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PYGDAL-GEO-ENGINEER

repo-status: work in progress

A collection of scripts to perform geospatial engineering tasks using GDAL, Python and SQL. The scripts are organized in a modular way under src/ and are designed to be run from the command line or within a Jupyter notebook.

Getting Started

This project utilizes GDAL, known for its complex installation. To simplify this, a Dockerfile is included, leveraging a GDAL image to run the project within a container. Alternatively, if GDAL bindings are already installed on your machine, the pyproject.toml file can be used to set up a virtual environment.

Virtual Environment

You can use the pyproject.toml file to set up a virtual environment. This ensures all Python dependencies and the scripts in src\py-scripts are installed in the environment.

To set up a Poetry virtual environment, follow these steps:

# Navigate to your project directory where your pyproject.toml is located
cd /path/to/your/project

# Install the project dependencies
poetry install

# Activate the virtual environment
poetry shell
# Create a virtual environment
python3 -m venv .venv
# Activate the virtual environment
source .venv/bin/activate
# Install the requirements
pip install -r requirements.txt

Docker

The project root contains a Dockerfile that utilizes a GDAL image and automatically installs dependencies from pyproject.toml. This Dockerfile also configures a Jupyter server, enabling the execution of notebooks within the server environment.

Build the Docker image

```bash
docker build -t gdal-python:0.0.1 .
```

Run the Docker image

There are several methods to interact with and run the code within the container. You can utilize Visual Studio Code or a Jupyter server for an interactive approach, or execute scripts directly via the command line.

Please note, the container does not include any data. Ensure to mount your data into the container accordingly.

1. Execute in a Container via Visual Studio Code

  • Add the volumes to the VS Code docker configuration file devcontainer.json. See the template here.

  • Open the command palette and select "Remote-Containers: Reopen in Container"

  • Exit the container by clicking the "Remote-Containers: Reopen Locally" button in the bottom right corner of the window.

2. Launch a Jupyter Notebook via Docker Compose

  • Run the container with docker-compose: docker-compose up
  • The docker-compose.yml file is configured to mount the data/ and config/ directories to the container.
  • The Jupyter notebook server is available at http://localhost:8888
  • The authentication token is printed in the terminal when the server starts http://localhost:8888/tree?token=...
  • Exit the server by pressing Ctrl+C in the terminal and then running docker-compose down

3. Run Scripts or Notebooks from the Command Line

# $PWD is the current working directory
docker run -it --rm -v $(pwd)/data:/data -v $(pwd)/src:/src gdal-python:0.0.1 /bin/bash

# ls / to see the mounted volumes
ls /data
ls /src

# Run the script
cd /src/shell
./gdal_filegdb-gpkg.sh

# Exit the container
exit

Scripts and Notebooks

More information about the standalone scripts and Jupyter notebooks can be found in the src/README file and the notebooks/README file.

For each project a new notebook is created in the notebooks/ directory. The notebooks are designed to be run in a Jupyter environment and they utilize the scripts in the src/ directory.

Note: If you run the notebooks in Colab, you will need to upload the /data and /src directories to the Colab environment.

# upload the src/ directory to the Colab environment
from google.colab import files
uploaded = files.upload()

# unzip the file
!unzip src.zip
!ls src