def main():
# Without turbulence
#model.settings['turbulence']['include'] = False
model.settings['turbulence']['include'] = False
model.settings['turbulence']['amplitude'] = False
model.settings['turbulence']['phase'] = False
signal = mono(rcv.auralise())
signal = Signal(signal.take(nsamples).toarray(), fs)
without = signal
# With turbulence (logamp)
model.settings['turbulence']['include'] = True
model.settings['turbulence']['amplitude'] = True
model.settings['turbulence']['phase'] = False
signal = mono(rcv.auralise())
signal = Signal(signal.take(nsamples).toarray(), fs)
#_ = signal.plot_spectrogram(ylim=(0.0, 4000.0), clim=(-40, +60))
with_logamp = signal
# With turbulence (phase)
model.settings['turbulence']['include'] = True
model.settings['turbulence']['amplitude'] = False
model.settings['turbulence']['phase'] = True
signal = mono(rcv.auralise())
signal = Signal(signal.take(nsamples).toarray(), fs)
# In[21]:
#Audio(data=signal, rate=signal.fs)
# In[22]:
_ = signal.plot_spectrogram(ylim=(0.0, 4000.0), clim=(-40, +60))
# In[23]:
with_phase = signal
# ## With turbulence (logamp and phase)
# In[24]:
model.settings['turbulence']['include'] = True
model.settings['turbulence']['amplitude'] = True
model.settings['turbulence']['phase'] = True
signal = mono(rcv.auralise())
signal = Signal(signal.take(nsamples).toarray(), fs)
# In[25]:
#Audio(data=signal, rate=signal.fs)
# In[26]:
_ = signal.plot_spectrogram(ylim=(0.0, 4000.0), clim=(-40, +60))
# In[27]:
with_logamp_and_phase = signal
# In[28]:
signals = Signal([without, with_logamp, with_phase, with_logamp_and_phase], fs)
# In[29]:
labels = ['Without', 'Logamp', 'Phase', 'Both']
# In[30]:
fig = signals.plot_levels(labels=labels)
# In[31]:
_ = signals.plot_third_octaves(labels=labels)
# ## Save figures and audio files
# In[34]:
#with sns.axes_style(rc={"axes.grid":False}):
clim = (0.0, +70)
ylim = (0.0, 4000.0)
for signal, label in zip(signals, labels):
signal.normalize().to_wav("../audio/auralisation_flight_{}.wav".format(label.lower()))
fig = signal.plot_spectrogram(ylim=ylim, clim=clim, title="")
fig.subplots_adjust(bottom=0.2, left=0.2)
fig.savefig("../figures/auralisation_flight_{}.eps".format(label.lower()))
speeds = np.array([1, 10, 20, 100, 110])
fmin = (5. * speeds / correlation_length).max() * 10.0
_linestyles = itertools.cycle(iter(linestyles))
results = []
for speed in speeds:
signal = sine(frequency, fs)
result = modulate(signal,
fs,
nhop,
correlation_length,
speed,
distance,
soundspeed,
mean_mu_squared,
fmin,
ntaps_corr,
state=np.random.RandomState(seed=seed),
include_saturation=True)
result = Signal(result.take(nsamples).toarray(), fs)
saveaudio(result, "scintillations_speed_{}.wav".format(speed))
results.append(result)
modulated = Signal(results, fs)
ax = modulated.plot_levels(labels=speeds, title="")
fig = ax.get_figure()
fig.tight_layout()
savefig(fig, "scintillations_levels")