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import scipy.io.wavfile
import time
import numpy as np
import matplotlib.pyplot as plt
import pylab
from scipy.signal import butter, lfilter, iirdesign
from pylab import *
import scipy.signal as signal
from scipy.fftpack import fft, ifft
def robs_lfilter(b, a, x):
max_asum = 0
max_bsum = 0
lenx = len(x)
#lenx = 13
y = [0 for i in range(0,lenx)]
for n in range(0,lenx):
bsum = 0
asum = 0
#print 'n %g' % n
for nb,num in enumerate(b):
#print 'nb %g' % nb
if (n-nb) < 0:
bsum += 0
#print 'b[%g] %g, x[%g] %g, term %g, bsum %g' % (nb, b[nb], n-nb, 0, 0, bsum)
else:
bsum += b[nb]*x[n-nb]
#print 'b[%g] %g, x[%g] %g, term %g, bsum %g' % (nb, b[nb], n-nb, x[n-nb], b[nb]*x[n-nb], bsum)
for na,num in enumerate(a[1:]):
rna = na+1
if (n-rna) < 0:
asum += 0
#print 'a[%g] %g, y[%g] %g, term %g, asum %g' % (rna, a[rna], n-rna, 0, 0, asum)
else:
asum += a[rna]*y[n-rna]
#print 'a[%g] %g, y[%g] %g, term %g, asum %g' % (rna, a[rna], n-rna, y[n-rna], a[rna]*y[n-rna], asum)
#print 'term %g' % a[rna]*y[n-rna]
y[n] = int(((int(bsum) - int(asum)) / int(a[0])))
if max_asum < asum:
max_asum = asum
if max_bsum < bsum:
max_bsum = bsum
#print 'bsum %.1f asum %.1f y[%g] %.1f' % (bsum, asum, n, y[n])
#print 'y[%g] %g' % (n, y[n])
#y[n] = (b*x - a*y)/2
#y[n] = (b[0]*x[n] + b[1]*x[n-1] + b[2]*x[n-2] - a[1]*y[n-1] -a[2]*y[n-2])/2
print 'max %g %g' % (max_asum, max_bsum)
return y
#a[0]*y[n] = b[0]*x[n] + b[1]*x[n-1] + ... + b[nb]*x[n-nb]
#- a[1]*y[n-1] - ... - a[na]*y[n-na]
def makegraph(data, filename):
plt.clf()
plt.plot(data)
plt.savefig(filename)
def ellip_bandpass_filter(data):
print data
print type(data[1])
bgain = 100000000
again = 100000000
#b,a = iirdesign(wp = [0.2, 0.225], ws= [0.19, 0.23], gstop= 50, gpass=6, ftype='ellip') # 8000 hz version
#b,a = iirdesign(wp = [0.25, 0.75], ws= [0.23, 0.77], gstop= 50, gpass=6, ftype='ellip') # 8000 hz version
#b,a = iirdesign(wp = 0.25, ws= 0.30, gstop= 50, gpass=6, ftype='ellip') # 8000 hz version
b,a = iirdesign(wp = 0.046, ws= 0.055, gstop= 50, gpass=6, ftype='ellip') # 44100 hz version
#b,a = iirdesign(wp = [0.61, 0.67], ws= [0.63, 0.65], gstop= 50, gpass=6, ftype='ellip') # notch
#for i,number in enumerate(b):
#if abs(number) < 1e-15:
#b[i] = 0
#else:
#b[i] = int(number*bgain)
#for i,number in enumerate(a):
#if abs(number) < 1e-15:
#a[i] = 0
#else:
#a[i] = int(number*again)
print 'int64_t a[] = ',
print '{',
for num in a:
print '%g,' % num,
print '\b\b };'
print 'int64_t b[] = ',
print '{',
for num in b:
print('%g,' % num),
print '\b\b };'
print len(a)
print len(b)
y = robs_lfilter(b, a, data)
for i,num in enumerate(y):
#y[i] = y[i]/100000000
y[i] = y[i]
return y
data = np.genfromtxt('data.txt', delimiter=', ', names=True, skiprows=5)
list_data = [list(row) for row in data]
matt = np.mat(list_data)
array_matt1 = np.array(matt[:,1])
data_2d_list = []
fft_2d_list = []
num_columns = 7
