added rewrite of some core fcts (max_amps and max_freqs)

compare_plot.py

@@ -8,9 +8,10 @@ def compare_plot():
     beatmap = sl.Beatmap.from_path(filename)
     timing = beatmap.timing_points[0]
     bpm = timing.bpm
-    offset = timing.offset
+    offset = timing.offset.total_seconds() * 1000
-    timings, amplitudes = sound_process.process_song(beatmap.audio_filename, bpm, offset=offset, n_iter_2=-1)
+    data = sound_process.process_song(beatmap.audio_filename, bpm, offset0=offset, n_iter_2=48, divisor=4)
-    timings = [x.total_seconds() for x in timings]
+    
+    timings, amplitudes, freqs = [x[0].total_seconds() for x in data], [x[1] for x in data], [x[2] for x in data]
 
     original_times = [x.time.total_seconds() for x in beatmap.hit_objects(spinners=False) if x.time.total_seconds() <= timings[len(timings) - 1]]
 

debug.py

@@ -0,0 +1,279 @@
+from math import *
+import numpy as np
+from scipy.io import wavfile
+from scipy import signal
+import matplotlib.pyplot as plt
+import subprocess
+import wave as wv
+import struct
+import librosa
+import heapq
+import scipy
+import os
+import random
+from pathlib import Path
+from time import sleep
+from datetime import timedelta
+
+def adjust_timings(raw_data, snapped_data, indexes, thr=100):
+    """
+    adjusts weirdly snapped notes
+    """
+    current = 0
+
+    while(current < len(indexes)):
+    
+        if(current < len(indexes) - 3 and current % 2 == 1): # on a 1/4 beat
+            if(snapped_data[indexes[current]] > thr and snapped_data[indexes[current+1]] > thr and snapped_data[indexes[current+2]] > thr and snapped_data[indexes[current+3]] <= thr):
+                # -XXX_
+                snapped_data[indexes[current+3]] = snapped_data[indexes[current+2]]
+                snapped_data[indexes[current+2]] = 0
+
+        if(current > 0 and current < len(indexes) - 1 and current % 2 == 1):
+            if(snapped_data[indexes[current]] > thr and (snapped_data[indexes[current+1]] < thr or snapped_data[indexes[current-1]] < thr)):
+                #_X_
+                '''if(snapped_data[indexes[current-1]] < thr and raw_data[indexes[current-1]] > raw_data[indexes[current+1]]):
+                    snapped_data[indexes[current-1]] = snapped_data[indexes[current]]
+                else:
+                    snapped_data[indexes[current+1]] = snapped_data[indexes[current]]'''
+
+                snapped_data[indexes[current]] = 0
+
+        current += 1
+
+    print("Resnap done")
+    return snapped_data
+
+
+
+def snap(data, sample_rate, bpm, divisor, show=False):
+    # adjust time amplitudes to match the given BPM 
+    new = [0 for x in range(int(1000*len(data)/sample_rate))] # 1pt per millisecond
+    print("old =", len(data))
+    print("len =", 1000*len(data)/sample_rate)
+    k = 0
+    t = 0
+    percent = 0
+    for i in range(len(data)):
+
+        while(t < i/sample_rate):
+            t = k/(bpm*divisor)
+            k += 60
+        '''
+        if(np.abs(i/sample_rate - k/(bpm*divisor)) > np.abs(i/sample_rate - (k-60)/(bpm*divisor))):
+            k -= 60
+            t = k/(bpm*divisor)'''
+
+        if(i%(len(data)//100) == 0):
+            print(percent, "%")
+            percent += 1
+
+        if(int(t*1000) < len(new)):
+            new[int(t*1000)] = max(data[i], new[int(t*1000)])
+        else:
+            new[len(new)-1] = max(data[i], new[len(new)-1])
+
+    if(show):
+        t = [j/1000 for j in range(len(new))]
+        plt.plot(t, new)
+        plt.xlabel("Time (e)")
+        plt.ylabel("Amplitude")
+        plt.grid()
+        plt.show()
+
+    return new
+
+def snap2(data, sample_rate, bpm, first_offset=0, div=4, show=False, adjust=False):
+    """
+    data : list(int)
+    sample_rate : int
+    bpm = float
+    """
+
+    song_len = int(len(data)/sample_rate)
+
+    indexes = []
+    app = True
+
+    reduced = [0 for i in range(song_len*1000)]
+    new = [0 for i in range(song_len*1000)]
+
+    # build the reduced version
+    for i in range(len(data)):
+        x = int(i*1000/sample_rate)
+        if(x < len(reduced)):
+            reduced[x] = max(reduced[x], data[i])
+
+
+    print("Build done")
+    # snap
+    k = 0
+    current_t = first_offset
+
+    while(current_t < 0):
+        k += 1
+        current_t = first_offset + k*60/(bpm*div)
+
+    for j in range(len(new)):
+        if(j/1000 > current_t):
+            k += 1
+            current_t = first_offset + k*60/(bpm*div)
+            app = True
+
+        y = int(current_t*1000)
+        if(y < len(new)):
+            new[y] = max(new[y], reduced[j])
+
+            if(app):
+                indexes.append(y)
