A demo project to demonstrate the fair-data prinicples. This demo project interpolates grain sizes for different locations in the Netherlands based on a few points with measurements. Next to the interpolation, the data is written to a asci file and the results are plotted.
To install the envionment with anaconda:
conda env create -f environment.yml
To run snake make:
snakemake --cores 1
To get the data run:
dvc pull
The project organization is created with cookiecutter:
cookiecutter gl:deltares/imod/cookiecutter-reproducible-project
This results in the following structure:
.
├── AUTHORS.md
├── LICENSE
├── README.md
├── bin <- Your compiled model code can be stored here (not tracked by git)
├── config <- Configuration files, e.g., for doxygen or for your model if needed
├── data
│ ├── 1-external <- Data external to the project.
│ ├── 2-interim <- Intermediate data that has been altered.
│ ├── 3-input <- The processed data sets, ready for modeling.
│ ├── 4-output <- Data dump from the model.
│ └── 5-visualization <- Post-processed data, ready for visualisation.
├── docs <- Documentation, e.g., doxygen or scientific papers (not tracked by git)
├── notebooks <- Jupyter notebooks
├── reports <- For a manuscript source, e.g., LaTeX, Markdown, etc., or any project reports
│ └── figures <- Figures for the manuscript or reports
└── src <- Source code for this project
├── 0-setup <- Install necessary software, dependencies, pull other git projects, etc.
├── 1-prepare <- Scripts and programs to process data, from 1-external to 2-interim.
├── 2-build <- Scripts to create model specific inputm from 2-interim to 3-input.
├── 3-model <- Scripts to run model and convert or compress model results, from 3-input to 4-output.
├── 4-analyze <- Scripts to post-process model results, from 4-output to 5-visualization.
└── 5-visualize <- Scripts for visualisation of your results, from 5-visualization to ./report/figures.