#integrate SPL values from beamforming results using the shapes levels_circ = acoular.integrate(bf.result, rg, circle) levels_rect = acoular.integrate(bf.result, rg, rect) levels_poly = acoular.integrate(bf.result, rg, poly) #integrate SPL values from beamforming results using sector classes levels_circ_sector = acoular.integrate(bf.result, rg, circle_sector) levels_rekt_sector = acoular.integrate(bf.result, rg, rect_sector) levels_poly_sector = acoular.integrate(bf.result, rg, poly_sector) levels_multi_sector = acoular.integrate(bf.result, rg, multi_sector) #plot map and sectors figure() map = bf.synthetic(2000,1) mx = acoular.L_p(map.max()) imshow(acoular.L_p(map.T), origin='lower', vmin=mx-15,interpolation='nearest', extent=rg.extend(),cmap=cm.hot_r) colorbar() circle1 = plt.Circle((-0.3,0.1), 0.05, color='k', fill=False) plt.gcf().gca().add_artist(circle1) polygon = Polygon(poly.reshape(-1,2), color='k', fill=False) plt.gcf().gca().add_artist(polygon) rect = Rectangle((-0.5,-0.15),0.1,0.3,linewidth=1,edgecolor='k',facecolor='none') plt.gcf().gca().add_artist(rect) #plot from shapes figure() plot(fftfreqs,acoular.L_p(levels_circ)) plot(fftfreqs,acoular.L_p(levels_rect)) plot(fftfreqs,acoular.L_p(levels_poly))
#integrate SPL values from beamforming results using the shapes
levels_circ = bf.integrate(circle)
levels_rect = bf.integrate(rect)
levels_poly = bf.integrate(poly)
#integrate SPL values from beamforming results using sector classes
levels_circ_sector = bf.integrate(circle_sector)
levels_rect_sector = bf.integrate(rect_sector)
levels_poly_sector = bf.integrate(poly_sector)
levels_multi_sector = bf.integrate(multi_sector)
#plot map and sectors
figure()
map = bf.synthetic(2000, 1)
mx = acoular.L_p(map.max())
imshow(acoular.L_p(map.T),
origin='lower',
vmin=mx - 15,
interpolation='nearest',
extent=rg.extend(),
cmap=cm.hot_r)
colorbar()
circle1 = plt.Circle((-0.3, 0.1), 0.05, color='k', fill=False)
plt.gcf().gca().add_artist(circle1)
polygon = Polygon(poly.reshape(-1, 2), color='k', fill=False)
plt.gcf().gca().add_artist(polygon)
rect = Rectangle((-0.5, -0.15),
0.1,
0.3,
linewidth=1,
# plot result maps for different beamformers in time domain
#===============================================================================
i2 = 1 # no of figure
i1 = 1 #no of subplot
for b in (cacht, cachts, cachct, cachcts):
# first, plot time-dependent result (block-wise)
figure(i2,(7,7))
i2 += 1
res = zeros(rg.size) # init accumulator for average
i3 = 1 # no of subplot
for r in b.result(1): #one single block
subplot(4,4,i3)
i3 += 1
res += r[0] # average accum.
map = r[0].reshape(rg.shape)
mx = acoular.L_p(map.max())
imshow(acoular.L_p(map.T), vmax=mx, vmin=mx-15, origin='lower',
interpolation='nearest', extent=rg.extend())
title('%i' % ((i3-1)*1024))
res /= i3-1 # average
tight_layout()
# second, plot overall result (average over all blocks)
figure(10)
subplot(4,4,i1)
i1 += 1
map = res.reshape(rg.shape)
mx = acoular.L_p(map.max())
imshow(acoular.L_p(map.T), vmax=mx, vmin=mx-15, origin='lower',
interpolation='nearest', extent=rg.extend())
colorbar()
#------------------------------------------------------------------------------
"""
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()
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')
#plt.savefig("test.png", bbox_inches='tight')
# 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)
bts = ac.BeamformerTimeSqTraj(source=fi, steer=st, trajectory=tr_beam, \
rvec = np.array((0,0,0)))
avgts = ac.TimeAverage(source=bts, naverage=int(fs*tmax/2))