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()
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()
#ww.save() #=============================================================================== # fixed focus frequency domain beamforming #=============================================================================== 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):
#=============================================================================== # this provides the cross spectral matrix and defines the beamformer # usually, another type of beamformer (e.g. CLEAN-SC) would be more appropriate # to be really fast, we restrict ourselves to only 10 frequencies # in the range 2000 - 6000 Hz (5*400 - 15*400) #=============================================================================== f = EigSpectra(time_data=t, window='Hanning', overlap='50%', block_size=128, \ ind_low=5, ind_high=15) b = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04) #=============================================================================== # reads the data, finds the maximum value (to properly scale the views) #=============================================================================== map = b.synthetic(4000,1) L1 = L_p(map) mx = L1.max() #=============================================================================== # print out the result integrated over an 3d-sector of the 3d map #=============================================================================== print(L_p(b.integrate((-0.3,-0.1,0.58,-0.1,0.1,0.78)))[f.ind_low:f.ind_high]) #=============================================================================== # displays the 3d view #=============================================================================== X,Y,Z = mgrid[g.x_min:g.x_max:1j*g.nxsteps,\ g.y_min:g.y_max:1j*g.nysteps,\
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)
# uncomment to save the signal to a wave file #ww = WriteWAV(source = t) #ww.channels = [0,14] #ww.save() #=============================================================================== # fixed focus frequency domain beamforming #=============================================================================== 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) st = SteeringVector(grid=g, mics=m) b = BeamformerBase(freq_data=f, steer=st, r_diag=True) map1 = b.synthetic(freq,3) #=============================================================================== # fixed focus time domain beamforming #=============================================================================== fi = FiltFiltOctave(source=t, band=freq, fraction='Third octave') bt = BeamformerTimeSq(source=fi, steer=st, r_diag=True) 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
r_diag=False, c=346.04, steer='inverse') bb3Full = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='true level') bb4Full = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='true location') Lbb1Rem = L_p(bb1Rem.synthetic(4000, 1)) Lbb2Rem = L_p(bb2Rem.synthetic(4000, 1)) Lbb3Rem = L_p(bb3Rem.synthetic(4000, 1)) Lbb4Rem = L_p(bb4Rem.synthetic(4000, 1)) Lbb1Full = L_p(bb1Full.synthetic(4000, 1)) Lbb2Full = L_p(bb2Full.synthetic(4000, 1)) Lbb3Full = L_p(bb3Full.synthetic(4000, 1)) Lbb4Full = L_p(bb4Full.synthetic(4000, 1)) bf1Rem = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='classic', gamma=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, grid=g, mpos=m, r_diag=True, c=346.04, steer='true location') bb1Full = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='classic') bb2Full = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='inverse') bb3Full = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='true level') bb4Full = BeamformerBase(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='true location') Lbb1Rem = L_p(bb1Rem.synthetic(4000,1)) Lbb2Rem = L_p(bb2Rem.synthetic(4000,1)) Lbb3Rem = L_p(bb3Rem.synthetic(4000,1)) Lbb4Rem = L_p(bb4Rem.synthetic(4000,1)) Lbb1Full = L_p(bb1Full.synthetic(4000,1)) Lbb2Full = L_p(bb2Full.synthetic(4000,1)) Lbb3Full = L_p(bb3Full.synthetic(4000,1)) Lbb4Full = L_p(bb4Full.synthetic(4000,1)) bf1Rem = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='classic', gamma=3) bf2Rem = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='inverse', gamma=3) bf3Rem = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='true level', gamma=3) bf4Rem = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=True, c=346.04, steer='true location', gamma=3) bf1Full = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='classic', gamma=3) bf2Full = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='inverse', gamma=3) bf3Full = BeamformerFunctional(freq_data=f, grid=g, mpos=m, r_diag=False, c=346.04, steer='true level', gamma=3)
# usually, another type of beamformer (e.g. CLEAN-SC) would be more appropriate # to be really fast, we restrict ourselves to only 10 frequencies # in the range 2000 - 6000 Hz (5*400 - 15*400) #=============================================================================== f = PowerSpectra(time_data=t, window='Hanning', overlap='50%', block_size=128, \ ind_low=5, ind_high=15) env = Environment(c=346.04) st = SteeringVector(grid=g, mics=m, env=env) b = BeamformerBase(freq_data=f, steer=st, r_diag=True) #=============================================================================== # reads the data, finds the maximum value (to properly scale the views) #=============================================================================== map = b.synthetic(4000,1) L1 = L_p(map) mx = L1.max() #=============================================================================== # print out the result integrated over an 3d-sector of the 3d map #=============================================================================== print(L_p(b.integrate((-0.3,-0.1,0.58,-0.1,0.1,0.78)))[f.ind_low:f.ind_high]) #=============================================================================== # displays the 3d view #=============================================================================== X,Y,Z = mgrid[g.x_min:g.x_max:1j*g.nxsteps,\ g.y_min:g.y_max:1j*g.nysteps,\
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')