def setup(self):
self.log.info('Configure EventSourceFactory...')
self.event_source = EventSourceFactory.produce(
config=self.config, tool=self, product='HESSIOEventSource')
self.event_source.allowed_tels = self.config['Analysis'][
'allowed_tels']
self.calibrator = CameraCalibrator(config=self.config,
tool=self,
eventsource=self.event_source)
self.writer = HDF5TableWriter(filename=self.outfile,
group_name='image_infos',
overwrite=True)
# Define Pre-selection for images
preselcuts = self.config['Preselect']
self.image_cutflow = CutFlow('Image preselection')
self.image_cutflow.set_cuts(
dict(no_sel=None,
n_pixel=lambda s: np.count_nonzero(s) < preselcuts['n_pixel'][
'min'],
image_amplitude=lambda q: q < preselcuts['image_amplitude'][
'min']))
# Define Pre-selection for events
self.event_cutflow = CutFlow('Event preselection')
self.event_cutflow.set_cuts(dict(no_sel=None))
def setup(self):
self.log.info('Configure EventSourceFactory...')
self.event_source = EventSourceFactory.produce(
config=self.config, tool=self, product='SimTelEventSource'
)
self.event_source.allowed_tels = self.config['Analysis']['allowed_tels']
self.calibrator = CameraCalibrator(
config=self.config, tool=self, eventsource=self.event_source
)
self.writer = HDF5TableWriter(
filename=self.outfile, group_name='image_infos', overwrite=True
)
# Define Pre-selection for images
preselcuts = self.config['Preselect']
self.image_cutflow = CutFlow('Image preselection')
self.image_cutflow.set_cuts(dict(
no_sel=None,
n_pixel=lambda s: np.count_nonzero(s) < preselcuts['n_pixel']['min'],
image_amplitude=lambda q: q < preselcuts['image_amplitude']['min']
))
# Define Pre-selection for events
self.event_cutflow = CutFlow('Event preselection')
self.event_cutflow.set_cuts(dict(
no_sel=None
))
def setup(self):
self.log.info('Configure EventSource...')
self.event_source = self.add_component(
EventSource.from_config(config=self.config, parent=self))
self.calibrator = self.add_component(CameraCalibrator(parent=self))
self.writer = self.add_component(
HDF5TableWriter(filename=self.outfile,
group_name='image_infos',
overwrite=True))
# Define Pre-selection for images
preselcuts = self.config['Preselect']
self.image_cutflow = CutFlow('Image preselection')
self.image_cutflow.set_cuts(
dict(no_sel=None,
n_pixel=lambda s: np.count_nonzero(s) < preselcuts['n_pixel'][
'min'],
image_amplitude=lambda q: q < preselcuts['image_amplitude'][
'min']))
# Define Pre-selection for events
self.event_cutflow = CutFlow('Event preselection')
self.event_cutflow.set_cuts(dict(no_sel=None))
def test_set_cuts_clear():
flow = CutFlow("TestFlow")
flow.set_cut("smaller5", smaller5)
flow.set_cuts(OrderedDict([
("smaller3", smaller3),
("smaller2", smaller2)
]), clear=True)
assert flow.cuts == OrderedDict([
("smaller3", [smaller3, 0]),
("smaller2", [smaller2, 0])
])
def __init__(
self,
calib=None,
cleaner=None,
hillas_parameters=None,
shower_reco=None,
event_cutflow=None,
image_cutflow=None,
# event/image cuts:
allowed_cam_ids=None,
min_ntel=1,
min_charge=0,
min_pixel=2):
# configuration for the camera calibrator
# modifies the integration window to be more like in MARS
cfg = Config()
cfg["ChargeExtractorFactory"]["extractor"] = 'LocalPeakIntegrator'
cfg["ChargeExtractorFactory"]["window_width"] = 5
cfg["ChargeExtractorFactory"]["window_shift"] = 2
self.calib = calib or CameraCalibrator(config=cfg, tool=None)
self.cleaner = cleaner or ImageCleaner(mode=None)
self.hillas_parameters = hillas_parameters or hillas.hillas_parameters
self.shower_reco = shower_reco or \
raise_error("need to provide a shower reconstructor....")
# adding cutflows and cuts for events and images
self.event_cutflow = event_cutflow or CutFlow("EventCutFlow")
self.image_cutflow = image_cutflow or CutFlow("ImageCutFlow")
self.event_cutflow.set_cuts(
OrderedDict([("noCuts", None),
("min2Tels trig", lambda x: x < min_ntel),
("min2Tels reco", lambda x: x < min_ntel),
("position nan", lambda x: np.isnan(x.value).any()),
("direction nan", lambda x: np.isnan(x.value).any())
]))
self.image_cutflow.set_cuts(
OrderedDict([
("noCuts", None),
("min pixel", lambda s: np.count_nonzero(s) < min_pixel),
("min charge", lambda x: x < min_charge),
("poor moments", lambda m: m.width <= 0 or m.length <= 0)
]))
def __init__(
self,
calib=None,
cleaner=None,
hillas_parameters=None,
shower_reco=None,
event_cutflow=None,
image_cutflow=None,
# event/image cuts:
allowed_cam_ids=None,
min_ntel=1,
min_charge=0,
min_pixel=2):
self.calib = calib or CameraCalibrator(None, None)
self.cleaner = cleaner or ImageCleaner(mode=None)
self.hillas_parameters = hillas_parameters or hillas.hillas_parameters
self.shower_reco = shower_reco or \
raise_error("need to provide a shower reconstructor....")
