def test_pgc1():
assert nb_channel==1
in_buffer = hls.moving_sinus(length, sample_rate=sample_rate, speed = .5, f1=500., f2=2000., ampl = .8)
in_buffer = np.tile(in_buffer[:, None],(1, nb_channel))
loss_params = { 'left' : {'freqs' : [ 125*2**i for i in range(7) ], 'compression_degree': [0]*7, 'passive_loss_db' : [0]*7 } }
loss_params['right'] = loss_params['left']
processing_conf = dict(nb_freq_band=5, level_step=10, debug_mode=True, chunksize=chunksize, backward_chunksize=backward_chunksize, loss_params=loss_params)
processing, online_arrs = hls.run_class_offline(hls.InvCGC, in_buffer, chunksize, sample_rate, processing_conf=processing_conf, buffersize_margin=backward_chunksize)
n = processing.nb_freq_band
in_buffer2 = np.tile(in_buffer,(1, processing.nb_freq_band))
online_arr = online_arrs['pgc1']
offline_arr = in_buffer2.copy()
for i in range(n):
offline_arr[:, i] = scipy.signal.sosfilt(processing.coefficients_pgc[i,:,:], in_buffer2[:,i])
offline_arr = offline_arr[:online_arr.shape[0]]
residual = np.abs((online_arr.astype('float64')-offline_arr.astype('float64'))/np.mean(np.abs(offline_arr.astype('float64'))))
#~ print(np.max(residual))
freq_band = 4
fig, ax = plt.subplots(nrows = 2, sharex=True)
#~ ax[0].plot(in_buffer2[:, freq_band], color = 'k')
ax[0].plot(offline_arr[:, freq_band], color = 'g')
ax[0].plot(online_arr[:, freq_band], color = 'r', ls='--')
ax[1].plot(residual[:, freq_band], color = 'm')
for i in range(nloop):
ax[1].axvline(i*chunksize)
plt.show()
assert np.max(residual)<1e-5, 'pgc1 online differt from offline'
def test_levels():
assert nb_channel == 1
#~ in_buffer = hls.moving_sinus(length, sample_rate=sample_rate, speed = .5, f1=500., f2=2000., ampl = .8)
in_buffer = hls.moving_erb_noise(length)
in_buffer = np.tile(in_buffer[:, None], (1, nb_channel))
loss_params = {
'left': {
'freqs': [125 * 2**i for i in range(7)],
'compression_degree': [0] * 7,
'passive_loss_db': [0] * 7
}
}
loss_params['right'] = loss_params['left']
processing_conf = dict(nb_freq_band=5,
level_step=10,
debug_mode=True,
chunksize=chunksize,
backward_chunksize=backward_chunksize,
loss_params=loss_params)
processing, online_arrs = hls.run_class_offline(
hls.InvCGC,
in_buffer,
chunksize,
sample_rate,
processing_conf=processing_conf,
buffersize_margin=backward_chunksize)
freq_band = 2
out_pgc1 = online_arrs['pgc1']
hilbert_env = np.abs(scipy.signal.hilbert(out_pgc1[:, freq_band], axis=0))
hilbert_level = 20 * np.log10(hilbert_env) + processing.calibration
#~ online_levels= online_arrs['levels'][:, freq_band]*processing.level_step
online_levels = online_arrs['levels'][:, freq_band]
online_env = 10**((online_levels - processing.calibration) / 20.)
