y_min=-3.0,
y_max=+3.0,
z=Z,
increment=0.3)
b = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=c0)
map1 = b.synthetic(freq, 3)
#===============================================================================
# fixed focus time domain beamforming
#===============================================================================
fi = FiltFiltOctave(source=t, band=freq, fraction='Third octave')
bt = BeamformerTimeSq(source=fi, grid=g, mpos=m, r_diag=True, c=c0)
avgt = TimeAverage(source=bt,
naverage=int(sfreq * tmax / 16)) # 16 single images
cacht = TimeCache(source=avgt) # cache to prevent recalculation
map2 = zeros(g.shape) # accumulator for average
# plot single frames
figure(1, (8, 7))
i = 1
for res in cacht.result(1):
res0 = res[0].reshape(g.shape)
map2 += res0 # average
i += 1
subplot(4, 4, i)
mx = L_p(res0.max())
imshow(L_p(transpose(res0)), vmax=mx, vmin=mx-10, interpolation='nearest',\
extent=g.extend(), origin='lower')
colorbar()
map2 /= i
origin='lower',
vmin=mx - 15,
interpolation='nearest',
extent=g.extend())
colorbar()
title(b.__class__.__name__)
#===============================================================================
# delay and sum beamformer in time domain
# processing chain: beamforming, filtering, power, average
#===============================================================================
bt = BeamformerTime(source=t1, grid=g, mpos=m, c=346.04)
ft = FiltFiltOctave(source=bt, band=cfreq)
pt = TimePower(source=ft)
avgt = TimeAverage(source=pt, naverage=1024)
cacht = TimeCache(source=avgt) # cache to prevent recalculation
#===============================================================================
# delay and sum beamformer in time domain with autocorrelation removal
# processing chain: zero-phase filtering, beamforming+power, average
#===============================================================================
fi = FiltFiltOctave(source=t1, band=cfreq)
bts = BeamformerTimeSq(source=fi, grid=g, mpos=m, r_diag=True, c=346.04)
avgts = TimeAverage(source=bts, naverage=1024)
cachts = TimeCache(source=avgts) # cache to prevent recalculation
#===============================================================================
# plot result maps for different beamformers in time domain
#===============================================================================
i2 = 2 # no of figure
for b in (cacht, cachts):
f = PowerSpectra(time_data=t, window='Hanning', overlap='50%', block_size=128, \
ind_low=1,ind_high=30) # CSM calculation
g = RectGrid(x_min=-3.0, x_max=+3.0, y_min=-3.0, y_max=+3.0, z=Z, increment=0.3)
st = SteeringVector(grid=g, mics=m)
b = BeamformerBase(freq_data=f, steer=st, r_diag=True)
map1 = b.synthetic(freq,3)
#===============================================================================
# fixed focus time domain beamforming
#===============================================================================
fi = FiltFiltOctave(source=t, band=freq, fraction='Third octave')
bt = BeamformerTimeSq(source=fi, steer=st, r_diag=True)
avgt = TimeAverage(source=bt, naverage=int(sfreq*tmax/16)) # 16 single images
cacht = TimeCache(source=avgt) # cache to prevent recalculation
map2 = zeros(g.shape) # accumulator for average
# plot single frames
figure(1,(8,7))
i = 1
for res in cacht.result(1):
res0 = res[0].reshape(g.shape)
map2 += res0 # average
i += 1
subplot(4,4,i)
mx = L_p(res0.max())
imshow(L_p(transpose(res0)), vmax=mx, vmin=mx-10, interpolation='nearest',\
extent=g.extend(), origin='lower')
colorbar()
map2 /= i