# adding cutflows and cuts for events and images
self.event_cutflow = event_cutflow or CutFlow("EventCutFlow")
self.image_cutflow = image_cutflow or CutFlow("ImageCutFlow")
self.event_cutflow.set_cuts(
OrderedDict([("noCuts", None),
("min2Tels trig", lambda x: x < min_ntel),
("min2Tels reco", lambda x: x < min_ntel),
("position nan", lambda x: np.isnan(x.value).any()),
("direction nan", lambda x: np.isnan(x.value).any())
]))
self.image_cutflow.set_cuts(
OrderedDict([
("noCuts", None),
("min pixel", lambda s: np.count_nonzero(s) < min_pixel),
("min charge", lambda x: x < min_charge),
("poor moments", lambda m: m.width <= 0 or m.length <= 0)
]))
def test_set_cuts_clear():
flow = CutFlow("TestFlow")
flow.set_cut("smaller5", smaller5)
flow.set_cuts(OrderedDict([("smaller3", smaller3),
("smaller2", smaller2)]),
clear=True)
assert flow.cuts == OrderedDict([("smaller3", [smaller3, 0]),
("smaller2", [smaller2, 0])])
def test_set_cuts_no_clear():
with warns(FutureWarning):
flow = CutFlow("TestFlow")
flow.set_cut("smaller5", smaller5)
flow.set_cuts(OrderedDict([("smaller3", smaller3),
("smaller2", smaller2)]),
clear=False)
assert flow.cuts == OrderedDict([
("smaller5", [smaller5, 0]),
("smaller3", [smaller3, 0]),
("smaller2", [smaller2, 0]),
])
def main():
# Argument parser
parser = make_argparser()
parser.add_argument(
"--debug",
action="store_true",
help="Print debugging information",
)
parser.add_argument("--regressor_dir",
default="./",
help="regressors directory")
parser.add_argument("--classifier_dir",
default="./",
help="regressors directory")
parser.add_argument(
"--force_tailcut_for_extended_cleaning",
type=str2bool,
default=False,
help="For tailcut cleaning for energy/score estimation",
)
parser.add_argument(
"--save_images",
action="store_true",
help="Save images in images.h5 (one file testing)",
)
parser.add_argument(
"--regressor_config",
type=str,
default=None,
help="Configuration file used to produce regressor model")
parser.add_argument(
"--classifier_config",
type=str,
default=None,
help="Configuration file used to produce classification model")
args = parser.parse_args()
# Read configuration file
cfg = load_config(args.config_file)
try: # If the user didn't specify a site and/or and array...
site = cfg["General"]["site"]
array = cfg["General"]["array"]
except KeyError: # ...raise an error and exit.
print(bcolors.FAIL +
"ERROR: make sure that both 'site' and 'array' are " +
"specified in the analysis configuration file!" + bcolors.ENDC)
exit()
# Add force_tailcut_for_extended_cleaning in configuration
cfg["General"][
"force_tailcut_for_extended_cleaning"] = args.force_tailcut_for_extended_cleaning
cfg["General"]["force_mode"] = "tail"
force_mode = args.mode
if cfg["General"]["force_tailcut_for_extended_cleaning"] is True:
force_mode = "tail"
print("force_mode={}".format(force_mode))
print("mode={}".format(args.mode))
if args.infile_list:
filenamelist = []
for f in args.infile_list:
filenamelist += glob("{}/{}".format(args.indir, f))
filenamelist.sort()
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
# Get the IDs of the involved telescopes and associated cameras together
# with the equivalent focal lengths from the first event
allowed_tels, cams_and_foclens, subarray = prod3b_array(
filenamelist[0], site, array)
# keeping track of events and where they were rejected
evt_cutflow = CutFlow("EventCutFlow")
img_cutflow = CutFlow("ImageCutFlow")
# Event preparer
preper = EventPreparer(
config=cfg,
subarray=subarray,
cams_and_foclens=cams_and_foclens,
mode=args.mode,
event_cutflow=evt_cutflow,
image_cutflow=img_cutflow,
)
# Regressor and classifier methods
regressor_method = cfg["EnergyRegressor"]["method_name"]
classifier_method = cfg["GammaHadronClassifier"]["method_name"]
use_proba_for_classifier = cfg["GammaHadronClassifier"]["use_proba"]
if regressor_method in ["None", "none", None]:
print(bcolors.OKBLUE +
"The energy of the event will NOT be estimated." + bcolors.ENDC)
use_regressor = False
else:
use_regressor = True
if classifier_method in ["None", "none", None]:
if args.debug:
print(bcolors.OKBLUE +
"The particle type of the event will NOT be estimated." +
bcolors.ENDC)
use_classifier = False
else:
use_classifier = True
# Classifiers
if use_classifier:
# Read configuration file
classifier_config = load_config(args.classifier_config)
classifier_files = (args.classifier_dir +
"/classifier_{cam_id}_{classifier}.pkl.gz")
clf_file = classifier_files.format(
**{
"mode": force_mode,
"wave_args": "mixed",
"classifier": classifier_method,
"cam_id": "{cam_id}",
})
classifiers = load_models(clf_file,
cam_id_list=cams_and_foclens.keys())
if args.debug:
print(bcolors.OKBLUE +
"The particle type of the event will be estimated" +
" using the models stored in" + f" {args.classifier_dir}\n" +
bcolors.ENDC)
# Regressors
if use_regressor:
# Read configuration file
regressor_config = load_config(args.regressor_config)
regressor_files = (args.regressor_dir +
"/regressor_{cam_id}_{regressor}.pkl.gz")
reg_file = regressor_files.format(
**{
"mode": force_mode,
