Learning Objectives

By completing this assignment, you will:

• Set up VS Code with GitHub Copilot as your AI pair programming environment
• Configure your Python development environment using uv
• Verify PyTorch installation and GPU access (if available)
• Practice basic Python skills needed for the course
• Learn the Git workflow used throughout this course

Instructions

Part 0: Tell Us About Yourself (10 points)

Open reflection.md and answer the questions about your background and goals. This helps us understand where you’re coming from and tailor the course.

Questions:

1. What is your program and year?
2. What programming experience do you have?
3. Have you used Git/GitHub before? If so, in what context?
4. What do you hope to learn in this course?
5. Is there a specific clinical AI application you’re interested in?

Part 1: Environment Setup (15 points)

1.1 Install VS Code and GitHub Copilot (9 points)

We’ll use Visual Studio Code as our IDE with GitHub Copilot as your AI pair programmer. This mirrors how many professional developers work today.

Step 1: Install VS Code

Download and install Visual Studio Code for your operating system.

Step 2: Get GitHub Education Benefits

As a student, you get free access to GitHub Copilot and other developer tools:

1. Go to GitHub Education
2. Click “Get your pack” and verify your student status with your Penn email
3. Once approved, Copilot will be free on your GitHub account

Step 3: Install VS Code Extensions

Open VS Code and install these extensions (Ctrl/Cmd + Shift + X):

1. Python (by Microsoft) — Python language support
2. GitHub Copilot — AI pair programming assistant
3. Jupyter — Notebook support

Step 4: Sign into GitHub Copilot

1. Click the Copilot icon in the VS Code sidebar
2. Sign in with your GitHub account (the one with Education benefits)
3. Verify Copilot is working by typing a comment like # function to calculate BMI and see if it suggests code

Deliverable: Screenshot showing VS Code with the Python and GitHub Copilot extensions installed and Copilot active.

A note on AI pair programming: Copilot is a tool to accelerate your work, not replace your learning. Use it to get unstuck, explore syntax, and see alternative approaches—but make sure you understand every line of code you submit. Your commit history should reflect your problem-solving process.

1.2 Install uv Package Manager (6 points)

uv is a fast Python package and environment manager that we’ll use throughout the course. It’s written in Rust and is significantly faster than pip—it handles both virtual environment creation and package installation in one tool.

Install it by following the instructions for your operating system:

macOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Verify the installation by running:

uv --version

Deliverable: Screenshot showing uv --version output.

1.3 Set Up Course Environment (10 points)

You’ll create one shared Python environment for the entire course. This saves disk space (PyTorch is large!) and keeps things simple.

Step 1: Create your course folder and initialize the environment

macOS/Linux:

mkdir ~/MPHY6120
cd ~/MPHY6120
uv init
uv add torch numpy pandas matplotlib jupyter ipykernel

Windows (PowerShell):

mkdir ~\MPHY6120
cd ~\MPHY6120
uv init
uv add torch numpy pandas matplotlib jupyter ipykernel

This creates:

• pyproject.toml — declares your dependencies
• uv.lock — locks exact versions (reproducibility!)
• .venv/ — virtual environment (shared by all homeworks)

Step 2: Clone your homework INTO this folder

cd ~/MPHY6120
git clone <your-hw1-repo-url>
cd hw1-python-setup-<your-username>

Your folder structure should look like:

~/MPHY6120/
├── .venv/                          ← shared environment
├── pyproject.toml
├── uv.lock
└── hw1-python-setup-<username>/    ← your homework repo
    ├── hw1_exercises.py
    └── reflection.md

Note: If you have an NVIDIA GPU and want CUDA support, follow the PyTorch installation guide for your specific setup.

Step 3: Verify your installation

cd ~/MPHY6120
uv run python -c "import torch; import numpy; import pandas; print('All packages working!')"

Deliverable: Screenshot showing successful package imports.

