def main():
if args.run_number:
file_list = sorted([
os.path.join(args.srcdir, f) for f in os.listdir(args.srcdir)
if (f.endswith('.h5') and args.run_number in f)
])
else:
file_list = sorted([
os.path.join(args.srcdir, f) for f in os.listdir(args.srcdir)
if f.endswith('.h5')
])
if args.noimage:
keys = get_dataset_keys(file_list[0])
for k in keys:
if 'image' in k:
keys.remove(k)
else:
keys = None
if args.smart:
smart_merge_h5files(file_list, args.outfile, node_keys=keys)
else:
auto_merge_h5files(file_list, args.outfile, nodes_keys=keys)
def main():
std_config = get_standard_config()
if args.config_file is not None:
config = replace_config(std_config, read_configuration_file(args.config_file))
else:
config = std_config
print(config['tailcut'])
geom = CameraGeometry.from_name('LSTCam-002')
foclen = OpticsDescription.from_name('LST').equivalent_focal_length
dl1_container = DL1ParametersContainer()
parameters_to_update = list(HillasParametersContainer().keys())
parameters_to_update.extend(['wl', 'r', 'leakage', 'n_islands', 'intercept', 'time_gradient'])
nodes_keys = get_dataset_keys(args.input_file)
if args.noimage:
nodes_keys.remove(dl1_images_lstcam_key)
auto_merge_h5files([args.input_file], args.output_file, nodes_keys=nodes_keys)
with tables.open_file(args.input_file, mode='r') as input:
image_table = input.root[dl1_images_lstcam_key]
with tables.open_file(args.output_file, mode='a') as output:
params = output.root[dl1_params_lstcam_key].read()
for ii, row in enumerate(image_table):
if ii%10000 == 0:
print(ii)
image = row['image']
pulse_time = row['pulse_time']
signal_pixels = tailcuts_clean(geom, image, **config['tailcut'])
if image[signal_pixels].shape[0] > 0:
num_islands, island_labels = number_of_islands(geom, signal_pixels)
hillas = hillas_parameters(geom[signal_pixels], image[signal_pixels])
dl1_container.fill_hillas(hillas)
dl1_container.set_timing_features(geom[signal_pixels],
image[signal_pixels],
pulse_time[signal_pixels],
hillas)
dl1_container.set_leakage(geom, image, signal_pixels)
dl1_container.n_islands = num_islands
dl1_container.wl = dl1_container.width / dl1_container.length
width = np.rad2deg(np.arctan2(dl1_container.width, foclen))
length = np.rad2deg(np.arctan2(dl1_container.length, foclen))
dl1_container.width = width.value
dl1_container.length = length.value
dl1_container.r = np.sqrt(dl1_container.x**2 + dl1_container.y**2)
for p in parameters_to_update:
params[ii][p] = Quantity(dl1_container[p]).value
else:
for p in parameters_to_update:
params[ii][p] = 0
output.root[dl1_params_lstcam_key][:] = params
def main():
file_list = [
args.srcdir + '/' + f for f in os.listdir(args.srcdir)
if f.endswith('.h5')
]
if args.noimage:
keys = get_dataset_keys(file_list[0])
for k in keys:
if 'image' in k:
keys.remove(k)
else:
keys = None
if args.smart:
smart_merge_h5files(file_list, args.outfile, node_keys=keys)
else:
auto_merge_h5files(file_list, args.outfile, nodes_keys=keys)
def main():
if args.run_number:
run = f'Run{args.run_number:05d}'
file_list = sorted(
filter(lambda f: run in f,
glob(os.path.join(args.srcdir, args.pattern))))
else:
file_list = sorted(glob(os.path.join(args.srcdir, args.pattern)))
if args.noimage:
keys = get_dataset_keys(file_list[0])
keys = {k for k in keys if 'image' not in k}
else:
keys = None
if args.smart:
smart_merge_h5files(file_list, args.outfile, node_keys=keys)
else:
auto_merge_h5files(file_list, args.outfile, nodes_keys=keys)
def main():
args = parser.parse_args()
if args.run_number:
run = f'Run{args.run_number:05d}'
file_list = sorted(filter(
lambda f: run in f,
glob(os.path.join(args.input_dir, args.pattern))
))
else:
file_list = sorted(glob(os.path.join(args.input_dir, args.pattern)))
if args.no_image:
keys = get_dataset_keys(file_list[0])
keys = {k for k in keys if 'image' not in k}
else:
keys = None
auto_merge_h5files(
