TRIMBOTH
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The TRIMBOTH node is based on a numpy or scipy function. The description of that function is as follows:
Slice off a proportion of items from both ends of an array.
Slice off the passed proportion of items from both ends of the passed array
(i.e., with 'proportiontocut' = 0.1, slices leftmost 10% and rightmost 10% of scores).
The trimmed values are the lowest and highest ones.
Slice off less if proportion results in a non-integer slice index (i.e. conservatively slices off 'proportiontocut'). Params: a : array_like Data to trim. proportiontocut : float Proportion (in range 0-1) of total data set to trim of each end. 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 TRIMBOTH(
default: OrderedPair | Matrix,
proportiontocut: float = 0.1,
axis: int = 0,
) -> OrderedPair | Matrix | Scalar:
"""The TRIMBOTH node is based on a numpy or scipy function.
The description of that function is as follows:
Slice off a proportion of items from both ends of an array.
Slice off the passed proportion of items from both ends of the passed array
(i.e., with 'proportiontocut' = 0.1, slices leftmost 10% and rightmost 10% of scores).
The trimmed values are the lowest and highest ones.
Slice off less if proportion results in a non-integer slice index (i.e. conservatively slices off 'proportiontocut').
Parameters
----------
a : array_like
Data to trim.
proportiontocut : float
Proportion (in range 0-1) of total data set to trim of each end.
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.trimboth(
a=default.y,
proportiontocut=proportiontocut,
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