i1 += 1
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
#===============================================================================
mg.mpos_tot = array([M1, M2, M3,
M4]).T # add microphone positions to MicGeom object
# define evaluation grid
# Hier könntest du vielleicht eine neue Spherical Grid Klasse schreiben oder
# eine ArbitraryGrid Klasse, damit wir ein sinnvolles Gitter zur Lokalisierung
# verwenden können.
# Als Anregung siehe: https://spaudiopy.readthedocs.io/en/latest/spaudiopy.grids.html
#
rg = SphericalGrid_Equiangular(NPOINTS_AZI, NPOINTS_ELE)
st = SteeringVector(grid=rg, mics=mg)
# analyze the data and generate map
name = AUDIO_DIR + TRACK
ts = WavSamples(name=name, start=STARTFRAME * NUM, stop=ENDFRAME * NUM)
bf = BeamformerTime(source=ts, steer=st)
#ft = FiltFiltOctave(source=bf,band=4000)
tp = TimePower(source=bf)
tavg = TimeAverage(source=tp, naverage=NUM)
# =============================================================================
# plot first data block
# =============================================================================
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
gen = tavg.result(1) # make generator object
## show map
#imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend, \
#interpolation='bicubic')
bl = BeamformerClean(beamformer=bb, n_iter=10, cached=False)
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):
# write H5 File 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):