+                app = False 
+
+    print("Snap done")
+    
+    if(adjust):
+        print("Len :", len(indexes))
+
+        new = adjust_timings(reduced, new, indexes)
+
+    
+    if(show):
+        new2 = [0.9 if new[i] != 0 else 0 for i in range(len(new))]
+
+        t = [j/1000+first_offset for j in range(len(new))]
+        beats_1 = [0 for j in range(len(new))]
+        beats_2 = [0 for k in range(len(new))]
+        beats_4 = [0 for l in range(len(new))]
+
+        k = 0
+        current_t = first_offset
+
+        while(current_t < 0):
+            k += 1
+            current_t = first_offset + k*60/(bpm*div)
+
+        while(1000*current_t < len(new)):
+            beats_4[int(1000*current_t)] = 0.9
+
+            if(k % 2 == 0):
+                beats_2[int(1000*current_t)] = 0.92
+
+            if(k % 4 == 0):
+                beats_1[int(1000*current_t)] = 0.94
+
+            k += 1
+            current_t = first_offset + k*60/(bpm*div)
+
+        plt.plot(t, new2, "bo")
+        plt.plot(t, beats_4, "r-")
+        plt.plot(t, beats_2, "y-")
+        plt.plot(t, beats_1, "g-")
+        plt.xlabel("Time (s)")
+        plt.ylabel("Amplitude")
+        plt.grid()
+        plt.show()
+
+    return new
+
+def filter_peaks(data, sample_rate=44100, thr=1000):
+    tdata = []
+    times = []
+
+    for i in range(len(data)):
+        if data[i] > thr:
+            tdata.append(data[i])
+            times.append(i/sample_rate)
+
+    return (tdata, times)
+    '''
+    times is in seconds
+    '''
+
+def get_spacing(data, sample_rate=44100, show=False, retrieve=False):
+    tdata, times = filter_peaks(data, sample_rate=sample_rate)
+
+    absc = [i for i in range(len(times))]
+    dt = [0]
+
+    for i in range(1, len(times)):
+        dt.append(1000*(times[i]-times[i-1]))
+
+    if(show):
+        plt.plot(absc, dt)
+        plt.xlabel("x")
+        plt.ylabel("T(peak x) - T(peak x-1) (ms)")
+        plt.grid()
+        plt.show()
+
+    if(retrieve):
+        return dt
+
+    '''
+    post-condition : 
+    - dt[i] = time(peak number i) - time(peak number i-1)
+    - dt is in ms
+    '''
+
+def avg(data, i, j):
+    res = 0
+    for e in range(i, min(len(data), j)):
+        res += data[e]
+    return (res/(min(len(data), j) - i))
+
+def snap3(data, sample_rate=44100, mintime=10, initial_plot=False, after_plot=False):
+    '''
+    explaination : 
+    1) get the time differences (cf get_spacing)
+    2) for eack peak : 2 cases
+        - if it's farther than mintime (in ms) :
+            > calculate the weighted mean if all elements in temp_list
+            > place a note at that mean
+            > empty temp_list
+            > push the current peak to temp_list
+        - else :
+            > push the current peak to temp_list
+    '''
+    data_peaks, peak_times = filter_peaks(data, sample_rate=sample_rate)
+    time_diff = get_spacing(data, show=initial_plot, retrieve=True)
+
+    res_peaks = []
+    res_times = []
+
+    segments = []
+    seglen = []
+
+    current_left = 0
+
+    for i in range(len(peak_times)):
+        if(time_diff[i] > mintime):
+            segments.append([current_left, i])
+            seglen.append(peak_times[i]-peak_times[current_left])
+            res_peaks.append(avg(data_peaks, current_left, i))
+            res_times.append(peak_times[i])
+            current_left = i
+
+    for i in range(len(segments)):
+        print(segments[i], ":", seglen[i])
+
+    if(after_plot):
+        peakplot = []
+        diffplot = []
+        for x in range(len(peak_times)):
+            peakplot.append(peak_times[x]-peak_times[x]/1000)
+            peakplot.append(peak_times[x])
+            peakplot.append(peak_times[x]+peak_times[x]/1000)
+            diffplot.append(0)
+            diffplot.append(time_diff[x])
+            diffplot.append(0)
+
+        plt.plot(res_times, res_peaks, "ro", label="placed beats")
+        plt.plot(peakplot, diffplot, label="derivatine of time")
+        plt.xlabel("t (s)")
+        plt.ylabel(".")
+        plt.legend(loc="upper left")
+        plt.grid()
+        plt.show()
+
+    return (res_peaks, res_times)
+    """ res_times is in seconds """
+
+    

main.py

@@ -3,18 +3,21 @@ import slider as sl
 from datetime import timedelta
 import place
 import sound_process
+import os
 import numpy as np
 
 def main():
     filename = fd.askopenfilename()
+    os.chdir(os.path.dirname(filename))
     beatmap = sl.Beatmap.from_path(filename)
     timing = beatmap.timing_points[0]
     bpm = timing.bpm
-    offset = timing.offset
+    offset = timing.offset.total_seconds() * 1000
     print(beatmap.audio_filename)
 
