incline.InterpolatingSpline

class incline.InterpolatingSpline(function_order=3, s=None, s_factor=1.0)

Smoothing spline that chooses its own knots.

UnivariateSpline places knots to satisfy a residual budget, so the fitted map depends on the data and no operator exists – probing it misses by a factor of order one, not by rounding error. Uncertainty comes from the bootstrap.

Variables:

• function_order (int) – Spline degree.

• s (float | None) – Residual budget. Derived from the data when None.

Parameters:

• function_order (int)

• s (float | None)

• s_factor (float)

__init__(function_order=3, s=None, s_factor=1.0)

Parameters:

• function_order (int)

• s (float | None)

• s_factor (float)

Return type:

None

Methods

__init__([function_order, s, s_factor])
analytic_operators(axis, order)	State the smoothing and derivative operators directly, if known.
evaluate(axis, y, order)	Fit the spline and differentiate it.
fit(axis, y[, order, se, noise, ...])	Estimate the trend and, optionally, its uncertainty.
native_posterior(axis, y, order, ...)	Uncertainty from the smoother's own probability model.
operators(axis, order)	The smoothing and derivative operators for this configuration.
params()	Report the spline degree and budget.
scale_of(axis)	Approximate scale implied by the budget multiplier.
with_scale(scale, axis)	Scale the residual budget; larger budget means a smoother fit.

Attributes

function_order
has_native_posterior
is_linear	Whether the derivative is a fixed linear map of the data.
linear
name
requires_regular_grid
s
s_factor
supported_orders

name: ClassVar[str] = 'spline'

supported_orders: ClassVar[frozenset[int]] = frozenset({0, 1, 2, 3})

function_order: int = 3

s: float | None = None

s_factor: float = 1.0

evaluate(axis, y, order)

Fit the spline and differentiate it.

Parameters:

• axis (TimeAxis)

• y (npt.NDArray[np.float64])

• order (int)

Return type:

Evaluation

with_scale(scale, axis)

Scale the residual budget; larger budget means a smoother fit.

Parameters:

• scale (float)

• axis (TimeAxis)

Return type:

Self

scale_of(axis)

Approximate scale implied by the budget multiplier.

Parameters:

axis (TimeAxis)

Return type:

float

params()

Report the spline degree and budget.

Return type:

dict[str, Any]