# SHAPIRO

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


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