tabular_function

tabular_function(
    x,
    distributions,
    at,
    inverse=False,
    x_transform='none',
    y_transform='none',
    confidence_level=None,
    allow_negative_y_values=False,
)

Evaluate a tabular function.

Mirrors the C# TabularFunction: builds an uncertain paired curve from x and distributions, samples it once (the mean curve, or confidence_level if given), and evaluates Function()/InverseFunction() at at. Unlike :func:curve_interpolate and friends, the underlying curve’s shape contract is not configurable here – TabularFunction is always built strict, ascending on both axes, matching every use in the ported C# test suite.

Parameters

Name	Type	Description	Default
x	array_like	The curve’s x positions, at least one element.	required
distributions	Distribution or list of Distribution	One distribution per element of x, or a single distribution recycled across every x.	required
at	array_like	Points to evaluate at (or, when inverse=True, points to evaluate the inverse function at).	required
inverse	bool	If True, evaluates InverseFunction() instead of Function().	False
x_transform	('none', 'logarithmic', 'log', 'normal_z')	“log” is an accepted alias for “logarithmic” (both parse to the same value); “logarithmic” is the spelling used in this package’s own examples.	"none"
y_transform	('none', 'logarithmic', 'log', 'normal_z')	“log” is an accepted alias for “logarithmic” (both parse to the same value); “logarithmic” is the spelling used in this package’s own examples.	"none"
confidence_level	float	Quantile in [0, 1] to sample the curve at; None (default) samples the mean.	None
allow_negative_y_values	bool	Allow a negative or NaN result to pass through unmodified, rather than clamping it to 0. Default False (clamp), matching every use in the ported C# test suite – the C# class default is True.	False

Returns

Name	Type	Description
	numpy.ndarray	Same length as at.

Examples

>>> from corehydropy import Distribution, tabular_function
>>> x = [50, 100, 150, 200, 250]
>>> d = [Distribution("Deterministic", [v]) for v in [100, 200, 300, 400, 500]]
>>> tabular_function(x, d, at=50, x_transform="logarithmic")
array([100.])