def update(self):
self.opacity = self.opacity / self.rate
self.image.opacity = int(self.opacity)
self.image.position = (self.x, self.y)
if self.opacity < self.minopacity:
signal('kill', self)
elif time.time() - self.created_at > self.seconds_to_live:
signal('kill', self)
def demonstrate_single(sample_rate, signal_params):
signal_type = SignalType.from_string(signal_params['signal'])
analog_signal = signal(signal_type, sample_rate, signal_params)
widgets = demonstrate_signal(sample_rate, analog_signal,
f'{signal_params["signal"].title()} Signal')
return widgets
def on_mouse_press(self, x, y, button, modifiers):
"""
Create a new bullet at the turret. (LEFT CLICK)
Create a new gravity well. (RIGHT CLICK).
"""
# left click make a bullet.
if button == 1:
if self.ballMeter.active:
# signal that we are making a bullet.
signal('shoot')
self._bullet(x, y)
# make a new X marks the spot.
x = self._xmark(x, y)
elif button == 4:
self._gravity(x, y)
self.ballMeter.add(3)
def demonstrate_polyharmonic(sample_rate, params_of_signals):
analog_signals = []
for signal_params in params_of_signals:
signal_type = SignalType.from_string(signal_params['signal'])
analog_signals.append(signal(signal_type, sample_rate, signal_params))
polyharmonic_analog_signal = aggregate_to_polyharmonic_signal(
analog_signals)
widgets = demonstrate_signal(sample_rate, polyharmonic_analog_signal,
'Polyharmonic Signal')
return widgets
def custom_update(self):
"""
Find the net force from all of the gravities to me!
Then work out if I have hit a cat or something...
also lay a trail as we go.
"""
gravities = self.parent.gravities
forcex,forcey = 0,0
for gravity in gravities:
_forcex, _forcey = gravity.force_on(self)
forcex += _forcex
forcey += _forcey
# have I hit a vortex?
if self.hit(gravity):
signal('vortexhit', gravity=gravity, ball=self)
# well if that is the case, then time for me to die.
signal('kill', self)
self.force = (forcex, forcey)
# have I hit a cat?
if self.hit(self.parent.cat):
signal('cathit')
# well I did. I should surely die now.
signal('kill', self)
# leave a trail but only if we are on the screen.
if not self.parent.onscreen(self.x, self.y):
return
trail = X(self.x, self.y, self.parent.batch, self.parent.images['trail'])
trail.minopacity = 1
trail.rate = 1.01
self.parent.actors.append(trail)
def demonstrate_modulation(sample_rate, signal_params,
modulating_signal_params):
fig, axes = plt.subplots(2, sharex=True)
fig.canvas.set_window_title('Advanced Signals Modelling')
fig.suptitle('Amplitude & Frequency Modulation')
n = range(sample_rate)
modulating_signal_type = SignalType.from_string(
modulating_signal_params['signal'])
modulating_analog_signal = signal(modulating_signal_type, sample_rate,
modulating_signal_params)
sampled_modulating_signal = tuple(map(modulating_analog_signal, n))
signal_type = SignalType.from_string(signal_params['signal'])
analog_signal = signal_for_modulation(signal_type, sample_rate,
signal_params)
sampled_signal_with_amplitude_modulation = tuple(
analog_signal(i, sampled_modulating_signal[i],
signal_params['frequency']) for i in n)
accumulator = [0]
sampled_signal_with_frequency_modulation = \
tuple(analog_signal(1, signal_params['amplitude'],
signal_params['frequency'] + signal_params['frequency'] * sampled_modulating_signal[i], accumulator) for i in n)
am_soundax = plt.axes([0.79, 0.54, 0.1, 0.04])
fm_soundax = plt.axes([0.79, 0.12, 0.1, 0.04])
widgets = [
show_signal_modulation(axes[0], am_soundax, sample_rate, n,
sampled_signal_with_amplitude_modulation,
sampled_modulating_signal, 'AM'),
show_signal_modulation(axes[1], fm_soundax, sample_rate, n,
sampled_signal_with_frequency_modulation,
sampled_modulating_signal, 'FM')
]
return widgets
try:
path2 = Path(argv[2])
path3 = Path(argv[3])
except:
path2 = Path('queries/q1.wav')
path3 = Path('queries/q2.wav')
freq2, samples2 = wavfile.read(path2)
freq3, samples3 = wavfile.read(path3)
# normalizacia
samples1 = samples1 / 2**15
samples2 = samples2 / 2**15
samples3 = samples3 / 2**15
pdf = PdfPages(path1.stem + '_nas.pdf')
fig = signal(samples1, freq1, 'gigantic', 'parking')
pdf.savefig(fig)
fig.clf()
fig = features(samples1, freq1, pdf=True)
pdf.savefig(fig)
fig.clf()
fig = scores(samples1, freq1, samples2, freq2, samples3, freq3, 'gigantic',
'parking')
pdf.savefig(fig)
fig.clf()
pdf.close()
