from os import path
#from mayavi import mlab
from numpy import amax
#from cPickle import dump, load
from pickle import dump, load
# see example3
t = TimeSamples(name='example_data.h5')
cal = Calib(from_file='example_calib.xml')
m = MicGeom(from_file=path.join(\
path.split(acoular.__file__)[0], 'xml', 'array_56.xml'))
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,
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) #to save computational effort, only
# frequencies with index 1-30 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)
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')