JARQUE_BERA
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The JARQUE_BERA node is based on a numpy or scipy function. The description of that function is as follows:
Perform the Jarque-Bera goodness of fit test on sample data.
The Jarque-Bera test tests whether the sample data has the skewness and kurtosis matching a normal distribution.
Note that this test only works for a large enough number of data samples (>2000) as the test statistic asymptotically has a Chi-squared distribution with 2 degrees of freedom. Params: select_return : 'jb_value', 'p' Select the desired object to return.
See the respective function docs for descriptors. x : array_like Observations of a random variable. 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 JARQUE_BERA(
default: OrderedPair | Matrix,
select_return: Literal["jb_value", "p"] = "jb_value",
) -> OrderedPair | Matrix | Scalar:
"""The JARQUE_BERA node is based on a numpy or scipy function.
The description of that function is as follows:
Perform the Jarque-Bera goodness of fit test on sample data.
The Jarque-Bera test tests whether the sample data has the skewness and kurtosis matching a normal distribution.
Note that this test only works for a large enough number of data samples (>2000) as the test statistic asymptotically has a Chi-squared distribution with 2 degrees of freedom.
Parameters
----------
select_return : 'jb_value', 'p'
Select the desired object to return.
See the respective function docs for descriptors.
x : array_like
Observations of a random variable.
Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""
result = scipy.stats.jarque_bera(
x=default.y,
)
return_list = ["jb_value", "p"]
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