def is_data_stereo(raw_global_data:list) -> bool:
    """
    self-explainatory
    """
    try:
        assert(raw_global_data[0][0])
    except IndexError:
        return False
    except AssertionError:
        return True
    return True

def dist_to_integer(x):
    ent = np.floor(x+0.5)
    return np.abs(x - ent)

def is_note_within(fr1, fr2):
    if(fr1 > fr2):
        return (fr1/fr2 <= NOTE_DIST and dist_to_integer(fr1/fr2) >= OCTAVE_DIST)
    else:
        return (fr2/fr1 <= NOTE_DIST and dist_to_integer(fr2/fr1) >= OCTAVE_DIST)

def keep_highest(song_name, offset, songlen, segsize, count, output_name, minfreq=110, maxfreq=5000, ampthr=250):
    # extracting data from cropped song
    sample_rate, raw_song_data = wavfile.read(song_name)
    blit = int(sample_rate*segsize) # Te

    song_data = [0 for i in range(len(raw_song_data))]

    id_start = int(offset*sample_rate)
    id_end = min(len(raw_song_data), int((offset+songlen)*sample_rate))

    a = 0
    if(is_data_stereo(raw_song_data)):
        print("Converting to mono...")
        for x in range(id_start, id_end):
            song_data[x] = raw_song_data[x][0]/2 + raw_song_data[x][1]/2

            if(x % (int(len(raw_song_data)/100)) == 0):
                print(a, "/ 100")
                a += 1
    else:
        song_data = raw_song_data

    print("\nSampleRate : ", sample_rate)
    print("SegSize : ", blit)
    
    # calculate the frequencies associated to the FFTs
    pfreq = scp.fft.rfftfreq(blit, 1/sample_rate)

    # left boundary of segment to crop
    current_time = offset

    # list of FFTs
    fft_list = []
    fft_list_untouched = []
    
    # number of samples
    k = 0

    print("Retrieving freqs from", offset, "to", songlen+offset, "...")
    while(current_time < songlen+offset-segsize):
        # index corresponding to left boundary
        left_id = int(current_time*sample_rate)
        
        # index corresponding to right boundary
        right_id = int((current_time+segsize)*sample_rate)
        
        # calculate the fft, append it to fft_list
        pff = scp.fft.rfft(song_data[int(current_time*sample_rate):int(sample_rate*(current_time+segsize))])
        fft_list.append(pff)
        fft_list_untouched.append([ee for ee in pff])

        # just to avoid what causes 0.1 + 0.1 == 0.2 to be False
        k += 1
        current_time = offset + k*segsize
        #print(current_time)

    print("\n\nSegSize :", segsize, "\nFFT :", len(fft_list), "\nFFT[0] :", len(fft_list[0]), "\npfreq :", len(pfreq), "\n\n")

    # -------------------------------------------- Clean song -------------------------------------------- #
    pfreq_minid = 0
    pfreq_maxid = len(pfreq) -1
    while(pfreq[pfreq_minid] < minfreq):
        for t in range(len(fft_list)):
            fft_list[t][pfreq_minid] = 0+0j
        pfreq_minid += 1

    while(pfreq[pfreq_maxid] > maxfreq):
        for t in range(len(fft_list)):
            fft_list[t][pfreq_maxid] = 0+0j
        pfreq_maxid -= 1

    new_times = []
    new_freqs = []
    new_ampls = []
    new_kept = []

    # i = time, j = freq
    for i in range(len(fft_list)):
        #returns a list of couples [id, value]
        elements = heapq.nlargest(count, enumerate(fft_list[i]), key=lambda x: x[1])

        for idx in range(len(elements)):
            if(elements[idx][0] < len(pfreq)):
                new_times.append(offset + i*segsize)
                new_freqs.append(pfreq[elements[idx][0]])
                new_ampls.append(fft_list[i][elements[idx][0]])

    # -------------------------------------------- Get amp distribution -------------------------------------------- #
    new_new_amps = [0 for i in range(int(sample_rate*songlen))]
    new_new_t = [offset + i/sample_rate for i in range(int(sample_rate*songlen))]

    amp_ct = 0
    incr_a = segsize*4
    len_seg_a = int(sample_rate*incr_a)
    count_a = len_seg_a//1250
    left_0 = int(sample_rate*(amp_ct+offset))
    while(amp_ct < songlen-segsize):
        left = int(sample_rate*(amp_ct+offset))
        right = int(sample_rate*(amp_ct+offset + incr_a))

