TRIM1
Download Flojoy Studio to try this app
The TRIM1 node is based on a numpy or scipy function. The description of that function is as follows:
Slice off a proportion from ONE end of the passed array distribution.
If 'proportiontocut' = 0.1, slices off 'leftmost' or 'rightmost' 10% of scores.
The lowest or highest values are trimmed (depending on the tail).
Slice off less if proportion results in a non-integer slice index (i.e. conservatively slices off 'proportiontocut'). Params: a : array_like Input array. proportiontocut : float Fraction to cut off of 'left' or 'right' of distribution. tail : {'left', 'right'} Defaults to 'right'. axis : int or None Axis along which to trim data.
Default is 0.
If None, compute over the whole array 'a'. 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 TRIM1(
default: OrderedPair | Matrix,
proportiontocut: float = 0.1,
tail: str = "right",
axis: int = 0,
) -> OrderedPair | Matrix | Scalar:
"""The TRIM1 node is based on a numpy or scipy function.
The description of that function is as follows:
Slice off a proportion from ONE end of the passed array distribution.
If 'proportiontocut' = 0.1, slices off 'leftmost' or 'rightmost' 10% of scores.
The lowest or highest values are trimmed (depending on the tail).
Slice off less if proportion results in a non-integer slice index (i.e. conservatively slices off 'proportiontocut').
Parameters
----------
a : array_like
Input array.
proportiontocut : float
Fraction to cut off of 'left' or 'right' of distribution.
tail : {'left', 'right'}, optional
Defaults to 'right'.
axis : int or None, optional
Axis along which to trim data.
Default is 0.
If None, compute over the whole array 'a'.
Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""
result = scipy.stats.trim1(
a=default.y,
proportiontocut=proportiontocut,
tail=tail,
axis=axis,
)
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