def input_to_image_grid(self, inputs, nrows=None):
nrows, ncols = get_layout(n=len(inputs))
outputs = self.generate_from_condition(inputs=inputs)
image_array = [[
image.reshape([self._image_shape[0], self._image_shape[1]])
for image in outputs[r * ncols:(r + 1) * ncols]
] for r in range(nrows)]
return image_array
def build_simulated_events(self, condition, tracker_image, calo_image, eval_functions,
cgan_image=None, n=10, title=None, reference_images=None, func_titles=None, x_labels=None,
scaler=1):
inputs = np.array([condition for _ in range(n)])
outputs = self.generate_batches(list_of_inputs=inputs, batch_size=100)*scaler
mean_output = np.mean(outputs, axis=0).reshape(calo_image.shape)
layout = get_layout(n=5+len(eval_functions)) # Tracker, Calo, GAN, Mean generated
fig, ax = plt.subplots(nrows=2, ncols=5, figsize=(16, 12))
fig.subplots_adjust(left=0.05, bottom=0.07, right=0.99, top=0.99, wspace=0.2, hspace=0.)
ax = ax.ravel()
if cgan_image is None:
E_max = np.max(np.stack((calo_image, mean_output), axis=0))
titles = ["Tracker", "Calorimeter", "Generated average (n={})".format(n)]
show_images = [tracker_image, calo_image, mean_output]
else:
E_max = np.max(np.stack((calo_image, cgan_image, mean_output), axis=0))
titles = ["Tracker", "Calorimeter", "STP", "CIR average (n={})".format(n)]
show_images = [tracker_image, calo_image, cgan_image, mean_output]
if reference_images is not None:
E_max = np.max([E_max, np.max(reference_images)])
titles.append("TIP average (n={})".format(len(reference_images)))
show_images.append(np.mean(reference_images, axis=0).reshape(calo_image.shape))
for i, image in enumerate(show_images):
if titles[i] == "Tracker":
ax[i].imshow(image)
else:
im = ax[i].imshow(image)#, vmin=0, vmax=E_max)
ax[i].get_xaxis().set_visible(False)
ax[i].get_yaxis().set_visible(False)
ax[i].set_title(titles[i])
# fig.colorbar(im, ax=ax[4], shrink=0.6)
if func_titles is None:
func_titles = [func.__name__ for func in eval_functions]
if x_labels is None:
x_labels = ["" for _ in eval_functions]
for i, (func, params) in enumerate(eval_functions.items()):
if ("cells" in func.__name__) or ("max" in func.__name__):
fake_values = np.array(func(crop_images(outputs, **self._padding), **params))
real_value = np.array(func(crop_images(np.array([calo_image]), **self._padding), **params))
if reference_images is not None:
ref_values = np.array(func(crop_images(reference_images, **self._padding), **params))
else:
fake_values = np.array(func(outputs, **params))
real_value = np.array(func(np.array([calo_image]), **params))
if reference_images is not None:
ref_values = np.array(func(reference_images, **params))
if "mass_x" in func.__name__:
fake_values /= self._image_shape[1]
real_value /= self._image_shape[1]
if reference_images is not None:
ref_values /= self._image_shape[1]
ax[i+5].set_xlim(-1, 1)
elif "mass_y" in func.__name__:
fake_values /= self._image_shape[0]
real_value /= self._image_shape[0]
if reference_images is not None:
ref_values /= self._image_shape[0]
ax[i+5].set_xlim(-1, 1)
elif "resolution" in func.__name__:
ax[i+5].set_xlabel("tracker-reco")
if reference_images is not None:
ax[i+5].hist([ref_values, fake_values], bins=20, histtype="step", stacked=False,
label=["TIP", "CIR"], density=True)
ax[i+5].legend()
else:
ax[i+5].hist(fake_values, bins=20)
ax[i+5].axvline(real_value, color='k', linestyle='dashed')
ax[i+5].set_title(func_titles[i])
ax[i+5].set_xlabel(x_labels[i])
if title is not None:
plt.text(0.05, 0.95, title, transform=fig.transFigure, size=24)
return fig, ax
def build_simulated_events(self,
condition,
tracker_image,
calo_image,
eval_functions,
gen_scaler=1,
n=10,
title=None,
reference_images=None):
tracker_image = np.sum(tracker_image, axis=0)
calo_image = np.sum(calo_image, axis=0).reshape(self._image_shape[:-1])
inputs = [condition for _ in range(n)]
outputs = self.generate_multiple_overlay(
list_of_inputs=inputs) * gen_scaler
mean_output = np.mean(outputs, axis=0).reshape(calo_image.shape)
layout = get_layout(
n=4 +
len(eval_functions)) # Tracker, Calo, Mean generated, Reference
fig, ax = plt.subplots(nrows=layout[0],
ncols=layout[1],
figsize=(layout[1] * 6, layout[0] * 6))
