##################### DeepLearning-Matrix ##########################
################ Elevation Azimuth Frequenzbänder Frames
DL_Matrix = zeros((NPOINTS_ELE, NPOINTS_AZI, len(FREQBANDS), FRAMES))
# Wenn .npz, dann keine FRAMES, weil die in einzelne .npy-Tabellen?!
# DL_Matrix = zeros((NPOINTS_ELE, NPOINTS_AZI, len(FREQBANDS)))
for frame_index, frame in enumerate(range(STARTFRAME, ENDFRAME)):
print('### FRAME: ', frame - STARTFRAME, ' (', frame, ') ###')
for freq_index, freq in enumerate(FREQBANDS):
print('FREQ =', freq)
ts.start = frame * NUM
ts.stop = (frame + 1) * NUM
result = zeros((4, rg.shape[0], rg.shape[1]))
for i in range(4):
be.n = i
result[i] = be.synthetic(freq, 3)
maxind = argmax(result.max((1, 2)))
# WARUM IMMER MAXINDEX = 3 ???
# print('Result Beamforming: Maxindex = ', maxind)
Lm = L_p(result[maxind]).reshape(rg.shape).flatten()
max_idx = argmax(
Lm.flatten()) # position in grid with max source strength
max_cartcoord = rg.gpos[:, max_idx]
max_idx = argmax(
Lm.flatten()) # position in grid with max source strength
max_value = amax(Lm.flatten())
# Opt 1
maxval1 = zeros(len(FREQBANDS))
maxval2 = zeros(len(FREQBANDS))
# Opt 2
tot_maxval1 = 0
tot_maxval2 = 0
# Opt 3
tot_maxval = 0
# Befüllen von Src1_Matrix und Src2_Matrix
for freq_index, freq in enumerate(FREQBANDS):
be.n = -1 #Eigenwerte der Größe nach sortiert -> größter Eigenwert (default)
Lm = L_p(be.synthetic(freq, 3)).reshape(rg.shape).flatten()
Src1_Matrix[:,:,freq_index] = Lm.reshape(rg.shape).T
max_idx1 = argmax(Lm.flatten()) # position in grid with max source strength
max_value1 = amax(Lm.flatten())
be.n = -2 #Eigenwerte der Größe nach sortiert -> größter Eigenwert (default)
Lm = L_p(be.synthetic(freq, 3)).reshape(rg.shape).flatten()
Src2_Matrix[:,:,freq_index] = Lm.reshape(rg.shape).T
max_idx2 = argmax(Lm.flatten()) # position in grid with max source strength
max_value2 = amax(Lm.flatten())
# Opt 1