adctoolbox.calibration.calibrate_weight_sine_lite 源代码

"""Minimal ADC calibration using sinewave input at known frequency."""

import numpy as np
from scipy.linalg import lstsq



def calibrate_weight_sine_lite(
    bits: np.ndarray,
    freq: float
) -> np.ndarray:
    """
    Minimal calibration at known frequency. Returns normalized weights.
    Expects well-conditioned binary data (N samples x M bits).

    The returned weights are in the same solver-unit-sine convention as
    ``calibrate_weight_sine``: the fitted fundamental sine magnitude is fixed
    to one for identifiability. Apply an explicit scale factor before using
    the reconstructed waveform for physical dBFS or NSD interpretation.
    """
    if freq is not None and freq > 0.5:
        raise ValueError(
            f"freq must be normalized Fin/Fs (Nyquist range [0, 0.5]); got {freq}. "
            f"If you have Fin in Hz, pass freq=Fin/Fs instead."
        )
    n_samples, bit_width = bits.shape

    # Build fundamental basis
    t = np.arange(n_samples)
    phase = 2.0 * np.pi * freq * t
    cos_basis = np.cos(phase)
    sin_basis = np.sin(phase)

    # Cosine=1 assumption
    offset_col = np.ones((n_samples, 1))
    A = np.column_stack([bits, offset_col, sin_basis])
    b = -cos_basis
    coeffs, _, _, _ = lstsq(A, b)

    # Extract weights and normalization
    weights_raw = coeffs[:bit_width]
    sin_coeff = coeffs[-1]
    norm_factor = np.sqrt(1.0 + sin_coeff**2)
    weights = weights_raw / norm_factor

    # Polarity correction (ensure positive sum)
    if np.sum(weights) < 0:
        weights = -weights

    return weights