"wave_args": "mixed",
"regressor": regressor_method,
"cam_id": "{cam_id}",
})
regressors = load_models(reg_file, cam_id_list=cams_and_foclens.keys())
if args.debug:
print(bcolors.OKBLUE +
"The energy of the event will be estimated" +
" using the models stored in" + f" {args.regressor_dir}\n" +
bcolors.ENDC)
# catch ctr-c signal to exit current loop and still display results
signal_handler = SignalHandler()
signal.signal(signal.SIGINT, signal_handler)
# Declaration of the column descriptor for the (possible) images file
StoredImages = dict(
event_id=tb.Int32Col(dflt=1, pos=0),
tel_id=tb.Int16Col(dflt=1, pos=1)
# reco_image, true_image and cleaning_mask_reco
# are defined later sicne they depend on the number of pixels
)
# this class defines the reconstruction parameters to keep track of
class RecoEvent(tb.IsDescription):
obs_id = tb.Int16Col(dflt=-1, pos=0)
event_id = tb.Int32Col(dflt=-1, pos=1)
NTels_trig = tb.Int16Col(dflt=0, pos=2)
NTels_reco = tb.Int16Col(dflt=0, pos=3)
NTels_reco_lst = tb.Int16Col(dflt=0, pos=4)
NTels_reco_mst = tb.Int16Col(dflt=0, pos=5)
NTels_reco_sst = tb.Int16Col(dflt=0, pos=6)
pointing_az = tb.Float32Col(dflt=np.nan, pos=7)
pointing_alt = tb.Float32Col(dflt=np.nan, pos=8)
true_az = tb.Float32Col(dflt=np.nan, pos=9)
true_alt = tb.Float32Col(dflt=np.nan, pos=10)
true_energy = tb.Float32Col(dflt=np.nan, pos=11)
reco_energy = tb.Float32Col(dflt=np.nan, pos=12)
reco_alt = tb.Float32Col(dflt=np.nan, pos=13)
reco_az = tb.Float32Col(dflt=np.nan, pos=14)
offset = tb.Float32Col(dflt=np.nan, pos=15)
xi = tb.Float32Col(dflt=np.nan, pos=16)
ErrEstPos = tb.Float32Col(dflt=np.nan, pos=17)
ErrEstDir = tb.Float32Col(dflt=np.nan, pos=18)
gammaness = tb.Float32Col(dflt=np.nan, pos=19)
success = tb.BoolCol(dflt=False, pos=20)
score = tb.Float32Col(dflt=np.nan, pos=21)
h_max = tb.Float32Col(dflt=np.nan, pos=22)
reco_core_x = tb.Float32Col(dflt=np.nan, pos=23)
reco_core_y = tb.Float32Col(dflt=np.nan, pos=24)
true_core_x = tb.Float32Col(dflt=np.nan, pos=25)
true_core_y = tb.Float32Col(dflt=np.nan, pos=26)
is_valid = tb.BoolCol(dflt=False, pos=27)
reco_outfile = tb.open_file(
mode="w",
# if no outfile name is given (i.e. don't to write the event list to disk),
# need specify two "driver" arguments
**({
"filename": args.outfile
} if args.outfile else {
"filename": "no_outfile.h5",
"driver": "H5FD_CORE",
"driver_core_backing_store": False,
}))
reco_table = reco_outfile.create_table("/", "reco_events", RecoEvent)
reco_event = reco_table.row
# Create the images file only if the user want to store the images
if args.save_images is True:
images_outfile = tb.open_file("images.h5", mode="w")
images_table = {}
images_phe = {}
for i, filename in enumerate(filenamelist):
source = EventSource(input_url=filename,
allowed_tels=allowed_tels,
max_events=args.max_events)
# loop that cleans and parametrises the images and performs the reconstruction
for (
event,
reco_image,
cleaning_mask_reco,
cleaning_mask_clusters,
true_image,
n_pixel_dict,
hillas_dict,
hillas_dict_reco,
leakage_dict,
n_tels,
max_signals,
n_cluster_dict,
reco_result,
impact_dict,
good_event,
good_for_reco,
) in preper.prepare_event(source,
save_images=args.save_images,
debug=args.debug):
# True direction
true_az = event.simulation.shower.az
true_alt = event.simulation.shower.alt
# Array pointing in AltAz frame
pointing_az = event.pointing.array_azimuth
pointing_alt = event.pointing.array_altitude
if good_event: # aka it has been successfully reconstructed
# Angular separation between
# - true direction
# - reconstruted direction
xi = angular_separation(event.simulation.shower.az,
event.simulation.shower.alt,
reco_result.az, reco_result.alt)
# Angular separation between
# - center of the array's FoV
# - reconstructed direction
offset = angular_separation(
pointing_az,
pointing_alt,
reco_result.az,
reco_result.alt,
)
# Reconstructed height of shower maximum
h_max = reco_result.h_max
# Reconstructed position of the shower's core on the ground
reco_core_x = reco_result.core_x
reco_core_y = reco_result.core_y
# Reconstructed direction of the shower's in the sky
alt, az = reco_result.alt, reco_result.az
# Successfully reconstructed shower
is_valid = True
else: # no successful reconstruction assign dummy values
xi = np.nan * u.deg
offset = np.nan * u.deg
reco_core_x = np.nan * u.m
reco_core_y = np.nan * u.m
h_max = np.nan * u.m
alt = np.nan * u.deg
az = np.nan * u.deg
is_valid = False
reco_energy = np.nan
score = np.nan
gammaness = np.nan
reco_event["success"] = False
# Estimate particle energy
if use_regressor and is_valid:
energy_tel = np.zeros(len(hillas_dict.keys()))
energy_tel_classifier = {}
weight_tel = np.zeros(len(hillas_dict.keys()))
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = source.subarray.tel[tel_id].camera.camera_name
moments = hillas_dict[tel_id]
model = regressors[cam_id]
############################################################
# GET FEATURES
############################################################
# Read feature list from model configutation file
features_basic = regressor_config["FeatureList"]["Basic"]
