#===============================================================================
# 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):
# first, plot time-dependent result (block-wise)
figure(i2, (7, 7))
i2 += 1
res = zeros(g.size) # init accumulator for average
i3 = 1 # no of subplot
for r in b.result(1): #one single block
subplot(4, 4, i3)
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
for cfreq in cfreqs:
for beam, bfname in zip(
(bbase, bc, beig, bm, bl, bo, bs, bd, bcmf, bf, bdp, bgib),
('bb', 'bc', 'be', 'bm', 'bl', 'bo', 'bs', 'bd', 'bcmf', 'bf',
'bdp', 'bgib')):
for i in range(len(beam.synthetic(cfreq, 1).flatten())):
h5f1 = WriteH5(name='ps', source=ps).save()
#==============================================================================
#load H5 File
#==============================================================================
#load from h5 File
mt1 = MaskedTimeSamples(name='ps')
#==============================================================================
#fixed focus time domain beamforming point source
#==============================================================================
bt_ps = BeamformerTime(source=ps, grid=g, mpos=m, c=c0)
bt_ps_h5 = BeamformerTime(source=mt1, grid=g, mpos=m, c=c0)
btSq_ps = BeamformerTimeSq(source=ps, grid=g, mpos=m, r_diag=True, c=c0)
btSq_ps_h5 = BeamformerTimeSq(source=mt1, grid=g, mpos=m, r_diag=True, c=c0)
avgt_ps = TimeAverage(source=bt_ps, naverage=int(sfreq * tmax / 2))
avgt_psSq = TimeAverage(source=btSq_ps, naverage=int(sfreq * tmax / 2))
avgt_ps_h5 = TimeAverage(source=bt_ps_h5, naverage=int(sfreq * tmax / 2))
avgt_psSq_h5 = TimeAverage(source=btSq_ps_h5, naverage=int(sfreq * tmax / 2))
#==============================================================================
#plot point source
#==============================================================================
figure(1)
for res in avgt_ps.result(1):
res_ps = res[0].reshape(g.shape)
subplot(2, 2, 1)
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',\
extent=g.extend(), origin='lower')
g = RectGrid(x_min=-3.0,
x_max=+3.0,
y_min=-3.0,
y_max=+3.0,
z=Z,
increment=0.3)
#===============================================================================
# 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,
weights='none')
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)
i = 0
for res in avgt.result(1):
res0 = res[0].reshape(g.shape)
map2 += res0 # average
i += 1
subplot(4, 4, i)
mx = L_p(res0.max())