def spectrogram(self):
#plotting the spectrogram for .wav####
if self.fileName.endswith(".wav"):
fig = plot.figure()
plot.subplot(111)
self.powerSpectrum, self.freqenciesFound, self.time, self.imageAxis = plot.specgram(
self.audio2, Fs=self.samplingrate, cmap=self.cmap)
plot.colorbar()
# plot.ylim(self.min_freq_slider.value(),self.max_freq_slider.value())
plot.xlabel('Time')
plot.ylabel('Frequency')
fig.savefig('plot.png')
plot.close()
self.upload()
#plotting the spectrogram for csv####
if self.fileName.endswith(".csv"):
# Define the list of frequencies
self.frequencies = np.arange(5, 105, 5)
# Sampling Frequency
self.data = pd.DataFrame(pd.read_csv(self.fileName,
delimiter=None))
self.Data = self.data.iloc[1:][1:]
self.sub2 = self.Data.values.tolist()
self.samplingFrequency = 2 * (len(self.sub2)) + 1
# Create two ndarrays
self.s1 = np.empty([0]) # For samples
self.s2 = np.empty([0]) # For signal
# Start Value of the sample
self.start = 1
# Stop Value of the sample
self.stop = self.samplingFrequency
for self.frequency in self.frequencies:
self.sub1 = np.arange(self.start, self.stop, 1)
self.s1 = np.append(self.s1, self.sub1)
self.s2 = np.append(self.s2, self.sub2)
self.start = self.stop + 1
self.stop = self.start + self.samplingFrequency
# Plot the spectrogram
fig = plot.figure()
plot.subplot(111)
self.powerSpectrum, self.freqenciesFound, self.time, self.imageAxis = plot.specgram(
self.s2, Fs=self.samplingFrequency, cmap=self.cmap)
plot.xlabel('Time')
plot.ylabel('Frequency')
plot.colorbar()
fig.savefig('plot.png')
plot.close()
self.upload()
def newsignal(self):
mag = np.abs(self.new_yfft)
phase = np.angle(self.new_yfft)
mod_wave = np.multiply(mag, np.exp(1j * phase))
self.signal = ifft(mod_wave)
pen = pg.mkPen(color=(255, 255, 255))
self.sc2.clear()
# self.sc.clear()
# self.signal=ifft(self.new_yfft)
# mag= np.abs(self.signal)
# phase= np.angle(self.signal)
# mod_wave=np.multiply(mag,np.exp(1j*phase))
# self.sc2.plot(mod_wave[0:].real, pen=pen)
self.sc2.plot(self.signal.real, pen=pen)
# self.sc.plot(self.audio2[0:], pen=pen)
max = self.max_freq_slider.value()
min = self.min_freq_slider.value()
fig = plot.figure()
self.powerSpectrum, self.freqenciesFound, self.time, self.imageAxis = plot.specgram(
self.signal.real,
Fs=self.samplingrate,
cmap=self.cmap,
vmin=min,
vmax=max)
plot.colorbar()
# plot.ylim(min,max)
plot.xlabel('Time')
plot.ylabel('Frequency')
fig.savefig('plot.png')
plot.close()
self.upload()
def savepdf(self):
self.combbox()
fig = plot.figure()
# plot.subplot(2,1,1)
# plot.plot(self.audio2[0:],color='black',linewidth=0.005,scalex=True)
# plot.title("before")
plot.subplot(2, 1, 2)
max = self.max_freq_slider.value()
min = self.min_freq_slider.value()
self.powerSpectrum, self.freqenciesFound, self.time, self.imageAxis = plot.specgram(
self.signal.real,
Fs=self.samplingrate,
cmap=self.cmap,
vmin=min,
vmax=max)
plot.colorbar()
plot.xlabel('Time')
plot.ylabel('Frequency')
plot.subplot(2, 1, 1)
plot.plot(self.signal[0:].real,
color='blue',
linewidth=0.003,
scalex=True)
plot.title("after")
fn, _ = QtWidgets.QFileDialog.getSaveFileName(
self, "Export PDF", None, "PDF files (.pdf);;All Files()")
if fn:
if QtCore.QFileInfo(fn).suffix() == "": fn += ".pdf"
fig.savefig(fn)
plot.close()
def spectrogram2(self):
#plotting the spectrogram####
# Define the list of frequencies
self.frequencies = np.arange(5, 105, 5)
# Sampling Frequency
self.data = pd.DataFrame(pd.read_csv(self.fileName2, delimiter=None))
self.Data = self.data.iloc[1:][1:]
self.sub2 = self.Data.values.tolist()
self.samplingFrequency = 2 * (len(self.sub2)) + 1
# Create two ndarrays
self.s1 = np.empty([0]) # For samples
self.s2 = np.empty([0]) # For signal
# Start Value of the sample
self.start = 1
# Stop Value of the sample
self.stop = self.samplingFrequency
for self.frequency in self.frequencies:
self.sub1 = np.arange(self.start, self.stop, 1)
self.s1 = np.append(self.s1, self.sub1)
self.s2 = np.append(self.s2, self.sub2)
self.start = self.stop + 1
self.stop = self.start + self.samplingFrequency
# Plot the spectrogram
fig = plot.figure()
plot.subplot(111)
self.powerSpectrum, self.freqenciesFound, self.time, self.imageAxis = plot.specgram(
self.s2, Fs=self.samplingFrequency)
plot.xlabel('Time')
plot.ylabel('Frequency')
fig.savefig('plot2.png')
self.upload2()
def savepdf(self):
fig = plot.figure()
if (self.channel1box.isChecked() == True):
if len(self.x) == 0:
pass
else:
plot.subplot(3, 2, 1)
plot.plot(self.x,
self.y,
color='red',
linewidth=2,
scalex=True)
plot.subplot(3, 2, 2)
self.powerSpectrum, self.freqenciesFound, self.time, self.imageAxis = plot.specgram(
self.s2, Fs=self.samplingFrequency)
plot.xlabel('Time')
plot.ylabel('Frequency')
# if(self.channel2box.isChecked()==True):
# if len(self.x1) ==0 :
# pass
# else:
# plot.subplot(3,2,3)
# plot.plot(self.x1,self.y1,color='red',linewidth=2,scalex=True)
# plot.subplot(3,2,4)
# self.powerSpectrum1, self.freqenciesFound1, self.time1, self.imageAxis1 = plot.specgram(self.s2, Fs=self.samplingFrequency)
# plot.xlabel('Time')
plot.ylabel('Frequency')
if (self.channel3box.isChecked() == True):
if len(self.x2) == 0:
pass
else:
plot.subplot(3, 2, 5)
plot.plot(self.x2,
self.y2,
color='red',
linewidth=2,
scalex=True)
plot.subplot(3, 2, 6)
self.powerSpectrum2, self.freqenciesFound2, self.time2, self.imageAxis2 = plot.specgram(
self.s2, Fs=self.samplingFrequency)
plot.xlabel('Time')
plot.ylabel('Frequency')
# plot.show()
fig.savefig("x.pdf")