file_list,
args.output_file,
nodes_keys=keys,
progress_bar=not args.no_progress
)
def main():
std_config = get_standard_config()
log.setLevel(logging.INFO)
handler = logging.StreamHandler()
logging.getLogger().addHandler(handler)
if args.config_file is not None:
config = replace_config(std_config, read_configuration_file(args.config_file))
else:
config = std_config
log.info(f"Tailcut config used: {config['tailcut']}")
foclen = OpticsDescription.from_name('LST').equivalent_focal_length
cam_table = Table.read(args.input_file, path="instrument/telescope/camera/LSTCam")
camera_geom = CameraGeometry.from_table(cam_table)
dl1_container = DL1ParametersContainer()
parameters_to_update = list(HillasParametersContainer().keys())
parameters_to_update.extend([
'concentration_cog',
'concentration_core',
'concentration_pixel',
'leakage_intensity_width_1',
'leakage_intensity_width_2',
'leakage_pixels_width_1',
'leakage_pixels_width_2',
'n_islands',
'intercept',
'time_gradient',
'n_pixels',
'wl',
'log_intensity'
])
nodes_keys = get_dataset_keys(args.input_file)
if args.noimage:
nodes_keys.remove(dl1_images_lstcam_key)
auto_merge_h5files([args.input_file], args.output_file, nodes_keys=nodes_keys)
with tables.open_file(args.input_file, mode='r') as input:
image_table = input.root[dl1_images_lstcam_key]
dl1_params_input = input.root[dl1_params_lstcam_key].colnames
disp_params = {'disp_dx', 'disp_dy', 'disp_norm', 'disp_angle', 'disp_sign'}
if set(dl1_params_input).intersection(disp_params):
parameters_to_update.extend(disp_params)
with tables.open_file(args.output_file, mode='a') as output:
params = output.root[dl1_params_lstcam_key].read()
for ii, row in enumerate(image_table):
dl1_container.reset()
image = row['image']
peak_time = row['peak_time']
signal_pixels = tailcuts_clean(camera_geom, image, **config['tailcut'])
n_pixels = np.count_nonzero(signal_pixels)
if n_pixels > 0:
num_islands, island_labels = number_of_islands(camera_geom, signal_pixels)
n_pixels_on_island = np.bincount(island_labels.astype(np.int))
n_pixels_on_island[0] = 0 # first island is no-island and should not be considered
max_island_label = np.argmax(n_pixels_on_island)
signal_pixels[island_labels != max_island_label] = False
hillas = hillas_parameters(camera_geom[signal_pixels], image[signal_pixels])
dl1_container.fill_hillas(hillas)
dl1_container.set_timing_features(camera_geom[signal_pixels],
image[signal_pixels],
peak_time[signal_pixels],
hillas)
dl1_container.set_leakage(camera_geom, image, signal_pixels)
dl1_container.set_concentration(camera_geom, image, hillas)
dl1_container.n_islands = num_islands
dl1_container.wl = dl1_container.width / dl1_container.length
dl1_container.n_pixels = n_pixels
width = np.rad2deg(np.arctan2(dl1_container.width, foclen))
length = np.rad2deg(np.arctan2(dl1_container.length, foclen))
dl1_container.width = width
dl1_container.length = length
dl1_container.log_intensity = np.log10(dl1_container.intensity)
if set(dl1_params_input).intersection(disp_params):
disp_dx, disp_dy, disp_norm, disp_angle, disp_sign = disp(
dl1_container['x'].to_value(u.m),
dl1_container['y'].to_value(u.m),
params['src_x'][ii],
params['src_y'][ii]
)
dl1_container['disp_dx'] = disp_dx
dl1_container['disp_dy'] = disp_dy
dl1_container['disp_norm'] = disp_norm
dl1_container['disp_angle'] = disp_angle
dl1_container['disp_sign'] = disp_sign
for p in parameters_to_update:
params[ii][p] = u.Quantity(dl1_container[p]).value
output.root[dl1_params_lstcam_key][:] = params
def main():
std_config = get_standard_config()
if args.config_file is not None:
config = replace_config(std_config,
read_configuration_file(args.config_file))
else:
config = std_config
print(config['tailcut'])
foclen = OpticsDescription.from_name('LST').equivalent_focal_length
cam_table = Table.read(args.input_file,
path="instrument/telescope/camera/LSTCam")
