def desmodulando_wav(sinal_modulado):
# modulando
# sinal_modulado = sinal_modulado[:, 0]
portadora_freq = 14000 # hz
portadora = np.cos(2 * np.pi * portadora_freq * time)
sinal_desmodulado = sinal_modulado * portadora
X, Y = calcFFT(sinal_desmodulado, FS)
fig = plt.figure(figsize=(12, 7))
plt.subplot(2, 1, 1)
plt.plot(time, sinal_desmodulado)
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.title('Wav Modulado')
# fourier
plt.subplot(2, 1, 2)
limite_x = 24000
plt.xlim(-limite_x, limite_x)
plt.title("Fourier Do Sinal Modulado")
plt.plot(X, np.abs(Y), 'red')
fig.tight_layout(pad=2.0)
plt.show()
return sinal_desmodulado
def analyze(basename,
suffix,
savedir,
start,
no_of_samples,
fft_width=512,
overlap=265,
height=64):
for i in range(start, no_of_samples):
filename = basename + str(i) + suffix
sample = mat_to_nparray(filename)
print i, sample.shape
no_channels = sample.shape[1]
for ch in range(no_channels):
magnitude = calcFFT(sample[:, ch], fft_width, overlap)[:, :height]
fig = plt.figure()
plt.imshow(np.flipud(magnitude.transpose()),
vmin=20,
vmax=4000,
aspect='auto',
interpolation='none')
fig.savefig('figures/' + savedir + '/ch' + str(ch) + '/test' +
str(i) + suffix + '.png')
fig, axs = plt.subplots(2,
5,
figsize=(15, 6),
facecolor='w',
edgecolor='k')
def read_wav(audio_path):
# read wav
wavedata = audio_path # plot this wav file ~/audio/aaa.wav
sampleRate, audioBuffer = scipy.io.wavfile.read(wavedata)
#audioBuffer = audioBuffer[:, 1]
duration = len(audioBuffer) / sampleRate
time = np.arange(0, duration, 1 / sampleRate) # time vector
X, Y = calcFFT(audioBuffer, FS)
fig = plt.figure(figsize=(12, 7))
plt.subplot(2, 1, 1)
plt.plot(time, audioBuffer)
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.title('Wav Recebido')
# fourier
plt.subplot(2, 1, 2)
limite_x = 24000
plt.xlim(-limite_x, limite_x)
plt.title("Fourier Do Sinal Original")
plt.plot(X, np.abs(Y), 'red')
fig.tight_layout(pad=2.0)
plt.show()
return audioBuffer, time
def update_voxel_data(self):
self.vals = self.Data[self.pos_t,:,:,:]
self.Voxel = self.Data[:,self.slice_x,self.slice_y,self.slice_z]
self.VoxelZeroPad = num.zeros(32048)
self.VoxelZeroPad[0:self.num_figs] = self.Voxel
[self.FFTVoxel,self.frqs] = utils.calcFFT(self.Voxel,self.Fs)
[self.maxtab, self.mintab ]= utils.peakdet(self.FFTVoxel,0.01)
self.fftPeaks = self.maxtab[:,1]
self.frqsPeaks = self.frqs[list(self.maxtab[:,0])]
def update_voxel_data(self):
self.vals = self.Data[self.pos_t, :, :, :]
self.Voxel = self.Data[:, self.slice_x, self.slice_y, self.slice_z]
self.VoxelZeroPad = num.zeros(32048)
self.VoxelZeroPad[0:self.num_figs] = self.Voxel
[self.FFTVoxel, self.frqs] = utils.calcFFT(self.Voxel, self.Fs)
[self.maxtab, self.mintab] = utils.peakdet(self.FFTVoxel, 0.01)
self.fftPeaks = self.maxtab[:, 1]
self.frqsPeaks = self.frqs[list(self.maxtab[:, 0])]
def analyze_1h(basename, suffix, savedir, start, end, fft_width=512, overlap=265, height=64):
session = 0
for i in range(start, end, 6):
print "session nr:",session
sample = read_data_1h(basename,suffix, i)
no_channels = sample.shape[1]
for ch in range(no_channels):
magnitude = calcFFT(sample[:,ch],fft_width,overlap)[:,:height]
print "five percentile", np.percentile(magnitude,5)
fig = plt.figure()
plt.imshow(np.flipud(magnitude.transpose()),vmin=20 , vmax=4000, aspect='auto', interpolation='none')
fig_filename = 'figures/'+savedir+'/ch'+str(ch)+'/test_1h_'+str(i)+suffix+'.png'
print fig_filename
fig.savefig(fig_filename)
session += 1
def norm_wav(audioBuffer):
normalized_wav = normalize_wav(audioBuffer)
X, Y = calcFFT(normalized_wav, FS)
fig = plt.figure(figsize=(12, 7))
plt.subplot(2, 1, 1)
plt.plot(time, normalized_wav)
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.title('Wav Normalizado')
# fourier
plt.subplot(2, 1, 2)
plt.xlim(-5000, 5000)
plt.title("Fourier Do Sinal Normalizado")
plt.plot(X, np.abs(Y), 'red')
fig.tight_layout(pad=2.0)
plt.show()
return normalized_wav
def filter_wav(normalized_wav):
cuttoff = 4000 #
filtered_wav = low_pass_filter(normalized_wav, cuttoff, FS)
X, Y = calcFFT(filtered_wav, FS)
fig = plt.figure(figsize=(12, 7))
plt.subplot(2, 1, 1)
plt.plot(time, filtered_wav)
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.title('Wav Filtrado')
# fourier
plt.subplot(2, 1, 2)
plt.xlim(-5000, 5000)
plt.title("Fourier Do Sinal Filtrado")
plt.plot(X, np.abs(Y), 'red')
fig.tight_layout(pad=2.0)
plt.show()
return filtered_wav