def main():
parser = argparse.ArgumentParser()
parser.add_argument('source', type=str)
parser.add_argument('target', type=str)
args = parser.parse_args()
filenames = os.listdir(args.source)
paths = (os.path.join(args.source, filename) for filename in filenames)
signals = list(map(load_signal, paths))
# Create spectrograms
plotargs = {
'clim': (-40, +40),
'title': '',
}
# Create spectrograms
for signal in signals:
create_spectrogram(
signal,
os.path.join(args.target, "{}.{}".format(signal.basename,
EXTENSION)), plotargs)
# Time-level
s = Signal(signals, signals[0].fs)
name = os.path.join(args.target, "levels.{}".format(EXTENSION))
ax = s.plot_levels()
ax.legend(labels=[
create_label(signal.method, signal.kernelsize) for signal in signals
])
fig = ax.get_figure()
fig.tight_layout()
fig.savefig(name, dpi=600)
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()))
mean_mu_squared,
ntaps_corr=ntaps_corr,
window=window,
include_saturation=include_saturation,
state=np.random.RandomState(seed=seed),
include_amplitude=include_amplitude,
include_phase=include_phase)
both = Signal([tone, modulated], fs)
print(both.levels()[1].std(axis=-1))
saveaudio(tone, "tone.wav")
saveaudio(modulated, "modulated.wav")
# In[9]:
labels = ["Tone", "Modulated"]
ax = both.plot_levels(title="", labels=labels)
fig = ax.get_figure()
fig.tight_layout()
savefig(fig, "modulated_levels")
ax = both.plot_power_spectrum(xlim=(985, 1015),
ylim=(30.0, 100.0),
xscale='linear',
labels=labels,
title="")
fig = ax.get_figure()
fig.tight_layout()
savefig(fig, "tone_broadening")
# # Correlation for different speeds