Setup

1) Code

Setup begins with downloading all the code from the GitHub repository. Open up a terminal in the folder where you intend to set up the project. You can download the codebase using git as follows in the terminal:

Clone the GitHub repository:

git clone https://github.com/Brent-Murray/DeepLearningEFI

Change to the repository directory:

cd DeepLearningEFI

2) Data

The preprocessed data can be downloaded here. Ensure the data is stored in the same folder where your code is stored.

For specific data preprocessing information, see the Data Section

3) Python Environment Setup

Environment setup is performed using the uv package manager which will need to be installed to run the tutorial code.

The uv package manager allows for the installation of specific package versions to maintain reproducibility. The list of packages and their versions is included in the pyproject.toml file.

This code has been tested on Windows 11 and Ubuntu 24.04.2

Install uv package manager by following the instructions at https://docs.astral.sh/uv/getting-started/installation

On a Windows system, this can be done by running the following command in PowerShell:
```
-ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```
On a Linux or MacOS system, run the following command in the terminal:
```
curl -LsSf https://astral.sh/uv/install.sh | sh
```
Open a terminal and check that uv is installed by running
```
uv --help
```
Ensure uv is up-to-date by running
```
uv self update
```
Intialize the uv environment and install dependencies.
```
uv sync
```

4) Directory structure

After downloading the data and setting up the python environment (see above), your directory structure should include the following (among other miscellaneous files):

01_intro.qmd
02_data.qmd
03_train.qmd
04_evaluation.qmd
05_examples.qmd
compute_lidar_metrics.qmd
data
pyproject.toml
random_forest.qmd
schedule.qmd
setup.qmd
src
uv.lock
.venv