bf = BeamformerFunctional(freq_data=f,
steer=st,
r_diag=False,
gamma=3,
cached=False)
bgib = BeamformerGIB(freq_data=f,
steer=st,
method='LassoLars',
n=2,
cached=False)
bbase = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=False)
beig = BeamformerEig(freq_data=f, steer=st, r_diag=True, n=54, cached=False)
#timebeamformer test
bt = BeamformerTime(source=t1, steer=st)
ft = FiltFiltOctave(source=bt, band=1000)
pt = TimePower(source=ft)
avgt = TimeAverage(source=pt, naverage=1024)
res = next(avgt.result(1))
#squared
fi = FiltFiltOctave(source=t1, band=4000)
bts = BeamformerTimeSq(source=fi, steer=st, r_diag=True)
avgts = TimeAverage(source=bts, naverage=1024)
resq = next(avgts.result(1))
class acoular_beamformer_test(unittest.TestCase):
#@unittest.skip('test time bf first')
def test_beamformer_freq_results(self):
#test all fbeamformers for 1000 and 8000 hertz
map = b.synthetic(cfreq, 1)
mx = L_p(map.max())
imshow(L_p(map.T),
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
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)
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',\
