adctoolbox.aout.analyze_error_envelope_spectrum 源代码

"""
Error envelope spectrum analysis using Hilbert transform.

Extracts envelope spectrum to reveal amplitude modulation patterns.

MATLAB counterpart: errevspec.m
"""

import numpy as np
import matplotlib.pyplot as plt

from scipy.signal import hilbert
from adctoolbox.spectrum import analyze_spectrum
from adctoolbox.fundamentals.fit_sine_4param import fit_sine_4param
from adctoolbox.aout._fit_diagnostics import extract_fit_diagnostics



def analyze_error_envelope_spectrum(signal, fs=1, frequency=None, create_plot: bool = True,
                                     ax=None, title: str = None, max_iterations: int = 1,
                                     tolerance: float = 1e-9, return_fit: bool = False,
                                     input_kind: str = "signal"):
    """
    Compute envelope spectrum using Hilbert transform.

    By default this function fits an ideal sine to the signal, computes the
    error, extracts the error envelope using Hilbert transform, and analyzes
    its spectrum to reveal amplitude modulation patterns. Pass
    ``input_kind="error"`` when the input is already a precomputed error or
    residual signal, matching MATLAB ``errevspec``.

    Parameters
    ----------
    signal : np.ndarray
        ADC output signal (1D array)
    fs : float, default=1
        Sampling frequency in Hz
    frequency : float, optional
        Normalized frequency (0-0.5). If None, auto-detected
    create_plot : bool, default=True
        If True, plot the envelope spectrum on current axes
    ax : matplotlib.axes.Axes, optional
        Axes to plot on. If None, uses current axes (plt.gca())
    title : str, optional
        Title for the plot. If None, no title is set
    max_iterations : int, default=1
        Frequency-refinement iterations passed to fit_sine_4param.
    tolerance : float, default=1e-9
        Frequency-refinement convergence threshold passed to fit_sine_4param.
    return_fit : bool, default=False
        If True, include scalar sine-fit diagnostics under result['fit'].
    input_kind : {'signal', 'error'}, default='signal'
        Input contract. ``'signal'`` treats the input as an ADC output signal
        and fits/subtracts a sine internally. ``'error'`` treats the input as
        an already computed error or residual and skips sine fitting.

    Returns
    -------
    result : dict
        Dictionary with keys:
        - 'enob': Effective Number of Bits
        - 'sndr_db': Signal-to-Noise and Distortion Ratio (dB)
        - 'sfdr_db': Spurious-Free Dynamic Range (dB)
        - 'snr_db': Signal-to-Noise Ratio (dB)
        - 'thd_db': Total Harmonic Distortion (dB)
        - 'sig_pwr_dbfs': Signal power (dBFS)
        - 'noise_floor_dbfs': Noise floor (dBFS)
        - 'error_signal': Error signal (signal - fitted sine)
        - 'envelope': Error envelope extracted via Hilbert transform
        - 'input_kind': Input contract used ('signal' or 'error')
        - 'fit': Optional sine-fit diagnostics when return_fit=True

    Notes
    -----
    - With ``input_kind="signal"``, error = signal - ideal_sine (fitted using
      fit_sine_4param).
    - With ``input_kind="error"``, the input is used directly as the error.
    - Envelope = ``abs(Hilbert(error))``
    - Analyzes spectrum of envelope to reveal AM patterns
    """
    valid_input_kinds = {"signal", "error"}
    if input_kind not in valid_input_kinds:
        raise ValueError(
            f"Unknown input_kind {input_kind!r}. "
            f"Expected one of {sorted(valid_input_kinds)}."
        )

    if input_kind == "signal":
        # Fit ideal sine to extract reference
        fit_kwargs = {"max_iterations": max_iterations, "tolerance": tolerance}
        if frequency is None:
            fit_result = fit_sine_4param(signal, **fit_kwargs)
        else:
            fit_result = fit_sine_4param(signal, frequency_estimate=frequency, **fit_kwargs)

        sig_ideal = fit_result['fitted_signal']
        error_signal = signal - sig_ideal
    else:
        fit_result = None
        error_signal = np.asarray(signal)

    # Ensure column data
    e = np.asarray(error_signal).flatten()

    # Envelope extraction via Hilbert transform
    env = np.abs(hilbert(e))

    # Analyze envelope spectrum
    if create_plot:
        # Use provided axes or set current axes
        if ax is not None:
            plt.sca(ax)

        result = analyze_spectrum(env, fs=fs, show_label=False, max_harmonic=5)
        plt.xlabel("Frequency (Hz)")
        plt.ylabel("Envelope Spectrum (dB)")
        plt.grid(True, alpha=0.3)

        # Set title if provided
        if title is not None:
            plt.gca().set_title(title, fontsize=10, fontweight='bold')
    else:
        # Analyze without plotting
        import matplotlib
        backend_orig = matplotlib.get_backend()
        matplotlib.use('Agg')  # Non-interactive backend

        result = analyze_spectrum(env, fs=fs, show_label=False, max_harmonic=5)
        plt.close()

        matplotlib.use(backend_orig)  # Restore original backend

    # Add error signal and envelope to result
    result['error_signal'] = e
    result['envelope'] = env
    result['input_kind'] = input_kind
    if return_fit:
        result['fit'] = (
            extract_fit_diagnostics(fit_result)
            if fit_result is not None
            else None
        )

    return result