Tools for data curation in the Polaris ecosystem.
from auroris.curation import Curator
from auroris.curation.actions import MoleculeCuration, OutlierDetection, Discretization
# Define the curation workflow
curator = Curator(
steps=[
MoleculeCuration(input_column="smiles"),
OutlierDetection(method="zscore", columns=["SOL"]),
Discretization(input_column="SOL", thresholds=[-3]),
],
parallelized_kwargs = { "n_jobs": -1 }
)
# Run the curation
dataset, report = curator(dataset)A Curator object is serializable, so you can save it to and load it from a JSON file to reproduce the curation.
auroris [config_file] [destination] --dataset-path [data_path]
Please refer to the documentation, which contains tutorials for getting started with auroris and detailed descriptions of the functions provided.
You can install auroris using conda/mamba/micromamba:
conda install -c conda-forge aurorisYou can also use pip:
pip install aurorisconda env create -n auroris -f env.yml
conda activate auroris
pip install --no-deps -e .Other installation options
Alternatively, using [uv](https://github.com/astral-sh/uv):
```shell
uv venv -p 3.12 auroris
source .venv/auroris/bin/activate
uv pip compile pyproject.toml -o requirements.txt --all-extras
uv pip install -r requirements.txt
```
You can run tests locally with:
pytestUnder the Apache-2.0 license. See LICENSE.