        #returns a list of couples [id, value]
        elements = heapq.nlargest(count_a, enumerate([song_data[i] for i in range(left, right)]), key=lambda x: x[1])

        amp_ct += incr_a

        for idx in range(len(elements)):
            new_new_amps[elements[idx][0]+left-left_0] = song_data[left+elements[idx][0]]
      
    mmxx = max(new_new_amps)
    new_new_amps = [nnw*1000/mmxx for nnw in new_new_amps]

    # localize peaks
    left_id = 0
    right_id = 0
    a_ampl = 0
    in_seg = False
    time_d = 0.035
    cur_t = 0

    locs = []
    loct = []
    for i in range(len(new_new_amps)):
        if(new_new_amps[i] > 100):
            if(not in_seg):
                in_seg = True
                left_id = i
            right_id = i
            a_ampl = max(a_ampl, new_new_amps[i])
            cur_t = 0
        else:
            cur_t += 1/sample_rate
            if(in_seg and cur_t >= time_d):
                in_seg = False
                locs.append(a_ampl)
                loct.append((left_id + right_id)/(2*sample_rate) + offset)

                a_ampl = 0

    # detect sliders
    sl_a = []
    sl_t = []
    in_slider = False
    slider_dtct = segsize
    for i in range(1, len(loct)-1):
        delta = loct[i] - loct[i-1]
        delta2 = loct[i+1] - loct[i]
        if(delta < slider_dtct and delta2 < slider_dtct):
            if(in_slider):
                sl_t.append(loct[i])
                sl_a.append(locs[i])
            else:
                in_slider = True
                sl_t.append(loct[i-1])
                sl_a.append(locs[i-1])
                sl_t.append(loct[i])
                sl_a.append(locs[i])
        else:
            in_slider = False

    plt.plot(new_new_t, new_new_amps, "b-")
    plt.plot(loct, locs, "ro")
    plt.plot(sl_t, sl_a, "go")
    plt.grid()
    plt.show()

    # -------------------------------------------- Localize -------------------------------------------- #

    timing_points = []
    last_freq = new_freqs[0]
    for i in range(len(new_times)):
        if(np.abs(new_ampls[i]) > ampthr and (i == 0 or not is_note_within(new_freqs[i], last_freq))):
            last_freq = new_freqs[i]
            timing_points.append(new_times[i])
            new_kept.append(new_freqs[i])
        else:
            new_kept.append(0)

    mx = max(new_ampls)
            
    plt.plot(new_times, new_freqs)
    plt.plot(new_times, [elt*1000/mx for elt in new_ampls])
    plt.plot(new_times, new_kept, "ro")
    plt.grid()
    plt.show()
    
    # -------------------------------------------- Write -------------------------------------------- #
    i0 = 0
    timing_points.append(999999)

    write_freq = 880
    write_cur = 0
    write_id = -1
    while(write_cur <= write_freq): # shouldnt seg fault
        write_id += 1
        write_cur = pfreq[write_id]
    
    
    # remove
    # i = time, j = freq
    for i in range(len(fft_list)):
        # retrieve dominant freq
        if(segsize*i >= timing_points[i0]-offset):
            i0 += 1

            maxfreq = 0
            maxfreqid = 0
            maxamp = 0
            for j in range(len(fft_list[0])):
                if(np.abs(fft_list[i][j]) > maxamp):
                    maxamp = np.abs(fft_list[i][j])
                    maxfreq = pfreq[j]
                    maxfreqid = j

            fft_list_untouched[i][write_id] = max(maxamp*2, 32767)
            fft_list_untouched[i][write_id-1] = max(maxamp*2, 32767)
            fft_list_untouched[i][write_id+1] = max(maxamp*2, 32767)

    res = []
    print("Converting...")
    for i in range(len(fft_list_untouched)):
        ift = scp.fft.irfft(fft_list_untouched[i], n=blit)
        for k in ift:
            res.append(k)
    #print(type(res[0]))
    mx = 0
    for j in range(len(res)):
        if(res[j] > mx):
            mx = res[j]

