Source code for suboptimumg.loganalysis.fitted_curve

"""Polynomial curve fitting with Horner evaluation."""

from __future__ import annotations

import numpy as np




class FittedCurve:
    """Polynomial fit of tabulated [x, y] data with Horner evaluation.

    Parameters
    ----------
    data : list of [x, y] pairs
        Raw tabulated input data.
    poly_order : int
        Polynomial degree for the least-squares fit.

    Attributes
    ----------
    x_raw, y_raw : np.ndarray
        Original input arrays.
    poly_order : int
    coeffs : np.ndarray
        Polynomial coefficients in **descending** degree order
        ``[c_n, c_{n-1}, ..., c_1, c_0]``, matching ``np.polyfit``
        output and the order Horner's method consumes directly.
    """

    def __init__(self, data: list[list[float]], poly_order: int):
        print(
            """
DEPRECATION WARNING: FittedCurve from suboptimumg.loganalysis is deprecated and will be removed in a future release.

Please use suboptimumg.vehicle.FittedCurve instead.
"""
        )
        self.x_raw = np.array([p[0] for p in data], dtype=np.float64)
        self.y_raw = np.array([p[1] for p in data], dtype=np.float64)
        self.poly_order = poly_order
        # np.polyfit returns highest-degree-first: [c_n, ..., c_1, c_0]
        # This is exactly the order Horner's method iterates over.
        self.coeffs = np.polyfit(self.x_raw, self.y_raw, poly_order)

    def __call__(self, x):
        return self.evaluate(x)



    def evaluate(self, x):
        """Evaluate the fitted polynomial via Horner's method.

        Parameters
        ----------
        x : float or array-like
            Input value(s) at which to evaluate.

        Returns
        -------
        np.ndarray or np.floating
            Evaluated polynomial value(s).
        """
        x = np.asarray(x, dtype=np.float64)
        result = np.full_like(x, self.coeffs[0])
        for c in self.coeffs[1:]:
            result = result * x + c
        return result


    def __repr__(self):
        return (
            f"FittedCurve(order={self.poly_order}, "
            f"{len(self.x_raw)} points, "
            f"x=[{self.x_raw[0]:.4g}..{self.x_raw[-1]:.4g}])"
        )