def animate_update(block_count, iq_data, block_size, rf_coeff, audio_coeff):
global audio_data, state_i_lpf_100k, state_q_lpf_100k, state_phase
if (block_count+1)*block_size > len(iq_data):
print('Finished processing the raw I/Q samples')
# write audio data to file (assumes audio_data samples are -1 to +1)
wavfile.write("../data/fmMonoAnim.wav", int(audio_Fs), np.int16((audio_data/2)*32767))
sys.exit()
# filter to extract the FM channel (I samples are even, Q samples are odd)
i_filt, state_i_lpf_100k = signal.lfilter(rf_coeff, 1.0, \
iq_data[(block_count)*block_size:(block_count+1)*block_size:2],
zi=state_i_lpf_100k)
q_filt, state_q_lpf_100k = signal.lfilter(rf_coeff, 1.0, \
iq_data[(block_count)*block_size+1:(block_count+1)*block_size:2],
zi=state_q_lpf_100k)
# downsample the FM channel
i_ds = i_filt[::rf_decim]
q_ds = q_filt[::rf_decim]
# FM demodulator
fm_demod, state_phase = fmDemodArctan(i_ds, q_ds, state_phase)
# PSD after FM demodulation
ax0.clear()
ax0.psd(fm_demod, NFFT=512, Fs=(rf_Fs/rf_decim)/1e3)
# freq, my_psd = estimatePSD(fm_demod, NFFT=512, Fs=(rf_Fs/rf_decim)/1e3)
# ax0.plot(freq, my_psd)
ax0.set_ylabel('PSD (dB/Hz)')
ax0.set_xlabel('Freq (kHz)')
ax0.set_title('Demodulated FM (block ' + str(block_count) + ')')
def rf_frontend():
# coefficients for the front-end low-pass filter
rf_coeff = signal.firwin(rf_taps, rf_Fc / (rf_Fs / 2), window=('hann'))
# rf_coeff = lp_impulse(rf_Fc, rf_Fs, rf_taps)
# filter to extract the FM channel (I samples are even, Q samples are odd)
i_filt = signal.lfilter(rf_coeff, 1.0, iq_data[0::2])
q_filt = signal.lfilter(rf_coeff, 1.0, iq_data[1::2])
block_size = int(len(iq_data[0::2]))
# print(block_size)
# i_filt, buf_wave = conv(iq_data[0::2], rf_coeff, np.zeros(len(rf_coeff)-1))
# q_filt, buf_wave = conv(iq_data[1::2], rf_coeff, np.zeros(len(rf_coeff)-1))
# downsample the FM channel
i_ds = i_filt[::rf_decim]
q_ds = q_filt[::rf_decim]
print("ARCTAN DEMOD BEGIN (LEN I = " + str(len(i_ds)) + ")")
# FM demodulator (check the library)
fm_demod = fmDemodArctan(i_ds, q_ds)
print("ARCTAN DEMOD EMD")
return fm_demod
print('Processing block ' + str(block_count))
# filter to extract the FM channel (I samples are even, Q samples are odd)
i_filt, state_i_lpf_100k = signal.lfilter(rf_coeff, 1.0, \
iq_data[(block_count)*block_size:(block_count+1)*block_size:2],
zi=state_i_lpf_100k)
q_filt, state_q_lpf_100k = signal.lfilter(rf_coeff, 1.0, \
iq_data[(block_count)*block_size+1:(block_count+1)*block_size:2],
zi=state_q_lpf_100k)
# downsample the I/Q data from the FM channel
i_ds = i_filt[::rf_decim]
q_ds = q_filt[::rf_decim]
# FM demodulator
fm_demod, state_phase = fmDemodArctan(i_ds, q_ds, state_phase)
tone_filt, state_tone = signal.lfilter(tone_pass_coeff, 1.0, \
fm_demod,
zi=state_tone)
stereo_filt, state_stereo = signal.lfilter(stereo_pass_coeff, 1.0, \
fm_demod,
zi=state_stereo)
ncoOut, state_integrator, state_phaseEst, state_feedbackI, state_feedbackQ, state_ncoOut0, state_trigOffset \
= fmPll(tone_filt,19e3,rf_Fs/rf_decim,ncoScale = 2, \
# rf_coeff = lp_impulse(rf_Fc, rf_Fs, rf_taps)
# filter to extract the FM channel (I samples are even, Q samples are odd)
i_filt = signal.lfilter(rf_coeff, 1.0, iq_data[0::2])