features_derived = regressor_config["FeatureList"][
"Derived"]
features = features_basic + list(features_derived)
# Create a pandas Dataframe with basic quantities
# This is needed in order to connect the I/O system of the
# model inputs to the in-memory computation of this script
data = pd.DataFrame({
"hillas_intensity": [moments.intensity],
"hillas_width": [moments.width.to("deg").value],
"hillas_length": [moments.length.to("deg").value],
"hillas_x": [moments.x.to("deg").value],
"hillas_y": [moments.y.to("deg").value],
"hillas_phi": [moments.phi.to("deg").value],
"hillas_r": [moments.r.to("deg").value],
"leakage_intensity_width_1_reco":
[leakage_dict[tel_id]['leak1_reco']],
"leakage_intensity_width_2_reco":
[leakage_dict[tel_id]['leak2_reco']],
"leakage_intensity_width_1":
[leakage_dict[tel_id]['leak1']],
"leakage_intensity_width_2":
[leakage_dict[tel_id]['leak2']],
"az": [reco_result.az.to("deg").value],
"alt": [reco_result.alt.to("deg").value],
"h_max": [h_max.value],
"impact_dist": [impact_dict[tel_id].to("m").value],
})
# Compute derived features and add them to the dataframe
for key, expression in features_derived.items():
if key not in data:
data.eval(f'{key} = {expression}', inplace=True)
# sort features_to_use alphabetically to ensure order
# preservation with model.fit in protopipe.mva
features = sorted(features)
# Select the values for the full set of features
features_values = data[features].to_numpy()
############################################################
if good_for_reco[tel_id] == 1:
energy_tel[idx] = model.predict(features_values)
else:
energy_tel[idx] = np.nan
weight_tel[idx] = moments.intensity
# Record the values regardless of the validity
# We don't use this now, but it should be recorded
energy_tel_classifier[tel_id] = energy_tel[idx]
# Use only images with valid estimated energies to calculate
# the average
energy_tel_selected = energy_tel[~np.isnan(energy_tel)]
weight_tel_selected = weight_tel[~np.isnan(energy_tel)]
# Try getting the average weighted energy of the shower
# If no image had a valid estimated energy record it as nan
if len(energy_tel_selected) == 0:
reco_energy = np.nan
energy_estimated = False
else:
reco_energy = np.sum(
weight_tel_selected *
energy_tel_selected) / sum(weight_tel_selected)
energy_estimated = True
else:
reco_energy = np.nan
energy_estimated = False
# Estimate particle score/gammaness
if use_classifier and is_valid:
score_tel = np.zeros(len(hillas_dict.keys()))
gammaness_tel = np.zeros(len(hillas_dict.keys()))
weight_tel = np.zeros(len(hillas_dict.keys()))
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = source.subarray.tel[tel_id].camera.camera_name
moments = hillas_dict[tel_id]
model = classifiers[cam_id]
############################################################
# GET FEATURES
############################################################
# Read feature list from model configutation file
features_basic = classifier_config["FeatureList"]["Basic"]
features_derived = classifier_config["FeatureList"][
"Derived"]
features = features_basic + list(features_derived)
# Create a pandas Dataframe with basic quantities
# This is needed in order to connect the I/O system of the
# model inputs to the in-memory computation of this script
data = pd.DataFrame({
"hillas_intensity": [moments.intensity],
"hillas_width": [moments.width.to("deg").value],
"hillas_length": [moments.length.to("deg").value],
"hillas_x": [moments.x.to("deg").value],
"hillas_y": [moments.y.to("deg").value],
"hillas_phi": [moments.phi.to("deg").value],
"hillas_r": [moments.r.to("deg").value],
"leakage_intensity_width_1_reco":
[leakage_dict[tel_id]['leak1_reco']],
"leakage_intensity_width_2_reco":
[leakage_dict[tel_id]['leak2_reco']],
"leakage_intensity_width_1":
[leakage_dict[tel_id]['leak1']],
"leakage_intensity_width_2":
[leakage_dict[tel_id]['leak2']],
"az": [reco_result.az.to("deg").value],
"alt": [reco_result.alt.to("deg").value],
"h_max": [h_max.value],
"impact_dist": [impact_dict[tel_id].to("m").value],
"reco_energy":
reco_energy,
"reco_energy_tel":
energy_tel_classifier[tel_id],
})
# Compute derived features and add them to the dataframe
for key, expression in features_derived.items():
if key not in data:
data.eval(f'{key} = {expression}', inplace=True)
# sort features_to_use alphabetically to ensure order
# preservation with model.fit in protopipe.mva
features = sorted(features)
# Select the values for the full set of features
features_values = data[features].to_numpy()
############################################################
# Here we check for valid telescope-wise energies
# Because it means that it's a good image
# WARNING: currently we should REQUIRE to estimate both
# energy AND particle type
if not np.isnan(energy_tel_classifier[tel_id]):
# Output of classifier according to type of classifier
if use_proba_for_classifier is False:
score_tel[idx] = model.decision_function(
features_values)
else:
gammaness_tel[idx] = model.predict_proba(
features_values)[:, 1]
weight_tel[idx] = np.sqrt(moments.intensity)
else:
# WARNING:
# this is true only because we use telescope-wise
# energies as a feature of the model!!!
score_tel[idx] = np.nan
gammaness_tel[idx] = np.nan
# Use only images with valid estimated energies to calculate
# the average
if use_proba_for_classifier is False:
score_tel_selected = score_tel[~np.isnan(score_tel)]
weight_tel_selected = weight_tel[~np.isnan(score_tel)]
else:
gammaness_tel_selected = gammaness_tel[
~np.isnan(gammaness_tel)]
weight_tel_selected = weight_tel[~np.isnan(gammaness_tel)]
# Try getting the average weighted score or gammaness
# If no image had a valid estimated energy record it as nan
if len(weight_tel_selected) > 0:
# Weight the final decision/proba
if use_proba_for_classifier is True:
gammaness = np.sum(
weight_tel_selected *
gammaness_tel_selected) / sum(weight_tel_selected)
else:
score = np.sum(
weight_tel_selected *
score_tel_selected) / sum(weight_tel_selected)
particle_type_estimated = True
else:
score = np.nan
gammaness = np.nan
particle_type_estimated = False
else:
score = np.nan
gammaness = np.nan
particle_type_estimated = False
if energy_estimated and particle_type_estimated:
reco_event["success"] = True
else:
if args.debug:
print(
bcolors.WARNING +
f"energy_estimated = {energy_estimated}\n" +
f"particle_type_estimated = {particle_type_estimated}\n"
+ bcolors.ENDC)
reco_event["success"] = False
# If the user wants to save the images of the run
if args.save_images is True:
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = source.subarray.tel[tel_id].camera.camera_name
if cam_id not in images_phe:
n_pixels = source.subarray.tel[
tel_id].camera.geometry.n_pixels
StoredImages["true_image"] = tb.Float32Col(
shape=(n_pixels), pos=2)
StoredImages["reco_image"] = tb.Float32Col(
shape=(n_pixels), pos=3)
StoredImages["cleaning_mask_reco"] = tb.BoolCol(
shape=(n_pixels), pos=4) # not in ctapipe
StoredImages["cleaning_mask_clusters"] = tb.BoolCol(
shape=(n_pixels), pos=5) # not in ctapipe
images_table[cam_id] = images_outfile.create_table(
"/", "_".join(["images", cam_id]), StoredImages)
images_phe[cam_id] = images_table[cam_id].row
images_phe[cam_id]["event_id"] = event.index.event_id
images_phe[cam_id]["tel_id"] = tel_id
images_phe[cam_id]["reco_image"] = reco_image[tel_id]
images_phe[cam_id]["true_image"] = true_image[tel_id]
images_phe[cam_id][
"cleaning_mask_reco"] = cleaning_mask_reco[tel_id]
images_phe[cam_id][
"cleaning_mask_clusters"] = cleaning_mask_clusters[
tel_id]
images_phe[cam_id].append()
# Now we start recording the data to file
reco_event["event_id"] = event.index.event_id
reco_event["obs_id"] = event.index.obs_id
reco_event["NTels_trig"] = len(event.r1.tel.keys())
reco_event["NTels_reco"] = len(hillas_dict)
reco_event["NTels_reco_lst"] = n_tels["LST_LST_LSTCam"]
reco_event["NTels_reco_mst"] = (n_tels["MST_MST_NectarCam"] +
n_tels["MST_MST_FlashCam"] +
n_tels["MST_SCT_SCTCam"])
reco_event["NTels_reco_sst"] = (n_tels["SST_1M_DigiCam"] +
n_tels["SST_ASTRI_ASTRICam"] +
n_tels["SST_GCT_CHEC"])
reco_event["pointing_az"] = pointing_az.to("deg").value
reco_event["pointing_alt"] = pointing_alt.to("deg").value
reco_event["reco_energy"] = reco_energy
reco_event["reco_alt"] = alt.to("deg").value
reco_event["reco_az"] = az.to("deg").value
reco_event["offset"] = offset.to("deg").value
reco_event["xi"] = xi.to("deg").value
reco_event["h_max"] = h_max.to("m").value
reco_event["reco_core_x"] = reco_core_x.to("m").value
reco_event["reco_core_y"] = reco_core_y.to("m").value
reco_event["is_valid"] = is_valid
if use_proba_for_classifier is True:
reco_event["gammaness"] = gammaness
else:
reco_event["score"] = score
reco_event["ErrEstPos"] = np.nan
reco_event["ErrEstDir"] = np.nan
# Simulated information
shower = event.simulation.shower
mc_core_x = shower.core_x
mc_core_y = shower.core_y
reco_event["true_energy"] = shower.energy.to("TeV").value
reco_event["true_az"] = true_az.to("deg").value
reco_event["true_alt"] = true_alt.to("deg").value
reco_event["true_core_x"] = mc_core_x.to("m").value
reco_event["true_core_y"] = mc_core_y.to("m").value
# Fill table
reco_table.flush()
reco_event.append()
if signal_handler.stop:
break
if signal_handler.stop:
break
# make sure everything gets written out nicely
reco_table.flush()
if args.save_images is True:
for table in images_table.values():
table.flush()
try:
print()
evt_cutflow()
print()
img_cutflow()
except ZeroDivisionError:
pass
print("Job done!")
def main():
# your favourite units here
energy_unit = u.TeV
angle_unit = u.deg
dist_unit = u.m
agree_threshold = .5
min_tel = 3
parser = make_argparser()
parser.add_argument('--classifier',
type=str,
default=expandvars(
"$CTA_SOFT/tino_cta/data/classifier_pickle/"
"classifier_{mode}_{cam_id}_{classifier}.pkl"))
parser.add_argument('--regressor',
type=str,
default=expandvars(
"$CTA_SOFT/tino_cta/data/classifier_pickle/"
"regressor_{mode}_{cam_id}_{regressor}.pkl"))
parser.add_argument('-o',
'--outfile',
type=str,
default="",
help="location to write the classified events to.")