residual = np.abs(
(online_levels.astype('float64') - hilbert_level.astype('float64')) /
np.mean(np.abs(online_levels.astype('float64'))))
residual[:100] = 0
residual[-100:] = 0
print(np.max(residual))
#~ assert np.max(residual)<3e-2, 'levelfrom hilbert offline'
fig, ax = plt.subplots(nrows=2, sharex=True)
ax[0].plot(out_pgc1[:, freq_band], color='k', alpha=.8)
ax[0].plot(np.abs(out_pgc1[:, freq_band]), color='k', ls='--', alpha=.8)
ax[0].plot(hilbert_env, color='g', lw=2)
ax[0].plot(online_env, color='r', lw=2)
ax[1].plot(online_levels, color='r')
ax[1].plot(hilbert_level, color='g')
ax[1].set_ylabel('level dB')
plt.show()
def test_pgc2():
assert nb_channel==1
#~ in_buffer = hls.moving_sinus(length, sample_rate=sample_rate, speed = .5, f1=50., f2=2000., ampl = .8)
#~ in_buffer = hls.moving_erb_noise(length, sample_rate=sample_rate,)
in_buffer = hls.whitenoise(length, sample_rate=sample_rate,)
in_buffer = np.tile(in_buffer[:, None],(1, nb_channel))
loss_params = { 'left' : {'freqs' : [ 125*2**i for i in range(7) ], 'compression_degree': [0]*7, 'passive_loss_db' : [0]*7 } }
loss_params['right'] = loss_params['left']
processing_conf = dict(nb_freq_band=32, level_max=120, level_step=1, debug_mode=True,
low_freq = 60., high_freq = 15000.,
loss_params = loss_params,
chunksize=chunksize, backward_chunksize=backward_chunksize)
processing, online_arrs = hls.run_class_offline(hls.InvCGC, in_buffer, chunksize, sample_rate, processing_conf=processing_conf, buffersize_margin=backward_chunksize)
freq_band = 4
online_hpaf = online_arrs['hpaf']
online_pgc2 = online_arrs['pgc2']
offline_pgc2 = online_pgc2.copy()
n = processing.nb_freq_band
for i in range(n):
offline_pgc2[:, i] = scipy.signal.sosfilt(processing.coefficients_pgc[i, :,:], online_hpaf[::-1,i])[::-1]
online_pgc2 = online_pgc2[:-backward_chunksize]
offline_pgc2 = offline_pgc2[:-backward_chunksize]
residual = np.abs((online_pgc2.astype('float64')-offline_pgc2.astype('float64'))/np.mean(np.abs(offline_pgc2.astype('float64')), axis=0))
print(np.max(residual, axis=0))
print(np.max(residual))
#~ freq_band = 4
fig, ax = plt.subplots(nrows = 2, sharex=True)
#~ ax[0].plot(online_hpaf[:, freq_band], color = 'b')
ax[0].plot(offline_pgc2[:, freq_band], color = 'g')
ax[0].plot(online_pgc2[:, freq_band], color = 'r', ls='--')
ax[1].plot(residual[:, freq_band], color = 'm')
ax[1].set_ylabel('residual for band {:0.2f}'.format(processing.freqs[freq_band]))
for i in range(nloop):
ax[1].axvline(i*chunksize)
plt.show()
# TODO make one values per band
assert np.max(residual)<2e-2, 'hpaf online differt from offline'
def test_invcomp():
#~ in_buffer = hls.moving_erb_noise(length)
in_buffer = hls.moving_sinus(length, sample_rate=sample_rate, speed = .5, f1=100., f2=2000., ampl = .8)
in_buffer = np.tile(in_buffer[:, None],(1, nb_channel))
#~ print(in_buffer.shape)
#~ exit()
loss_params = { 'left' : {'freqs' : [ 125*2**i for i in range(7) ], 'compression_degree': [0]*7, 'passive_loss_db' : [0]*7 } }
loss_params['right'] = loss_params['left']
processing_conf = dict(nb_freq_band=32, level_step=1., level_max = 100., loss_params=loss_params,
low_freq=100., high_freq=15000.,
debug_mode=True, chunksize=chunksize, backward_chunksize=backward_chunksize)
processing, online_arrs = hls.run_class_offline(hls.InvComp, in_buffer, chunksize, sample_rate, processing_conf=processing_conf, buffersize_margin=backward_chunksize)
print('nlevel', processing.levels.size, 'nb_freq_band', processing.nb_freq_band)
freq_band = 15
fig, ax = plt.subplots(nrows = 7, sharex=True) #, sharey=True)
ax[0].plot(in_buffer[:, 0], color = 'k')
steps = ['pgc1', 'levels', 'dyngain', 'pgc2', 'passive']
for i, k in enumerate(steps):
online_arr = online_arrs[k]
print(online_arr.shape)
ax[i+1].plot(online_arr[:, freq_band], color = 'b')
ax[i+1].set_ylabel(k)
#~ ax[0].plot(offline_arr[:, 0], color = 'g')
out_buffer = online_arrs['main_output']
ax[-1].plot(out_buffer[:, 0], color = 'k')
if nb_channel==2:
#test stereo is like mono
#~ fig, ax = plt.subplots()
#~ ax.plot(out_buffer[:,0], color='b')
#~ ax.plot(out_buffer[:,1], color='r')
#~ fig, ax = plt.subplots()
#~ ax.plot(out_buffer[:,0]-out_buffer[:,1], color='b')
#~ plt.show()
assert np.all(np.abs(out_buffer[:,0]-out_buffer[:,1])<1e-5)
plt.show()
def test_dyngain():
"""
For testing dynamic gain we take coefficient with only one level so
it is dynamic with alwas the same coefficient.
