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KURTOSISTEST

The KURTOSISTEST node is based on a numpy or scipy function. The description of that function is as follows: Test whether a dataset has normal kurtosis. This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution. Params: select_return : This function has returns multiple objects ['statistic', 'pvalue']. Select the desired one to return. See the respective function docs for descriptors. a : array Array of the sample data. axis : int or None Axis along which to compute test. Default is 0. If None, compute over the whole array 'a'. nan_policy : {'propagate', 'raise', 'omit'} Defines how to handle when input contains nan. The following options are available (default is 'propagate'): 'propagate' : returns nan 'raise' : throws an error 'omit' : performs the calculations ignoring nan values alternative : {'two-sided', 'less', 'greater'} Defines the alternative hypothesis. The following options are available (default is 'two-sided'): 'two-sided' : the kurtosis of the distribution underlying the sample is different from that of the normal distribution 'less' : the kurtosis of the distribution underlying the sample is less than that of the normal distribution 'greater' : the kurtosis of the distribution underlying the sample is greater than that of the normal distribution .. versionadded : : 1.7.0 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 KURTOSISTEST(
    default: OrderedPair | Matrix,
    axis: int = 0,
    nan_policy: str = "propagate",
    alternative: str = "two-sided",
    select_return: Literal["statistic", "pvalue"] = "statistic",
) -> OrderedPair | Matrix | Scalar:
    """The KURTOSISTEST node is based on a numpy or scipy function.

    The description of that function is as follows:

        Test whether a dataset has normal kurtosis.

        This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution.

    Parameters
    ----------
    select_return : This function has returns multiple objects ['statistic', 'pvalue'].
        Select the desired one to return.
        See the respective function docs for descriptors.
    a : array
        Array of the sample data.
    axis : int or None, optional
        Axis along which to compute test. Default is 0.
        If None, compute over the whole array 'a'.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan.
        The following options are available (default is 'propagate'):
        'propagate' : returns nan
        'raise' : throws an error
        'omit' : performs the calculations ignoring nan values
    alternative : {'two-sided', 'less', 'greater'}, optional
        Defines the alternative hypothesis.
        The following options are available (default is 'two-sided'):
        'two-sided' : the kurtosis of the distribution underlying the sample is different from that of the normal distribution
        'less' : the kurtosis of the distribution underlying the sample is less than that of the normal distribution
        'greater' : the kurtosis of the distribution underlying the sample is greater than that of the normal distribution

    .. versionadded:: 1.7.0

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

    result = scipy.stats.kurtosistest(
        a=default.y,
        axis=axis,
        nan_policy=nan_policy,
        alternative=alternative,
    )

    return_list = ["statistic", "pvalue"]
    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

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