"""Detect peaks in data based on their amplitude and other features."""

from __future__ import division, print_function
import numpy as np

__author__ = "Marcos Duarte, https://github.com/demotu/BMC, Modified by Malte Deiseroth"
__version__ = "1.0.4.1"
__license__ = "MIT"


def detect_peaks(x, mph=None, mpd=1, threshold=0, edge='rising',
                 kpsh=False, valley=False, show=False, ax=None):

    """Detect peaks in data based on their amplitude and other features.

    Parameters
    ----------
    x : 1D array_like
        data.
    mph : {None, number}, optional (default = None)
        detect peaks that are greater than minimum peak height.
    mpd : positive integer, optional (default = 1)
        detect peaks that are at least separated by minimum peak distance (in
        number of data).
    threshold : positive number, optional (default = 0)
        detect peaks (valleys) that are greater (smaller) than `threshold`
        in relation to their immediate neighbors.
    edge : {None, 'rising', 'falling', 'both'}, optional (default = 'rising')
        for a flat peak, keep only the rising edge ('rising'), only the
        falling edge ('falling'), both edges ('both'), or don't detect a
        flat peak (None).
    kpsh : bool, optional (default = False)
        keep peaks with same height even if they are closer than `mpd`.
    valley : bool, optional (default = False)
        if True (1), detect valleys (local minima) instead of peaks.
    show : bool, optional (default = False)
        if True (1), plot data in matplotlib figure.
    ax : a matplotlib.axes.Axes instance, optional (default = None).

    Returns
    -------
    ind : 1D array_like
        indices of the peaks in `x`.

    Notes
    -----
    The detection of valleys instead of peaks is performed internally by simply
    negating the data: `ind_valleys = detect_peaks(-x)`
    
    The function can handle NaN's 

    See this IPython Notebook [1]_.

    References
    ----------
    .. [1] http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb

    Examples
    --------
    >>> from detect_peaks import detect_peaks
    >>> x = np.random.randn(100)
    >>> x[60:81] = np.nan
    >>> # detect all peaks and plot data
    >>> ind = detect_peaks(x, show=True)
    >>> print(ind)

    >>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
    >>> # set minimum peak height = 0 and minimum peak distance = 20
    >>> detect_peaks(x, mph=0, mpd=20, show=True)

    >>> x = [0, 1, 0, 2, 0, 3, 0, 2, 0, 1, 0]
    >>> # set minimum peak distance = 2
    >>> detect_peaks(x, mpd=2, show=True)

    >>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
    >>> # detection of valleys instead of peaks
    >>> detect_peaks(x, mph=0, mpd=20, valley=True, show=True)

    >>> x = [0, 1, 1, 0, 1, 1, 0]
    >>> # detect both edges
    >>> detect_peaks(x, edge='both', show=True)

    >>> x = [-2, 1, -2, 2, 1, 1, 3, 0]
    >>> # set threshold = 2
    >>> detect_peaks(x, threshold = 2, show=True)
    """

    x = np.atleast_1d(x).astype('float64')
    if x.size < 3:
        return np.array([], dtype=int)
    if valley:
        x = -x
    # find indices of all peaks
    dx = x[1:] - x[:-1]
    # handle NaN's
    indnan = np.where(np.isnan(x))[0]
    if indnan.size:
        x[indnan] = np.inf
        dx[np.where(np.isnan(dx))[0]] = np.inf
    ine, ire, ife = np.array([[], [], []], dtype=int)
    if not edge:
        ine = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) > 0))[0]
    else:
        if edge.lower() in ['rising', 'both']:
            ire = np.where((np.hstack((dx, 0)) <= 0) & (np.hstack((0, dx)) > 0))[0]
        if edge.lower() in ['falling', 'both']:
            ife = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) >= 0))[0]
    ind = np.unique(np.hstack((ine, ire, ife)))
    # handle NaN's
    if ind.size and indnan.size:
        # NaN's and values close to NaN's cannot be peaks
        ind = ind[np.in1d(ind, np.unique(np.hstack((indnan, indnan-1, indnan+1))), invert=True)]
    # first and last values of x cannot be peaks
    if ind.size and ind[0] == 0:
        ind = ind[1:]
    if ind.size and ind[-1] == x.size-1:
        ind = ind[:-1]
    # remove peaks < minimum peak height
    if ind.size and mph is not None:
        ind = ind[x[ind] >= mph]
    # remove peaks - neighbors < threshold
    if ind.size and threshold > 0:
        dx = np.min(np.vstack([x[ind]-x[ind-1], x[ind]-x[ind+1]]), axis=0)
        ind = np.delete(ind, np.where(dx < threshold)[0])
    # detect small peaks closer than minimum peak distance
    if ind.size and mpd > 1:
        ind = ind[np.argsort(x[ind])][::-1]  # sort ind by peak height
        idel = np.zeros(ind.size, dtype=bool)
        for i in range(ind.size):
            if not idel[i]:
                # keep peaks with the same height if kpsh is True
                idel = idel | (ind >= ind[i] - mpd) & (ind <= ind[i] + mpd) \
                    & (x[ind[i]] > x[ind] if kpsh else True)
                idel[i] = 0  # Keep current peak
        # remove the small peaks and sort back the indices by their occurrence
        ind = np.sort(ind[~idel])

