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()
def get_beamformer_traj_result(Beamformer, num=32):
"""
returns the result for a given Beamformer class
Parameters
----------
Beamformer : cls
trajectory beamformer.
num : int, optional
number of samples to return. The default is 32.
Returns
-------
array
first block returned by the trajectory beamformers result() function.
"""
## with moving grid
ts = MaskedTimeSamples(name=FNAME)
gMoving = RectGrid(x_min=-.1,
x_max=.1,
y_min=0,
y_max=0,
z=0,
increment=.1)
stMoving = SteeringVector(grid=gMoving, mics=MGEOM)
bt = Beamformer(source=ts, trajectory=TRAJ, steer=stMoving)
if hasattr(bt, 'n_iter'):
bt.n_iter = 2
return next(bt.result(num)).astype(np.float32)
def get_acoular_essentials(self):
#Set the mic array geometry
mg = MicGeom(from_file=self.array_arrngmnt)
#Set rectangular plane and grid parameters for Acoular
self.set_grid()
rg = RectGrid(x_min=self.x_min_grid, x_max=self.x_max_grid, y_min=self.y_min_grid, y_max=self.y_max_grid, z=self.distance, \
increment=self.grid_increment)
st = SteeringVector(grid=rg, mics=mg)
return mg, rg, st
def get_acoular_grid(conn, grid_id, ap):
'''
function returns acoular grid from given grid id and aperture value
'''
grid_type, x1_min, x1_max, x2_min, x2_max, x3_min, dx1 = read_columns(
conn=conn,
table_name='grid',
column_names=[
'type', 'x1_min', 'x1_max', 'x2_min', 'x2_max', 'x3_min', 'dx1'
],
condition_column_name=['id'],
condition_value=[grid_id])
if grid_type == 'rect':
acoular_grid = RectGrid(x_min=float(x1_min) * ap,
x_max=float(x1_max) * ap,
y_min=float(x2_min) * ap,
y_max=float(x2_max) * ap,
z=float(x3_min) * ap,
increment=float(dx1) * ap)
return acoular_grid
# 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, 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()
micgeofile = path.join(path.split(acoular.__file__)[0], 'xml', 'array_56.xml')
#calc all values from example 1
cfreq = 4000
t1 = MaskedTimeSamples(name=datafile)
t1.start = 0 # first sample, default
t1.stop = 16000 # last valid sample = 15999
invalid = [1, 7] # list of invalid channels (unwanted microphones etc.)
t1.invalid_channels = invalid
t1.calib = Calib(from_file=calibfile)
m = MicGeom(from_file=micgeofile)
m.invalid_channels = invalid
g = RectGrid(x_min=-0.6,
x_max=-0.0,
y_min=-0.3,
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)
#octave band of interest
cfreq = 4000
t1 = MaskedTimeSamples(name=datafile)
t1.start = 0 # first sample, default
t1.stop = 16000 # last valid sample = 15999
invalid = [1,7] # list of invalid channels (unwanted microphones etc.)
t1.invalid_channels = invalid
#t1.calib = Calib(from_file=calibfile)
m = MicGeom(from_file=micgeofile)
m.invalid_channels = invalid
g = RectGrid(x_min=-0.6, x_max=-0.0, y_min=-0.3, y_max=0.3, z=0.68,
increment=0.05)
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)))
#t = p1 # use only fix source
# 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)
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
micGeoFile = path.join(path.split(acoular.__file__)[0], 'xml', 'array_56.xml')
freqInt = 4000
t1 = MaskedTimeSamples(name = dataFile)
t1.start = 0
t1.stop = 16000
invalid = [1, 7]
t1.invalid_channels = invalid
t1.calib = Calib(from_file = calibFile)
m = MicGeom(from_file = micGeoFile)
m.invalid_channels = invalid
g = RectGrid(x_min = -0.6, x_max = -0.0, y_min = -0.3, y_max = 0.3,
z = 0.68, increment = 0.05)
f = EigSpectra(time_data = t1,
window = 'Hanning', overlap = '50%', block_size = 128,
ind_low = 7, ind_high = 15)
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 = range(38, 54))
bs = BeamformerCleansc(freq_data = f, grid = g, mpos = m, r_diag = True, c = 346.04)
bf = BeamformerCMF(freq_data = f, grid = g, mpos = m, c = 346.04, method = 'LassoLarsBIC')
# 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, 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()
"obj.plane = array((1., 0., 0.))"),
# "OpenJet.origin item assignment": (OpenJet(), "obj.origin[0] = 1."),
"OpenJet.origin array assignment": (OpenJet(),
"obj.origin = array((1., 0., 0.))"),
# "RotatingFlow.origin item assignment": (RotatingFlow(), "obj.origin[0] = 1."),
"RotatingFlow.origin array assignment":
(RotatingFlow(), "obj.origin = array((1., 0., 0.))"),
#fbeamform.py
# "SteeringVector.ref item assignment": (SteeringVector(), "obj.ref[0] = 1."),
"SteeringVector.ref array assignment": (SteeringVector(),
"obj.ref = array((1., 1., 1.))"),
# "BeamformerOrth.eva_list item assignment": (BeamformerOrth(eva_list=array((0, 1))), "obj.eva_list[0] = 2"),
"BeamformerOrth.eva_list array assignment":
(BeamformerOrth(eva_list=array((0, 1))), "obj.eva_list = array((2))"),
#grids.py
"MergeGrid.grids item assignment": (MergeGrid(grids=[RectGrid()]),
"obj.grids[0] = RectGrid()"),
"MergeGrid.grids list assignment": (MergeGrid(),
"obj.grids = [RectGrid()]"),
# signals.py
# "FiltWNoiseGenerator.ar item assignment": (FiltWNoiseGenerator(ar=[1.,2.,3.]), "obj.ar[0] = 0."),
"FiltWNoiseGenerator.ar array assignment":
