have downloaded some code from a repository and opened a folder. Whenever we open a folder in VSCode, this is referred to as a “Workspace” - essentially, a collection of a project’s files and directories. So within this workspace, you’ll see the following:

  • data/ - a directory containing some example CSV files, each representing inflammation data from a series of hypothetical clinical trials for 60 patients over 40 days
  • .gitignore - a text file that contains things that shouldn’t be tracked by Git version control
  • inflammation-plot.py - which plots three graphs of the mean, maximum, and minimum values for each day of a trial for all patients

You’ll also see venv/ which is not part of the repository, but the virtual environment we created and configured earlier.

Examining the Code

Select the inflammation-plot.py file in the explorer window, which will bring up the contents of the file in the code editor.

PYTHON 

import glob

import numpy as np
from matplotlib import pyplot as plt

filenames = glob.glob('data/inflammation-*.csv')
filenames.sort()

for filename in filenames:
    print(filename)

    data = np.loadtxt(fname=filename, delimiter=',')

    fig = plt.figure(figsize=(10.0, 3.0))

    axes1 = fig.add_subplot(1, 3, 1)
    axes2 = fig.add_subplot(1, 3, 2)
    axes3 = fig.add_subplot(1, 3, 3)

    axes1.set_ylabel('average')
    axes1.plot(data.mean(axis=0))

    axes2.set_ylabel('max')
    axes2.plot(data.max(axis=0))

    axes3.set_ylabel('min')
    axes3.plot(data.min(axis=0))

    fig.tight_layout()
    fig.savefig(filename + '.png')

The aim will be to use GitHub Copilot within VSCode to improve this script throughout the session.

Note that as an example, the code is deliberately written to have flaws. Things like the line spacing is inconsistent, there are no code comments, there’s some code duplication, and you may spot other issues too. It’s also deliberately been kept relatively simple. This is for two reasons:

  • Most importantly, from a training perspective, when we use Copilot later to suggest changes, we’ll be able to quickly reason about the changes and how they impact the codebase
  • To give us enough scope to improve it

But in essence, the code is designed to do the following:

  • Loop through a list of all inflammation data files (sorted by their filename) in the data/ subdirectory
  • For each file, load the data into a Numpy array
  • For that array, create a plot containing three graphs, one for each of the mean, minimum and maximum of the data
  • Save the plot image to a file (essentially the same path and filename, with a .png added to it)

Running the Example Code

Next, you may recall we needed NumPy and Matplotlib to run this code; if you look at the the bottom right of VSCode’s status bar, it should mention the version of Python being used, e.g. 3.14.2 (venv). So here, we can see that VSCode has picked up our virtual environment we configured earlier and will use that by default.

Then, select the “Play”-looking icon at the top right of the code editor.

You should see the program run in a terminal window that appears at the bottom, along with the following output from the program:

OUTPUT 

data/inflammation-01.csv
data/inflammation-02.csv
data/inflammation-03.csv
data/inflammation-04.csv
data/inflammation-05.csv
data/inflammation-06.csv
data/inflammation-07.csv
data/inflammation-08.csv
data/inflammation-09.csv
data/inflammation-10.csv
data/inflammation-11.csv
data/inflammation-12.csv

After it’s completed, we should see corresponding plot image files in the data directory, essentially each with a .png on the end. For example, inflammation-01.csv.png looks like:

Plot generated from data/inflammation-01.csv
Plot generated from data/inflammation-01.csv
Callout

Error: the term conda is not recognised

If you’re running an Anaconda distribution of Python on Windows, if you see this error it means that VSCode is not looking in the right place for Anaconda’s installation. In this case, you may need to configure VSCode accordingly:

  1. Activate the Command Palette, either by selecting View and Command Palette in the menu, or by pressing Ctrl + Shift + P (Linux), Mac/Windows Key + Shift + P (Mac/Windows) simultaneously
  2. Type Terminal: Select Default Profile
  3. From the options, select the entry that’s something like Command Prompt C:\WINDOWS\...

Hopefully that should resolve the issue.

Callout

What Does the Patient Inflammation Data Contain?

Each dataset file in data/ records inflammation measurements from a separate clinical trial of the drug, and each dataset contains information for 60 patients, who had their inflammation levels recorded (in some arbitrary units of inflammation measurement) for 40 days whilst participating in the trial.

These datasets are reused from the Software Carpentry Python novice lesson.

Snapshot of the inflammation dataset

Each of the data files uses the popular comma-separated (CSV) format to represent the data, where:

  • each row holds inflammation measurements for a single patient
  • each column represents a successive day in the trial
  • each cell represents an inflammation reading on a given day for a patient
Key Points
  • The example Python code generates a basic set of statistical plots for all data files within the data/ directory.
  • The code has a number of deliberate issues that we want to resolve throughout this lesson using GitHub Copilot.