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TTEST_1SAMP

The TTEST_1SAMP node is based on a numpy or scipy function. The description of that function is as follows: Calculate the T-test for the mean of ONE group of scores. This is a test for the null hypothesis that the expected value (mean) of a sample of independent observations 'a' is equal to the given population mean, 'popmean'. Params: select_return : 'statistic', 'pvalue' Select the desired object to return. See the respective function docs for descriptors. a : array_like Sample observation. popmean : float or array_like Expected value in null hypothesis. If array_like, then it must have the same shape as 'a' excluding the axis dimension. 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' : raises 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 mean of the underlying distribution of the sample is different than the given population mean (`popmean`) 'less' : the mean of the underlying distribution of the sample is less than the given population mean (`popmean`) 'greater' : the mean of the underlying distribution of the sample is greater than the given population mean (`popmean`) .. versionadded : : 1.6.0 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 TTEST_1SAMP(
    default: OrderedPair | Matrix,
    popmean: float = 0.1,
    axis: int = 0,
    nan_policy: str = "propagate",
    alternative: str = "two-sided",
    select_return: Literal["statistic", "pvalue"] = "statistic",
) -> OrderedPair | Matrix | Scalar:
    """The TTEST_1SAMP node is based on a numpy or scipy function.

    The description of that function is as follows:

        Calculate the T-test for the mean of ONE group of scores.

        This is a test for the null hypothesis that the expected value (mean) of a sample of independent observations 'a' is equal to the given population mean, 'popmean'.

    Parameters
    ----------
    select_return : 'statistic', 'pvalue'
        Select the desired object to return.
        See the respective function docs for descriptors.
    a : array_like
        Sample observation.
    popmean : float or array_like
        Expected value in null hypothesis.
        If array_like, then it must have the same shape as 'a' excluding the axis dimension.
    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' : raises 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 mean of the underlying distribution of the sample
        is different than the given population mean (`popmean`)
        'less' : the mean of the underlying distribution of the sample is
        less than the given population mean (`popmean`)
        'greater' : the mean of the underlying distribution of the sample is
        greater than the given population mean (`popmean`)

    .. versionadded:: 1.6.0

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

    result = scipy.stats.ttest_1samp(
        a=default.y,
        popmean=popmean,
        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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