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# NORMALTEST

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The NORMALTEST node is based on a numpy or scipy function. The description of that function is as follows: Test whether a sample differs from a normal distribution. This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D'Agostino and Pearson's _, _ test that combines skewness and kurtosis to produce an omnibus test of normality. Params: select_return : 'statistic', 'pvalue' Select the desired object to return. See the respective function docs for descriptors. a : array_like The array containing the sample to be tested. 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 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 NORMALTEST(
default: OrderedPair | Matrix,
axis: int = 0,
nan_policy: str = "propagate",
select_return: Literal["statistic", "pvalue"] = "statistic",
) -> OrderedPair | Matrix | Scalar:
"""The NORMALTEST node is based on a numpy or scipy function.

The description of that function is as follows:

Test whether a sample differs from a normal distribution.

This function tests the null hypothesis that a sample comes from a normal distribution.
It is based on D'Agostino and Pearson's _, _ test that combines skewness and kurtosis to produce an omnibus test of normality.

Parameters
----------
select_return : 'statistic', 'pvalue'
Select the desired object to return.
See the respective function docs for descriptors.
a : array_like
The array containing the sample to be tested.
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

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

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

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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