-    timings, amplitudes = sound_process.process_song(beatmap.audio_filename, bpm, offset=offset, n_iter_2=-1)
+    amplitudes, timings, frequencies = sound_process.process_song(beatmap.audio_filename, bpm, offset0=offset, n_iter_2=-1)
     # NOTE : remove n_iter_2 to map the whole music
+    timings = [timedelta(seconds=x) for x in timings]
 
     beatmap._hit_objects = place.greedy(bpm, offset, timings, amplitudes)
     #beatmap._hit_objects = [sl.Slider(sl.Position(0, 0), timedelta(milliseconds=3), timedelta(milliseconds=130), 0, sl.curve.Linear([sl.Position(0, 0), sl.Position(100, 100)], 100), 100, 2, 1, 1, 1, timing.ms_per_beat, [], [],)]

place.py

@@ -12,23 +12,36 @@ def beatify(bpm:float, offset:int, time:timedelta) -> float:
 def debeatify(bpm:float, offset:int, beat:int) -> timedelta:
     return timedelta(milliseconds=(beat*60000/bpm) + offset)
 
-def f(intensity): return np.pi/2 - np.arctan(2*intensity - 5)
+def f(intensity): return np.pi/2 - np.arctan(2*intensity) + np.pi/5
+#def f(intensity): return np.pi/2 
 
 def greedy(bpm:int, offset:int, timings:list, amplitudes:list):
     """
     input: takes Alexandre's note selection / intensity data
     output: list of object type / position
     """
-    flow = 1
+    new_combo = True
-    notes = [sl.HitObject(0, timedelta(milliseconds=0), 0)] * len(timings)
+    flow = 0
-    beats = [beatify(bpm, offset, timing) for timing in timings]
+    notes = [sl.HitObject(sl.Position(260, 188), timedelta(milliseconds=0), 0)] * len(timings)
+    beats = [int(beatify(bpm, offset, timing)*4) for timing in timings]
+    last_position = (260, 188)
+    max_x, max_y, min_x, min_y = -np.inf, -np.inf, np.inf, np.inf
     for (delta, note_beat, intensity, i) in zip(timings, beats, amplitudes, range(len(timings))):
         try:
-            duration = note_beat - beats[i + 1]
+            if int(note_beat/32) % 2: #test si la mesure est impaire ou non
+                new_combo = not (flow == 1)
+                flow = 1
+            else:
+                new_combo = (flow == 1)
+                flow = -1
+            duration = abs(note_beat - beats[i + 1])
+            print(duration)
+            """
             if duration in (QUARTER, HALF):
                 notes[i] = sl.Circle(sl.Position(0, 0), delta, 0)
             notes[i] = sl.Circle(sl.Position(0, 0), delta, 0)
             """
+            """
             elif duration % 2 == 0:
                 rhythms.insert(0, f"slider {duration}")
             else:
@@ -36,18 +49,37 @@ def greedy(bpm:int, offset:int, timings:list, amplitudes:list):
         """
         except IndexError:
             notes[i] = sl.Circle(sl.Position(0, 0), delta, 0)
-        # TODO mettre à jour flow
+
-        """
         if len(notes) > 2:
-            angle = flow * f(rhythm.intensite)
+            if duration == QUARTER:
-            x1, y1 = notes[i-2].position
+                x2, y2 = int(notes[i-1].position.x), int(notes[i-1].position.y)
-            x2, y2 = notes[i-1].position
+                notes[i] = sl.Circle(sl.Position(x2, y2), delta, 0)
-            old_angle = np.arctan2((y1, y2), (x1, x2))
+           
-            x3 = x2 + (intensity * np.cos(angle + old_angle))
+            else:
-            y3 = y2 + (intensity * np.sin(angle + old_angle))
+                    angle = flow * f(intensity/100)
+                    x1, y1 = int(notes[i-2].position.x), int(notes[i-2].position.y)
+                    x2, y2 = int(notes[i-1].position.x), int(notes[i-1].position.y)
+                    old_angle = np.arctan2([y2-y1], [x2-x1])
+                    x3 = x2 + ((duration + new_combo*4)* np.cos(angle + old_angle))
+                    y3 = y2 + ((duration + new_combo*4) * np.sin(angle + old_angle))
+                    notes[i] = sl.Circle(sl.Position(int(x3[0]), int(y3[0])), delta, 0)
         else:
-            pass
+            notes[i] = sl.Circle(sl.Position(260, 188), delta, 0)
-        """
+        if notes[i].position.x > max_x:
+            max_x = notes[i].position.x
+        elif notes[i].position.x < min_x:
+            min_x = notes[i].position.x
+        if notes[i].position.y > max_y:
+            max_y = notes[i].position.y
+        elif notes[i].position.y < min_y:
+            min_y = notes[i].position.y
+
+        notes[i].new_combo = new_combo
+    factor_x = 1/(max_x - min_x)
+    factor_y = 1/(max_y - min_y)
+    for note in notes:
+        note.position = sl.Position((note.position.x-min_x)*factor_x*512, (note.position.y-min_y)*factor_y*384)
+
     return notes
         
