import matplotlib.pyplot as plt
from matplotlib.patches import Polygon,Rectangle
#fft block size
block = 128
#example data
micgeofile = path.join( path.split(acoular.__file__)[0],'xml','array_56.xml')
mg = acoular.MicGeom( from_file=micgeofile )
ts = acoular.TimeSamples( name='example_data.h5' )
ps = acoular.PowerSpectra( time_data=ts, block_size=128, window='Hanning' )
rg = acoular.RectGrid(x_min=-0.6, x_max=-0.0, y_min=-0.3, y_max=0.3, z=0.68,
increment=0.02)
st = acoular.SteeringVector( grid = rg, mics=mg )
f = acoular.PowerSpectra(time_data=ts,block_size=block)
bf = acoular.BeamformerBase(freq_data = f,steer= st)
#Integrate function can deal with multiple methods for integration:
#1. a circle containing of three values: x-center, y-center and radius
circle = array([-0.3,-0.1, 0.05])
#2. a rektange containing of 4 values: lower corner(x1, y1) and upper corner(x2, y2).
rect = array([-0.5, -0.15, -0.4 , 0.15])
#3. a polygon containing of vector tuples: x1,y1,x2,y2,...,xi,yi
poly = array([ -0.25, -0.1, -0.1, -0.1, -0.1, -0.2, -0.2, -0.25, -0.3, -0.2])
#4 alternative: define those sectors as Classes
circle_sector = acoular.CircSector(x=-0.3,y= -0.1, r= 0.05)
#------------------------------------------------------------------------------
"""
Loads the three sources test data set, analyzes them and generates a
map of the three sources.
"""
from os import path
import acoular
from pylab import figure, plot, axis, imshow, colorbar, show
micgeofile = path.join(path.split(acoular.__file__)[0], 'xml', 'array_64.xml')
datafile = 'three_sources.h5'
mg = acoular.MicGeom(from_file=micgeofile)
ts = acoular.TimeSamples(name='three_sources.h5')
ps = acoular.PowerSpectra(time_data=ts, block_size=128, window='Hanning')
rg = acoular.RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \
increment=0.01 )
bb = acoular.BeamformerBase(freq_data=ps, grid=rg, mpos=mg)
pm = bb.synthetic(8000, 3)
Lm = acoular.L_p(pm)
imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \
interpolation='bicubic')
colorbar()
figure(2)
plot(mg.mpos[0], mg.mpos[1], 'o')
axis('equal')
show()
"""
Loads the three sources test data set, analyzes them and generates a
map of the three sources.
"""
from os import path
import acoular
from pylab import figure, plot, axis, imshow, colorbar, show
micgeofile = path.join(path.split(acoular.__file__)[0], 'xml', 'array_64.xml')
datafile = 'three_sources.h5'
mg = acoular.MicGeom(from_file=micgeofile)
ts = acoular.TimeSamples(name='three_sources.h5')
ps = acoular.PowerSpectra(time_data=ts, block_size=128, window='Hanning')
rg = acoular.RectGrid( x_min=-0.2, x_max=0.2, y_min=-0.2, y_max=0.2, z=0.3, \
increment=0.01 )
st = acoular.SteeringVector(grid=rg, mics=mg)
bb = acoular.BeamformerBase(freq_data=ps, steer=st)
pm = bb.synthetic(8000, 3)
Lm = acoular.L_p(pm)
imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=rg.extend(), \
interpolation='bicubic')
colorbar()
figure(2)
plot(mg.mpos[0], mg.mpos[1], 'o')
axis('equal')
show()
sag = "4mic_sagust.h5"
orta = "4mic_orta.h5"
sol2 = "sol_ust_ses_20_01.h5"
sag2 = "sag_alt_ses_20_01.h5"
micgeofile = path.join(path.split(ac.__file__)[0], 'xml', 'array_4_2.xml')
m = ac.MicGeom(from_file=micgeofile)
g = ac.RectGrid(x_min=-4, x_max=4, y_min=-2, y_max=2, z=0.3, increment=0.01)
t1 = ac.TimeSamples(name=sag2)
cal = ac.Calib(from_file='calibration.xml')
es = ac.EigSpectra(time_data=t1,
block_size=512,
window="Hanning",
overlap='50%',
calib=cal)
bb = ac.BeamformerBase(freq_data=es, grid=g, mpos=m, r_diag=False)
Lm = ac.L_p(bb.synthetic(500, 0))
"""
plt.figure()
plt.imshow( Lm.T, origin='lower', vmin=Lm.max()-10, extent=g.extend(), interpolation='bicubic')
"""
img = plt.imshow(Lm.T,
origin='lower',
vmin=Lm.max() - 10,
extent=g.extend(),
interpolation='bicubic')
plt.axis('off')
t = ac.Mixer(source = p0, sources = [p1,])
# Saving 32 channel wav
# ww = ac.WriteWAV(source = t)
# ww.channels = [0,32]
# ww.save
##################### Frequency beamforming fixed focus ######################
fi = ac.PowerSpectra(time_data=t, window='Hanning', overlap='50%', block_size=128, \
ind_low=1,ind_high=15) # CSM calculation
g = ac.RectGrid(x_min=-2, x_max=+2, y_min=0, y_max=+4, z=5, increment=0.1)
st = ac.SteeringVector(grid=g, mics=m)
b = ac.BeamformerBase(freq_data=fi, steer=st)
map1 = b.synthetic(f,3)
mx = ac.L_p(map1.max())
plot.figure()
plot.imshow(ac.L_p(np.transpose(map1)), vmax=mx, vmin=mx-5, interpolation='bilinear',\
extent=g.extend(), origin='lower')
plot.colorbar()
##################### Time beamforming moving focus ##########################
fi = ac.FiltFiltOctave(source=t, band=f, fraction='Third octave')
g = ac.RectGrid(x_min=-2.0, x_max=+2.0, y_min=0.0, y_max=+4.0, z=0.0, \
increment=0.1)# grid point of origin is at trajectory (thus z=0)
st = ac.SteeringVector(grid=g, mics=m)
# beamforming with trajectory (rvec axis perpendicular to trajectory)