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)
def test_beamformer_caching(self): # within each subcase, we need new beamformer objects because result is not updated when # global_caching or cached changes with self.subTest("global_caching = 'none'"): acoular.config.global_caching = 'none' b0 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=True) b1 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=True) self.assertNotEqual(id(b0.result), id(b1.result)) with self.subTest("global_caching = 'individual'"): acoular.config.global_caching = 'individual' b0 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=True) b1 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=True) self.assertEqual(id(b0.result), id(b1.result)) b1 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=False) self.assertNotEqual(id(b0.result), id(b1.result)) with self.subTest("global_caching = 'all'"): acoular.config.global_caching = 'all' b0 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=True) b1 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=False) self.assertEqual(id(b0.result), id(b1.result)) with self.subTest("global_caching = 'readonly'"): acoular.config.global_caching = 'readonly' b0 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=True) b1 = BeamformerBase(freq_data=f, steer=st, r_diag=True, cached=True) self.assertEqual(id(b0.result), id(b1.result))
def run(): from os import path from acoular import __file__ as bpath, MicGeom, WNoiseGenerator, PointSource,\ Mixer, WriteH5, TimeSamples, PowerSpectra, RectGrid, SteeringVector,\ BeamformerBase, L_p from pylab import figure, plot, axis, imshow, colorbar, show # set up the parameters sfreq = 51200 duration = 1 nsamples = duration * sfreq micgeofile = path.join(path.split(bpath)[0], 'xml', 'array_64.xml') h5savefile = 'three_sources.h5' # generate test data, in real life this would come from an array measurement mg = MicGeom(from_file=micgeofile) n1 = WNoiseGenerator(sample_freq=sfreq, numsamples=nsamples, seed=1) n2 = WNoiseGenerator(sample_freq=sfreq, numsamples=nsamples, seed=2, rms=0.7) n3 = WNoiseGenerator(sample_freq=sfreq, numsamples=nsamples, seed=3, rms=0.5) p1 = PointSource(signal=n1, mics=mg, loc=(-0.1, -0.1, 0.3)) p2 = PointSource(signal=n2, mics=mg, loc=(0.15, 0, 0.3)) p3 = PointSource(signal=n3, mics=mg, loc=(0, 0.1, 0.3)) pa = Mixer(source=p1, sources=[p2, p3]) wh5 = WriteH5(source=pa, name=h5savefile) wh5.save() # analyze the data and generate map ts = TimeSamples(name=h5savefile) ps = PowerSpectra(time_data=ts, block_size=128, window='Hanning') rg = RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \ increment=0.01 ) st = SteeringVector(grid=rg, mics=mg) bb = BeamformerBase(freq_data=ps, steer=st) pm = bb.synthetic(8000, 3) Lm = L_p(pm) # show map imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \ interpolation='bicubic') colorbar() # plot microphone geometry figure(2) plot(mg.mpos[0], mg.mpos[1], 'o') axis('equal') show()
# 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=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 #===============================================================================
n1 = WNoiseGenerator( sample_freq=sfreq, numsamples=nsamples, seed=1 ) n2 = WNoiseGenerator( sample_freq=sfreq, numsamples=nsamples, seed=2, rms=0.7 ) n3 = WNoiseGenerator( sample_freq=sfreq, numsamples=nsamples, seed=3, rms=0.5 ) p1 = PointSource( signal=n1, mpos=mg, loc=(-0.1,-0.1,0.3) ) p2 = PointSource( signal=n2, mpos=mg, loc=(0.15,0,0.3) ) p3 = PointSource( signal=n3, mpos=mg, loc=(0,0.1,0.3) ) pa = Mixer( source=p1, sources=[p2,p3] ) wh5 = WriteH5( source=pa, name=h5savefile ) wh5.save() # analyze the data and generate map ts = TimeSamples( name=h5savefile ) ps = PowerSpectra( time_data=ts, block_size=128, window='Hanning' ) rg = RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \ increment=0.01 ) bb = BeamformerBase( freq_data=ps, grid=rg, mpos=mg ) pm = bb.synthetic( 8000, 3 ) Lm = L_p( pm ) # show map imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \ interpolation='bicubic') colorbar() # plot microphone geometry figure(2) plot(mg.mpos[0],mg.mpos[1],'o') axis('equal') show()