 

requirements.txt

@@ -1,3 +1,4 @@
+slider
 audioread==3.0.1
 certifi==2024.2.2
 cffi==1.16.0

script (1).py

@@ -0,0 +1,145 @@
+import numpy as np
+import scipy as scp
+import heapq
+
+def retrieve_dominant_freqs(song_name, offset, songlen, segsize):
+    # returns a list with peak frequencies alongside the sample rate
+    # /!\ song_name is specified to be a list, NOT a list of couples (aka song is mono)
+    # segsize is in seconds
+    
+    # remove high_pitched/low-pitched frequencies
+    minfreq = 110
+    maxfreq = 440*8
+    
+    # cutting the song to only keep the one we're interested in
+    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen+offset), "-i", song_name, "crop.wav"], shell=False) 
+
+    # extracting data from cropped song
+    sample_rate, song_data = wavfile.read("crop.wav")
+    blit = int(sample_rate*segsize) # Te
+
+    # remove the copy of the song
+    subprocess.run(["rm", "crop.wav"], shell=False)
+    
+    # calculate the frequencies associated to the FFTs
+    pfreq = scipy.fft.rfftfreq(blit, 1/sample_rate)
+
+    # left boundary of segment to crop
+    current_time = offset
+
+    # list of FFTs
+    fft_list = []
+    
+    # number of samples
+    k = 0
+
+    while(current_time <= songlen+offset):
+        # 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(global_data[left:right])
+        fft_list.append(pff)
+
+        # just to avoid what causes 0.1 + 0.1 == 0.2 to be False
+        k += 1
+        current_time = offset + k*segsize
+
+    # spacing between samples (time)
+    fe = segsize/sample_rate
+    
+    # list that will contain the maximum frequencies/amplitudes for all FFTs
+    maxlist = []
+    maxamps = []
+    
+    # find all maximums
+    for i in range(len(fft_list)):
+        current_max = -1
+        current_fmax = 0
+        
+        for j in range(len(fft_list[i])): 
+            if(pfreq[j] < maxfreq & pfreq[j] >= minfreq & np.abs(fft_list[i][j]) > current_max):
+                current_max = np.abs(fft_list[i][j])
+                current_fmax = pfreq[j]
+                
+        maxlist.append(current_fmax)
+        maxamps.append(current_max)
+        
+    # gg
+    # maxlist[i] corresponds to time (offset + i*segsize)
+    return (maxlist, maxamps, segsize)
+
+def retrieve_dominant_amps(song_name, offset, songlen, segsize, percent):
+    # returns a list with the percent% peak amplitudes alongside the sample rate
+    # /!\ song_name is specified to be a list, NOT a list of couples (aka song is mono)
+    # segsize is in seconds
+    
+    # cutting the song to only keep the one we're interested in
+    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen+offset), "-i", song_name, "crop.wav"], shell=False) 
+
+    # extracting data from cropped song
+    sample_rate, song_data = wavfile.read("crop.wav")
+    blit = int(sample_rate*segsize) # Te
+
+    # remove the copy of the song
+    subprocess.run(["rm", "crop.wav"], shell=False)
+    
+    # which notes will be voided
+    is_locked = [False for i in range(len(song_data))]
+    x = int((len(song_data)*threshold)//100)
+
+    print("Retreiving the", int(x), "/", len(song_data), "highest values")
+    elements = heapq.nlargest(int(x), enumerate(song_data), key=lambda x: x[1])
+    #returns a list of couples [id, value]
+
+    for idx in range(len(elements)):
+        is_locked[elements[idx][0]] = True
+
+    for r in range(len(song_data)):
+        if(is_locked[r] == False):
+            song_data[r] = 0
+    
+    # now we need to reduce song_data so that it matches the length of the previous function's return
+    res = []
+    k = 0
+    current_time = offset 
+
+    while(current_time <= songlen+offset):
+        # 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)
+        
+        # merge the segment into one value
+        cmax = 0
+        for i in range(left_id, right_id):
+            if(i < len(song_data) & cmax < song_data[i]):
+                cmax = song_data[i]
+                
+        res.append(cmax)
+            
+        k += 1
+        current_time = current_time + k*segsize
+        
+    # gg
+    # res[i] corresponds to time (offset + i*segsize)
+    return res
+        
+print("done")
+
+
+
+
+
+
+
+
+
+
+
+
+

sound_process.py

@@ -15,7 +15,7 @@ from pathlib import Path
 from time import sleep
 from datetime import timedelta
 
-WORKING_SAMPLE_RATE = 1000
+import debug
 
 print("Starting...\n")
 
@@ -30,14 +30,12 @@ def filter_n_percent_serial(song_name, offset, n_iter, step, threshold):
     filter data associated with song_name to keep only the highest threshold% values
     """
 
-    offset = offset.total_seconds()
+    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(offset+step*n_iter), "-i", song_name, "crop.wav"], shell=False) 
-
-    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(offset+step*n_iter), "-i", song_name, "crop.wav"]) 
 
     sample_rate, global_data = wavfile.read('crop.wav')
 
-    subprocess.run(["clear"])
+    subprocess.run(["clear"], shell=False)
-    subprocess.run(["rm", "crop.wav"])
+    subprocess.run(["rm", "crop.wav"], shell=False)
 
     for i in range(n_iter):
         print(i, "/", n_iter)
@@ -94,46 +92,38 @@ def round_t(id, sample_rate, bpm, div, offset, k0):
 def compress(Zxx):
     res = []
 
