Development Environment Setup

Materials¶

• Slides: Foundations of Scientific Computing

Learning Objectives¶

By the end of this lecture, students will be able to:

1. Create and configure a GitHub account

2. Install and configure VS Code with Python extensions

3. Install the uv package manager on their operating system

4. Initialize a Python project with uv

5. Add scientific computing packages to their project

6. Run Python scripts and Jupyter notebooks

Prerequisites¶

Part 1: GitHub Account Setup¶

Why GitHub?¶

GitHub provides version control and collaboration tools essential for modern computational work. We will use it to:

• Submit assignments

• Track changes to your code

• Collaborate on projects

• Build a portfolio of your work

Creating an Account¶

1. Navigate to github.com

2. Click “Sign up”

3. Use your JHU email address for educational benefits

4. Choose a professional username (this will be visible to future employers)

5. Complete email verification

GitHub Student Developer Pack¶

Installing GitHub Desktop¶

GitHub Desktop provides a graphical interface for Git, making version control more accessible.

1. Download from desktop.github.com

2. Install and sign in with your GitHub account

3. Grant permissions when prompted

GitHub Desktop handles authentication automatically, so you won’t need to configure SSH keys or tokens.

Configuring Git (Optional)¶

git config --global user.name "Your Name"
git config --global user.email "your-email@jhu.edu"

Part 2: Installing VS Code¶

Visual Studio Code is a free, powerful code editor with excellent support for Python, Jupyter notebooks, and MyST Markdown.

Download and Install¶

1. Download from code.visualstudio.com

2. Run the installer for your operating system

3. Launch VS Code after installation

Recommended Extensions¶

Install these extensions from the Extensions sidebar (Ctrl+Shift+X / Cmd+Shift+X):

To install an extension, search for its name and click Install.

Part 3: Installing uv¶

uv is a fast Python package and project manager written in Rust. It replaces pip, virtualenv, and pyenv with a single tool.

Windows Installation¶

Open PowerShell and run:

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

macOS Installation¶

Open Terminal and run:

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

Linux Installation¶

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

Verifying Installation¶

Confirm uv is installed correctly:

uv --version

Part 4: Creating Your First Project¶

Directory Structure¶

We will use a consistent directory structure throughout the course. Create a github folder in your home directory to store all your repositories:

macOS/Linux:

mkdir -p ~/github
cd ~/github

Windows (PowerShell):

mkdir ~\github
cd ~\github

Initialize a New Project¶

Create your course repository folder and initialize it:

mkdir moma
cd moma
uv init

Your full path will be ~/github/moma, which will later sync with your GitHub repository online.

This creates:

• pyproject.toml — project configuration and dependencies

• .python-version — specifies the Python version

• main.py — a sample Python file

• README.md — project documentation

• .gitignore — files to exclude from version control

Project Structure¶

~/github/
└── moma/
    ├── .gitignore
    ├── .python-version
    ├── README.md
    ├── main.py
    └── pyproject.toml

Part 5: Adding Dependencies¶

Course Packages¶

Add the packages we will use throughout the course:

uv add numpy scipy matplotlib mystmd jupyter

This installs:

• numpy — numerical arrays and linear algebra

• scipy — optimization, integration, and scientific routines

• matplotlib — plotting and visualization

• mystmd — MyST Markdown for writing and publishing documents

• jupyter — interactive notebooks (includes JupyterLab, Notebook, and related tools)

Verify Installation¶

Test that packages are available:

uv run python -c "import numpy; import scipy; import matplotlib; print('Success!')"

Verify MyST is installed:

uv run myst --version

Part 6: Running Python Code¶

Using uv run¶

Execute Python scripts within the project environment:

uv run python main.py

Interactive Python¶

Start an interactive session:

uv run python

JupyterLab¶

Launch JupyterLab for interactive notebook computing:

uv run jupyter lab

This opens a browser window with the JupyterLab interface. You can also open notebooks directly in VS Code using the Jupyter extension.

Quick Test¶

In VS Code, create a new file test_setup.py in your moma folder:

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import numpy as np
import scipy.optimize as opt
import matplotlib.pyplot as plt

# Simple optimization example
f = lambda x: (x - 2)**2
result = opt.minimize_scalar(f)
print(f"Minimum at x = {result.x:.4f}")

# Plot
x = np.linspace(0, 4, 100)
plt.plot(x, f(x))
plt.xlabel("x")
plt.ylabel("f(x)")
plt.title("Quadratic Function")
plt.savefig("test_plot.png")
print("Plot saved to test_plot.png")

Run it:

uv run python test_setup.py

Summary¶

Homework¶