def main():
ds = DefaultSignal(id=signal_id())
dbg = DefaultBackground(id=background_id())
x = dbg.background_range()
plt.plot(x, psi_fluctuations(ds, dbg))
plt.title(r"Background + Signal \w Fluctuations", fontsize=18)
plt.ylabel("Scales", fontsize=16)
plt.xlabel("Mass", fontsize=16)
plt.show()
def generate_classifier_toy_dataset():
signals = all_signals()
background_config = all_backgrounds()[0]
wavelet_config = all_wavelets()[0]
arrays = []
ids = []
for signl_config in signals:
ds = DefaultSignal(signl_config['id'])
dbg = DefaultBackground(background_config['id'])
cmor = DefaultCWTFluctuations(wavelet_config['id'])
for j in range(2000):
data = psi_fluctuations(ds, dbg)
[coeffs, freqs] = cmor.generate_coefficients(data)
amp = np.abs(coeffs)
arrays.append(amp)
ids.append(signl_config['id'])
x = np.stack(arrays, axis=0)
y = np.array(ids)
toy_data_set, labels = randomize(x, y)
np.save(PATH_CLASSIFIER_TOY_DATASET, toy_data_set)
np.save(PATH_CLASSIFIER_TOY_LABELS, labels)
print('generated classifier_toy_dataset successfully ', toy_data_set.shape)
def main():
ds = DefaultSignal(id=signal_id())
dbg = DefaultBackground(id=background_id())
cmor = DefaultCWTFluctuations(id=wavelet_id())
print(cmor.wavelet)
data = psi_fluctuations(ds, dbg)
# data = psi_clean(ds, dbg)
coeffs, freqs = cmor.generate_coefficients(data)
amp = np.abs(coeffs)
# new_shape = [48, 64]
new_shape = [48, 64]
amp_p_value = p_value_transformation_local(rebin(amp, new_shape),
new_shape)
fig, ax = plt.subplots(figsize=(12, 12))
img = ax.imshow(rebin(amp, new_shape),
extent=(dbg.min_bg, dbg.max_bg, cmor.max_scales,
cmor.min_scales),
interpolation='sinc',
aspect='auto',
cmap='bwr')
ax.set_ylim(cmor.min_scales, cmor.max_scales)
fig.colorbar(img, ax=ax)
# plt.title('CWT - scales range: (%s, %s)' % (cmor.min_scales, cmor.max_scales))
plt.title('CWT of Background + Signal w/ Fluctuations', fontsize=18)
plt.ylabel('Scales', fontsize=16)
plt.xlabel('Mass', fontsize=16)
plt.show()
def save_clean_plot(path, s_id, bg_id):
ds = DefaultSignal(id=s_id)
dbg = DefaultBackground(id=bg_id)
x = dbg.background_range()
plt.plot(x, psi_clean(ds, dbg))
plt.title(r"Signal + Background")
plt.ylabel("Amplitude")
plt.xlabel("Mass")
if os.path.isfile(path):
os.remove(path)
plt.savefig(path)
plt.close('all')
def main():
ds = DefaultSignal(id=signal_id())
dbg = DefaultBackground(id=background_id())
x = dbg.background_range()
fig, ax = plt.subplots(figsize=(9, 7))
plt.plot(x, psi_clean(ds, dbg))
plt.title(r"Background + Signal", fontsize=20)
plt.ylabel("Events/Mass Unit", fontsize=18)
plt.xlabel("Mass", fontsize=18)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
path = 'docs/output/cwt/clean_bg_signal.jpeg'
plt.savefig(path)
plt.close('all')
def generate_cwt_fluctuations(signal_id=0, bg_id=0, wavelet_id=0):
ds = DefaultSignal(signal_id)
dbg = DefaultBackground(bg_id)
cmor = DefaultCWTClean(wavelet_id)
data = psi_fluctuations(ds, dbg)
[coeffs, freqs] = cmor.generate_coefficients(data)
amp = np.abs(coeffs)
return amp
def main():
ds = DefaultSignal(id=signal_id())
dbg = DefaultBackground(id=background_id())
cmor = DefaultCWTFluctuations(id=wavelet_id())
print(cmor.wavelet)
x = dbg.background_range()
data = psi_fluctuations(ds, dbg)
# data = psi_clean(ds, dbg)
coeffs, freqs = cmor.generate_coefficients(data)
amp = np.abs(coeffs)
new_shape = [48, 64]
amp_rebinned = rebin(amp, new_shape)
amp_p_value = p_value_transformation_local(amp_rebinned, new_shape)
plot_bg_signal_fluc(x, data)
plot_bg_signal_og(amp, dbg, cmor)
plot_bg_signal_rebinned(amp_rebinned, dbg, cmor)
plot_bg_signal_rebinned_p_value(amp_p_value, dbg, cmor)
def generate_rebined_sample_fluctuations(rebined_shape, signal_id=0, bg_id=0, wavelet_id=0):
ds = DefaultSignal(signal_id)
dbg = DefaultBackground(bg_id)
cmor = DefaultCWTClean(wavelet_id)
data = psi_fluctuations(ds, dbg)
[coeffs, freqs] = cmor.generate_coefficients(data)
amp = np.abs(coeffs)
rebined_amp = rebin(amp, rebined_shape)
return rebined_amp
def main():
ds = DefaultSignal(id=signal_id())
dbg = DefaultBackground(id=background_id())
cmor = DefaultCWTClean(id=wavelet_id())
print(cmor.wavelet)
data = psi_clean(ds, dbg)
coeffs, freqs = cmor.generate_coefficients(data)
amp = np.abs(coeffs)
fig, ax = plt.subplots(figsize=(9, 7))
img = ax.imshow(amp,
extent=(dbg.min_bg, dbg.max_bg, cmor.max_scales,
cmor.min_scales),
interpolation='sinc',
aspect='auto',
cmap='bwr')
ax.set_ylim(cmor.min_scales, cmor.max_scales)
cbar = fig.colorbar(img, ax=ax)
cbar.ax.tick_params(labelsize=18)
plt.title('CWT w/o noise - scales range: (%s, %s)' %
(cmor.min_scales, cmor.max_scales))
plt.title('CWT Background + Signal', fontsize=20)
plt.ylabel('Scales', fontsize=18)
plt.xlabel('Mass', fontsize=18)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
path = 'docs/output/cwt/cwt_bg_signal_clean.jpeg'
plt.savefig(path)
plt.close('all')