def fbeamformers():
bb = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=False)
be = BeamformerEig(freq_data=f, steer=st, r_diag=True, n=54, cached=False)
#frequency beamformers to test
bbase = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=False)
bc = BeamformerCapon(freq_data=f, steer=st, cached=False)
beig = BeamformerEig(freq_data=f,
steer=st,
r_diag=True,
n=54,
cached=False)
bm = BeamformerMusic(freq_data=f, steer=st, n=6, cached=False)
bd = BeamformerDamas(beamformer=bb, n_iter=10, cached=False)
bdp = BeamformerDamasPlus(beamformer=bb, n_iter=100, cached=False)
bo = BeamformerOrth(beamformer=be,
eva_list=list(range(38, 54)),
cached=False)
bs = BeamformerCleansc(freq_data=f, steer=st, r_diag=True, cached=False)
bcmf = BeamformerCMF(freq_data=f,
steer=st,
method='LassoLarsBIC',
cached=False)
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)
return (bbase, bc, beig, bm, bl, bo, bs, bd, bcmf, bf, bdp, bgib)
block_size=128, #FFT-parameters
ind_low=8,
ind_high=16) #to save computational effort, only
# frequencies with indices 8..15 are used
#===============================================================================
# different beamformers in frequency domain
#===============================================================================
bb = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04)
bc = BeamformerCapon(freq_data=f, grid=g, mpos=m, c=346.04, cached=False)
be = BeamformerEig(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, n=54)
bm = BeamformerMusic(freq_data=f, grid=g, mpos=m, c=346.04, n=6)
bd = BeamformerDamas(beamformer=bb, n_iter=100)
bo = BeamformerOrth(beamformer=be, eva_list=list(range(38, 54)))
bs = BeamformerCleansc(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04)
bcmf = BeamformerCMF(freq_data=f, grid=g, mpos=m, c=346.04, \
method='LassoLarsBIC')
bl = BeamformerClean(beamformer=bb, n_iter=100)
bf = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, \
gamma=4)
#===============================================================================
# plot result maps for different beamformers in frequency domain
#===============================================================================
figure(1, (10, 6))
i1 = 1 #no of subplot
for b in (bb, bc, be, bm, bl, bo, bs, bd, bcmf, bf):
subplot(3, 4, i1)
i1 += 1
map = b.synthetic(cfreq, 1)
mx = L_p(map.max())
imshow(L_p(map.T),
# eigenvalues and eigenvectors, if only the matrix is needed then class
# PowerSpectra can be used instead
#===============================================================================
f = EigSpectra(
time_data=t1,
window='Hanning',
overlap='50%',
block_size=256, #FFT-parameters
ind_low=15,
ind_high=31) #to save computational effort, only
# frequencies with index 15-31 are used
#===============================================================================
# beamformers in frequency domain
#===============================================================================
b = BeamformerCMF(freq_data=f, grid=g, mpos=m, c=346.04, alpha=1e-8)
#===============================================================================
# plot result maps for different beamformers in frequency domain
#===============================================================================
figure(1) #no of figure
i1 = 1 #no of subplot
from time import time
for method in ('LassoLars', 'LassoLarsBIC', \
'OMPCV', 'NNLS'):
b.method = method
subplot(2, 2, i1)
i1 += 1
ti = time()
map = b.synthetic(cfreq, 1)
print time() - ti
time_data=t1,
window='Hanning',
overlap='50%',
block_size=128, #FFT-parameters
cached=False) #cached = False
bb = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=False)
bc = BeamformerCapon(freq_data=f, steer=st, cached=False)
be = BeamformerEig(freq_data=f, steer=st, r_diag=True, n=54, cached=False)