-def get_freq(song_name, offset, step, songlen, data, display=False):
+def get_freq(song_name, times, width=1000, display=False):
     """
-    for a given list of amplitudes, returns the corresponding peak frequencies
+    for a given list of times (in seconds), returns the corresponding peak frequencies
     """
-    offset = offset.total_seconds()
-    fft_list = []
-    times = []
-    current_time = offset
-    k = 0
-    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(offset+songlen),"-i",song_name, "crop.wav"]) 
 
-    sample_rate, global_data = wavfile.read("crop.wav")
+    subprocess.run(["ffmpeg", "-ss", str(0), "-t", str(max(np.array(times))), "-i", song_name, "crop.wav"], shell=False) 
-    #blit = int(len(global_data) / len(data))
-    blit = int(sample_rate*step)
 
-    subprocess.run(["clear"])
+    sample_rate, global_data = wavfile.read(song_name)
-    subprocess.run(["rm", "crop.wav"])
+    #blit = int(sample_rate*step)
 
-    pfreq = scipy.fft.rfftfreq(blit, 1/sample_rate)
+    subprocess.run(["clear"], shell=False)
+    subprocess.run(["rm", "crop.wav"], shell=False)
 
-    print("len : ", len(global_data))
+    pfreq = scipy.fft.rfftfreq(2*width, 1/sample_rate)
-    print("len : ", len(data))
 
-    frequencies = [0 for s in range(len(data))]
+    frequencies = [0 for s in range(len(times))]
     print(len(pfreq))
 
-    for s in range(len(data)):
+    for s in range(len(times)):
-        if(data[s] != 0):
+        left = max(0, int(times[s]*44100)-width)
-            pff = scipy.fft.rfft(global_data[int(s*len(global_data)/len(data)):int(WORKING_SAMPLE_RATE*step+int(s*len(global_data)/len(data)))])
+        right = min(len(global_data), int(times[s]*44100)+width)
-            
+        pff = scipy.fft.rfft(global_data[left:right])
-            mx = max(np.abs(pff))
-            for id in range(len(pff)):
-                if frequencies[s] == 0 and np.abs(pff[id]) == mx:
-                    frequencies[s] = pfreq[id]
 
-        elif s != 0:
+        #print(len(pff), len(pfreq))
-            frequencies[s] = 0
+            
+        mx = max(np.abs(pff))
+        for id in range(len(pff)):
+            if frequencies[s] == 0 and np.abs(pff[id]) == mx:
+                frequencies[s] = pfreq[id]
 
     if(display):
-        plt.plot([offset+t/1000 for t in range(len(data))], frequencies)
+        plt.plot(times, frequencies)
         plt.grid()
         plt.xlabel("Time (s)")
         plt.ylabel("Dominant frequency (Hz)")
@@ -171,22 +161,20 @@ def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr,
     write : bool (should be set to True)
     output_file : technical
     """
-    offset = offset.total_seconds()
-
     fft_list = []
     times = []
     current_time = offset
     k = 0
 
-    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen+offset), "-i", song_name, "crop.wav"]) 
+    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(songlen+offset), "-i", song_name, "crop.wav"], shell=False) 
 
     sample_rate, raw_global_data = wavfile.read("crop.wav")
     blit = int(sample_rate*increment)
 
     global_data = [0 for i in range(len(raw_global_data))]
 
-    subprocess.run(["clear"])
+    #subprocess.run(["clear"])
-    subprocess.run(["rm", "crop.wav"])
+    subprocess.run(["rm", "crop.wav"], shell=False)
 
     a = 0
 
@@ -263,7 +251,7 @@ def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr,
             res[i] = np.int16(32767*res[i]/mx)
 
         res = np.array(res)
-        wavfile.write(output_file, WORKING_SAMPLE_RATE, res)
+        wavfile.write(output_file, 44100, res)
 
     #plt.plot(np.abs(pfreq[:len(fft_list[0])]), np.abs(fft_list[0]))
     #plt.grid()
@@ -271,29 +259,11 @@ def void_freq(song_name, offset, songlen, increment, minfreq, maxfreq, upperthr,
 
     print("Done")
 
-def get_tpts(data, sample_rate, thr):
+def convert_tuple(data, times):
-    res = []
-    for i in range(len(data)):
-        if(data[i] > thr):
-            res.append(i/sample_rate)
-    
-    for i in res:
-        print(i)
-    return res
-
-def test_sample(timelist):
-    for i in range(1,len(timelist)):
-        #os.system('play -n synth %s sin %s' % (0.05, 440))
-        for k in range(random.randint(1, 10)):
-            print("E", end="")
-        print("F")
-        sleep(timelist[i]-timelist[i-1])
-
-def convert_tuple(datares, freq):
     """
-    Takes datares and converts it to a list of tuples (amplitude, datetimes)
+    Takes data and converts it to a list of tuples (amplitude, datetimes)
     """
-    return [(timedelta(milliseconds=i), datares[i], freq[i]) for i in range(len(datares)) if datares[i] > 0]
+    return [(times[i], data[i]) for i in range(len(data))]
 
 def get_songlen(filename):
     """
@@ -304,201 +274,21 @@ def get_songlen(filename):
 
     return (len(global_data)/sample_rate)
 