#=============================================================================== # the environment, i.e. medium characteristics # (in this case, the speed of sound is set) #=============================================================================== env = Environment(c=346.04) # ============================================================================= # a steering vector instance. SteeringVector provides the standard freefield # sound propagation model in the steering vectors. # ============================================================================= st = SteeringVector(grid=g, mics=m, env=env) #=============================================================================== # beamformers in frequency domain #=============================================================================== bb = BeamformerBase(freq_data=f, steer=st, r_diag=True) bd = BeamformerDamas(beamformer=bb, n_iter=100) be = BeamformerEig(freq_data=f, steer=st, r_diag=True, n=54) bo = BeamformerOrth(beamformer=be, eva_list=list(range(38, 54))) bs = BeamformerCleansc(freq_data=f, steer=st, r_diag=True) #=============================================================================== # plot result maps for different beamformers in frequency domain #=============================================================================== fi = 1 #no of figure for r_diag in (True, False): figure(fi, (10, 6)) fi += 1 bb.r_diag = r_diag be.r_diag = r_diag bs.r_diag = r_diag
y_max=0.3, z=0.68, increment=0.05) env = Environment(c=346.04) st = SteeringVector(grid=g, mics=m, env=env) f = PowerSpectra( 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,
p2 = PointSource(signal=n2, mics=mg, loc=(0.15, 0, 0.3)) p3 = PointSource(signal=n3, mics=mg, loc=(0, 0.1, 0.3)) pa = Mixer(source=p1, sources=[p2, p3]) wh5 = WriteH5(source=pa, name=h5savefile) wh5.save() # analyze the data and generate map ts = TimeSamples(name=h5savefile) ps = PowerSpectra(time_data=ts, block_size=128, window='Hanning') rg = RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \ increment=0.01 ) st = SteeringVector(grid=rg, mics=mg) bb = BeamformerBase(freq_data=ps, steer=st) pm = bb.synthetic(8000, 3) Lm = L_p(pm) # show map imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \ interpolation='bicubic') colorbar() # plot microphone geometry figure(2) plot(mg.mpos[0], mg.mpos[1], 'o') axis('equal') show()
csm = f.csm[:] eva = f.eva[:] f32 = EigSpectra(time_data=t1, window='Hanning', overlap='50%', block_size=128, #FFT-parameters ind_low=7, ind_high=15, precision='complex64') #to save computational effort, only csm32 = f32.csm[:] eva32 = f32.eva[:] psf32 = PointSpreadFunction(grid=g, mpos=m, c=346.04, precision='float32') psf32Res = psf32.psf[:] psf64 = PointSpreadFunction(grid=g, mpos=m, c=346.04, precision='float64') psf64Res = psf64.psf[:] bb32 = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, precision='float32') bb32Res = bb32.synthetic(cfreq,1) bb64 = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, precision='float64') bb64Res = bb64.synthetic(cfreq,1) bf = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, gamma = 60, precision='float32') bfRes = bf.synthetic(cfreq,1) # 32 Bit PSF precision bd3232 = BeamformerDamas(beamformer=bb32, n_iter=100, psf_precision='float32') bd3232Res = bd3232.synthetic(cfreq,1) bc3232 = BeamformerClean(beamformer=bb32, psf_precision='float32') bc3232Res = bc3232.synthetic(cfreq,1) bdp3232 = BeamformerDamasPlus(beamformer=bb32, n_iter=100, psf_precision='float32') bdp3232Res = bdp3232.synthetic(cfreq,1)
g = RectGrid3D(x_min=-0.6, x_max=-0.0, y_min=-0.3, y_max=0.3, \ z_min=0.48, z_max=0.88, increment=0.1) f = EigSpectra(time_data=t, window='Hanning', overlap='50%', block_size=128, ind_low=5, ind_high=15) csm = f.csm[:] eva = f.eva[:] eve = f.eve[:] #""" Creating the beamformers bb1Rem = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='classic') bb2Rem = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='inverse') bb3Rem = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='true level') bb4Rem = BeamformerBase(freq_data=f,
object_name.configure_traits() m = MicGeom(from_file='UCA8.xml') import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt from os import path import acoular from acoular import L_p, Calib, MicGeom, TimeSamples, \ RectGrid, BeamformerBase, EigSpectra, BeamformerOrth, BeamformerCleansc, \ MaskedTimeSamples, FiltFiltOctave, BeamformerTimeSq, TimeAverage, \ TimeCache, BeamformerTime, TimePower, BeamformerCMF, \ BeamformerCapon, BeamformerMusic, BeamformerDamas, BeamformerClean, \ BeamformerFunctional object_name.configure_traits() m = MicGeom(from_file='UCA8.xml') m = MicGeom(from_file='UCA8.xml') g = RectGrid(x_min=-0.8, x_max=-0.2, y_min=-0.1, y_max=0.3, z=0.8, increment=0.01) t1 = TimeSamples(name='cry_n0000001.wav') f1 = EigSpectra(time_data=t1, block_size=256, window="Hanning", overlap='75%') e1 = BeamformerBase(freq_data=f1, grid=g, mpos=m, r_diag=False) fr = 4000 L1 = L_p(e1.synthetic(fr, 0)) object_name.configure_traits() m = MicGeom(from_file='UCA8.xml')