"""
assert nb_channel==1
#~ in_buffer = hls.moving_sinus(length, sample_rate=sample_rate, speed = .5, f1=500., f2=2000., ampl = .8)
in_buffer = hls.moving_erb_noise(length)
in_buffer = np.tile(in_buffer[:, None],(1, nb_channel))
loss_params = { 'left' : {'freqs' : [ 125*2**i for i in range(7) ], 'compression_degree': [0]*7, 'passive_loss_db' : [0]*7 } }
loss_params['right'] = loss_params['left']
processing_conf = dict(nb_freq_band=5, level_max=120, level_step=120, debug_mode=True, chunksize=chunksize, backward_chunksize=backward_chunksize, loss_params=loss_params)
processing, online_arrs = hls.run_class_offline(hls.InvComp, in_buffer, chunksize, sample_rate, processing_conf=processing_conf, buffersize_margin=backward_chunksize)
#~ assert len(processing.levels)==1
freq_band = 2
online_pgc1 = online_arrs['pgc1']
online_dyngain = online_arrs['dyngain']
n = processing.nb_freq_band
offline_dyngain = online_pgc1.copy()
for i in range(n):
offline_dyngain[:, i] = online_pgc1[:, i] * processing.gain_controlled[i,0]
print(processing.gain_controlled[i,0])
residual = np.abs((online_dyngain.astype('float64')-offline_dyngain.astype('float64'))/np.mean(np.abs(offline_dyngain.astype('float64'))))
print(np.max(residual))
freq_band = 4
fig, ax = plt.subplots(nrows = 2, sharex=True)
#~ ax[0].plot(online_pgc1[:, freq_band], color = 'b')
ax[0].plot(offline_dyngain[:, freq_band], color = 'g')
ax[0].plot(online_dyngain[:, freq_band], color = 'r', ls='--')
ax[1].plot(residual[:, freq_band], color = 'm')
for i in range(nloop):
ax[1].axvline(i*chunksize)
plt.show()
assert np.max(residual)<2e-2, 'hpaf online differt from offline'
def test_passive_loss():
in_buffer = hls.moving_sinus(length, sample_rate=sample_rate, speed = .5, f1=500., f2=2000., ampl = .8)
in_buffer = np.tile(in_buffer[:, None],(1, nb_channel))
loss_params = { 'left' : {'freqs' : [ 125*2**i for i in range(7) ], 'compression_degree': [1.]*7, 'passive_loss_db' : [-20.]*7 } }
loss_params['right'] = loss_params['left']
processing_conf = dict(nb_freq_band=32, level_step=10, debug_mode=True, chunksize=chunksize, backward_chunksize=backward_chunksize, loss_params=loss_params)
processing, online_arrs = hls.run_class_offline(hls.InvCGC, in_buffer, chunksize, sample_rate, processing_conf=processing_conf, buffersize_margin=backward_chunksize)
n = processing.nb_freq_band
online_pgc2 = online_arrs['pgc2']
online_passive = online_arrs['passive']
#~ offline_passive = online_pgc2.copy()
#~ channels = ('left', 'right')[:nb_channel]
#~ for c, chan in enumerate(channels):
#~ for i in range(n):
#~ offline_passive[:, c*n + i] = processing.passive_gain[c*n + i] * online_pgc2
offline_passive = processing.passive_gain.T * online_pgc2
residual = np.abs((online_passive.astype('float64')-offline_passive.astype('float64'))/np.mean(np.abs(offline_passive.astype('float64'))))
print(np.max(residual))
freq_band = 15
fig, ax = plt.subplots(nrows = 2, sharex=True)
#~ ax[0].plot(in_buffer2[:, freq_band], color = 'k')
ax[0].plot(offline_passive[:, freq_band], color = 'g')
ax[0].plot(online_passive[:, freq_band], color = 'r', ls='--')
ax[1].plot(residual[:, freq_band], color = 'm')
for i in range(nloop):
ax[1].axvline(i*chunksize)
plt.show()
assert np.max(residual)<1e-4, 'passive online differt from offline'
