UT Python Carpentries Training

Welcome to the Python Software Carpentries training. Please follow these instructions to set up your environment before the workshop.

Trainers: Robert Ohuru (Software Developer at UT-ITC) & Adhitya Bhawiyuga (PhD Candidate at UT-ITC)

Prerequisites

• Python 3.x installed on your computer
• Internet connection for downloading files
• Terminal (in Linux or MacOS), Powershell (Windows) command Line access

Installing Python 3.x

Windows

Python is not pre-installed on Windows, so you'll need to install it:

1. Download Python:

   • Visit https://www.python.org/downloads/
   • Download the latest Python 3.x installer (Python 3.11 or newer recommended)

2. Run the Installer:

   • Double-click the downloaded .exe file
   • Important: Check the box "Add Python to PATH" at the bottom
   • Click "Install Now"

3. Verify Installation:

   • Open PowerShell or Command Prompt
   • Run: python --version
   • You should see something like Python 3.11.x

macOS and Linux

Python 3 is already pre-installed on most modern macOS and Linux systems. To verify, run following command in your Terminal:

python3 --version

If Python 3 is not installed or you need a newer version:

• macOS: Download from python.org or use Homebrew: brew install python@3
• Linux: Use your package manager (e.g., sudo apt install python3 python3-pip python3-venv on Ubuntu/Debian)

Setup Instructions

1. Create a directory called swc-python, preferably in your Desktop for easier finding.

2. Download the data and code files using the following links for code and data.

3. Extract both zip files and move them to swc-python

4. Download the requirements.txt from this repository file into your swc-python directory. Alternatively, create a requirements.txt file with the following content:

   numpy
   jupyterlab
   matplotlib
   

5. Open Terminal/Powershell into swc-python directory. Then, create a Python virtual environment:

   python -m venv venv

6. Activate the virtual environment:

   On macOS/Linux:

   source venv/bin/activate

   On Windows:

   venv\Scripts\activate

   You should see (venv) appear at the beginning of your terminal prompt.

7. Install the required Python packages:

   pip install -r requirements.txt

8. Launch Jupyter Lab:

   jupyter lab

   This will open Jupyter Lab in your default web browser. You're now ready to start coding!

Verification

To verify your setup is correct, ensure:

• You are in the swc-python directory
• The virtual environment is activated (you see (venv) in your prompt)
• All packages are installed without errors
• Jupyter Lab opens in your browser

Troubleshooting

Python Command Not Found

If python doesn't work, try python3 instead:

python3 -m venv venv

Permission Issues

If you encounter permission errors during installation, ensure your virtual environment is activated.

Jupyter Lab Won't Start

Make sure you've installed all packages from requirements.txt and your virtual environment is activated.

Getting Help

If you encounter any issues during setup, please contact your instructor before the workshop begins.

Deactivating Virtual Environment

When you're done working, you can deactivate the virtual environment:

deactivate

Dataset: Arthritis Inflammation Cure

Our imaginary colleague "Dr. Maverick" has invented a new miracle drug that promises to cure arthritis inflammation flare-ups after only 3 weeks since initially taking the medication!

Naturally, we wish to see the clinical trial data, and after months of asking for the data they have finally provided us with a CSV spreadsheet containing the clinical trial data.

The CSV file contains the number of inflammation flare-ups per day for the 60 patients in the initial clinical trial, with the trial lasting 40 days.

Each row corresponds to a patient, and each column corresponds to a day in the trial. Once a patient has their first inflammation flare-up they take the medication and wait a few weeks for it to take effect and reduce flare-ups.

To see how effective the treatment is we would like to:

• Calculate the average inflammation per day across all patients.
• Plot the result to discuss and share with colleagues.

Data Format

The data sets are stored in comma-separated values (CSV) format:

• each row holds information for a single patient,
• columns represent successive days.

The first three rows of our first file look like this:

0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1
0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1

• Each number represents the number of inflammation bouts that a particular patient experienced on a given day.
• For example, value 6 at row 3 column 7 of the data set above means that the third patient was experiencing inflammation six times on the seventh day of the clinical study.