Source code for openalea.phenomenal.segmentation.peak_detection

# -*- python -*-
#
#       Copyright INRIA - CIRAD - INRA
#
#       Distributed under the Cecill-C License.
#       See accompanying file LICENSE.txt or copy at
#           http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html
#
# ==============================================================================
from __future__ import division, print_function, absolute_import

import numpy
import scipy.signal
# ==============================================================================


def smooth(x, window_len=11, window="hanning"):
    """smooth the data using a window with requested size.

    This method is based on the convolution of a scaled window with the signal.
    The signal is prepared by introducing reflected copies of the signal
    (with the window size) in both ends so that transient parts are minimized
    in the beginning and end part of the output signal.

    input:
        x: the input signal
        window_len: the dimension of the smoothing window; should be an odd integer
        window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
            flat window will produce a moving average smoothing.

    output:
        the smoothed signal

    example:

    t=linspace(-2,2,0.1)
    x=sin(t)+randn(len(t))*0.1
    y=smooth(x)

    see also:

    numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
    scipy.signal.lfilter

    TODO: the window parameter could be the window itself if an array instead of a string
    NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.
    """

    if x.ndim != 1:
        raise ValueError("smooth only accepts 1 dimension arrays.")

    if x.size < window_len:
        raise ValueError("Input vector needs to be bigger than window size.")

    if window_len < 3:
        return x

    if window not in ["flat", "hanning", "hamming", "bartlett", "blackman"]:
        raise ValueError(
            "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
        )

    s = numpy.r_[x[window_len - 1 : 0 : -1], x, x[-2 : -window_len - 1 : -1]]
    # print(len(s))
    if window == "flat":  # moving average
        w = numpy.ones(window_len, "d")
    else:
        w = eval("numpy." + window + "(window_len)")

    y = numpy.convolve(w / w.sum(), s, mode="valid")

    return y




def peak_detection(values, order=3):
    order = max(1, int(order))
    max_peaks = scipy.signal.argrelextrema(
        numpy.array(values), numpy.greater_equal, order=order
    )[0]

    min_peaks = scipy.signal.argrelextrema(
        numpy.array(values), numpy.less_equal, order=order
    )[0]

    max_peaks = [(i, values[i]) for i in max_peaks]
    min_peaks = [(i, values[i]) for i in min_peaks]

    return max_peaks, min_peaks