-def snap(data, sample_rate, bpm, divisor, show=False):
+def convert_to_wav(song_name:str, output_file="audio.wav") -> str:
-    # adjust time amplitudes to match the given BPM 
-    new = [0 for x in range(int(1000*len(data)/sample_rate))] # 1pt per millisecond
-    print("old =", len(data))
-    print("len =", 1000*len(data)/sample_rate)
-    k = 0
-    t = 0
-    percent = 0
-    for i in range(len(data)):
-
-        while(t < i/sample_rate):
-            t = k/(bpm*divisor)
-            k += 60
-        '''
-        if(np.abs(i/sample_rate - k/(bpm*divisor)) > np.abs(i/sample_rate - (k-60)/(bpm*divisor))):
-            k -= 60
-            t = k/(bpm*divisor)'''
-
-        if(i%(len(data)//100) == 0):
-            print(percent, "%")
-            percent += 1
-
-        if(int(t*1000) < len(new)):
-            new[int(t*1000)] = max(data[i], new[int(t*1000)])
-        else:
-            new[len(new)-1] = max(data[i], new[len(new)-1])
-
-    if(show):
-        t = [j/1000 for j in range(len(new))]
-        plt.plot(t, new)
-        plt.xlabel("Time (e)")
-        plt.ylabel("Amplitude")
-        plt.grid()
-        plt.show()
-
-    return new
-
-def adjust_timings(raw_data, snapped_data, indexes, thr=100):
     """
-    adjusts weirdly snapped notes
+    Converts the song to .wav, only if it's not already in wave format.
-    """
+    Currently relies on file extension.
-    current = 0
-
-    while(current < len(indexes)):
-    
-        if(current < len(indexes) - 3 and current % 2 == 1): # on a 1/4 beat
-            if(snapped_data[indexes[current]] > thr and snapped_data[indexes[current+1]] > thr and snapped_data[indexes[current+2]] > thr and snapped_data[indexes[current+3]] <= thr):
-                # -XXX_
-                snapped_data[indexes[current+3]] = snapped_data[indexes[current+2]]
-                snapped_data[indexes[current+2]] = 0
-
-        if(current > 0 and current < len(indexes) - 1 and current % 2 == 1):
-            if(snapped_data[indexes[current]] > thr and (snapped_data[indexes[current+1]] < thr or snapped_data[indexes[current-1]] < thr)):
-                #_X_
-                '''if(snapped_data[indexes[current-1]] < thr and raw_data[indexes[current-1]] > raw_data[indexes[current+1]]):
-                    snapped_data[indexes[current-1]] = snapped_data[indexes[current]]
-                else:
-                    snapped_data[indexes[current+1]] = snapped_data[indexes[current]]'''
-
-                snapped_data[indexes[current]] = 0
-
-        current += 1
-
-    print("Resnap done")
-    return snapped_data
-
-def snap2(data, sample_rate, bpm, first_offset=0, div=4, show=False, adjust=False):
-    """
-    data : list(int)
-    sample_rate : int
-    bpm = float
-    """
-
-    song_len = int(len(data)/sample_rate)
-
-    indexes = []
-    app = True
-
-    reduced = [0 for i in range(song_len*1000)]
-    new = [0 for i in range(song_len*1000)]
-
-    # build the reduced version
-    for i in range(len(data)):
-        x = int(i*1000/sample_rate)
-        if(x < len(reduced)):
-            reduced[x] = max(reduced[x], data[i])
-
-
-    print("Build done")
-    # snap
-    k = 0
-    current_t = first_offset
-
-    while(current_t < 0):
-        k += 1
-        current_t = first_offset + k*60/(bpm*div)
-
-    for j in range(len(new)):
-        if(j/1000 > current_t):
-            k += 1
-            current_t = first_offset + k*60/(bpm*div)
-            app = True
-
-        y = int(current_t*1000)
-        if(y < len(new)):
-            new[y] = max(new[y], reduced[j])
-
-            if(app):
-                indexes.append(y)
-                app = False 
-
-    print("Snap done")
-    
-    if(adjust):
-        print("Len :", len(indexes))
-
-        new = adjust_timings(reduced, new, indexes)
-
-    
-    if(show):
-
-        t = [j/1000+first_offset for j in range(len(new))]
-        scatter_t = [t[i] for i in range(len(new)) if new[i] != 0]
-        scatter_chosen_rhythm = [0.9 for i in range(len(new)) if new[i] != 0 ]
-        beats_1 = [0 for j in range(len(new))]
-        beats_2 = [0 for k in range(len(new))]
-        beats_4 = [0 for l in range(len(new))]
-
-        k = 0
-        current_t = first_offset
-
-        while(current_t < 0):
-            k += 1
-            current_t = first_offset + k*60/(bpm*div)
-
-        while(1000*current_t < len(new)):
-            beats_4[int(1000*current_t)] = 0.9
-
-            if(k % 2 == 0):
-                beats_2[int(1000*current_t)] = 0.902
-
-            if(k % 4 == 0):
-                beats_1[int(1000*current_t)] = 0.91
-
-            if(k % 16 == 0):
-                beats_1[int(1000*current_t)] = 0.915
-
-            k += 1
-            current_t = first_offset + k*60/(bpm*div)
-
-        points = plt.scatter(scatter_t, scatter_chosen_rhythm, marker="o", label="Detected Rhythm")
-        div1_plot, = plt.plot(t, beats_4, "b-", label="1/4")
-        div2_plot, = plt.plot(t, beats_2, "r-", label="1/2")
-        div3_plot, = plt.plot(t, beats_1, "black", label="1/1")
-        plt.xlabel("Time (s)")
-        #plt.ylabel("Amplitude")
-        plt.legend(handles=[points, div1_plot, div2_plot, div3_plot])
-        plt.grid()
-        plt.show()
-
-    return new
-
-def convert_song(song_name:str, output_file="audio.wav") -> str:
-    """
-    Converts the song to .wav AND lower its sample rate to 1000.
     Returns: the song_name that should be used afterwards.
     """
-    subprocess.run(["ffmpeg", "-y", "-i", song_name, "-ar", "1000", output_file]) 
+    extension = Path(song_name).suffix
-    return output_file
+    match extension:
+        case ".mp3" | ".ogg":
+            print("Converting to .wav...")
+            subprocess.run(["ffmpeg", "-y", "-i", song_name, output_file], shell=False) 
+            return output_file
+    return song_name
 
