algorithms/asa.py
def _compute_peaks_or_valleys_of_first_derivative(s, do_peaks=True):
    """
    Takes a spectrogram and returns a 2D array of the form:

    0 0 0 1 0 0 1 0 0 0 1   <-- Frequency 0
    0 0 1 0 0 0 0 0 0 1 0   <-- Frequency 1
    0 0 0 0 0 0 1 0 1 0 0   <-- Frequency 2
    *** Time axis *******

    Where a 1 means that the value in that time bin in the spectrogram corresponds to
    a peak/valley in the first derivative.

    This function is used as part of the ASA algorithm and is not meant to be used publicly.
    """
    # Get the first derivative of each frequency in the time domain
    gradient = np.nan_to_num(np.apply_along_axis(np.gradient, 1, s), copy=False)

    # Calculate the value we will use for determinig whether something is an event or not
    threshold = np.squeeze(np.nanmean(gradient, axis=1) + np.nanstd(gradient, axis=1))

    # Look for relative extrema along the time dimension
    half_window = 4
    if do_peaks:
        indexes = [signal.argrelextrema(gradient[i, :], np.greater, order=half_window)[0] for i in range(gradient.shape[0])]
    else:
        indexes = [signal.argrelextrema(gradient[i, :], np.less, order=half_window)[0] for i in range(gradient.shape[0])]

    # indexes should now contain the indexes of possible extrema
    # But we need to filter out values that are not large enough, and we want the end result
    # to be a 1 or 0 mask corresponding to locations of extrema
    extrema = np.zeros(s.shape)
    for row_index, index_array in enumerate(indexes):
        # Each index_array is a list of indexes corresponding to all the extrema in a given row
        for col_index in index_array:
            if do_peaks and (gradient[row_index, col_index] > threshold[row_index]):
                extrema[row_index, col_index] = 1
            elif not do_peaks:
                # Note that we do not remove under-threshold values from the offsets - these will be taken care of later in the algo
                extrema[row_index, col_index] = 1
    return extrema, gradient
def _compute_peaks_or_valleys_of_first_derivative(s, do_peaks=True):
    """
    Takes a spectrogram and returns a 2D array of the form:

    0 0 0 1 0 0 1 0 0 0 1   <-- Frequency 0
    0 0 1 0 0 0 0 0 0 1 0   <-- Frequency 1
    0 0 0 0 0 0 1 0 1 0 0   <-- Frequency 2
    *** Time axis *******

    Where a 1 means that the value in that time bin in the spectrogram corresponds to
    a peak/valley in the first derivative.

    This function is used as part of the ASA algorithm and is not meant to be used publicly.
    """
    # Get the first derivative of each frequency in the time domain
    gradient = np.nan_to_num(np.apply_along_axis(np.gradient, 1, s), copy=False)

    # Calculate the value we will use for determinig whether something is an event or not
    threshold = np.squeeze(np.nanmean(gradient, axis=1) + np.nanstd(gradient, axis=1))

    # Look for relative extrema along the time dimension
    half_window = 4
    if do_peaks:
        indexes = [signal.argrelextrema(gradient[i, :], np.greater, order=half_window)[0] for i in range(gradient.shape[0])]
    else:
        indexes = [signal.argrelextrema(gradient[i, :], np.less, order=half_window)[0] for i in range(gradient.shape[0])]

    # indexes should now contain the indexes of possible extrema
    # But we need to filter out values that are not large enough, and we want the end result
    # to be a 1 or 0 mask corresponding to locations of extrema
    extrema = np.zeros(s.shape)
    for row_index, index_array in enumerate(indexes):
        # Each index_array is a list of indexes corresponding to all the extrema in a given row
        for col_index in index_array:
            if do_peaks and (gradient[row_index, col_index] > threshold[row_index]):
                extrema[row_index, col_index] = 1
            elif not do_peaks:
                # Note that we do not remove under-threshold values from the offsets - these will be taken care of later in the algo
                extrema[row_index, col_index] = 1
    return extrema, gradient
def _compute_peaks_or_valleys_of_first_derivative ( s , do_peaks = True ) : # Get the first derivative of each frequency in the time domain gradient = np . nan_to_num ( np . apply_along_axis ( np . gradient , 1 , s ) , copy = False ) # Calculate the value we will use for determinig whether something is an event or not threshold = np . squeeze ( np . nanmean ( gradient , axis = 1 ) + np . nanstd ( gradient , axis = 1 ) ) # Look for relative extrema along the time dimension half_window = 4 if do_peaks : indexes = [ signal . argrelextrema ( gradient [ i , : ] , np . greater , order = half_window ) [ 0 ] for i in range ( gradient . shape [ 0 ] ) ] else : indexes = [ signal . argrelextrema ( gradient [ i , : ] , np . less , order = half_window ) [ 0 ] for i in range ( gradient . shape [ 0 ] ) ] # indexes should now contain the indexes of possible extrema # But we need to filter out values that are not large enough, and we want the end result # to be a 1 or 0 mask corresponding to locations of extrema extrema = np . zeros ( s . shape ) for row_index , index_array in enumerate ( indexes ) : # Each index_array is a list of indexes corresponding to all the extrema in a given row for col_index in index_array : if do_peaks and ( gradient [ row_index , col_index ] > threshold [ row_index ] ) : extrema [ row_index , col_index ] = 1 elif not do_peaks : # Note that we do not remove under-threshold values from the offsets - these will be taken care of later in the algo extrema [ row_index , col_index ] = 1 return extrema , gradient
Takes a spectrogram and returns a 2D array of the form:

    0 0 0 1 0 0 1 0 0 0 1   <-- Frequency 0
    0 0 1 0 0 0 0 0 0 1 0   <-- Frequency 1
    0 0 0 0 0 0 1 0 1 0 0   <-- Frequency 2
    *** Time axis *******

    Where a 1 means that the value in that time bin in the spectrogram corresponds to
    a peak/valley in the first derivative.

    This function is used as part of the ASA algorithm and is not meant to be used publicly.
Takes a spectrogram and returns a 2D array of the form :