parser.add_argument('--wave_dir',
type=str,
default=None,
help="directory where to find mr_filter. "
"if not set look in $PATH")
parser.add_argument(
'--wave_temp_dir',
type=str,
default='/dev/shm/',
help="directory where mr_filter to store the temporary fits "
"files")
group = parser.add_mutually_exclusive_group()
group.add_argument('--proton',
action='store_true',
help="do protons instead of gammas")
group.add_argument('--electron',
action='store_true',
help="do electrons instead of gammas")
args = parser.parse_args()
if args.infile_list:
filenamelist = []
for f in args.infile_list:
filenamelist += glob("{}/{}".format(args.indir, f))
filenamelist.sort()
elif args.proton:
filenamelist = sorted(glob("{}/proton/*gz".format(args.indir)))
elif args.electron:
filenamelist = glob("{}/electron/*gz".format(args.indir))
channel = "electron"
else:
filenamelist = sorted(glob("{}/gamma/*gz".format(args.indir)))
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
# keeping track of events and where they were rejected
Eventcutflow = CutFlow("EventCutFlow")
Imagecutflow = CutFlow("ImageCutFlow")
# takes care of image cleaning
cleaner = ImageCleaner(mode=args.mode,
cutflow=Imagecutflow,
wavelet_options=args.raw,
tmp_files_directory=args.wave_temp_dir,
skip_edge_events=False,
island_cleaning=True)
# the class that does the shower reconstruction
shower_reco = HillasReconstructor()
preper = EventPreparer(
cleaner=cleaner,
hillas_parameters=hillas_parameters,
shower_reco=shower_reco,
event_cutflow=Eventcutflow,
image_cutflow=Imagecutflow,
# event/image cuts:
allowed_cam_ids=[],
min_ntel=2,
min_charge=args.min_charge,
min_pixel=3)
# wrapper for the scikit-learn classifier
classifier = EventClassifier.load(args.classifier.format(
**{
"mode": args.mode,
"wave_args": "mixed",
"classifier": 'RandomForestClassifier',
"cam_id": "{cam_id}"
}),
cam_id_list=args.cam_ids)
# wrapper for the scikit-learn regressor
regressor = EnergyRegressor.load(args.regressor.format(
**{
"mode": args.mode,
"wave_args": "mixed",
"regressor": "RandomForestRegressor",
"cam_id": "{cam_id}"
}),
cam_id_list=args.cam_ids)
ClassifierFeatures = namedtuple(
"ClassifierFeatures",
("impact_dist", "sum_signal_evt", "max_signal_cam", "sum_signal_cam",
"N_LST", "N_MST", "N_SST", "width", "length", "skewness", "kurtosis",
"h_max", "err_est_pos", "err_est_dir"))
EnergyFeatures = namedtuple(
"EnergyFeatures",
("impact_dist", "sum_signal_evt", "max_signal_cam", "sum_signal_cam",
"N_LST", "N_MST", "N_SST", "width", "length", "skewness", "kurtosis",
"h_max", "err_est_pos", "err_est_dir"))
# catch ctr-c signal to exit current loop and still display results
signal_handler = SignalHandler()
signal.signal(signal.SIGINT, signal_handler)
# this class defines the reconstruction parameters to keep track of
class RecoEvent(tb.IsDescription):
Run_ID = tb.Int16Col(dflt=-1, pos=0)
Event_ID = tb.Int16Col(dflt=-1, pos=1)
NTels_trig = tb.Int16Col(dflt=0, pos=0)
NTels_reco = tb.Int16Col(dflt=0, pos=1)
NTels_reco_lst = tb.Int16Col(dflt=0, pos=2)
NTels_reco_mst = tb.Int16Col(dflt=0, pos=3)
NTels_reco_sst = tb.Int16Col(dflt=0, pos=4)
MC_Energy = tb.Float32Col(dflt=np.nan, pos=5)
reco_Energy = tb.Float32Col(dflt=np.nan, pos=6)
reco_phi = tb.Float32Col(dflt=np.nan, pos=7)
reco_theta = tb.Float32Col(dflt=np.nan, pos=8)
off_angle = tb.Float32Col(dflt=np.nan, pos=9)
xi = tb.Float32Col(dflt=np.nan, pos=10)
DeltaR = tb.Float32Col(dflt=np.nan, pos=11)
ErrEstPos = tb.Float32Col(dflt=np.nan, pos=12)
ErrEstDir = tb.Float32Col(dflt=np.nan, pos=13)
gammaness = tb.Float32Col(dflt=np.nan, pos=14)
success = tb.BoolCol(dflt=False, pos=15)
channel = "gamma" if "gamma" in " ".join(filenamelist) else "proton"
reco_outfile = tb.open_file(
mode="w",
# if no outfile name is given (i.e. don't to write the event list to disk),
# need specify two "driver" arguments
**({
"filename": args.outfile
} if args.outfile else {
"filename": "no_outfile.h5",
"driver": "H5FD_CORE",
"driver_core_backing_store": False
}))
reco_table = reco_outfile.create_table("/", "reco_events", RecoEvent)
reco_event = reco_table.row
allowed_tels = None # all telescopes
allowed_tels = prod3b_tel_ids("L+N+D")
for i, filename in enumerate(filenamelist[:args.last]):
# print(f"file: {i} filename = {filename}")
source = hessio_event_source(filename,
allowed_tels=allowed_tels,
max_events=args.max_events)
# loop that cleans and parametrises the images and performs the reconstruction
for (event, hillas_dict, n_tels, tot_signal, max_signals, pos_fit,
dir_fit, h_max, err_est_pos,
err_est_dir) in preper.prepare_event(source, True):
# now prepare the features for the classifier
cls_features_evt = {}
reg_features_evt = {}
if hillas_dict is not None:
for tel_id in hillas_dict.keys():
Imagecutflow.count("pre-features")
