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||< |d	7 }||d|…  }|d|… }n2t  |¡}|| }||	| k�r¤|| }ng }g }|dk �r¾|d9 }t|t jƒ�sÜt  |¡ d¡}t|t jƒ�sút  |¡ d¡}t|ƒdk�rt d¡ nt dt|ƒt|ƒf ¡ ||fS )a  Noise-tolerant fast peak-finding algorithm.

    Parameters
    ----------
    x0 : 1d array
        A real vector from the maxima will be found (required).
    thresh : float | None
        The amount above surrounding data for a peak to be
        identified. Larger values mean the algorithm is more selective in
        finding peaks. If ``None``, use the default of
        ``(max(x0) - min(x0)) / 4``.
    extrema : {-1, 1}
        1 if maxima are desired, -1 if minima are desired
        (default = maxima, 1).
    %(verbose)s

    Returns
    -------
    peak_loc : array
        The indices of the identified peaks in x0.
    peak_mag : array
        The magnitude of the identified peaks.

    Notes
    -----
    If repeated values are found the first is identified as the peak.
    Conversion from initial Matlab code from:
    Nathanael C. Yoder (ncyoder@purdue.edu)

    Examples
    --------
    >>> import numpy as np
    >>> from mne.preprocessing import peak_finder
    >>> t = np.arange(0, 3, 0.01)
    >>> x = np.sin(np.pi*t) - np.sin(0.5*np.pi*t)
    >>> peak_locs, peak_mags = peak_finder(x) # doctest: +SKIP
    >>> peak_locs # doctest: +SKIP
    array([36, 260]) # doctest: +SKIP
    >>> peak_mags # doctest: +SKIP
    array([0.36900026, 1.76007351]) # doctest: +SKIP
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atleast_1dZastypeÚlenÚinfor   )Zx0ZthreshZextremar   ÚsZdx0ÚindÚxÚlengthÚmin_magZtemp_magZ
found_peakZleft_minZsignDxÚiiZmaxPeaksZpeak_locZpeak_magZc_indZtemp_locZ	peak_indsZ	peak_magsZx_ind© r#   ú]/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/preprocessing/_peak_finder.pyÚpeak_finder   s®    +
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