def find_peak(f0, osc_ctrl_queue, audio_in_queue,
comedi, serial, alsa_stream_in):
firstsamp = 0 # Location of first sample of pluck
fftlen = SECONDS_PLUCK_DECAY * INPUT_RATE # Number of bins for pitch determination FFT
fnb = plot_file_name(f0, serial) # Base filename for plot pngs
pluck = stimulate(f0, SECONDS_PLUCK_DECAY,
osc_ctrl_queue, audio_in_queue, comedi, alsa_stream_in)
save_pluck_data(f0, serial, pluck)
#exit(1)
spectrum = fft.fft(pluck, 2*len(pluck)) #[:4*len(tofft)/2]
fftlen = len(spectrum)
spectrum = spectrum[:fftlen/2]
sampname = fnb + "-samples.png"
freqname = fnb + "-spect.png"
if PLOT_TRIAL:
pluckplt = BackgroundPlot(5, sampname,
range(len(pluck)), pluck, title=fnb)
bin_l = int((f0-50)*fftlen/INPUT_RATE)
bin_h = int((f0+50)*fftlen/INPUT_RATE)
estimate = estimate_in(spectrum, bin_l, bin_h)
print "\n>>>>>>>> Sent %fHz; Peak estimate: %fHz\n" % (f0, estimate)
if not PLOT_TRIAL :
time.sleep(10) # This now should happen at top of stimulate
else:
freqs = [float(i)*INPUT_RATE/fftlen for i in range(bin_l, bin_h+1)]
pluckplt.waitForPlot()
os.chmod(sampname, 0666)
gtitle = "investigating %.3fHz: estimate %f" % (f0, estimate)
freqplt = BackgroundPlot(5, freqname, freqs,
20*numpy.log(abs(spectrum[bin_l:bin_h+1])),
gtitle, estimate, '.-')
freqplt.waitForPlot()
os.chmod(freqname, 0666)
return estimate
def main():
### init
comedi = comedi_interface.Comedi()
comedi.send_all(0)
#comedi.send(comedi_interface.CHANNEL_A0, 1)
audio = alsa_interface.Audio()
audio_output_lock = multiprocessing.Lock()
audio_output_lock.acquire()
global logfile
logfile = open(LOGFILE, 'w')
# Later on, we'll say (in stimulate(), actually):
# audio_output_lock.release()
# sleep(some time)
# pluck_data = wait_left(...
# and this should play the whole precomputed output block
# starting the input some time into it.
freq_queue, audio_in_queue = audio.begin(audio_output_lock)
alsa_stream_in = audio.alsa_stream_in
### get silence
if False:
global silence
print "getting silence..."
silence = stimulate(0.0, 1*SECONDS_MEASUREMENT,
freq_queue, audio_output_lock,
audio_in_queue,
comedi, alsa_stream_in)
print "... silence gotten"
wavname = "silence.wav"
if FORMAT_NUMPY == numpy.int16:
scipy.io.wavfile.write(wavname, INPUT_RATE, silence)
else:
scipy.io.wavfile.write(wavname, INPUT_RATE,
numpy.int32((2**31-1)*silence))
#f0=438.0 # Expected frequency of fundamental
#f0=658.0 # Expected frequency of fundamental
#f0=2*658.0
#f0= .0 # Expected frequency of fundamental
#f0 = 143.0
f0 = 220.0
B = 0.0
tries = LOCATE_F0_ATTEMPTS
max_partials = MAX_PARTIAL
series = [0] * (max_partials+1)
start_partial = 1
tested_modes = []
tested_favgs = []
### kick-start process
#start_partial = 10
#tested_modes = [1, 2, 3, 4, 5, 6, 7, 8, 9]
#tested_favgs = [ 439.69665405 , 879.34497282 ,1321.67762859, 1763.11055972 , 2204.01194094 ,
# 2649.08665505 , 3094.1608183 , 3533.91024435 , 3986.87554256]
for p in range(start_partial, max_partials+1):
text = "--- mode %i" % (p)
logfile.write(text+"\n")
logfile.flush()
outfile = open("estimates-%i.txt" % p, "w")
f0, B, rsquared = estimate_f0_B.estimate_B(
tested_modes, tested_favgs, f0, B)
f_test = partials.mode_B2freq(f0, p, B)
