def beamformer(fileName,figNum=1):
param=param_read(fileName)
navg=param['Navg']
ts=TS.Sim_ts(param)
kw=KW.Kw(param)
kw.zero()
dtaIn=ts.instance()
gramOut=kw.instance()
for i in range(navg):
ts.zero()
ts.nb_sim()
ts.noise_sim()
kw.kw(dtaIn)
for i in range(vsip.getrowlength(gramOut)):
v=vsip.colview(gramOut,i)
vsip.freqswap(v)
vsip.destroy(v)
max = vsip.maxval(gramOut,None)
avg = vsip.meanval(gramOut)
vsip.clip(gramOut,0.0,max,avg/100000.0,max,gramOut)
vsip.log10(gramOut,gramOut)
min = vsip.minval(gramOut,None)
vsip.add(-min,gramOut,gramOut)
max=vsip.maxval(gramOut,None)
vsip.mul(1.0/max,gramOut,gramOut)
fig = plt.figure(figNum,figsize=(10,4))
ax = fig.add_axes([0.10,0.10,0.85,0.80])
ax.set_yticklabels(['0','0','30','60','90','120','150','180'])
ax.yaxis.set_ticks_position('right')
im=mToA(gramOut)
plt.imshow(im)
plt.title('K-Omega Beamformer Output')
plt.xlabel('Frequency')
plt.ylabel(r'$\frac{cos(\theta)}{\lambda}$',fontsize=16,rotation='horizontal')
plt.colorbar()
def noise_sim(self):
from numpy import pi, cos
d_t=self.d_t * self.Fs #sensor-to-sensor travel time at end-fire in samples
o_0 = d_t * self.Nsens + 1 # array travel time at end-fire in samples
a_stp = pi/self.Nsim_noise # angle step
bl_attr = vsip.getattrib(self.bl_noise)
for j in range(self.Nsim_noise):
a_crct = cos(float(j) * a_stp)
vsip.randn(self.rand,self.noise)
vsip.firfilt(self.fir,self.noise,self.bl_noise)
vsip.mul(12.0/float(self.Nsim_noise),self.bl_noise,self.bl_noise)
vsip.putlength(self.bl_noise,self.Nts);
for i in range(self.Nsens):
vsip.putoffset(self.bl_noise,int(o_0 + i * d_t * a_crct))
VU_rowview(self.v_data,self.m_data,i)
vsip.add(self.bl_noise,self.v_data,self.v_data)
vsip.putattrib(self.bl_noise,bl_attr);
vsip.add(-vsip.meanval(self.m_data),self.m_data,self.m_data);