TMAX
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The TMAX node is based on a numpy or scipy function. The description of that function is as follows:
Compute the trimmed maximum.
This function computes the maximum value of an array along a given axis, while ignoring values larger than a specified upper limit. Params: a : array_like Array of values. upperlimit : None or float Values in the input array greater than the given limit will be ignored.
When upperlimit is None, then all values are used.
The default value is None. axis : int or None Axis along which to operate.
Default is 0.
If None, compute over the whole array 'a'. inclusive : {True, False} This flag determines whether values exactly equal to the upper limit are included.
The default value is True. 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 TMAX(
default: OrderedPair | Matrix,
upperlimit: float = 0.1,
axis: int = 0,
inclusive: bool = True,
nan_policy: str = "propagate",
) -> OrderedPair | Matrix | Scalar:
"""The TMAX node is based on a numpy or scipy function.
The description of that function is as follows:
Compute the trimmed maximum.
This function computes the maximum value of an array along a given axis, while ignoring values larger than a specified upper limit.
Parameters
----------
a : array_like
Array of values.
upperlimit : None or float, optional
Values in the input array greater than the given limit will be ignored.
When upperlimit is None, then all values are used.
The default value is None.
axis : int or None, optional
Axis along which to operate.
Default is 0.
If None, compute over the whole array 'a'.
inclusive : {True, False}, optional
This flag determines whether values exactly equal to the upper limit are included.
The default value is True.
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.tmax(
a=default.y,
upperlimit=upperlimit,
axis=axis,
inclusive=inclusive,
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