-def quantify(time: timedelta, bpm, offset, snapping):
+def process_song(filename, bpm, offset0=0, div_len_factor=1, n_iter_2=-1, threshold=0.5, divisor=4):
-    """
-    Input: timedelta, bpm, offset, and snapping divisor (2 for 1/2, etc...)
-    Returns a timedelta that is properly timed to the map.
-    """
-    offset_ms = offset.total_seconds() / 1000
-    time_ms = time.total_seconds() * 1000
-    time_spacing = (60000/bpm)/snapping
-    beats_away = round((time_ms - offset_ms)/time_spacing)
-    new_time = timedelta(milliseconds=time_spacing*beats_away + offset_ms)
-    return new_time
-
-def quantify_all(amplitudes_ugly, bpm, offset_ms, divisor):
-    n = len(amplitudes_ugly)
-    covered = [False] * n
-    times = []
-    amplitudes = []
-    for i in range(n):
-        if amplitudes_ugly[i] != 0 and not covered[i]:
-            times.append(quantify(timedelta(milliseconds=i), bpm, offset_ms, divisor))
-            amplitudes.append(amplitudes_ugly[i])
-            covered[i] = True
-    return times, amplitudes
-
-def process_song(filename, bpm, offset=timedelta(milliseconds=0), div_len_factor=1, n_iter_2=-1, threshold=0.5, divisor=4):
     """
     filename : string (name of the song)
     offset : int [+] (song mapping will start from this time in seconds, default is 0)
@@ -509,7 +299,9 @@ def process_song(filename, bpm, offset=timedelta(milliseconds=0), div_len_factor
     divisor : int [+] (beat divisor used to snap the notes, default is 4)
     """
 
-    filename = convert_song(filename)
+    filename = convert_to_wav(filename)
+
+    offset = offset0/1000
 
     div_len = div_len_factor*60/bpm-0.01
 
@@ -517,22 +309,33 @@ def process_song(filename, bpm, offset=timedelta(milliseconds=0), div_len_factor
     song_len = get_songlen(filename)
 
     if(n_iter == -1):
-        n_iter = floor((song_len-offset.total_seconds())/div_len)-1
+        n_iter = int((song_len-offset/1000)/div_len)-1
 
     filtered_name = f"{filename}_trimmed.wav"
 
-    void_freq(filename, offset, min(song_len, offset.total_seconds()+div_len*(n_iter+1)+0.01), 4*60/bpm, minfreq=0, maxfreq=220, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 5.0, leniency = 0.005, write=True, linear=False, output_file=filtered_name)
+    void_freq(filename, offset, min(song_len, offset+div_len*(n_iter+1)+0.01), 4*60/bpm, minfreq=0, maxfreq=220, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 5.0, leniency = 0.005, write=True, linear=False, output_file=filtered_name)
-    #void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=2500, ampthr=60, ampfreq = 1200, ampval = 1/2000, leniency = 0.0, write=True, linear=True, output_file=filtered_name)
+    
-    amplitudes_ugly = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold) 
+    datares = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold) 
-    #datares = snap(datares, WORKING_SAMPLE_RATE, bpm, 4, True)
+
-    times, amplitudes = quantify_all(amplitudes_ugly, bpm, offset, divisor)
+    #snapped_data = amplitude
-    #frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True)
+    #times in ms
+    (snapped_data, times) = debug.snap3(datares, mintime=50, initial_plot=True, after_plot=True)
+
+    #frequencies=get_freq(filtered_name, offset, div_len, div_len*n_iter, snapped_data, True)
+    frequencies = get_freq(filtered_name, times, display=True)
+
     Path(f"{filename}_trimmed.wav").unlink()
-    #return convert_tuple(datares, frequencies)
+    return snapped_data, times, frequencies
-    return times, amplitudes
+
+    '''
+    datares = debug.snap2(datares, 44100, bpm, first_offset=offset, div=divisor, show=True, adjust=True)
+    frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True)
+    Path(f"{filename}_trimmed.wav").unlink()
+    return convert_tuple(datares, frequencies)
+    '''
 
 def main():
-    data = process_song("tetris_4.wav", 160, n_iter_2=48, threshold=100)
+    aa, bb, cc = process_song("tetris_4.wav", 160, n_iter_2=48)
     #print(data)
     print("Program finished with return 0")
 
