Please make sure to add dependencies using uv, so the project environment
is kept up to date and functional for other users.
Note that this should not be done while on the DRAC cluster and using pre-built wheels,
as those library versions do not exist elsewhere and uv will install Pypi versions,
not local versions - unless specifically configured to do.
To add a new dependency:
uv add <dependency_name>
To add a new dependency with a specific version:
uv add "<dependency_name>==<x.x.x>"
To add a new dependency and specify a version with some ability to update, use capped
versions, like so : ">=1.2.3,<1.3.0".
This is useful when you want to limit the version number but still allow bug fixes.
uv add "pandas>=2.3.0,<2.4.0"
To add a new dependency to a specific group of dependencies (for example, the development dependencies):
uv add --group dev <dependency_name>
To make a whole group optional, add the following to your pyproject.toml file, where
<group_name> is the name of your group:
[project.optional-dependencies]
If you do add dependencies directly with pip, make sure to also add them
(preferably with a version number) to the dependencies = [] section of
the pyproject.toml file.
Two main considerations should be made when contributing to this package.
First, a polymorphic approach, using abstract classes and their concrete implementation, should be prioritized in order to increase maintainability and extensibility.
Therefore, new additions should try to follow this design pattern and either implement new concrete classes or create new abstract classes and their implementations for completely new behavior or needs.
Avoid multiple levels of inheritance; the approach should be AbstractClass -> [ConcreteClass1, ConcreteClass2, ...] and not AbstractClass -> ChildClass -> GrandChildClass -> ...
Next, a dependency-injection approach is to be preferred, as well as a composition approach when creating new modules or extending existing ones.
Functional approaches are also acceptable when appropriate, but classes should still
be used for data management/representation. This can be done with either regular
classes, dataclasses, or pydantic models.
New contributions should include appropriate tests. Pytest is the preferred library to use for testing in this project.
To get started and to learn more about testing in Python:
Docstring format should follow the Google standard and type hinting should be used as per the PEP8 standard : https://docs.python.org/3/library/typing.html
Changes to this repository should be tracked in the CHANGES.md file.
Actual version handling should be done using the following targets:
