def get_bin_f0_B(bin_initial_estimate, fft_buffers,
noise_cutoff, bin_spread_below, bin_spread_above, fs):
### gradually discover/refine estimates of bin_f0 and B
bin_f0 = bin_initial_estimate
B = 1e-4
limit = defs.TOTAL_HARMONICS
for i in range(defs.B_INITIAL_PARTIALS, defs.TOTAL_HARMONICS):
#print fft_buffers.shape
bin_f0, B, rsquared, ok = align_with_B(i, bin_f0, B, fft_buffers,
noise_cutoff, bin_spread_below, bin_spread_above,
fs)
limit = stiff_ideal_conflict.find_limit(bin_f0, B,
bin_spread_below, bin_spread_above)
#print B, rsquared, limit
if i > limit:
#print "warning: exceeding lim?", i, limit
ok = False
if defs.B_PRINT_INDIVIDUAL_BS:
print "%i\t%.4f\t%.5e\t\t%i\t%i" % (
i, bin_f0, B, ok, 0)
if ok is False:
limit = i
break
rows, columns = fft_buffers.shape
partialss = []
idealss = []
for j in range(rows):
fft = fft_buffers[j]
#harmonics, ideal_harmonics = partials.get_freqs_mags(defs.TOTAL_HARMONICS, bin_f0, B,
harmonics, ideal_harmonics = partials.get_freqs_mags(limit, bin_f0, B,
fft, bin_spread_below, bin_spread_above,
only_peaks=True, Bsearch=True)
harmonics = [ h for h in harmonics if h.mag > noise_cutoff[h.fft_bin] ]
ideal_harmonics = [ h for h in ideal_harmonics if h.mag > noise_cutoff[h.fft_bin] ]
partialss.extend(harmonics)
idealss.extend(ideal_harmonics)
#if True:
if False:
pylab.figure()
for j in range(rows):
fft = fft_buffers[j]
pylab.plot(stft.amplitude2db(fft), color="orange")
xs = [ h.fft_bin for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
ys = [ h.mag for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
pylab.plot(xs, stft.amplitude2db(ys), 'p',
color="blue")
pylab.plot(stft.amplitude2db(noise_cutoff), color="black")
#ns = [ h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
#pylab.plot( ns,
# [ h.fft_bin - partials.mode_B2freq(bin_f0, h.n, B)
# for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ],
# '.')
pylab.show()
if defs.B_PLOT_FIT:
ns = [ h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
ys = [ h.fft_bin / h.n for h in partialss if h.n >
defs.B_MIN_HARMONIC_FIT_TO ]
omit_ns = [ h.n for h in partialss if h.n <= defs.B_MIN_HARMONIC_FIT_TO ]
omit_ys = [ h.fft_bin / h.n for h in partialss if h.n <=
defs.B_MIN_HARMONIC_FIT_TO ]
plots.plot_B_fit(ns, ys, omit_ns, omit_ys, bin_f0, B, fs, idealss)
pylab.show()
return bin_f0, B, rsquared, partialss, limit
def get_bin_f0_B(bin_initial_estimate, fft_buffers, noise_cutoff,
bin_spread_below, bin_spread_above, fs):
### gradually discover/refine estimates of bin_f0 and B
bin_f0 = bin_initial_estimate
B = 1e-4
limit = defs.TOTAL_HARMONICS
for i in range(defs.B_INITIAL_PARTIALS, defs.TOTAL_HARMONICS):
#print fft_buffers.shape
bin_f0, B, rsquared, ok = align_with_B(i, bin_f0, B, fft_buffers,
noise_cutoff, bin_spread_below,
bin_spread_above, fs)
limit = stiff_ideal_conflict.find_limit(bin_f0, B, bin_spread_below,
bin_spread_above)
#print B, rsquared, limit
if i > limit:
#print "warning: exceeding lim?", i, limit
ok = False
if defs.B_PRINT_INDIVIDUAL_BS:
print "%i\t%.4f\t%.5e\t\t%i\t%i" % (i, bin_f0, B, ok, 0)
if ok is False:
limit = i
break
rows, columns = fft_buffers.shape
partialss = []
idealss = []
for j in range(rows):
fft = fft_buffers[j]
#harmonics, ideal_harmonics = partials.get_freqs_mags(defs.TOTAL_HARMONICS, bin_f0, B,
harmonics, ideal_harmonics = partials.get_freqs_mags(limit,
bin_f0,
B,
fft,
bin_spread_below,
bin_spread_above,
only_peaks=True,
Bsearch=True)
harmonics = [h for h in harmonics if h.mag > noise_cutoff[h.fft_bin]]
ideal_harmonics = [
h for h in ideal_harmonics if h.mag > noise_cutoff[h.fft_bin]
]
partialss.extend(harmonics)
idealss.extend(ideal_harmonics)
#if True:
if False:
pylab.figure()
for j in range(rows):
fft = fft_buffers[j]
pylab.plot(stft.amplitude2db(fft), color="orange")
xs = [h.fft_bin for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
ys = [h.mag for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
pylab.plot(xs, stft.amplitude2db(ys), 'p', color="blue")
pylab.plot(stft.amplitude2db(noise_cutoff), color="black")
#ns = [ h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
#pylab.plot( ns,
# [ h.fft_bin - partials.mode_B2freq(bin_f0, h.n, B)
# for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ],
# '.')
pylab.show()
if defs.B_PLOT_FIT:
ns = [h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO]
ys = [
h.fft_bin / h.n for h in partialss
if h.n > defs.B_MIN_HARMONIC_FIT_TO
]
omit_ns = [h.n for h in partialss if h.n <= defs.B_MIN_HARMONIC_FIT_TO]
omit_ys = [
h.fft_bin / h.n for h in partialss
if h.n <= defs.B_MIN_HARMONIC_FIT_TO
]
plots.plot_B_fit(ns, ys, omit_ns, omit_ys, bin_f0, B, fs, idealss)
pylab.show()
return bin_f0, B, rsquared, partialss, limit
def align_with_B(num_harmonics, bin_f0, B, ffts,
noise_cutoff,
bin_spread_below, bin_spread_above, sample_rate):
rows, columns = ffts.shape
partialss = []
idealss = []
for j in range(rows):
fft = ffts[j]
harmonics, ideal_harmonics = partials.get_freqs_mags(num_harmonics, bin_f0, B,
fft, bin_spread_below, bin_spread_above,
only_peaks=True, Bsearch=True)
if harmonics is None:
return bin_f0, B, None, False
harmonics = [ h for h in harmonics if h.mag > noise_cutoff[h.fft_bin] ]
ideal_harmonics = [ h for h in ideal_harmonics if h.mag > noise_cutoff[h.fft_bin] ]
partialss.extend(harmonics)
idealss.extend(ideal_harmonics)
#ns = [ h.n for h in partialss if h.n > 1 ]
#ys = [ h.fft_bin / h.n for h in partialss if h.n > 1 ]
ns = [ h.n for h in partialss if h.n >
defs.B_MIN_HARMONIC_FIT_TO ]
ys = [ h.fft_bin / h.n for h in partialss if h.n >
defs.B_MIN_HARMONIC_FIT_TO ]
ns_orig = list(ns)
ys_orig = list(ys)
ns = []
ys = []
weights = []
#for i, h in enumerate(partialss):
# if h.n <= defs.B_MIN_HARMONIC_FIT_TO:
# continue
#if stiff_ideal_conflict.does_confict(bin_f0, B,
# bin_spread_below, bin_spread_above, h.n):
# #print "bail from conflict"
# continue
# ns.append( h.n )
# ys.append( h.fft_bin / h.n)
# weights.append( h.mag - noise_cutoff[h.fft_bin] )
ns = ns_orig
ys = ys_orig
bin_f0, B, rsquared = estimate_B(ns, ys,
weights,
initial_bin_f0=bin_f0, initial_B=B)
if defs.B_PLOT_FIT_INDIVIDUAL:
omit_ns = [ h.n for h in partialss if h.n <= defs.B_MIN_HARMONIC_FIT_TO ]
omit_ys = [ h.fft_bin / h.n for h in partialss if h.n <=
defs.B_MIN_HARMONIC_FIT_TO ]
plots.plot_B_fit(ns, ys, omit_ns, omit_ys, bin_f0, B,
sample_rate, idealss)
pylab.show()
ok = True
nums_per = []
#print '----'
for n in range(1,num_harmonics+1):
nums = len( [h.n for h in partialss if h.n == n])
nums_per.append(nums)
#print nums,
if nums_per[num_harmonics-1] < 4:
#ok = False
#print "bail", num_harmonics
pass
return bin_f0, B, rsquared, ok
def align_with_B(num_harmonics, bin_f0, B, ffts, noise_cutoff,
bin_spread_below, bin_spread_above, sample_rate):
rows, columns = ffts.shape
partialss = []
idealss = []
for j in range(rows):
fft = ffts[j]
harmonics, ideal_harmonics = partials.get_freqs_mags(num_harmonics,
bin_f0,
B,
fft,
bin_spread_below,
bin_spread_above,
only_peaks=True,
Bsearch=True)
if harmonics is None:
return bin_f0, B, None, False
harmonics = [h for h in harmonics if h.mag > noise_cutoff[h.fft_bin]]
ideal_harmonics = [
h for h in ideal_harmonics if h.mag > noise_cutoff[h.fft_bin]
]
partialss.extend(harmonics)
idealss.extend(ideal_harmonics)
#ns = [ h.n for h in partialss if h.n > 1 ]
#ys = [ h.fft_bin / h.n for h in partialss if h.n > 1 ]
ns = [h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO]
ys = [
h.fft_bin / h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO
]
ns_orig = list(ns)
ys_orig = list(ys)
ns = []
ys = []
weights = []
#for i, h in enumerate(partialss):
# if h.n <= defs.B_MIN_HARMONIC_FIT_TO:
# continue
#if stiff_ideal_conflict.does_confict(bin_f0, B,
# bin_spread_below, bin_spread_above, h.n):
# #print "bail from conflict"
# continue
# ns.append( h.n )
# ys.append( h.fft_bin / h.n)
# weights.append( h.mag - noise_cutoff[h.fft_bin] )
ns = ns_orig
ys = ys_orig
bin_f0, B, rsquared = estimate_B(ns,
ys,
weights,
initial_bin_f0=bin_f0,
initial_B=B)
if defs.B_PLOT_FIT_INDIVIDUAL:
omit_ns = [h.n for h in partialss if h.n <= defs.B_MIN_HARMONIC_FIT_TO]
omit_ys = [
h.fft_bin / h.n for h in partialss
if h.n <= defs.B_MIN_HARMONIC_FIT_TO
]
plots.plot_B_fit(ns, ys, omit_ns, omit_ys, bin_f0, B, sample_rate,
idealss)
pylab.show()
ok = True
nums_per = []
#print '----'
for n in range(1, num_harmonics + 1):
nums = len([h.n for h in partialss if h.n == n])
nums_per.append(nums)
#print nums,
if nums_per[num_harmonics - 1] < 4:
#ok = False
#print "bail", num_harmonics
pass
return bin_f0, B, rsquared, ok