Part 2: Python Skills Review (40 points)

Complete the following exercises in hw1_exercises.py from your cloned repository. The file contains function stubs—replace the None values with your implementations.

How to run your code:

Option 1: Using uv run (recommended)

From your course folder (~/MPHY6120), run:

cd ~/MPHY6120
uv run python hw1-python-setup-<your-username>/hw1_exercises.py

Or from inside your homework folder:

cd ~/MPHY6120/hw1-python-setup-<your-username>
uv run python hw1_exercises.py

uv run automatically finds the .venv in the parent folder.

Option 2: In VS Code

Since the .venv is in your course folder (not inside the homework), you need to point VS Code to it:

1. Open your homework folder in VS Code
2. Press Cmd+Shift+P (Mac) or Ctrl+Shift+P (Windows) to open the Command Palette
3. Type “Python: Select Interpreter” and press Enter
4. Click “Enter interpreter path…“
5. Enter the path to your .venv folder (not the python binary inside it):

   ~/MPHY6120/.venv
   

6. Press Enter, then click the ▶️ Run button (top right) or press F5

Important: Enter the path to the .venv folder, not .venv/bin/python. VS Code needs the folder path to properly detect the environment.

Option 3: With venv activated manually

source ~/MPHY6120/.venv/bin/activate  # Mac/Linux
# or: ~\MPHY6120\.venv\Scripts\activate  # Windows
cd ~/MPHY6120/hw1-python-setup-<your-username>
python hw1_exercises.py

2.1 NumPy Array Operations (15 points)

1. Array Creation (3 pts): Create a 1D NumPy array containing the integers 1 through 10.

2. Random Matrix (3 pts): Create a 3x3 matrix of random values between 0 and 1 using np.random.rand().

3. Statistics (3 pts): For your random matrix, compute and print the mean, standard deviation, min, and max.

4. Reshaping (3 pts): Create a 1D array of 12 elements and reshape it into a 3x4 matrix.

5. Slicing (3 pts): From your 3x3 random matrix, extract a 2x2 submatrix from the top-left corner.

2.2 Pandas DataFrames (15 points)

1. Create DataFrame (3 pts): Create a DataFrame with columns patient_id, age, heart_rate, and diagnosis with at least 10 rows of sample data.

2. Add Column (3 pts): Add a new column age_group that categorizes patients as “young” (< 40), “middle” (40-65), or “senior” (> 65).

3. Filter (3 pts): Filter the DataFrame to show only patients with heart rate > 80.

4. GroupBy (3 pts): Group by diagnosis and compute the mean age and heart rate for each group.

5. Missing Data (3 pts): Introduce some NaN values into your DataFrame, then demonstrate using fillna() or dropna().

2.3 Matplotlib Visualization (10 points)

Create the following visualizations with proper labels, titles, and legends:

1. Line Plot (2.5 pts): Plot $y = x^2$ for $x \in [0, 10]$.

2. Scatter Plot (2.5 pts): Create a scatter plot of random (x, y) data with at least 50 points.

3. Histogram (2.5 pts): Generate 1000 samples from a normal distribution and plot a histogram.

4. Bar Chart (2.5 pts): Create a bar chart showing the count of patients per diagnosis from your DataFrame.

Part 3: PyTorch Basics (25 points)

Continue working in hw1_exercises.py for these exercises.

3.1 Tensor Creation (10 points)

1. From List (2 pts): Create a 2D tensor from a nested Python list.

2. Special Tensors (4 pts): Create tensors using:
   • torch.zeros(3, 4)
   • torch.ones(2, 3)
   • torch.rand(3, 3)
   • torch.arange(0, 10, 2)
3. Tensor Properties (4 pts): For each tensor you created, print its dtype, shape, and device.

3.2 Tensor Operations (10 points)

1. Element-wise (3 pts): Create two 3x3 random tensors and perform element-wise addition, multiplication, and exponentiation.

