Source code for psc_sim.compare_metrics

"""Metrics for comparing Python vs LTspice I–V curves."""

from __future__ import annotations

import numpy as np




def interpolate_current_at(
    v_grid: np.ndarray,
    v_samples: np.ndarray,
    i_samples: np.ndarray,
) -> np.ndarray:
    """Linear interpolation of I(V) onto v_grid; NaN outside sample range."""
    v_grid = np.asarray(v_grid, dtype=float)
    v_samples = np.asarray(v_samples, dtype=float)
    i_samples = np.asarray(i_samples, dtype=float)
    ok = np.isfinite(v_samples) & np.isfinite(i_samples)
    if ok.sum() < 2:
        return np.full_like(v_grid, np.nan, dtype=float)
    order = np.argsort(v_samples[ok])
    vs = v_samples[ok][order]
    is_ = i_samples[ok][order]
    return np.interp(v_grid, vs, is_, left=np.nan, right=np.nan)





def iv_curve_rmse(
    v_ref: np.ndarray,
    i_ref: np.ndarray,
    v_other: np.ndarray,
    i_other: np.ndarray,
) -> tuple[float, float]:
    """Return (rmse_A, max_abs_error_A) of i_other interpolated onto v_ref."""
    i_interp = interpolate_current_at(v_ref, v_other, i_other)
    ok = np.isfinite(v_ref) & np.isfinite(i_ref) & np.isfinite(i_interp)
    if ok.sum() == 0:
        return float("nan"), float("nan")
    diff = i_ref[ok] - i_interp[ok]
    rmse = float(np.sqrt(np.mean(diff**2)))
    max_err = float(np.max(np.abs(diff)))
    return rmse, max_err





def eis_z_rmse(
    zre_a: np.ndarray,
    zim_a: np.ndarray,
    zre_b: np.ndarray,
    zim_b: np.ndarray,
) -> float:
    """RMSE on Re/Im components (same-length arrays)."""
    d = (zre_a - zre_b) ** 2 + (zim_a - zim_b) ** 2
    return float(np.sqrt(np.mean(d)))





def eis_impedance_rmse(
    freq_ref: np.ndarray,
    zre_ref: np.ndarray,
    zim_ref: np.ndarray,
    freq_other: np.ndarray,
    zre_other: np.ndarray,
    zim_other: np.ndarray,
) -> float:
    """RMSE of complex impedance (Re/Im) with other interpolated onto freq_ref."""
    zre_i = interpolate_current_at(freq_ref, freq_other, zre_other)
    zim_i = interpolate_current_at(freq_ref, freq_other, zim_other)
    ok = (
        np.isfinite(freq_ref)
        & np.isfinite(zre_ref)
        & np.isfinite(zim_ref)
        & np.isfinite(zre_i)
        & np.isfinite(zim_i)
    )
    if ok.sum() == 0:
        return float("nan")
    diff = np.hypot(zre_ref[ok] - zre_i[ok], zim_ref[ok] - zim_i[ok])
    return float(np.sqrt(np.mean(diff**2)))