    for i in range(len(res)):
        res[i] = np.int16(32767*res[i]/mx)

    res = np.array(res)
    wavfile.write(output_name, sample_rate, res)

    return (loct, sl_t, timing_points) # amplitude result, sliders and frequency result

def convert_to_wav(song_name:str, output_file="audio.wav") -> str:
    """
    Converts the song to .wav, only if it's not already in wave format.
    Currently relies on file extension.
    Returns: the song_name that should be used afterwards.
    """
    extension = Path(song_name).suffix
    if(extension == ".mp3" or extension == ".ogg"):
        print("Converting to .wav...")
        subprocess.run(["ffmpeg", "-y", "-i", song_name, output_file], shell=False) 
        return output_file
    return song_name

def retrieve_all_from_song(filename, t0, t1, bpm, dta=0.001, dtf=0.01, threshold=0.06, show=True):
    # dt = sample interval
    # threshold is in percent

    if(t1 <= t0):
        print("ERROR : t1 <= t0\n")
        exit(1)

    # converts format to .wav
    new_fn = convert_to_wav(filename)

    print("Filtering song...")
    #void_freq_clean(new_fn, t0, t1, dtf, 20, 20000, 0.05, "crop1.wav")
    #def void_freq_clean(song_name, offset, songlen, segsize, minfreq, maxfreq, ampthr, output_name):

    print("Now retrieving the frequencies")
    (maxlist, maxamps) = retrieve_dominant_freqs(new_fn, t0, t1, dtf)
    #def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
    
    print("Now retrieving the amplitudes")
    amps = retrieve_dominant_amps(new_fn, t0, t1, dta, threshold, (4/(bpm/60))/4)

    print("Len of freqs : ", len(maxlist), "|", len(maxamps))
    print("Len of amps : ", len(maxlist), "|", len(amps))

    maxa = amps[0]
    for jj in amps:
        if(jj > maxa):
            maxa = jj

    for i in range(len(amps)):
        amps[i] = (amps[i] * 2000) / maxa

    if(show):
        timesF = [t0 + dtf*k for k in range(len(maxlist))]
        timesA = [t0 + dta*k for k in range(len(amps))]

        plt.plot(timesA, amps)
        plt.plot(timesF, maxlist)
        plt.show()

    # free()

# c-type
SONG_LEN = 7
OFFSET = 0.042
BPM = 149.3
SEGSIZE = 1/(BPM/60)
wavved_song = convert_to_wav("ctype.mp3")

'''
# tetris_2
SONG_LEN = 10
OFFSET = 0
BPM = 157
SEGSIZE = 1/(BPM/60)
wavved_song = convert_to_wav("tetris_2.wav")
'''
'''
# test
SONG_LEN = 1
OFFSET = 0
BPM = 240
SEGSIZE = 1/(BPM/60)
'''
'''
# gmtn
SONG_LEN = 5
OFFSET = 1.652
BPM = 155
SEGSIZE = 1/(BPM/60)
wavved_song = convert_to_wav("furioso melodia.mp3")
'''
'''
# E
SONG_LEN = 30
OFFSET = 2.641
BPM = 155
SEGSIZE = 1/(BPM/60)
wavved_song = convert_to_wav("rushe.mp3")
'''
'''
# Tsubaki
SONG_LEN = 10
OFFSET = 35.659
BPM = 199
SEGSIZE = 1/(BPM/60)
wavved_song = convert_to_wav("TSUBAKI.mp3")
'''
'''
# death
SONG_LEN = 8
OFFSET = 21.750
BPM = 180
SEGSIZE = 1/(BPM/60)
wavved_song = convert_to_wav("songs/Night of Knights.mp3")
'''

#wavved_song = convert_to_wav("tetris_2.wav")

NOTE_DIST = (2**(1/4))
OCTAVE_DIST = 0.05
keep_highest(wavved_song, OFFSET, SONG_LEN, SEGSIZE/4, 1, "Zblit.wav", minfreq=300, maxfreq=3000, ampthr=500)

print("yipee")