#app_2.plot_tf()
#plt.show()
sp_vec_1 = app_1.to_array()[0]
sp_vec_2 = app_2.to_array()[0]
print "%1.5f, %1.5f"%(euclid_dist(sp_vec_1,sp_vec_2), hamming_dist(sp_vec_1,sp_vec_2))
# Now the information distance
from PyMP.mp_coder import joint_coding_distortion
# Measure the distortion of joint coding using approx of first patter as the reference
max_rate = 1000 # maximum bitrate allowed (in bits)
search_width = 1024 # maximum time shift allowed in samples
info_dist = joint_coding_distortion(sig_occ2, app_1, max_rate, search_width)
info_dist_rev = joint_coding_distortion(sig_occ1, app_2, max_rate, search_width)
print "%1.5f - %1.5f"%(info_dist/target_srr, info_dist_rev/target_srr)
# building the similarity matrix
# Now load the long version
from PyMP.signals import LongSignal
seg_size = 5*8192
long_signal = LongSignal(op.join(os.environ['PYMP_PATH'],'data/Bach_prelude_40s.wav'),
seg_size,
mono=True, Noverlap=0.5)
# decomposing the long signal
apps, decays = mp.mp_long(long_signal,
# decomposing the long signal
apps, decays = mp.mp_long(long_signal,
dico,
target_srr, max_atom_num)
mp._initialize_fftw(apps[0].dico, max_thread_num=1)
dists = np.zeros((long_signal.n_seg, len(apps)))
for idx in range(long_signal.n_seg):
# print idx
target_sig = long_signal.get_sub_signal(idx, 1, mono=True, pad=dico.get_pad()+1024,fast_create=True)
for jdx in range(idx+1):
# test all preceeding segments only
dists[idx,jdx] = joint_coding_distortion(target_sig, apps[jdx],max_rate,1024, debug=0, precut=15, initfftw=False)
mp._clean_fftw()
# remove everything that is under zero
cutscores = np.zeros((long_signal.n_seg, len(apps)))
# normalize by reference srr
cutscores[dists>0] = dists[dists>0]/ float(target_srr)
plt.figure()
plt.imshow(cutscores, origin='lower')
plt.colorbar()
plt.xlabel('Target Segment index')
plt.ylabel('Reference Segment index')
plt.show()