    if show:
        if indnan.size:
            x[indnan] = np.nan
        if valley:
            x = -x
        _plot(x, mph, mpd, threshold, edge, valley, ax, ind)

    return ind
    
def detect_peaks_vectorized(x, **kwargs):
    """
    vectorized version of detect_peaks.
    --------
    input:
    x   1d or 2d array like to search for peaks.
        if 2d, peaks are searched in a row wise patter,
        thus we loop over x.
    for the rest see detect_peaks, but with the addition, that all
    kwargs are also vectorized and thus instead of ont mph one can also
    enter a list of mphs where each mph corresponds to one row of the
    2d spectrum in x.
    --------
    return
    The return mimiks the behaviour of numpy masks. See e.g.
    `np.where(array([1,2,3,4,5] > 3))`
    
    In the case of 1d x-array, a numpy mask like tuple of peakpostitions
    is returned.
    In the case of a 2d x-array a numpy mask like tuple is returned. 0.
    element of the tuple is an array with the numbers of the spektrum the
    peak was found
    in, 1. element of the tubple is the position of the peak in the spectrum.
    """

    # check and vectorize user input
    D1, D2 = {}, {} # 1D and 2D kwarg input
    for key, value in kwargs.items(): 
        # print(key, value)
        if np.shape(value) == () : D1[key] = value
        elif np.shape(value) == (len(x),): D2[key] = value
        else: raise IOError("Cant handle dim of %s. Int, float or iterable of shape %s was expected, but %s was found."%(key, len(x), np.shape(value)))

    # pass everything to detect_peak in the 1d case
    if len(np.shape(x)) == 1: 
        peakpos = detect_peaks(x, **kwargs)
        return (peakpos) # format according to numpy masks

    # find peaks an format output according to numpy mask conventions
    peakpos = np.array([])
    pos = np.array([])    
    for i in range(len(x)):
        elm = x[i]
        kwrds = dict(D1)
        for key, value in D2.items():
            kwrds[key] = value[i]

        peaks = detect_peaks(elm, **kwrds )
        peakpos =  np.hstack((peakpos, peaks))
        pos = np.hstack((pos, i * np.ones(np.shape(peaks), dtype=np.int32)))
        
    return np.int32(pos), np.int32(peakpos)


def _plot(x, mph, mpd, threshold, edge, valley, ax, ind):
    """Plot results of the detect_peaks function, see its help."""
    try:
        import matplotlib.pyplot as plt
    except ImportError:
        print('matplotlib is not available.')
    else:
        if ax is None:
            _, ax = plt.subplots(1, 1, figsize=(8, 4))

        ax.plot(x, 'b', lw=1)
        if ind.size:
            label = 'valley' if valley else 'peak'
            label = label + 's' if ind.size > 1 else label
            ax.plot(ind, x[ind], '+', mfc=None, mec='r', mew=2, ms=8,
                    label='%d %s' % (ind.size, label))
            ax.legend(loc='best', framealpha=.5, numpoints=1)
        ax.set_xlim(-.02*x.size, x.size*1.02-1)
        ymin, ymax = x[np.isfinite(x)].min(), x[np.isfinite(x)].max()
        yrange = ymax - ymin if ymax > ymin else 1
        ax.set_ylim(ymin - 0.1*yrange, ymax + 0.1*yrange)
        ax.set_xlabel('Data #', fontsize=14)
        ax.set_ylabel('Amplitude', fontsize=14)
        mode = 'Valley detection' if valley else 'Peak detection'
        ax.set_title("%s (mph=%s, mpd=%d, threshold=%s, edge='%s')"
                     % (mode, str(mph), mpd, str(threshold), edge))
        # plt.grid()
        plt.show()
