#Example of how to extract data from a wav file using the modified svt library.
#Also useful for graphing or playing around with data.
import svt
import matplotlib
from matplotlib import pyplot
import pylab
from mpl_toolkits.mplot3d import Axes3D
import random
import numpy
#The line needed to get data from the first channel.
centroids, frequencies, volumes = svt.processWav("wubwub.wav", 1)
"""
#Samples some of the data.
print centroids[1]
print frequencies[1]
print volumes[1]
for i in range(0, int(len(frequencies[1])/20)):
print frequencies[1][i]
"""
#Graphs frequencies, centroids, and volumes on 2d plots; frequencies should really be 3d but I can't figure out how to 3d graph them so a number of 2d graphs should suffice for understanding the data.
#numbers = []
#for i in range(len(frequencies[1])):
# numbers.append(i)
#analysis codeeeeeeeee
#for i in range(len(frequencies)):
# matplotlib.pyplot.scatter(numbers, frequencies[i])
# matplotlib.pyplot.show()
centroids, frequencies, volumes = supercentroids, superfrequencies, supervolumes
elif str(request.form['song']) == "4":
centroids, frequencies, volumes = rivercentroids, riverfrequencies, rivervolumes
elif str(request.form['song']) == "5":
centroids, frequencies, volumes = starcentroids, starfrequencies, starvolumes
elif str(request.form['song']) == "6":
centroids, frequencies, volumes = cypruscentroids, cyprusfrequencies, cyprusvolumes
elif str(request.form['song']) == "7":
centroids, frequencies, volumes = sandscentroids, sandsfrequencies, sandsvolumes
elif str(request.form['song']) == "8":
centroids, frequencies, volumes = dubcentroids, dubfrequencies, dubvolumes
song = int(request.form['song'])
except:
pass
return render_template("vis4.html", centroids=centroids, frequencies=frequencies, volumes=volumes, song=song)
#if we use fileIO preprocessing
def preProcess():
pass
if __name__=="__main__":
centroids, frequencies, volumes = svt.processWav("wubwub.wav", 1)
reg1centroids, reg1frequencies, reg1volumes = svt.processWav("./static/reg1.wav", 1)
reg2centroids, reg2frequencies, reg2volumes = svt.processWav("./static/reg2.wav", 1)
cypruscentroids, cyprusfrequencies,cyprusvolumes = svt.processWav("./static/cyprus.wav", 1)
dubcentroids, dubfrequencies,dubvolumes = svt.processWav("./static/dubstep.wav", 1)
rivercentroids, riverfrequencies,rivervolumes = svt.processWav("./static/river.wav", 1)
sandscentroids, sandsfrequencies, sandsvolumes = svt.processWav("./static/sands.wav", 1)
starcentroids, starfrequencies, starvolumes = svt.processWav("./static/starstuff.wav", 1)
supercentroids, superfrequencies, supervolumes = svt.processWav("./static/superposition.wav", 1) #wormhole
app.run(debug=True)
#Example of how to extract data from a wav file using the modified svt library.
#Also useful for graphing or playing around with data.
import svt
import matplotlib
from matplotlib import pyplot
import pylab
from mpl_toolkits.mplot3d import Axes3D
import random
import numpy
#The line needed to get data from the first channel.
centroids, frequencies, volumes = svt.processWav("../pyaudio/ooo.wav", 1)
"""
#Samples some of the data.
print centroids[1]
print frequencies[1]
print volumes[1]
for i in range(0, int(len(frequencies[1])/20)):
print frequencies[1][i]
"""
#Graphs frequencies, centroids, and volumes on 2d plots; frequencies should really be 3d but I can't figure out how to 3d graph them so a number of 2d graphs should suffice for understanding the data.
numbers = []
for i in range(len(frequencies[1])):
numbers.append(i)
#analysis codeeeeeeeee
for i in range(3):
matplotlib.pyplot.scatter(numbers, frequencies[i])
matplotlib.pyplot.show()
from svt import processWav
from sklearn.cross_validation import StratifiedKFold
from sklearn import svm
from sklearn.metrics import confusion_matrix
from numpy import array
from math import sin, cos
from decimal import Decimal
from random import random, randint
ac, af, av = processWav('../pyaudio/aaa.wav', 1)
ec, ef, ev = processWav('../pyaudio/eee.wav', 1)
clf = svm.SVC()
clf.fit(af[:100] + ef[:100], [0 for i in range(100)] + [1 for i in range(100)])
print clf.predict(af[120])
#Example of how to extract data from a wav file using the modified svt library.
#Also useful for graphing or playing around with data.
import svt
import matplotlib
from matplotlib import pyplot
import pylab
from mpl_toolkits.mplot3d import Axes3D
import random
import numpy
#The line needed to get data from the first channel.
centroids, frequencies, volumes = svt.processWav("../pyaudio/ooo.wav", 1)
"""
#Samples some of the data.
print centroids[1]
print frequencies[1]
print volumes[1]
for i in range(0, int(len(frequencies[1])/20)):
print frequencies[1][i]
"""
#Graphs frequencies, centroids, and volumes on 2d plots; frequencies should really be 3d but I can't figure out how to 3d graph them so a number of 2d graphs should suffice for understanding the data.
numbers = []
for i in range(len(frequencies[1])):
numbers.append(i)
#analysis codeeeeeeeee
for i in range(3):
matplotlib.pyplot.scatter(numbers, frequencies[i])
matplotlib.pyplot.show()
