scoretree 1.1.0

Easy to use, multi-level grade weighting system

ScoreTree

ScoreTree is an easy to use, multi-level grade weighting system that serves as excellent tool for cascade grading methods.

Installation

This package has been released through PyPI, so it can be installed using Python's pip module:

python -m pip install scoretree

[!NOTE] The command above asumes that your Python interpreter is aliased to python and references a version equal to or greater than 3.10

Alternatively, it is also possible to clone this repository and install the package via pip and/or build.

Usage

Here is an usage example. Feel free to take a look at other examples in the corresponding section of the repository.

from scoretree import Score, ScoreArea, ScoreTree

# Define a score tree:
st = ScoreTree([
    # Add a list of areas to be evaluated:
    ScoreArea(name=f"Simulation", weight=1, items=[
        # Each area can contain other areas and/or scores:
        ScoreArea("Stop maneuver", .2, [
            # Scores are the minimal grading unit:
            Score(
                name="Distance to end",
                weight=.8,
                score_range=(0, 10),
                value=9.8848,
                inverse=True
            ),
            Score("Deceleration intensity", .2, (0, 20), value=185555)
        ]),
        # Different instance creation syntax:
        ScoreArea("Track performance", .8, [
            Score("Speed", .3, (0, 20), 5),
            ScoreArea("Efficiency", .7, [
                Score("Track time", .5, (0, 40), 30),
                Score("Track distance", .5, (0, 200), value=10, inverse=True)
            ])
        ])
    ])
], colorized=True)  # Enable or disable colorized output.

print(f"Total simulation score: {st.score}")

This would be the colorized output for the code snippet above:

Contributing

Since this is a very small project that can be easily improved and can expand its functionality way further down the development process, any contributions, suggestions or bug reports are more than welcome!