Contributing¶

We'd love to hear from you! Feel free to open an issue or a PR if you have any suggestions or find any bugs.

When contributing, please add tests to the tests directory. Additionally, we follow flake8 as a linter and pytest for testing. Please lint your code before submitting a pull request to maintain design consistency. For documentation, please write docstrings following the Google style guide. We use a variant of this style guide for docstrings as shown below:

def mean_tf(x: np.ndarray, where: Callable[[Union[int, float, np.int_, np.float_]], Union[bool, np.bool_]] = lambda x: not np.isnan(x)) -> Union[float, np.float_]:
    """
    Compute the mean of the values in `x` where `where` is `True`.

    Args:
        x: The array to compute the mean of.

        where: A function that takes a value and returns `True` or `False`. Default is `lambda x: not np.isnan(x)` i.e. a measurement is valid if it is not a `NaN` value.

    Returns:
        The mean of the values in `x` where `where` is `True`.
    """
    # Vectorize where fn
    where_fn = np.vectorize(pyfunc=where)
    return np.mean(x, where=where_fn(x))

We also encourage using type annotations where possible. For example, the mean_tf function above has type annotations for the input and output types.

The following commands can be run for verficiation before opening a PR:

# Unit tests
python -m pytest tests

# Linting
flake8 autonfeat --ignore=E501

If you enjoy using AutonFeat, please consider starring the repository ⭐️.