Aller au contenu

# SEM

Ce contenu n’est pas encore disponible dans votre langue.

The SEM node is based on a numpy or scipy function. The description of that function is as follows: Compute the standard error of the mean. Calculate the standard error of the mean (or standard error of measurement) of the values in the input array. Params: a : array_like An array containing the values for which the standard error is returned. axis : int or None Axis along which to operate. Default is 0. If None, compute over the whole array 'a'. ddof : int Delta degrees-of-freedom. How many degrees of freedom to adjust for bias in limited samples relative to the population estimate of variance. Defaults to 1. 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

import scipy.stats

@flojoy
def SEM(
default: OrderedPair | Matrix,
axis: int = 0,
ddof: int = 1,
nan_policy: str = "propagate",
) -> OrderedPair | Matrix | Scalar:
"""The SEM node is based on a numpy or scipy function.

The description of that function is as follows:

Compute the standard error of the mean.

Calculate the standard error of the mean (or standard error of measurement) of the values in the input array.

Parameters
----------
a : array_like
An array containing the values for which the standard error is returned.
axis : int or None, optional
Axis along which to operate.
Default is 0.
If None, compute over the whole array 'a'.
ddof : int, optional
Delta degrees-of-freedom. How many degrees of freedom to adjust
for bias in limited samples relative to the population estimate of variance.
Defaults to 1.
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.sem(
a=default.y,
axis=axis,
ddof=ddof,
nan_policy=nan_policy,
)

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


Find this Flojoy Block on GitHub