Containerized Development Environment for the Rio Grande Rift (RGR) NSF-INTERN Project

• Use PDAL to request pointclouds from The National Map and 3DEP Entwine Server
• Generate DEM grids using PDAL and GDAL-based python modules
• Compute metrics on DEMs
• Perform basic pixel classification using sci-kitlearn
• Example notebooks show analysis of Rio Grande rift normal faults and alluvial fan deposits

** Setup is tested for Windows running Docker & VSCode-Remote Containers

Required Software:

• Docker Desktop installed
• VScode
• VSCode Extensions: (Local) Docker, Dev Containers, WSL (DEV Container) Python, Pylance, isort, Docker, Jupyter

File List:

• Dockerfile -- builds a miniconda container for developing in VSCode
• devcontainer -- config files for VSCode Remote Container
• src -- folder containing DEM creation & Analysis Modules, tutorial jupyter_notebooks, test scripts, and testData
• .travis.yml, docker-compose.image.yml, docker-compose.yml -- configuration files for Docker.mini.rgr

Getting started:

• Clone this repo

For VSCode Container setup:

• Launch Docker Desktop & VSCode
• In VSCode, install extensions (listed above)
• Navigate to repo folder
• Select ><, in Remote options, select 'reopen folder in container'
• Once container is launched, add installed VSCode extensions to the Dev container
• Change Python Interpreter to 'Python 3.8 ('rgr':conda) /opt/conda/bin/python'