camera_geom = CameraGeometry.from_table(cam_table)
dl1_container = DL1ParametersContainer()
parameters_to_update = list(HillasParametersContainer().keys())
parameters_to_update.extend([
'concentration_cog',
'concentration_core',
'concentration_pixel',
'leakage_intensity_width_1',
'leakage_intensity_width_2',
'leakage_pixels_width_1',
'leakage_pixels_width_2',
'n_islands',
'intercept',
'time_gradient',
'n_pixels',
'wl',
'r',
])
nodes_keys = get_dataset_keys(args.input_file)
if args.noimage:
nodes_keys.remove(dl1_images_lstcam_key)
auto_merge_h5files([args.input_file],
args.output_file,
nodes_keys=nodes_keys)
with tables.open_file(args.input_file, mode='r') as input:
image_table = input.root[dl1_images_lstcam_key]
with tables.open_file(args.output_file, mode='a') as output:
params = output.root[dl1_params_lstcam_key].read()
for ii, row in enumerate(image_table):
if ii % 10000 == 0:
print(ii)
image = row['image']
peak_time = row['peak_time']
signal_pixels = tailcuts_clean(camera_geom, image,
**config['tailcut'])
n_pixels = np.count_nonzero(signal_pixels)
if n_pixels > 0:
num_islands, island_labels = number_of_islands(
camera_geom, signal_pixels)
n_pixels_on_island = np.bincount(
island_labels.astype(np.int))
n_pixels_on_island[
0] = 0 # first island is no-island and should not be considered
max_island_label = np.argmax(n_pixels_on_island)
signal_pixels[island_labels != max_island_label] = False
hillas = hillas_parameters(camera_geom[signal_pixels],
image[signal_pixels])
dl1_container.fill_hillas(hillas)
dl1_container.set_timing_features(
camera_geom[signal_pixels], image[signal_pixels],
peak_time[signal_pixels], hillas)
dl1_container.set_leakage(camera_geom, image,
signal_pixels)
dl1_container.set_concentration(camera_geom, image, hillas)
dl1_container.n_islands = num_islands
dl1_container.wl = dl1_container.width / dl1_container.length
dl1_container.n_pixels = n_pixels
width = np.rad2deg(np.arctan2(dl1_container.width, foclen))
length = np.rad2deg(
np.arctan2(dl1_container.length, foclen))
dl1_container.width = width
dl1_container.length = length
dl1_container.r = np.sqrt(dl1_container.x**2 +
dl1_container.y**2)
else:
# for consistency with r0_to_dl1.py:
for key in dl1_container.keys():
dl1_container[key] = \
u.Quantity(0, dl1_container.fields[key].unit)
dl1_container.width = u.Quantity(np.nan, u.m)
dl1_container.length = u.Quantity(np.nan, u.m)
dl1_container.wl = u.Quantity(np.nan, u.m)
for p in parameters_to_update:
params[ii][p] = u.Quantity(dl1_container[p]).value
output.root[dl1_params_lstcam_key][:] = params
def main():
std_config = get_standard_config()
log.setLevel(logging.INFO)
handler = logging.StreamHandler()
logging.getLogger().addHandler(handler)
if args.config_file is not None:
config = replace_config(std_config,
read_configuration_file(args.config_file))
else:
config = std_config
if args.pedestal_cleaning:
print("Pedestal cleaning")
clean_method_name = 'tailcuts_clean_with_pedestal_threshold'
sigma = config[clean_method_name]['sigma']
pedestal_thresh = get_threshold_from_dl1_file(args.input_file, sigma)
cleaning_params = get_cleaning_parameters(config, clean_method_name)
pic_th, boundary_th, isolated_pixels, min_n_neighbors = cleaning_params
log.info(
f"Fraction of pixel cleaning thresholds above picture thr.:"
f"{np.sum(pedestal_thresh>pic_th) / len(pedestal_thresh):.3f}")
picture_th = np.clip(pedestal_thresh, pic_th, None)
log.info(f"Tailcut clean with pedestal threshold config used:"
f"{config['tailcuts_clean_with_pedestal_threshold']}")
else:
clean_method_name = 'tailcut'
cleaning_params = get_cleaning_parameters(config, clean_method_name)
picture_th, boundary_th, isolated_pixels, min_n_neighbors = cleaning_params
log.info(f"Tailcut config used: {config['tailcut']}")
use_only_main_island = True
if "use_only_main_island" in config[clean_method_name]:
