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# GAUSS_SPLINE

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The GAUSS_SPLINE node is based on a numpy or scipy function. The description of that function is as follows: Gaussian approximation to B-spline basis function of order n. Params: x : array_like A knot vector. n : int The order of the spline. Must be non-negative, i.e. n >= 0. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np

import scipy.signal

@flojoy
def GAUSS_SPLINE(
default: OrderedPair | Matrix,
n: int = 2,
) -> OrderedPair | Matrix | Scalar:
"""The GAUSS_SPLINE node is based on a numpy or scipy function.

The description of that function is as follows:

Gaussian approximation to B-spline basis function of order n.

Parameters
----------
x : array_like
A knot vector.
n : int
The order of the spline. Must be non-negative, i.e. n >= 0.

Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""

result = scipy.signal.gauss_spline(
x=default.y,
n=n,
)

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


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