bm = BeamformerMusic(freq_data=f, steer=st, n=6, cached=False)
bd = BeamformerDamas(beamformer=bb, n_iter=100, cached=False)
bdp = BeamformerDamasPlus(beamformer=bb, n_iter=100, cached=False)
bo = BeamformerOrth(beamformer=be, eva_list=list(range(38, 54)), cached=False)
bs = BeamformerCleansc(freq_data=f, steer=st, r_diag=True, cached=False)
bcmf = BeamformerCMF(freq_data=f,
steer=st,
method='LassoLarsBIC',
cached=False)
bl = BeamformerClean(beamformer=bb, n_iter=100, cached=False)
bf = BeamformerFunctional(freq_data=f,
steer=st,
r_diag=False,
gamma=4,
cached=False)
bgib = BeamformerGIB(freq_data=f,
steer=st,
method='LassoLars',
n=10,
cached=False)
class acoular_test(unittest.TestCase):
st = SteeringVector(grid=g, mics=m, env=env)
#===============================================================================
# for frequency domain methods, this provides the cross spectral matrix and its
# eigenvalues and eigenvectors, if only the matrix is needed then class
# PowerSpectra can be used instead
#===============================================================================
f = PowerSpectra(time_data=t1,
window='Hanning', overlap='50%', block_size=256, #FFT-parameters
ind_low=15, ind_high=31) #to save computational effort, only
# frequencies with index 15-31 are used
#===============================================================================
# beamformers in frequency domain
#===============================================================================
b = BeamformerCMF(freq_data=f, steer=st, alpha=1e-8)
#===============================================================================
# plot result maps for different beamformers in frequency domain
#===============================================================================
figure(1,(7,7)) #no of figure
i1 = 1 #no of subplot
from time import time
for method in ('LassoLars', 'LassoLarsBIC', 'OMPCV', 'NNLS'):
b.method = method
subplot(2,2,i1)
i1 += 1
ti = time()
map = b.synthetic(cfreq,1)
print(time()-ti)
mx = L_p(map.max())
bort1Full = BeamformerOrth(beamformer=be1Full, eva_list=list(range(4, 8)))
bort2Full = BeamformerOrth(beamformer=be2Full, eva_list=list(range(4, 8)))
bort3Full = BeamformerOrth(beamformer=be3Full, eva_list=list(range(4, 8)))
bort4Full = BeamformerOrth(beamformer=be4Full, eva_list=list(range(4, 8)))
Lbort1Rem = L_p(bort1Rem.synthetic(4000, 1))
Lbort2Rem = L_p(bort2Rem.synthetic(4000, 1))
Lbort3Rem = L_p(bort3Rem.synthetic(4000, 1))
Lbort4Rem = L_p(bort4Rem.synthetic(4000, 1))
Lbort1Full = L_p(bort1Full.synthetic(4000, 1))
Lbort2Full = L_p(bort2Full.synthetic(4000, 1))
Lbort3Full = L_p(bort3Full.synthetic(4000, 1))
Lbort4Full = L_p(bort4Full.synthetic(4000, 1))
bcmf1Rem = BeamformerCMF(freq_data=f,
grid=g,
mpos=m,
r_diag=True,
c=346.04,
steer='classic')
bcmf2Rem = BeamformerCMF(freq_data=f,
grid=g,
mpos=m,
r_diag=True,
c=346.04,
steer='inverse')
bcmf3Rem = BeamformerCMF(freq_data=f,
grid=g,
mpos=m,
r_diag=True,
c=346.04,
steer='true level')
bcmf4Rem = BeamformerCMF(freq_data=f,
bort3Rem = BeamformerOrth(beamformer=be3Rem, eva_list=list(range(4,8))) bort4Rem = BeamformerOrth(beamformer=be4Rem, eva_list=list(range(4,8))) bort1Full = BeamformerOrth(beamformer=be1Full, eva_list=list(range(4,8))) bort2Full = BeamformerOrth(beamformer=be2Full, eva_list=list(range(4,8))) bort3Full = BeamformerOrth(beamformer=be3Full, eva_list=list(range(4,8))) bort4Full = BeamformerOrth(beamformer=be4Full, eva_list=list(range(4,8))) Lbort1Rem = L_p(bort1Rem.synthetic(4000,1)) Lbort2Rem = L_p(bort2Rem.synthetic(4000,1)) Lbort3Rem = L_p(bort3Rem.synthetic(4000,1)) Lbort4Rem = L_p(bort4Rem.synthetic(4000,1)) Lbort1Full = L_p(bort1Full.synthetic(4000,1)) Lbort2Full = L_p(bort2Full.synthetic(4000,1)) Lbort3Full = L_p(bort3Full.synthetic(4000,1)) Lbort4Full = L_p(bort4Full.synthetic(4000,1)) bcmf1Rem = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='classic') bcmf2Rem = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='inverse') bcmf3Rem = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='true level') bcmf4Rem = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='true location') bcmf1Full = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='classic') bcmf2Full = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='inverse') bcmf3Full = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='true level') bcmf4Full = BeamformerCMF(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='true location') Lbcmf1Rem = L_p(bcmf1Rem.synthetic(4000,1)) Lbcmf2Rem = L_p(bcmf2Rem.synthetic(4000,1)) Lbcmf3Rem = L_p(bcmf3Rem.synthetic(4000,1)) Lbcmf4Rem = L_p(bcmf4Rem.synthetic(4000,1)) Lbcmf1Full = L_p(bcmf1Full.synthetic(4000,1)) Lbcmf2Full = L_p(bcmf2Full.synthetic(4000,1)) Lbcmf3Full = L_p(bcmf3Full.synthetic(4000,1)) Lbcmf4Full = L_p(bcmf4Full.synthetic(4000,1))
increment=0.025)
#===============================================================================
# for frequency domain methods, this provides the cross spectral matrix and its
# eigenvalues and eigenvectors, if only the matrix is needed then class
# PowerSpectra can be used instead
#===============================================================================
f = EigSpectra(time_data=t1,
window='Hanning', overlap='50%', block_size=256, #FFT-parameters
ind_low=15, ind_high=31) #to save computational effort, only
# frequencies with index 15-31 are used
#===============================================================================
# beamformers in frequency domain
#===============================================================================
b = BeamformerCMF(freq_data=f, grid=g, mpos=m, c=346.04, alpha=1e-8)
#===============================================================================
# plot result maps for different beamformers in frequency domain
#===============================================================================
figure(1) #no of figure
i1 = 1 #no of subplot
from time import time
for method in ('LassoLars', 'LassoLarsBIC', \
'OMPCV', 'NNLS'):
b.method = method
subplot(2,2,i1)
i1 += 1
ti = time()
map = b.synthetic(cfreq,1)
print time()-ti
ind_low=8,
ind_high=16) #to save computational effort, only
# frequencies with indices 8..15 are used
#===============================================================================
# different beamformers in frequency domain
#===============================================================================
bb = BeamformerBase(freq_data=f, steer=st, r_diag=True)
bc = BeamformerCapon(freq_data=f, steer=st, cached=False)
be = BeamformerEig(freq_data=f, steer=st, r_diag=True, n=54)
bm = BeamformerMusic(freq_data=f, steer=st, n=6)
bd = BeamformerDamas(beamformer=bb, n_iter=100)
bdp = BeamformerDamasPlus(beamformer=bb, n_iter=100)
bo = BeamformerOrth(beamformer=be, eva_list=list(range(38, 54)))
bs = BeamformerCleansc(freq_data=f, steer=st, r_diag=True)
bcmf = BeamformerCMF(freq_data=f, steer=st, method='LassoLarsBIC')
bl = BeamformerClean(beamformer=bb, n_iter=100)
bf = BeamformerFunctional(freq_data=f, steer=st, r_diag=False, gamma=4)
bgib = BeamformerGIB(freq_data=f, steer=st, method='LassoLars', n=10)
#===============================================================================
# plot result maps for different beamformers in frequency domain
#===============================================================================
figure(1, (10, 6))
i1 = 1 #no of subplot
for b in (bb, bc, be, bm, bl, bo, bs, bd, bcmf, bf, bdp, bgib):
subplot(4, 4, i1)
i1 += 1
map = b.synthetic(cfreq, 1)
mx = L_p(map.max())
imshow(L_p(map.T),