SHAPIRO

Download Flojoy Studio to try this app

The SHAPIRO node is based on a numpy or scipy function. The description of that function is as follows: Perform the Shapiro-Wilk test for normality. The Shapiro-Wilk test tests the null hypothesis that the data was drawn from a normal distribution. Params: select_return : 'statistic', 'p-value' Select the desired object to return. See the respective function docs for descriptors. x : array_like Array of sample data. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'

Python Code

from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np
from typing import Literal

import scipy.stats


@flojoy
def SHAPIRO(
    default: OrderedPair | Matrix,
    select_return: Literal["statistic", "p-value"] = "statistic",
) -> OrderedPair | Matrix | Scalar:
    """The SHAPIRO node is based on a numpy or scipy function.

    The description of that function is as follows:

        Perform the Shapiro-Wilk test for normality.

        The Shapiro-Wilk test tests the null hypothesis that the data was drawn from a normal distribution.

    Parameters
    ----------
    select_return : 'statistic', 'p-value'
        Select the desired object to return.
        See the respective function docs for descriptors.
    x : array_like
        Array of sample data.

    Returns
    -------
    DataContainer
        type 'ordered pair', 'scalar', or 'matrix'
    """

    result = scipy.stats.shapiro(
        x=default.y,
    )

    return_list = ["statistic", "p-value"]
    if isinstance(result, tuple):
        res_dict = {}
        num = min(len(result), len(return_list))
        for i in range(num):
            res_dict[return_list[i]] = result[i]
        result = res_dict[select_return]
    else:
        result = result._asdict()
        result = result[select_return]

    if isinstance(result, np.ndarray):
        result = OrderedPair(x=default.x, y=result)
    else:
        assert isinstance(
            result, np.number | float | int
        ), f"Expected np.number, float or int for result, got {type(result)}"
        result = Scalar(c=float(result))

    return result

Find this Flojoy Block on GitHub