This repo contains data and code for the collaboration between UConn, TextProject, Emelex, and FSU on machine-based methods of teaching and learning concerning properties of curricular texts.

Team Members: Devin Kearns, UConn Nathan Crock, Emelex Elfrieda Hiebert, TextProject Mohammed Razzakuddin, UConn Madhuri Malyala, FSU Mario Shontz, FSU Pramodh Sairam, FSU Giulio Martini, FSU Matt Cooper Borkenhagen, FSU

Getting Started with the Development Environment

Welcome to the ConnTextUL Data Repository project. This guide will help you set up your development environment using our new Dev Container and Poetry for package management. Follow these steps to get started.

Prerequisites

• Install Docker Desktop on your machine.
• Install Visual Studio Code.
• Install the Remote - Containers extension in VS Code.

Setting Up the Development Container

1. Clone the Repository: First, clone this repository to your local machine.

git clone https://github.com/MCooperBorkenhagen/ConnTextUL_data.git
cd ConnTestUL_data

2. Open in VS Code: Open the cloned repository in Visual Studio Code.
3. Reopen in Container: VS Code may prompt you to reopen the project in a container. If so, click on "Reopen in Container". Otherwise, press F1, type "Remote-Containers: Reopen in Container", and press Enter.
4. Wait for the Container to Build: This may take a few minutes as it sets up the environment for the first time.

Working with Poetry

1. Accessing the Terminal: Once the Dev Container is running, you can access the terminal in VS Code. This terminal is already inside the container.
2. Using Poetry: To manage Python packages, use Poetry commands in the terminal. For example, to add a new package:

poetry add [package-name]

3. Verify Changes: Inspect the pyproject.toml file to ensure that the appropriate package has been added.
4. Add Dev Packages: To add a library specifically for development purposes, include the --dev flag

poetry add [package-name] --dev

Additional Features

• Jupyter Notebooks: Our Dev Container comes with Jupyter extensions. You can create and run notebooks directly in VS Code.
• R Support: For R projects, the container is pre-configured with necessary R packages.
• Linting and Formatting: Use the provided linters and formatters like Black for Python and tools for R to maintain code quality.