num_samples = len(array_matt1)
#sample_freq = 8000
sample_freq = 44100
nyquist_freq = sample_freq / 2
#plt.figure(1)
wf = np.linspace(0.0, nyquist_freq, num_samples/2)
for j in range(0,num_columns,1):
#if j not in data_2d_list:
data_2d_list.append([])
array_matt = np.array(matt[:,j])
for i,row in enumerate(array_matt):
for element in row:
data_2d_list[j].append(int64(element-1500))
if j not in fft_2d_list:
fft_2d_list.append([])
fft_temp_list = fft(data_2d_list[j])
for i,element in enumerate(fft_temp_list):
fft_2d_list[j].append(element)
#if j != 0:
#plt.plot(data_2d_list[j], label = str(j))
#plt.semilogy(wf, 2.0/num_samples * np.abs(fft_2d_list[j][0:num_samples/2]), label = str(j))
if j in [1,2]:
#plt.plot(data_2d_list[j], label = str(j))
plt.semilogy(wf, 2.0/num_samples * np.abs(fft_2d_list[j][0:num_samples/2]), label = str(j))
for i,sample in enumerate(data_2d_list[j]):
data_2d_list[j][i] = data_2d_list[j][i]*1000000000
filtered_snippet = ellip_bandpass_filter(data_2d_list[j])
#plt.plot(filtered_snippet)
plt.semilogy(wf, 2.0/num_samples * np.abs(filtered_snippet[0:num_samples/2]), label = str(j))
data = np.genfromtxt('test.txt', delimiter=', ', names=True, skiprows=5)
print data
list_data = [list(row) for row in data]
print list_data
sound_tup = scipy.io.wavfile.read('/mnt/hgfs/vmware_share/Unacuna Cry Analyzer Tests/short ben.wav', 'r')
sound_data = list(sound_tup[1])
print len(sound_data)
snippet_length = 750
num_samples = snippet_length
sample_freq = 44100
nyquist_freq = sample_freq / 2
#ben_times_of_interest = [0.299, 2.105, 3.422, 5.104, 9.160, 9.786, 10.742, 6.935, 7.020, 7.078, 7.161]
ben_times_of_interest = [0.299]
times_of_interest = ben_times_of_interest
samples_of_interest = [(int) (x*sample_freq - snippet_length/2) for x in times_of_interest]
thefile = open('test.txt', 'w')
#"""
for i in samples_of_interest:
sound_data_snippet = sound_data[i:i+snippet_length]
for item in sound_data_snippet:
print>>thefile, item
for i,sample in enumerate(sound_data_snippet):
sound_data_snippet[i] = sound_data_snippet[i]*100000000
filtered_snippet = ellip_bandpass_filter(sound_data_snippet)
for i,sample in enumerate(sound_data_snippet):
sound_data_snippet[i] = sound_data_snippet[i]/100000000
fft_of_sound = fft(sound_data_snippet)
fft_of_filtered_sound = fft(filtered_snippet)
sum_of_filter = 0
for number in fft_of_filtered_sound:
sum_of_filter = sum_of_filter + np.abs(number)
print sum_of_filter
wf = np.linspace(0, nyquist_freq, num_samples/2)
try:
pylab.figure(figsize=(12,9))
#plt.plot(sound_data_snippet)
#plt.plot(filtered_snippet)
#pylab.figure(figsize=(12,9))
plt.semilogy(wf[1:num_samples/2], 2.0/num_samples * np.abs(fft_of_sound[1:num_samples/2]))
plt.semilogy(wf[1:num_samples/2], 2.0/num_samples * np.abs(fft_of_filtered_sound[1:num_samples/2]))
except ValueError:
print "not plotting because data is all zeroes"
#pylab.xlim([0,4000])
plt.grid()
#"""
plt.show()