tel_pos = np.array(event.inst.tel_pos[tel_id][:2]) * u.m
moments = hillas_dict[tel_id]
impact_dist = linalg.length(tel_pos - pos_fit)
cls_features_tel = ClassifierFeatures(
impact_dist=impact_dist / u.m,
sum_signal_evt=tot_signal,
max_signal_cam=max_signals[tel_id],
sum_signal_cam=moments.size,
N_LST=n_tels["LST"],
N_MST=n_tels["MST"],
N_SST=n_tels["SST"],
width=moments.width / u.m,
length=moments.length / u.m,
skewness=moments.skewness,
kurtosis=moments.kurtosis,
h_max=h_max / u.m,
err_est_pos=err_est_pos / u.m,
err_est_dir=err_est_dir / u.deg)
reg_features_tel = EnergyFeatures(
impact_dist=impact_dist / u.m,
sum_signal_evt=tot_signal,
max_signal_cam=max_signals[tel_id],
sum_signal_cam=moments.size,
N_LST=n_tels["LST"],
N_MST=n_tels["MST"],
N_SST=n_tels["SST"],
width=moments.width / u.m,
length=moments.length / u.m,
skewness=moments.skewness,
kurtosis=moments.kurtosis,
h_max=h_max / u.m,
err_est_pos=err_est_pos / u.m,
err_est_dir=err_est_dir / u.deg)
if np.isnan(cls_features_tel).any() or np.isnan(
reg_features_tel).any():
continue
Imagecutflow.count("features nan")
cam_id = event.inst.subarray.tel[tel_id].camera.cam_id
try:
reg_features_evt[cam_id] += [reg_features_tel]
cls_features_evt[cam_id] += [cls_features_tel]
except KeyError:
reg_features_evt[cam_id] = [reg_features_tel]
cls_features_evt[cam_id] = [cls_features_tel]
if cls_features_evt and reg_features_evt:
predict_energ = regressor.predict_by_event([reg_features_evt
])["mean"][0]
predict_proba = classifier.predict_proba_by_event(
[cls_features_evt])
gammaness = predict_proba[0, 0]
try:
# the MC direction of origin of the simulated particle
shower = event.mc
shower_core = np.array(
[shower.core_x / u.m, shower.core_y / u.m]) * u.m
shower_org = linalg.set_phi_theta(az_to_phi(shower.az),
alt_to_theta(shower.alt))
# and how the reconstructed direction compares to that
xi = linalg.angle(dir_fit, shower_org)
DeltaR = linalg.length(pos_fit[:2] - shower_core)
except Exception:
# naked exception catch, because I'm not sure where
# it would break in non-MC files
xi = np.nan
DeltaR = np.nan
phi, theta = linalg.get_phi_theta(dir_fit)
phi = (phi if phi > 0 else phi + 360 * u.deg)
# TODO: replace with actual array pointing direction
array_pointing = linalg.set_phi_theta(0 * u.deg, 20. * u.deg)
# angular offset between the reconstructed direction and the array
# pointing
off_angle = linalg.angle(dir_fit, array_pointing)
reco_event["NTels_trig"] = len(event.dl0.tels_with_data)
reco_event["NTels_reco"] = len(hillas_dict)
reco_event["NTels_reco_lst"] = n_tels["LST"]
reco_event["NTels_reco_mst"] = n_tels["MST"]
reco_event["NTels_reco_sst"] = n_tels["SST"]
reco_event["reco_Energy"] = predict_energ.to(energy_unit).value
reco_event["reco_phi"] = phi / angle_unit
reco_event["reco_theta"] = theta / angle_unit
reco_event["off_angle"] = off_angle / angle_unit
reco_event["xi"] = xi / angle_unit
reco_event["DeltaR"] = DeltaR / dist_unit
reco_event["ErrEstPos"] = err_est_pos / dist_unit
reco_event["ErrEstDir"] = err_est_dir / angle_unit
reco_event["gammaness"] = gammaness
reco_event["success"] = True
else:
reco_event["success"] = False
# save basic event infos
reco_event["MC_Energy"] = event.mc.energy.to(energy_unit).value
reco_event["Event_ID"] = event.r1.event_id
reco_event["Run_ID"] = event.r1.run_id
reco_table.flush()
reco_event.append()
if signal_handler.stop:
break
if signal_handler.stop:
break
# make sure everything gets written out nicely
reco_table.flush()
try:
print()
Eventcutflow()
print()
Imagecutflow()
# do some simple event selection
# and print the corresponding selection efficiency
N_selected = len([
x for x in reco_table.where(
"""(NTels_reco > min_tel) & (gammaness > agree_threshold)""")
])
N_total = len(reco_table)
print("\nfraction selected events:")
print("{} / {} = {} %".format(N_selected, N_total,
N_selected / N_total * 100))
except ZeroDivisionError:
pass
print("\nlength filenamelist:", len(filenamelist[:args.last]))
# do some plotting if so desired
if args.plot:
gammaness = [x['gammaness'] for x in reco_table]
NTels_rec = [x['NTels_reco'] for x in reco_table]
NTel_bins = np.arange(np.min(NTels_rec), np.max(NTels_rec) + 2) - .5
NTels_rec_lst = [x['NTels_reco_lst'] for x in reco_table]
NTels_rec_mst = [x['NTels_reco_mst'] for x in reco_table]
NTels_rec_sst = [x['NTels_reco_sst'] for x in reco_table]
reco_energy = np.array([x['reco_Energy'] for x in reco_table])
mc_energy = np.array([x['MC_Energy'] for x in reco_table])
fig = plt.figure(figsize=(15, 5))
plt.suptitle(" ** ".join(
[args.mode, "protons" if args.proton else "gamma"]))
plt.subplots_adjust(left=0.05, right=0.97, hspace=0.39, wspace=0.2)
ax = plt.subplot(131)
histo = np.histogram2d(NTels_rec,
gammaness,
bins=(NTel_bins, np.linspace(0, 1, 11)))[0].T
