Merge pull request #40 from naik-aakash/main

Update general guidelines

README.md

@@ -3,18 +3,29 @@
 
 Software for automated fitting and benchmarking of ML potentials.
 
-Contributions are welcome. Please raise a pull request for contributions first. At least one person has to review the code. At the beginning, Janine will take care of the reviews.
+Contributions are welcome. Please raise a pull request for contributions first. At least one person has to review the code. In the beginning, Janine will take care of the reviews.
+
+# General Code Structure
+- We are currently aiming to follow the code structure below for each submodule (This is an initial idea; of course, this could change depending on the needs in the future)
+  - autoplex/submodule/job.py (any jobs defined will be inside this module)
+  - autoplex/submodule/flows.py (workflows defined will be hosted in this module)
+  - autoplex/submodule/utils.py (all functions that act as utilities for defining flow or job, for example, a small subtask to calculate some metric or plotting, will be hosted in this module)
 
 # General guidelines
 - variable names should be descriptive and should use snake case.
-- please use numpy docstrings (use an IDE and switch on thise docstring type; you can check examples in our code base; the doctring should be useful for other people)
-- please write unit tests (testing will be performed with pytest; please look into tests for examples)
+- If you define a Maker use python class naming convention (eg: PhononMaker, RssMaker)
+- please use numpy docstrings (use an IDE and switch on this docstring type; you can check examples in our code base; the doctring should be useful for other people)
+- ensure type hints are added for each variable, function, class, and method (helps code readability)
+- write the code in a way that users can also have control to change parameters (mainly applicable, for example, fitting protocols/ flows. In other words, avoid hardcoding. Defaults should be set, but with the possibility to modify them where required.)
+- please write unit tests. (testing will be performed with pytest; please look into tests for examples)
 - please ensure high coverage of the code based on the tests (you can test this with `coverage`)
 
 # Commit guidelines
 1. `pip install pre-commit`.
 2. Next, run `pre-commit install` (this will install all the hooks from pre-commit-config.yaml)
-3. Step 1 and 2, needs to be done only once in the local repository
-4. Procced with modifying the code and add commits as usual.
+3. Step 1 and 2 needs to be done only once in the local repository
+4. Procced with modifying the code and adding commits as usual. This should automatically run the linters.
+5. To manually run the pre-commit hooks on all files, just use `pre-commit run --all-files`
+6. To run pre-commit on a specific file, use `pre-commit run --files path/to/your/modified/module/`
 
 Please check out atomate2 for example code (https://github.com/materialsproject/atomate2)