# -*- coding: utf-8 -*- 
#Nicolas Enfon - LSIS DYNI - NortekMED
from pylab import *
from time import *
from dwt_1 import *

def detector(signal, wavelet='db2', level=6, normalized=False, freq_range=(20,40), detector_type='energy', smooth=False):
    '''Computes a DWT (Discrete Wavelet Transform) and a detector based on the energy of the signal

	@signal: numerized audio signal
	@wavelet: PyWavelets wavelet type
	@level: level of decomposition. Level=6 gives 2 ^ 6 = 64 rows
	@normalized: normalization of the scalogram
	@freq_range: frequency range where we look for information
	@detector_type: energy, diff(energy) or diff(diff(energy))
	@smooth: smoothing of the detector with a moving average ie convolution with ones(x)

	returns: an array detector made accordingly to the specified rules'''

    scalo = scalogram(signal, wavelet=wavelet, level=level)

    #The frequency range can be given in %, or in row numbers
    #This can be seen as where we are looking for the given species
    if type(freq_range[0]) != int:
	h_freq = int(freq_range[0] * values.shape[0])
	l_freq = int(freq_range[1] * values.shape[0])
    else:
	h_freq = freq_range[0]
	l_freq = freq_range[1]

    #Energy of the signal
    if detector_type == 'energy':
	detector = scalo[h_freq:l_freq,:]
	detector = detector.sum(0)
    #Derivative of the signal's energy
    if detector_type == 'd_energy':
        detector = scalo[h_freq:l_freq,:]
        detector = detector.sum(0)
	#We differentiate, and add a 0 in the first column to replace the missing value
	detector = diff(detector)
	detector = hstack((0,detector))
    #Second derivative of the signal's energy
    if detector_type == 'd2_energy':
        detector = scalo[h_freq:l_freq,:]
        detector = detector.sum(0)
        detector = diff(detector)
        detector = diff(detector)
        detector = hstack((0,0,detector))

    #smoothing with a moving average
    if smooth != False:
	detector = convolve(detector, ones(smooth) / float(smooth), mode='same')
    return scalo, detector

def booleanized(scalo, detector, threshold_type='mean', threshold_coeff=0.5, external=False, above=True):
    '''Transforms a detector array into a boolean array, with 1 where there is a detection and 0 otherwise

	@scalo: scalogram
	@detector: detector previously computed on the scalogram
	@threshold_type: mean or average
	@threshold_coeff: if threshold_coeff == 0.3, values 30% above the  signal.mean() will trigger a detection
	@external: absolute threshold, if previously computed on a broader time window
	@above: > or < threshold

	returns: a booleanized detector'''

    if not(external):
	if threshold_type == 'mean':
	    if above:
                threshold = (1 + threshold_coeff) * detector.mean()
	    else:
                threshold = (1 - threshold_coeff) * detector.mean()
	elif threshold_type == 'median':
	    if above:
                threshold = (1 + threshold_coeff) * median(detector)
	    else:
                threshold = (1 - threshold_coeff) * median(detector)

    if above:
	booleanized = detector > threshold
    else:
	booleanized = detector < threshold

    return booleanized

def decision(booleanized, threshold=0.2):
    '''Returns the decision based on the per sample detection

	@booleanized: booleanized detector
	@threshold: % of detection above which we decide the window is actually positive

	returns: a decision (boolean)'''

    if sum(booleanized) / float(len(booleanized)) >= threshold:
	decision = True
    else:
	decision = False

    return decision

def agregated(decisions, threshold=0.1):
    '''Returns the decision based on decisions made on several windows of signal
	
	@decisions: decisions made upon several windows of signal
	@threshold: % of positive decisions above which we decide the group is positive as a whole

	returns: a decision (boolean)'''

    decisions = array(decisions)

    if sum(decisions) / float( sum(decisions) + sum(decisions ^ 1)   ) >= threshold:
	decision = True
    else:
	decision = False

    return decision


if __name__ == '__main__':
    print asctime()
    tic = time()
    print 'Testing ...'
    soundfile = '/NAS3/SABIOD/SITE/NORTEKMED/SAMPLE_SOUNDS/BOTTLENOSE/04._Bottlenose_dolphin.wav'
    scal, detec = detector(soundfile)
    b = booleanized(scal, detec)
    d = decision(b)
    a = agregated(d)
    print 'Done in ',time() - tic, 'seconds (the count can be wrong if on utln servers)'