plt.plot(binsm[:-1], gen_mass_hist_diff, label='Generated', color='blue')
plt.xlabel('Mass', x=0.5)
plt.ylabel('# Exact - # EFP Jets')
lg = plt.legend(loc=1, prop={'size': 18})
plt.savefig(figpath + "_100000_jets_mass_diff.pdf", bbox_inches='tight')
plt.show()
num_pixels = 100
Njets = 100000
img_width = 0.8
average_real_jet_image = np.zeros((num_pixels, num_pixels, 1))
for i in range(Njets):
average_real_jet_image += ut.pixelate(X_efp_format[i, :, :],
npix=num_pixels,
img_width=img_width,
nb_chan=1,
norm=False,
charged_counts_only=False)
average_real_jet_image /= Njets
average_gen_jet_image = np.zeros((num_pixels, num_pixels, 1))
for i in range(Njets):
average_gen_jet_image += ut.pixelate(gen_out_efp_format[i, :, :],
npix=num_pixels,
img_width=img_width,
nb_chan=1,
norm=False,
charged_counts_only=False)
average_gen_jet_image /= Njets
plt.rcParams.update({'font.size': 12})
################################################################################
# load data
X, y = qg_jets.load(num_data=num_data)
# convert labels to categorical
Y = to_categorical(y, num_classes=2)
print('Loaded quark and gluon jets')
# make jet images
images = np.asarray([
pixelate(x,
npix=npix,
img_width=img_width,
nb_chan=nb_chan,
charged_counts_only=True,
norm=norm) for x in X
])
print('Done making jet images')
# do train/val/test split
(X_train, X_val, X_test, Y_train, Y_val, Y_test) = data_split(images,
Y,
val=val_frac,
test=test_frac)
print('Done train/val/test split')
# preprocess by zero centering images and standardizing each pixel
dataset = dataset.detach().numpy()
_ = plt.hist(dataset[:, :, 0].reshape(-1), bins=bins[0], histtype='step')
datasett = torch.load('datasets/all_t_jets_30p_polarrel.pt')
X = datasett.detach().numpy()
X_efp_format = np.concatenate(
(np.expand_dims(X[:, :, 2], 2), X[:, :, :2], np.zeros((len(X), 30, 1))),
axis=2)
Xims = []
for i in tqdm(range(len(X))):
Xims.append(
ut.pixelate(X_efp_format[i, :, :],
npix=25,
img_width=0.85,
nb_chan=1,
norm=False,
charged_counts_only=False))
datasetg = torch.load('datasets/all_g_jets_30p_polarrel.pt')
Xg = datasetg.detach().numpy()
Xg_efp_format = np.concatenate(
(np.expand_dims(Xg[:, :, 2], 2), Xg[:, :, :2], np.zeros((len(Xg), 30, 1))),
axis=2)
Xgims = []
for i in tqdm(range(len(Xg))):
Xgims.append(
ut.pixelate(Xg_efp_format[i, :, :],
npix=25,
img_width=0.85,
# network training parameters
num_epoch = 2
batch_size = 100
################################################################################
# load data
X, y = cepc.load(num_data=num_data)
# convert labels to categorical
Y = to_categorical(y, num_classes=2)
print('Loaded quark and gluon jets')
# make jet images
images = np.asarray([pixelate(x, npix=npix, img_width=img_width, nb_chan=nb_chan, norm=norm,charged_counts_only=True) for x in X])
print('Done making jet images')
# do train/val/test split
(X_train, X_val, X_test,
Y_train, Y_val, Y_test) = data_split(images, Y, val=val_frac, test=test_frac)
print('Done train/val/test split')
# preprocess by zero centering images and standardizing each pixel
X_train, X_val, X_test = standardize(*zero_center(X_train, X_val, X_test))
print('Finished preprocessing')
print('Model summary:')