@@ -587,7 +390,7 @@ if(False):
     #t, f, Zxx = fct("deltamax.wav", 9.992, 0.032, 20, 3000, False)
     #t, f, Zxx = fct("da^9.wav", 8.463, 0.032, 20, 5000, False)
     t, f, Zxx = fct("13.  Cosmic Mind.wav", 0, 0.032, 20, 5000, False)
-    #t, f, Zxx = fct("Furioso Melodia WORKING_SAMPLE_RATE.wav", 4, 0.032, 8, 3000, False)
+    #t, f, Zxx = fct("Furioso Melodia 44100.wav", 4, 0.032, 8, 3000, False)
     #t, f, Zxx = fct("changing.wav", 0, 0.05, 3.9, 5000, False)
     #fct("worlds_end_3.wav", 75, (60/178)/4, 75+2, 2500)
 
@@ -598,7 +401,7 @@ if(False):
     (t, data) = peaks("worlds_end_3.wav", 74.582, 6, False, 0.9)
     #(t, data) = peaks("da^9.wav", 8.463, 301.924 - 8.463, False, 0.95)
     #(t, data) = peaks("deltamax.wav", 8.463, 30101.924 - 8.463, False, 0.92)
-    da = find_bpm(t, WORKING_SAMPLE_RATE, data, 100, 200, 1, 10)
+    da = find_bpm(t, 44100, data, 100, 200, 1, 10)
     print("BPM data is", da)'''
 
     #data = [-1 for i in range(int(x))]
@@ -651,13 +454,13 @@ def fct(song_name, offset, increment, songlen, maxfreq, display):
     current_time = offset
     k = 0
     while(current_time <= songlen):
-        subprocess.run(["ffmpeg", "-ss", str(current_time), "-t", str(increment), "-i", song_name, "crop.wav"]) 
+        subprocess.run(["ffmpeg", "-ss", str(current_time), "-t", str(increment), "-i", song_name, "crop.wav"], shell=False) 
 
         sample_rate, audio_data = wavfile.read('crop.wav')
         size = audio_data.size
 
         #subprocess.run(["clear"])
-        subprocess.run(["rm", "crop.wav"])
+        subprocess.run(["rm", "crop.wav"], shell=False)
 
         # do stuff here
         #f, t, Zxx = signal.stft(audio_data, sample_rate, nperseg=1000)
@@ -800,16 +603,15 @@ def extract_peaks_v2(song_data, sample_rate, offset, display, threshold, seglen)
     return (t, song_data)
 
 def peaks(song_name, offset, length, display, thr):
-    offset = offset.total_seconds()
+    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"], shell=False) 
-    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"]) 
 
     sample_rate, audio_data = wavfile.read('crop.wav')
 
-    subprocess.run(["clear"])
+    #subprocess.run(["clear"])
-    subprocess.run(["rm", "crop.wav"])
+    subprocess.run(["rm", "crop.wav"], shell=False)
 
     #return extract_peaks(audio_data, sample_rate, offset, display, thr)
-    return extract_peaks_v2(audio_data, sample_rate, offset, display, thr, WORKING_SAMPLE_RATE*2)
+    return extract_peaks_v2(audio_data, sample_rate, offset, display, thr, 44100*2)
 
 def find_bpm(sample_rate, data, minbpm, maxbpm, step, width):
     optimal = minbpm
@@ -1011,17 +813,15 @@ def filter_n_percent(song_name, offset, length, threshold, reduce, show):
     # threshold is in ]0, 100]
     # filter data associated with song_name to keep only the highest threshold% values
 
-    offset = offset.total_seconds()
+    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"], shell=False) 
-
-    subprocess.run(["ffmpeg", "-ss", str(offset), "-t", str(length), "-i", song_name, "crop.wav"]) 
 
     sample_rate, song_data = wavfile.read('crop.wav')
 
-    subprocess.run(["clear"])
+    subprocess.run(["clear"], shell=False)
-    subprocess.run(["rm", "crop.wav"])
+    subprocess.run(["rm", "crop.wav"], shell=False)
 
     if(reduce):
-        (song_data,e) = to_ms(song_data, WORKING_SAMPLE_RATE, 1)
+        (song_data,e) = to_ms(song_data, 44100, 1)
         sample_rate = 1000
 
     mx = max(song_data)
@@ -1051,4 +851,22 @@ def filter_n_percent(song_name, offset, length, threshold, reduce, show):
         plt.show()
 
     return song_data
+
+def get_tpts(data, sample_rate, thr):
+    res = []
+    for i in range(len(data)):
+        if(data[i] > thr):
+            res.append(i/sample_rate)
+    
+    for i in res:
+        print(i)
+    return res
+
+def test_sample(timelist):
+    for i in range(1,len(timelist)):
+        #os.system('play -n synth %s sin %s' % (0.05, 440))
+        for k in range(random.randint(1, 10)):
+            print("E", end="")
+        print("F")
+        sleep(timelist[i]-timelist[i-1])
 '''