2. Matrix Operations (4 pts): Perform matrix multiplication (@ or torch.matmul) and transpose.

3. NumPy Conversion (3 pts): Convert a PyTorch tensor to a NumPy array and back. Demonstrate that changes to one affect the other (shared memory).

3.3 GPU/CUDA Check (10 points)

Write code that:

1. Check Availability (4 pts): Check if CUDA is available and print the result.

2. GPU Info (3 pts): If CUDA is available, print the GPU device name. If not, print a message saying CPU will be used.

3. Device Transfer (3 pts): Create a tensor and move it to the available device (GPU if available, otherwise CPU). Print the tensor’s device.

# Example structure
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")

x = torch.rand(3, 3).to(device)
print(f"Tensor device: {x.device}")

Submission via GitHub

All submissions for this course are done through GitHub. This mirrors real-world software development workflows and helps you build essential version control skills.

How to Submit

1. Complete your work in hw1_exercises.py (replace None values with your code)
2. Commit your changes with meaningful commit messages:

   git add hw1_exercises.py
   git commit -m "Complete NumPy exercises 2.1.1-2.1.5"
   

3. Push to GitHub before the deadline:

   git push
   

Commit Expectations

Good commit habits are part of your grade. We expect:

• Multiple commits showing your progress (not just one giant commit at the end)
• Meaningful commit messages that describe what you did (e.g., “Add patient DataFrame with age groups” not “stuff”)
• Working code in your final commit (we grade the last commit before the deadline)

Example Good Commit History

a]b3f2d1 Complete Part 3: PyTorch CUDA check
8c4e5f6 Add tensor operations and NumPy conversion
2d1a9b7 Complete Part 2: matplotlib visualizations
f7e8c3a Add pandas DataFrame exercises
9a2b4c5 Complete NumPy array operations
1e6d8f2 Verify environment setup - all packages installed

Why Git Matters

In the era of AI-assisted coding, your commit history tells the story of your work:

• It shows your problem-solving process
• It distinguishes your contributions from AI-generated code
• It’s a skill every employer expects

Grading Rubric

Resources

• Git Handbook — If you’re new to Git
• uv Documentation
• NumPy Quickstart
• Pandas Getting Started
• Matplotlib Tutorials
• PyTorch Tutorials

Troubleshooting

VS Code can’t find the Python interpreter / “No module named numpy”

Problem: You selected the interpreter but VS Code still uses the wrong Python.

Solution: When entering the interpreter path, enter the path to the .venv folder, not the python binary inside it:

1. Cmd+Shift+P (Mac) or Ctrl+Shift+P (Windows)
2. Type “Python: Select Interpreter” → Enter
3. Click “Enter interpreter path…“
4. Enter the path to your .venv folder (not .venv/bin/python):

   ~/MPHY6120/.venv
   

5. Press Enter

VS Code terminal shows (base) and ignores your interpreter

Problem: Conda’s base environment activates automatically and overrides your selection.

Solution: Disable conda auto-activation:

conda config --set auto_activate_base false

Then restart VS Code.

Can’t see the .venv folder

Problem: The folder is hidden because it starts with a dot.

Solution:

• Mac Finder: Press Cmd+Shift+. to show hidden files
• Windows Explorer: View menu → check “Hidden items”
• VS Code: The file explorer shows hidden files by default, but you may need to scroll up

uv run command not found

Problem: uv isn’t in your PATH after installation.

Solution: Close and reopen your terminal, or run:

source ~/.bashrc   # or ~/.zshrc on Mac

Tips

• Start early! Environment setup can sometimes have unexpected issues. GitHub Education verification can take 1-2 days.
• Commit often — Don’t wait until you’re done to make your first commit.
• Post on Canvas or come to office hours if you get stuck.
• If you don’t have a GPU, that’s completely fine—all coursework can be done on CPU.
• Use Copilot wisely — Let it help you learn syntax and explore approaches, but understand every line you submit.