q_filt = signal.lfilter(rf_coeff, 1.0, iq_data[1::2])
block_size = int(len(iq_data[0::2]))
# print(block_size)
# i_filt, buf_wave = conv(iq_data[0::2], rf_coeff, np.zeros(len(rf_coeff)-1))
# q_filt, buf_wave = conv(iq_data[1::2], rf_coeff, np.zeros(len(rf_coeff)-1))
# downsample the FM channel
i_ds = i_filt[::rf_decim]
q_ds = q_filt[::rf_decim]
# FM demodulator (check the library)
fm_demod, buf_I, buf_Q = fmDemodArctan(i_ds, q_ds)
# we use a dummy because there is no state for this single-pass model
# set up drawing
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3)
fig.subplots_adjust(hspace = 1.0)
# PSD after FM demodulation
ax0.psd(fm_demod, NFFT=512, Fs=(rf_Fs/rf_decim)/1e3)
ax0.set_ylabel('PSD (db/Hz)')
ax0.set_title('Demodulated FM')
# coefficients for the filter to extract mono audio
# audio_coeff = np.array([]) # to be updated by you during in-lab
audio_coeff = signal.firwin(audio_taps, audio_Fc/(24e4/2), window=('hann'))
# audio_coeff = lp_impulse(audio_Fc, 24e4, audio_taps)
zi=state_i_lpf_state)
#
q_filt, state_q_lpf_state = signal.lfilter(rf_coeff, 1.0, \
iq_data[(block_count)*block_size+1:(block_count+1)*block_size:2],
zi=state_q_lpf_state)
# i_filt, state_i_lpf_100k = conv(iq_data[(block_count)*block_size:(block_count+1)*block_size:2], rf_coeff, state_i_lpf_100k)
# q_filt, state_q_lpf_100k = conv(iq_data[(block_count)*block_size+1:(block_count+1)*block_size:2], rf_coeff, state_q_lpf_100k)
# downsample the FM channel
i_ds = i_filt[::rf_decim]
q_ds = q_filt[::rf_decim]
# FM demodulator
# fm_demod, state_phase = fmDemodArctan(i_ds, q_ds, state_phase)
fm_demod, buf_I, buf_Q = fmDemodArctan(i_ds, q_ds, block_count, buf_I,
buf_Q)
# extract the mono audio data through filtering
# audio_filt = ... change as needed
mono_filt, mono_lpf_state = signal.lfilter(audio_coeff, 1.0, \
fm_demod,
zi=mono_lpf_state)
# audio_filt, mono_lpf_100k = conv(fm_demod, audio_coeff, mono_lpf_100k)
# downsample audio data
# audio_block = ... change as needed
mono_block = mono_filt[::audio_decim]
stereo_channel, stereo_channel_state = signal.lfilter(
stereo_channel_coeff, 1.0, fm_demod, zi=stereo_channel_state)
iq_data = np.fromfile(in_fname, dtype='float32')
print("Read raw RF data from \"" + in_fname + "\" in float32 format")
# coefficients for the front-end low-pass filter
rf_coeff = signal.firwin(rf_taps, rf_Fc / (rf_Fs / 2), window=('hann'))
# filter to extract the FM channel (I samples are even, Q samples are odd)
i_filt = signal.lfilter(rf_coeff, 1.0, iq_data[0::2])
q_filt = signal.lfilter(rf_coeff, 1.0, iq_data[1::2])
# downsample the FM channel
i_ds = i_filt[::rf_decim]
q_ds = q_filt[::rf_decim]
# FM demodulator (check the library)
fm_demod, dummy = fmDemodArctan(i_ds, q_ds)
# we use a dummy because there is no state for this single-pass model
# set up drawing
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3)
fig.subplots_adjust(hspace=1.0)
# PSD after FM demodulation
ax0.psd(fm_demod, NFFT=512, Fs=(rf_Fs / rf_decim) / 1e3)
ax0.set_ylabel('PSD (db/Hz)')
ax0.set_title('Demodulated FM')
# coefficients for the filter to extract mono audio
audio_coeff = np.array([]) # to be updated by you during in-lab
# extract the mono audtio data through filtering
