import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 



audio = pd.read_csv('testaudio.csv')
audio = np.array(audio)
audio = np.delete(audio, 0,1)
final = np.array([])

def compress(audio, number):
	x = []
	clean = np.array(x)
	audio = audio[np.argmax(audio)-17000 :np.argmax(audio)+17000]
	
	#cuts the audio file in to 150 different strips and adds it together
	for i in range(0,len(audio)-(len(audio)/75),len(audio)/75):
		total = 0
		for x in range(len(audio)/75):
			total += abs(audio[i+x])
		clean = np.append(clean,total)

	#normalizes for the maximum value to be 1
	maximum = max(clean)
	for i in range(0,len(clean)):
		clean[i] = clean[i]/maximum
		
	clean = clean[np.argmax(clean)-35 :np.argmax(clean)+35]
	#plt.plot(clean)
	#plt.show()
	clean = np.insert(clean,0,number)
	global final
	final = np.concatenate([final,clean], axis=1)
	return clean

def reshaper():
	return np.reshape(final, (final.size/71, 71))
	

compress(audio,1)
compress(audio,2)
np.savetxt("foo.csv",reshaper(),delimiter=",")
e8c4804e38f1c73859d4f16b43a2de0d0b2ad2ae
<mask token>


def compress(audio, number):
    x = []
    clean = np.array(x)
    audio = audio[np.argmax(audio) - 17000:np.argmax(audio) + 17000]
    for i in range(0, len(audio) - len(audio) / 75, len(audio) / 75):
        total = 0
        for x in range(len(audio) / 75):
            total += abs(audio[i + x])
        clean = np.append(clean, total)
    maximum = max(clean)
    for i in range(0, len(clean)):
        clean[i] = clean[i] / maximum
    clean = clean[np.argmax(clean) - 35:np.argmax(clean) + 35]
    clean = np.insert(clean, 0, number)
    global final
    final = np.concatenate([final, clean], axis=1)
    return clean


<mask token>

<mask token>


def compress(audio, number):
    x = []
    clean = np.array(x)
    audio = audio[np.argmax(audio) - 17000:np.argmax(audio) + 17000]
    for i in range(0, len(audio) - len(audio) / 75, len(audio) / 75):
        total = 0
        for x in range(len(audio) / 75):
            total += abs(audio[i + x])
        clean = np.append(clean, total)
    maximum = max(clean)
    for i in range(0, len(clean)):
        clean[i] = clean[i] / maximum
    clean = clean[np.argmax(clean) - 35:np.argmax(clean) + 35]
    clean = np.insert(clean, 0, number)
    global final
    final = np.concatenate([final, clean], axis=1)
    return clean


def reshaper():
    return np.reshape(final, (final.size / 71, 71))


compress(audio, 1)
compress(audio, 2)
np.savetxt('foo.csv', reshaper(), delimiter=',')

<mask token>
audio = pd.read_csv('testaudio.csv')
audio = np.array(audio)
audio = np.delete(audio, 0, 1)
final = np.array([])


def compress(audio, number):
    x = []
    clean = np.array(x)
    audio = audio[np.argmax(audio) - 17000:np.argmax(audio) + 17000]
    for i in range(0, len(audio) - len(audio) / 75, len(audio) / 75):
        total = 0
        for x in range(len(audio) / 75):
            total += abs(audio[i + x])
        clean = np.append(clean, total)
    maximum = max(clean)
    for i in range(0, len(clean)):
        clean[i] = clean[i] / maximum
    clean = clean[np.argmax(clean) - 35:np.argmax(clean) + 35]
    clean = np.insert(clean, 0, number)
    global final
    final = np.concatenate([final, clean], axis=1)
    return clean


def reshaper():
    return np.reshape(final, (final.size / 71, 71))


compress(audio, 1)
compress(audio, 2)
np.savetxt('foo.csv', reshaper(), delimiter=',')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
audio = pd.read_csv('testaudio.csv')
audio = np.array(audio)
audio = np.delete(audio, 0, 1)
final = np.array([])


def compress(audio, number):
    x = []
    clean = np.array(x)
    audio = audio[np.argmax(audio) - 17000:np.argmax(audio) + 17000]
    for i in range(0, len(audio) - len(audio) / 75, len(audio) / 75):
        total = 0
        for x in range(len(audio) / 75):
            total += abs(audio[i + x])
        clean = np.append(clean, total)
    maximum = max(clean)
    for i in range(0, len(clean)):
        clean[i] = clean[i] / maximum
    clean = clean[np.argmax(clean) - 35:np.argmax(clean) + 35]
    clean = np.insert(clean, 0, number)
    global final
    final = np.concatenate([final, clean], axis=1)
    return clean


def reshaper():
    return np.reshape(final, (final.size / 71, 71))


compress(audio, 1)
compress(audio, 2)
np.savetxt('foo.csv', reshaper(), delimiter=',')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 



audio = pd.read_csv('testaudio.csv')
audio = np.array(audio)
audio = np.delete(audio, 0,1)
final = np.array([])

def compress(audio, number):
	x = []
	clean = np.array(x)
	audio = audio[np.argmax(audio)-17000 :np.argmax(audio)+17000]
	
	#cuts the audio file in to 150 different strips and adds it together
	for i in range(0,len(audio)-(len(audio)/75),len(audio)/75):
		total = 0
		for x in range(len(audio)/75):
			total += abs(audio[i+x])
		clean = np.append(clean,total)

	#normalizes for the maximum value to be 1
	maximum = max(clean)
	for i in range(0,len(clean)):
		clean[i] = clean[i]/maximum
		
	clean = clean[np.argmax(clean)-35 :np.argmax(clean)+35]
	#plt.plot(clean)
	#plt.show()
	clean = np.insert(clean,0,number)
	global final
	final = np.concatenate([final,clean], axis=1)
	return clean

def reshaper():
	return np.reshape(final, (final.size/71, 71))
	

compress(audio,1)
compress(audio,2)
np.savetxt("foo.csv",reshaper(),delimiter=",")