f = EigSpectra( time_data=t1, window='Hanning', overlap='50%', block_size=128, #FFT-parameters ind_low=1, ind_high=15) #to save computational effort, only # frequencies with index 1-30 are used #=============================================================================== # different beamformers in frequency domain #=============================================================================== # first, some simple delay and sum beamformers, # but with different steering vector types #=============================================================================== bb0 = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='true level') # this is the default bb1 = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='true location') # gives better results for 3D location at low freqs. bb2 = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='classic') # classical formulation (e.g. Johnson/Dudgeon) bb3 = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='inverse') # 'inverse' formulation (e.g. Brooks 2001) #=============================================================================== # second, the same with CleanSC #=============================================================================== bs0 = BeamformerCleansc(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='true level') bs1 = BeamformerCleansc(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='true location') bs2 = BeamformerCleansc(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \
# 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=128, #FFT-parameters ind_low=8, ind_high=16) #to save computational effort, only # frequencies with index 1-30 are used #=============================================================================== # different beamformers in frequency domain #=============================================================================== # first, some simple delay and sum beamformers, # but with different steering vector types #=============================================================================== bb0 = BeamformerBase(freq_data=f, steer=st0, r_diag=True) bb1 = BeamformerBase(freq_data=f, steer=st1, r_diag=True) bb2 = BeamformerBase(freq_data=f, steer=st2, r_diag=True) bb3 = BeamformerBase(freq_data=f, steer=st3, r_diag=True) #=============================================================================== # second, the same with CleanSC #=============================================================================== bs0 = BeamformerCleansc(freq_data=f, steer=st0, r_diag=True) bs1 = BeamformerCleansc(freq_data=f, steer=st1, r_diag=True) bs2 = BeamformerCleansc(freq_data=f, steer=st2, r_diag=True) bs3 = BeamformerCleansc(freq_data=f, steer=st3, r_diag=True) #=============================================================================== # third, caching is disabled here for BeamformerEig to save cache storage
# 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=128, #FFT-parameters ind_low=4, ind_high=15) #to save computational effort, only # frequencies with index 1-30 are used #=============================================================================== # different beamformers in frequency domain #=============================================================================== # first, some simple delay and sum beamformers, # but with different steering vector types #=============================================================================== bb0 = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='true level', cached = False) # this is the default bs0 = BeamformerCleansc(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, \ steer='true level', cached=False, n=200) be0 = BeamformerEig(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, n=-1, \ steer='true level', cached=False) bo0 = BeamformerOrth(beamformer=be0, eva_list=range(38, 54), cached=False) from time import time ti = time() map = bs0.result[:] print time() - ti, g.size