f_avg = 0
for attempt in range(tries):
f_measured = find_peak(f_test,
freq_queue, audio_output_lock,
audio_in_queue,
comedi, attempt, alsa_stream_in)
f_avg += f_measured
f_test = 0.1*f_test + 0.9*f_measured
outfile.write("%f\n" % f_measured)
#time.sleep(10)
f_avg /= tries # no, not useful
tested_modes.append(p)
tested_favgs.append(f_avg)
if p == 1:
f0 = f_test
f0, B, rsquared = estimate_f0_B.estimate_B(
tested_modes, tested_favgs, f0, B)
#text = "Estimated string f0: %.2f\tinharmonicity B: %.3g\tR^2 \"variability\": %.3g" % (f0, B, rsquared)
text = "Estimated string f0: %.2f\tinharmonicity B: %.3g\tR^2: %.3g" % (f0, B, rsquared)
logfile.write(text+"\n")
logfile.flush()
#f0 = f0avg * (p + 1) / p
series[p] = f_avg
outfile.close()
series = series[1:]
# High pass de-glitch filter
# dgfa, dgfb = sig.iirdesign(50.*0.5/INPUT_RATE, 20*0.5/INPUT_RATE, 3, 70)
# (but we still save the raw data)
numpy.savetxt( os.path.join(SAVE_PATH, "detected-freqs.txt"),
numpy.array(series) )
exit(1)
for f0_idx in range(len(series)) :
f0 = series[f0_idx]
for attempt in range(MEASUREMENTS) :
pluck = stimulate(f0, SECONDS_MEASUREMENT,
freq_queue, audio_output_lock,
audio_in_queue,
comedi, alsa_stream_in)
save_pluck_data(f0_idx+1, attempt, pluck)
if PLOT_TRIAL:
fnb = plot_file_name(f0_idx+1, attempt)
graphname = fnb + "-measured.png"
pluckplt = BackgroundPlot(7, graphname,
range(len(pluck)), pluck,
title=fnb)
pluckplt.waitForPlot()
os.chmod(graphname, 0666)
### clean up
audio.end()
audio.close()
logfile.close()
def find_peak(f0, osc_ctrl_queue, lock, audio_in_queue,
comedi, serial, alsa_stream_in):
firstsamp = 0 # Location of first sample of pluck
fftlen = SECONDS_PEAKFINDER_FFT * INPUT_RATE # Number of bins for pitch determination FFT
fnb = plot_file_name(f0, serial) # Base filename for plot pngs
pluck = stimulate(f0, SECONDS_PEAKFINDER_FFT,
osc_ctrl_queue, lock, audio_in_queue,
comedi, alsa_stream_in)
save_pluck_data(f0, serial, pluck)
#data_un_silenced = spectral_subtraction.spectral_subtraction_arrays(
# pluck, silence, INPUT_RATE)
fft_points = 2*len(pluck)
spectrum = fft.fft(pluck, fft_points) #[:4*len(tofft)/2]
fftlen = len(spectrum)
spectrum = spectrum[:fftlen/2+1] / fft_points
power_spectrum = abs(spectrum)**2
sampname = fnb + "-samples.png"
freqname = fnb + "-spect.png"
if PLOT_TRIAL:
pluckplt = BackgroundPlot(5, sampname,
range(len(pluck)), pluck, title=fnb)
#pylab.plot(pluck, '.-')
#pylab.show()
#freq_spread = 50.0
freq_spread = 5.0
bin_l = int((f0 - freq_spread)*fftlen/INPUT_RATE)
bin_h = int((f0 + freq_spread)*fftlen/INPUT_RATE)
bin_f0 = int((f0)*fftlen/INPUT_RATE)
estimate_bin, estimate_mag = estimate_in(spectrum, bin_l, bin_h)
estimate = estimate_bin
#estimate, decay, estimate_mag, _, _ = chemistry.fit_lorentzian(power_spectrum,
# bin_l, bin_h, bin_f0,
# False, False)
# #True, False)
peak_dB = 10*numpy.log10(estimate_mag)
decay = -1
#mean_spectral_energy = abs(spectrum).mean()
#peak_energy_above_mean = (
# 20*numpy.log10(estimate_mag) -
# 20*numpy.log10(mean_spectral_energy))
#print "\n>>>>>>>> Sent %fHz; Peak estimate: %fHz\n" % (f0, estimate)
text = "Sent %.2fHz; Peak estimate: %.2fHz; Peak magnitude: %.2f dB; Decay rate: %.3f" % (f0, estimate, peak_dB, decay)