use_only_main_island = config[clean_method_name][
"use_only_main_island"]
delta_time = None
if "delta_time" in config[clean_method_name]:
delta_time = config[clean_method_name]["delta_time"]
foclen = OpticsDescription.from_name('LST').equivalent_focal_length
cam_table = Table.read(args.input_file,
path="instrument/telescope/camera/LSTCam")
camera_geom = CameraGeometry.from_table(cam_table)
dl1_container = DL1ParametersContainer()
parameters_to_update = [
'intensity', 'x', 'y', 'r', 'phi', 'length', 'width', 'psi',
'skewness', 'kurtosis', 'concentration_cog', 'concentration_core',
'concentration_pixel', 'leakage_intensity_width_1',
'leakage_intensity_width_2', 'leakage_pixels_width_1',
'leakage_pixels_width_2', 'n_islands', 'intercept', 'time_gradient',
'n_pixels', 'wl', 'log_intensity'
]
nodes_keys = get_dataset_keys(args.input_file)
if args.noimage:
nodes_keys.remove(dl1_images_lstcam_key)
auto_merge_h5files([args.input_file],
args.output_file,
nodes_keys=nodes_keys)
with tables.open_file(args.input_file, mode='r') as input:
image_table = input.root[dl1_images_lstcam_key]
dl1_params_input = input.root[dl1_params_lstcam_key].colnames
disp_params = {
'disp_dx', 'disp_dy', 'disp_norm', 'disp_angle', 'disp_sign'
}
if set(dl1_params_input).intersection(disp_params):
parameters_to_update.extend(disp_params)
with tables.open_file(args.output_file, mode='a') as output:
params = output.root[dl1_params_lstcam_key].read()
for ii, row in enumerate(image_table):
dl1_container.reset()
image = row['image']
peak_time = row['peak_time']
signal_pixels = tailcuts_clean(camera_geom, image, picture_th,
boundary_th, isolated_pixels,
min_n_neighbors)
n_pixels = np.count_nonzero(signal_pixels)
if n_pixels > 0:
num_islands, island_labels = number_of_islands(
camera_geom, signal_pixels)
n_pixels_on_island = np.bincount(
island_labels.astype(np.int64))
n_pixels_on_island[
0] = 0 # first island is no-island and should not be considered
max_island_label = np.argmax(n_pixels_on_island)
if use_only_main_island:
signal_pixels[
island_labels != max_island_label] = False
# if delta_time has been set, we require at least one
# neighbor within delta_time to accept a pixel in the image:
if delta_time is not None:
cleaned_pixel_times = peak_time
# makes sure only signal pixels are used in the time
# check:
cleaned_pixel_times[~signal_pixels] = np.nan
new_mask = apply_time_delta_cleaning(
camera_geom, signal_pixels, cleaned_pixel_times, 1,
delta_time)
signal_pixels = new_mask
# count the surviving pixels
n_pixels = np.count_nonzero(signal_pixels)
if n_pixels > 0:
hillas = hillas_parameters(camera_geom[signal_pixels],
image[signal_pixels])
dl1_container.fill_hillas(hillas)
dl1_container.set_timing_features(
camera_geom[signal_pixels], image[signal_pixels],
peak_time[signal_pixels], hillas)
dl1_container.set_leakage(camera_geom, image,
signal_pixels)
dl1_container.set_concentration(
camera_geom, image, hillas)
dl1_container.n_islands = num_islands
dl1_container.wl = dl1_container.width / dl1_container.length
dl1_container.n_pixels = n_pixels
width = np.rad2deg(
np.arctan2(dl1_container.width, foclen))
length = np.rad2deg(
np.arctan2(dl1_container.length, foclen))
dl1_container.width = width
dl1_container.length = length
dl1_container.log_intensity = np.log10(
dl1_container.intensity)
if set(dl1_params_input).intersection(disp_params):
disp_dx, disp_dy, disp_norm, disp_angle, disp_sign = disp(
dl1_container['x'].to_value(u.m),
dl1_container['y'].to_value(u.m), params['src_x'][ii],
params['src_y'][ii])
dl1_container['disp_dx'] = disp_dx
dl1_container['disp_dy'] = disp_dy
dl1_container['disp_norm'] = disp_norm
dl1_container['disp_angle'] = disp_angle
dl1_container['disp_sign'] = disp_sign
for p in parameters_to_update:
params[ii][p] = u.Quantity(dl1_container[p]).value
output.root[dl1_params_lstcam_key][:] = params