(FiltWNoiseGenerator(ar=[1., 2., 3.]), "obj.ar = array((0., 0., 0.))"),
# "FiltWNoiseGenerator.ma item assignment": (FiltWNoiseGenerator(ma=[1.,2.,3.]), "obj.ma[0] = 0."),
"FiltWNoiseGenerator.mar array assignment":
(FiltWNoiseGenerator(ma=[1., 2., 3.]), "obj.ma = array((0., 0., 0.))"),
#sources.py
"MaskedTimeSamples.invalid_channels item assignment":
(MaskedTimeSamples(invalid_channels=[1]), "obj.invalid_channels[0] = 0"),
"MaskedTimeSamples.invalid_channels list assignment":
(MaskedTimeSamples(), "obj.invalid_channels = [0]"),
p1 = PointSource(signal=n1, mics=m, loc=(0,R,Z))
t = Mixer(source = p0, sources = [p1,]) # mix both signals
#t = p1 # use only fix source
# 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))
if sys.version_info > (3, ):
long = int
nsamples = long(sfreq * tmax)
n1 = WNoiseGenerator(sample_freq=sfreq, numsamples=nsamples)
s1 = SineGenerator(sample_freq=sfreq, numsamples=nsamples, freq=freq)
p1 = PNoiseGenerator(seed=1, numsamples=nsamples)
#===============================================================================
# define environment and grid
#===============================================================================
ufe = UniformFlowEnvironment(ma=0.5, fdv=(0, 1, 0))
g = RectGrid(x_min=-3.0,
x_max=+3.0,
y_min=-3.0,
y_max=+3.0,
z=Z,
increment=0.2)
#===============================================================================
# define some sources
#===============================================================================
mp = MovingPointSource(signal=s1, env=ufe, mpos=m, trajectory=tr1)
ps = PointSource(signal=s1, mpos=m, loc=(0, R, Z))
pd = PointSourceDipole(signal=s1,
mpos=m,
direction=(0.5, 0, 0),
loc=(0, -2, Z))
un = UncorrelatedNoiseSource(signal=n1, mpos=mg)
mix = SourceMixer(sources=[ps, pd])
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')
t = Mixer(source=p0, sources=[p1]) # mix both signals
# t = p1 # use only fix source
# 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)
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)
i = 0
nsamples = long(sfreq * 0.3)
n1 = WNoiseGenerator(sample_freq=sfreq, numsamples=nsamples)
s1 = SineGenerator(sample_freq=sfreq, numsamples=nsamples, freq=freq)
s2 = SineGenerator(sample_freq=sfreq, numsamples=nsamples, freq=freq, \
phase=pi)
#define a circular array of 8 microphones
m = MicGeom('array_64_8mic.xml')
m.mpos_tot = array([(r*sin(2*pi*i+pi/8), r*cos(2*pi*i+pi/8), 0) \
for i in linspace(0.0, 1.0, 8, False)]).T
t = MaskedTimeSamples(name=datafile)
f = PowerSpectra(time_data=t, window='Hanning', overlap='50%', block_size=128, \
ind_low=1,ind_high=30)
g = RectGrid(x_min=0 - .2,
x_max=0.3,
y_min=-0.13,
y_max=0.3,
z=0,
increment=0.05)
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)
t1.calib = Calib(from_file=calibfile)
#===============================================================================
# the microphone geometry must have the same number of valid channels as the
# TimeSamples object has
#===============================================================================
m = MicGeom(from_file=micgeofile)
m.invalid_channels = invalid
#===============================================================================
# the grid for the beamforming map; a RectGrid3D class is also available
# (the example grid is very coarse)
#===============================================================================
g = RectGrid(x_min=-0.6,
x_max=-0.0,
y_min=-0.3,
y_max=0.3,
z=0.68,
increment=0.05)
#===============================================================================
# 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=128, #FFT-parameters
ind_low=8,
ind_high=16) #to save computational effort, only
# object (for backwards compatibility)
#===============================================================================
t1.calib = Calib(from_file=calibfile)
#===============================================================================
# the microphone geometry must have the same number of valid channels as the
# TimeSamples object has
#===============================================================================
m = MicGeom(from_file=micgeofile)
m.invalid_channels = invalid
#===============================================================================
# the grid for the beamforming map; a RectGrid3D class is also available
# (the example grid is very coarse)
#===============================================================================
g = RectGrid(x_min=-0.6, x_max=-0.0, y_min=-0.3, y_max=0.3, z=0.68,
increment=0.05)
#===============================================================================
# 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=128, #FFT-parameters
ind_low=7, ind_high=15, precision='complex128') #to save computational effort, only
# frequencies with index 1-30 are used
csm = f.csm[:]
eva = f.eva[:]
f32 = EigSpectra(time_data=t1,
window='Hanning', overlap='50%', block_size=128, #FFT-parameters