fig.subplots_adjust(wspace=0.2, hspace=0.3)
ax = ax.ravel()
E_min = 0
E_max = np.max(np.stack((calo_image, mean_output), axis=0))
titles = [
"Tracker", "Calorimeter", "Generated average (n={})".format(n)
]
for i, image in enumerate([tracker_image, calo_image, mean_output]):
if titles[i] == "Tracker":
ax[i].imshow(image)
else:
im = ax[i].imshow(image, vmin=E_min, vmax=E_max)
ax[i].get_xaxis().set_visible(False)
ax[i].get_yaxis().set_visible(False)
ax[i].set_title(titles[i])
fig.colorbar(im, ax=ax[:3], shrink=0.6)
for i, (func, params) in enumerate(eval_functions.items()):
if ("cells" in func.__name__) or ("max" in func.__name__):
fake_values = np.array(
func(crop_images(outputs, **self._padding), **params))
real_value = np.array(
func(crop_images(np.array([calo_image]), **self._padding),
**params))
if reference_images is not None:
ref_values = np.array(
func(crop_images(reference_images, **self._padding),
**params))
else:
fake_values = np.array(func(outputs, **params))
real_value = np.array(func(np.array([calo_image]), **params))
if reference_images is not None:
ref_values = np.array(func(reference_images, **params))
if "mass_x" in func.__name__:
fake_values /= self._image_shape[1]
real_value /= self._image_shape[1]
if reference_images is not None:
ref_values /= self._image_shape[1]
ax[i + 3].set_xlim(-1, 1)
elif "mass_y" in func.__name__:
fake_values /= self._image_shape[0]
real_value /= self._image_shape[0]
if reference_images is not None:
ref_values /= self._image_shape[0]
ax[i + 3].set_xlim(-1, 1)
elif "resolution" in func.__name__:
ax[i + 3].set_xlabel("true-reco")
if reference_images is not None:
ax[i + 3].hist([ref_values, fake_values],
bins=20,
histtype="step",
stacked=False,
label=["reference", "generated"],
density=True)
ax[i + 3].legend()
else:
ax[i + 3].hist(fake_values, bins=20)
ax[i + 3].axvline(real_value, color='k', linestyle='dashed')
if "get_energies" == func.__name__:
ax[i + 3].axvline(real_value + 0.1 * real_value,
color='gray',
linestyle='dashed')
ax[i + 3].axvline(real_value - 0.1 * real_value,
color='gray',
linestyle='dashed')
ax[i + 3].set_title(func.__name__)
if reference_images is not None:
ax[-1].imshow(get_mean_image(reference_images))
ax[-1].set_title("{} references".format(len(reference_images)))
if title is not None:
plt.text(0.05, 0.95, title, transform=fig.transFigure, size=24)
return fig, ax
def create_image_summary(paths_to_outputs,
image_folder,
nr_images,
save_path,
ignore=None,
image_name="samples"):
if isinstance(paths_to_outputs, str):
paths_to_outputs = [paths_to_outputs]
if ignore is None:
ignore = "__//##//"
subfolders = [
f.path + "/" + image_folder for fpath in paths_to_outputs
for f in os.scandir(fpath) if f.is_dir() and ignore not in f.path
]
subfolders.sort(key=natural_keys)
figs = []
nrows, ncols = get_layout(n=nr_images)
for i, folder in enumerate(subfolders):
if i % 10 == 0:
print(i + 1, "/", len(subfolders), ":", folder)
if ignore in folder:
continue
try:
image_paths = [f.path for f in os.scandir(folder)]
except FileNotFoundError:
print("File not found for {}.".format(folder))
continue
image_paths.sort(key=natural_keys)
im_ids = (np.linspace(0, 1, nr_images) *
(len(image_paths) - 1)).astype(int)
fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(20, 16))
fig.suptitle(folder)
axs = np.ravel(axs)
for ax_idx, ax in enumerate(axs):
if im_ids[ax_idx] >= len(image_paths):
break
im_id = im_ids[ax_idx] if nr_images != 1 else -1
current_image_path = image_paths[im_id]
image = imageio.imread(current_image_path)
im_name = current_image_path.partition(image_folder + "/")[2]
ax.set_title(im_name)
ax.imshow(image)
figs.append(fig)
if (i + 1) % 10 == 0:
savefigs(figures=figs,
save_path=save_path + "/samples_{}.pdf".format(i + 1))
plt.cla()
plt.clf()
for fig in figs:
del fig
figs = []
elif (i + 1) == len(subfolders):
savefigs(figures=figs,
save_path=save_path + "/samples_{}.pdf".format(i + 1))
merger = PdfFileMerger()
pdfs = [
f.path for f in os.scandir(save_path)
if ".pdf" in f.path and "samples_" in f.path
]
pdfs.sort(key=natural_keys)
for pdf in pdfs:
merger.append(pdf)
os.remove(pdf)
merger.write(save_path + image_name + ".pdf")
merger.close()