def plot_graph(self, index):
gpx = self.get_gpx(index)
points = gpx.track.track_segment
plot = Plotter(points)
result = plot.setup()
if result:
plot.plot()
else:
self.show_warning("Нехватает данных о высоте!")
selection_mc = selection_mc,
selection_tight = selection_tight,
pandas_selection = pandas_selection,
lumi = 59700.,
model = '/'.join([env['NN_DIR'], 'trainings', training, 'net_model_weighted.h5' ]),
transformation = '/'.join([env['NN_DIR'], 'trainings', training, 'input_tranformation_weighted.pck']),
features = '/'.join([env['NN_DIR'], 'trainings', training, 'input_features.pck' ]),
process_signals = False, # switch off for control regions
mini_signals = False, # process only the signals that you'll plot
plot_signals = False,
blinded = False,
datacards = ['hnl_m_12_lxy_lt_0p5', 'hnl_m_12_lxy_0p5_to_1p5', 'hnl_m_12_lxy_1p5_to_4p0', 'hnl_m_12_lxy_mt_4p0'], # FIXME! improve this to accept wildcards / regex
mc_subtraction = True,
dir_suffix = 'check_alt_prompt_estimate',
relaxed_mc_scaling = 0.05,
)
if __name__ == '__main__':
plotter.plot()
# save the plotter and all
save_plotter_and_selections(plotter, selection, selection_mc, selection_tight)
pass
def task1(original_signal: Signal,
original_freq,
carrier_freq,
global_pref=None,
k=64,
noise_freq=None):
Plotter.plot(original_signal, _title_pref=global_pref)
Plotter.fourier_transform(original_signal, _title_pref=global_pref)
modulated_signal = DoubleSideband(original_signal, carrier_freq)
if noise_freq is not None:
modulated_signal = modulated_signal + Sine(noise_freq,
amplitude=amplitude,
t_start=0,
t_end=1,
discretization=10000)
Plotter.plot(modulated_signal, t_end=0.1, _title_pref=global_pref)
Plotter.fourier_transform(modulated_signal, _title_pref=global_pref)
signal_fft = abs(fftshift(fft(modulated_signal.get_y())))
signal_fft = 2 * signal_fft / len(signal_fft)
f_fft = fftshift(
fftfreq(len(modulated_signal.get_x()),
abs(modulated_signal.get_x()[1] -
modulated_signal.get_x()[0])))
if noise_freq is None:
print(
find_freqs(signal_fft[len(signal_fft) // 2 - 1:],
f_fft[len(f_fft) // 2 - 1:], 3))
else:
print(
find_freqs(signal_fft[len(signal_fft) // 2 - 1:],
f_fft[len(f_fft) // 2 - 1:], 4))
discrete_signal = Discrete(modulated_signal,
k,
signal_min=min(modulated_signal.get_y()),
signal_max=max(modulated_signal.get_y()))
Plotter.plot(discrete_signal, t_end=0.1, _title_pref=global_pref)
detected_signal = Detect(discrete_signal, carrier_freq, filter_freq=30)
Plotter.plot(detected_signal, t_end=0.1, _title_pref=global_pref)
Plotter.fourier_transform(detected_signal, _title_pref=global_pref)
signal_fft = abs(fftshift(fft(detected_signal.get_y())))
signal_fft = 2 * signal_fft / len(signal_fft)
f_fft = fftshift(
fftfreq(len(detected_signal.get_x()),
abs(detected_signal.get_x()[1] - detected_signal.get_x()[0])))
print(
find_freqs(signal_fft[len(signal_fft) // 2 - 1:],
f_fft[len(f_fft) // 2 - 1:], 2))
detected_signal2 = Detect(detected_signal, original_freq, filter_freq=7)
Plotter.plot(detected_signal2, _title_pref=global_pref)
Plotter.fourier_transform(detected_signal2, _title_pref=global_pref)
lbound = max(detected_signal2.get_y()) / pow(10, 3 / 20)
print("lbound: %d" % lbound)
sig_out = np.ones(len(detected_signal2.get_x()))
for i in range(0, len(detected_signal2.get_x())):
if detected_signal2.get_y()[i] <= lbound:
sig_out[i] = 0
else:
break
k = 1
while sig_out[k] == 0:
k = k + 1
if k == sig_out.__len__() - 1:
break
print('delay: %f' % detected_signal2.get_x()[k])
pyplot.plot(detected_signal.get_x(), sig_out, '.')
# pyplot.xlim(0, 1000)
pyplot.show()