logfile.write(text+"\n")
logfile.flush()
if PLOT_TRIAL :
freqs = [float(i)*INPUT_RATE/fftlen for i in range(bin_l, bin_h+1)]
pluckplt.waitForPlot()
os.chmod(sampname, 0666)
gtitle = "investigating %.3fHz: estimate %f" % (f0, estimate)
freqplt = BackgroundPlot(5, freqname, freqs,
20*numpy.log10(abs(spectrum[bin_l:bin_h+1])),
gtitle, estimate, '.-')
freqplt.waitForPlot()
os.chmod(freqname, 0666)
return estimate
def main():
### init
comedi = comedi_interface.Comedi()
comedi.send_all(0)
#comedi.send(comedi_interface.CHANNEL_A0, 1)
audio = alsa_interface.Audio()
audio_input_lock = multiprocessing.Lock()
audio_output_lock = multiprocessing.Lock()
audio_input_lock.acquire()
audio_output_lock.acquire()
# Later on, we'll say:
# audio_output_lock.release()
# sleep(some time)
# pluck_data = wait_left(6.0)
# ## audio_input_lock.release()
# # get some data
# # When everything settles down, both processes should
# # release their locks (might need some extra logic in them)
# audio_input_lock.acquire()
# audio_output_lock.acquire()
# queue.send(next buffer of stuff)
# as many times as is necessary,
# and this should play the whole precomputed output block
# starting the input some time into it.
# Seems the separate output thread tascam_input_process
# is deprecated in alsa_interface.py, so need some consultancy
# on whether this is what you're thinking about.
# Also need to precompute the blocks instead of using the
# freq_queue stuff which might be a bit of a big change.
freq_queue, audio_in_queue = audio.begin(audio_input_lock)
alsa_stream_in = audio.alsa_stream_in
f0=438.0 # Expected frequency of fundamental
B = 0.0
tries = LOCATE_F0_ATTEMPTS
max_partials = MAX_PARTIAL
series = [0] * (max_partials+1)
tested_modes = []
tested_favgs = []
for p in range(1, max_partials+1):
outfile = open("estimates-%i.txt" % p, "w")
f_test = partials.mode_B2freq(f0, p, B)
f_avg = 0
for attempt in range(tries):
f_measured = find_peak(f_test, freq_queue, audio_in_queue,
comedi, attempt, alsa_stream_in)
f_avg += f_measured
f_test = 0.1*f_test + 0.9*f_measured
outfile.write("%f\n" % f_measured)
#time.sleep(10)
f_avg /= tries
tested_modes.append(p)
tested_favgs.append(f_avg)
f0, B, rsquared = partials.estimate_B(
tested_modes, tested_favgs, f0, B)
print "--------------"
print "%.2f\t%.3g\t%.3g" % (f0, B, rsquared)
print "--------------"
#f0 = f0avg * (p + 1) / p
series[p] = f_avg
outfile.close()
series = series[1:]
# High pass de-glitch filter
dgfa, dgfb = sig.iirdesign(50.*0.5/INPUT_RATE, 20*0.5/INPUT_RATE, 3, 70)
numpy.savetxt( os.path.join(SAVE_PATH, "detected-freqs.txt"),
numpy.array(series) )
for f0_idx in range(len(series)) :
f0 = series[f0_idx]
for attempt in range(MEASUREMENTS) :
pluck = stimulate(f0, SECONDS_MEASUREMENT,
freq_queue, audio_in_queue,
comedi, alsa_stream_in)
save_pluck_data(f0_idx+1, attempt, pluck)
if PLOT_TRIAL:
fnb = plot_file_name(f0_idx+1, attempt)
graphname = fnb + "-measured.png"
pluckplt = BackgroundPlot(10, graphname,
range(len(pluck)), pluck,
title=fnb)
pluckplt.waitForPlot()
os.chmod(graphname, 0666)
else:
time.sleep(10)
### clean up
audio.end()
audio.close()
