Update README

README.md

@@ -4,7 +4,16 @@ license: mit
 
 # WannierDatasets
 
-Datasets for playing with Wannier functions.
+Datasets of input files for Wannier functions.
+
+## List of datasets
+
+- `Si2_valence`: Silicon valence band only
+- `Si2`: Silicon valence and conduction bands
+- `Cu`: copper, metal
+- `CrI3`: chromium triiodide, magnetic calculation
+
+## Why this repo?
 
 Specifically, this repo
 
@@ -30,7 +39,6 @@ to manage the datasets. This allows us to
 - [`datasets/`](./datasets/) each subfolder contains a dataset for a specific system
 - [`pseudo/`](./pseudo/) pseudopotentials used when generating the datasets
 - [`src/`](./src/) a fake folder just to make `Project.toml` happy
-- [`util/`](./util/) Several small scripts that help with running the examples
 
 ## Contributing
 

datasets/README.md

@@ -1,33 +0,0 @@
-# Datasets
-
-## List of datasets
-
-- `Si2_valence`: Silicon valence band only
-- `Si2`: Silicon valence and conduction bands
-- `Cu`: copper, metal
-- `CrI3`: chromium triiodide, magnetic calculation
-
-## Dataset generation
-
-These files are generated by the respective `creator/run.sh` script in each subdirectory.
-The `creator` subdirectory contains all the script and input files for DFT codes
-to generate the Wannier input `amn/mmn/eig/...` files.
-
-The Fortran binary (also called unformatted) files are written by QE binaries
-which are compiled with
-
-```bash
-GNU Fortran (Ubuntu 11.2.0-19ubuntu1) 11.2.0
-```
-
-To add a new dataset:
-
-- Create a new subdirectory, e.g. `Si2`
-- Put all the input files for the DFT code in the subdirectory `Si2/creator/`
-- Create a `run.sh` script in `creator/` which runs the DFT code and generates the `amn/mmn/eig/...` files
-- Move the `amn/mmn/eig/...` files to `Si2/`
-- (Optional) Create a `README.md` file in `Si2/` which describes the dataset
-- (Optional) Add reference results in a subdirectory `Si2/reference/`
-
-Out goal is that the `run.sh` script should be able to reproduce the `amn/mmn/eig/...` files,
-on any machine. So we can easily regenerate the dataset if we need to.

docs/CONTRIBUTING.md

@@ -11,6 +11,32 @@ In general, we would like to
     to ensure reproducibility. This also allows us to regenerate the datasets
     if needed.
 
+## Dataset generation
+
+Each folder in [`../datasets`](../datasets) folder is a standalone dataset
+for one material, these files are generated by the respective `inputs/run.sh`
+script. The `inputs` subdirectory contains all the script and input files for
+DFT codes to generate the Wannier input `amn/mmn/eig/...` files.
+
+The Fortran binary (also called unformatted) files are written by QE binaries
+which are compiled with
+
+```bash
+GNU Fortran (Ubuntu 11.2.0-19ubuntu1) 11.2.0
+```
+
+To add a new dataset:
+
+- Create a new subdirectory, e.g. `Si2`
+- Put all the input files for the DFT code in the subdirectory `Si2/inputs/`
+- Create a `run.sh` script in `inputs/` which runs the DFT code and generates the `amn/mmn/eig/...` files
+- Move the `amn/mmn/eig/...` files to `Si2/`
+- (Optional) Create a `README.md` file in `Si2/` which describes the dataset
+- (Optional) Add reference results in a subdirectory `Si2/outputs/`
+
+Out goal is that the `run.sh` script should be able to reproduce the `amn/mmn/eig/...` files,
+on any machine. So we can easily regenerate the dataset if needed.
+
 ## All Code Changes Happen Through Pull Requests
 
 Pull requests are the best way to propose changes to the codebase.

docs/large_files.md

@@ -1,31 +0,0 @@
-# Notes on large files
-
-## `util/GitHub-ForceLargeFiles`
-
-GitHub has a limit of 100MB per file, to bypass this limit, there is a
-python script `util/GitHub-ForceLargeFiles/src/main.py` that will auto
-compress large files and split them into chunks.
-
-To use it
-
-```shell
-python util/GitHub-ForceLargeFiles/src/main.py DIR_TO_CHECK
-```
-
-where `DIR_TO_CHECK` is the directory to check for large files.
-
-To decompress the files, use
-
-```shell
-python util/GitHub-ForceLargeFiles/src/reverse.py DIR_TO_CHECK
-```
-
-For more information, see the two scripts.
-
-## Adding large dataset files
-
-Therefore, in general we should avoid adding large files. However, if e.g.
-without enough kpoint sampling the band structure is really poor, then we
-can use `main.py` script to compress the files and git commit the 7z files.
-In the GitHub workflow, the `reverse.py` script will be run to decompress
-the files and pack them into artifact tarballs.