histo_normed = histo / histo.max(axis=0)
im = ax.imshow(
histo_normed,
interpolation='none',
origin='lower',
aspect='auto',
# extent=(*NTel_bins[[0, -1]], 0, 1),
cmap=plt.cm.inferno)
ax.set_xlabel("NTels")
ax.set_ylabel("drifted gammaness")
plt.title("Total Number of Telescopes")
# next subplot
ax = plt.subplot(132)
histo = np.histogram2d(NTels_rec_sst,
gammaness,
bins=(NTel_bins, np.linspace(0, 1, 11)))[0].T
histo_normed = histo / histo.max(axis=0)
im = ax.imshow(
histo_normed,
interpolation='none',
origin='lower',
aspect='auto',
# extent=(*NTel_bins[[0, -1]], 0, 1),
cmap=plt.cm.inferno)
ax.set_xlabel("NTels")
plt.setp(ax.get_yticklabels(), visible=False)
plt.title("Number of SSTs")
# next subplot
ax = plt.subplot(133)
histo = np.histogram2d(NTels_rec_mst,
gammaness,
bins=(NTel_bins, np.linspace(0, 1, 11)))[0].T
histo_normed = histo / histo.max(axis=0)
im = ax.imshow(
histo_normed,
interpolation='none',
origin='lower',
aspect='auto',
# extent=(*NTel_bins[[0, -1]], 0, 1),
cmap=plt.cm.inferno)
cb = fig.colorbar(im, ax=ax)
ax.set_xlabel("NTels")
plt.setp(ax.get_yticklabels(), visible=False)
plt.title("Number of MSTs")
plt.subplots_adjust(wspace=0.05)
# plot the energy migration matrix
plt.figure()
plt.hist2d(np.log10(reco_energy),
np.log10(mc_energy),
bins=20,
cmap=plt.cm.inferno)
plt.xlabel("E_MC / TeV")
plt.ylabel("E_rec / TeV")
plt.colorbar()
plt.show()
def test_CutFlow():
with warns(FutureWarning):
flow = CutFlow("TestFlow")
# set_cut and add_cut a aliases
flow.set_cut("smaller5", smaller5)
flow.add_cut("smaller3", lambda x: x < 3)
for i in range(2, 6):
flow.count("noCuts")
# .keep counts if the function returns True,
# i.e. when we "keep" the event
if flow.keep("smaller5", i):
# .cut counts if the function returns False,
# i.e. when we do NOT "cut" the event
if flow.cut("smaller3", i):
pass
else:
# do something else that could fail or be rejected
try:
assert i == 3
flow.count("something")
except:
pass
t = flow(sort_column=1)
assert np.all(t["selected Events"] == [4, 3, 2, 1])
with raises(UndefinedCut):
flow.cut("undefined", 5)
with raises(PureCountingCut):
flow.cut("noCuts")
plt.sca(ax)
plt.xticks(rotation=45)
for label in ax.get_xmajorticklabels():
label.set_horizontalalignment("right")
plt.subplots_adjust(top=0.9,
bottom=0.135,
left=0.034,
right=0.98,
hspace=0.478,
wspace=0.08)
plt.pause(.1)
# do some cross-validation now
if args.check:
# keeping track of events and where they were rejected
Eventcutflow = CutFlow("EventCutFlow")
Imagecutflow = CutFlow("ImageCutFlow")
# takes care of image cleaning
cleaner = ImageCleaner(mode=args.mode,
cutflow=Imagecutflow,
wavelet_options=args.raw,
skip_edge_events=False,
island_cleaning=True)
# the class that does the shower reconstruction
shower_reco = HillasReconstructor()
preper = EventPreparer(
cleaner=cleaner,
shower_reco=shower_reco,
def main():
# Argument parser
parser = make_argparser()
parser.add_argument("--regressor_dir",
default="./",
help="regressors directory")
parser.add_argument("--classifier_dir",
default="./",
help="regressors directory")
parser.add_argument(
"--force_tailcut_for_extended_cleaning",
type=str2bool,
default=False,
help="For tailcut cleaning for energy/score estimation",
)
parser.add_argument(
"--save_images",
action="store_true",
help="Save images in images.h5 (one file testing)",
)
args = parser.parse_args()
# Read configuration file
cfg = load_config(args.config_file)
# Read site layout
site = cfg["General"]["site"]
array = cfg["General"]["array"]
cameras = cfg["General"]["cam_id_list"]
# Add force_tailcut_for_extended_cleaning in configuration
cfg["General"][
"force_tailcut_for_extended_cleaning"] = args.force_tailcut_for_extended_cleaning
cfg["General"]["force_mode"] = "tail"
force_mode = args.mode
if cfg["General"]["force_tailcut_for_extended_cleaning"] is True:
force_mode = "tail"
print("force_mode={}".format(force_mode))
print("mode={}".format(args.mode))
if args.infile_list:
filenamelist = []
for f in args.infile_list:
filenamelist += glob("{}/{}".format(args.indir, f))
filenamelist.sort()
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
# keeping track of events and where they were rejected
evt_cutflow = CutFlow("EventCutFlow")
img_cutflow = CutFlow("ImageCutFlow")
# Event preparer
preper = EventPreparer(config=cfg,
mode=args.mode,
event_cutflow=evt_cutflow,
image_cutflow=img_cutflow)
# Regressor and classifier methods
regressor_method = cfg["EnergyRegressor"]["method_name"]
classifier_method = cfg["GammaHadronClassifier"]["method_name"]
use_proba_for_classifier = cfg["GammaHadronClassifier"]["use_proba"]
if regressor_method in ["None", "none", None]:
use_regressor = False
else:
use_regressor = True
if classifier_method in ["None", "none", None]:
use_classifier = False
else:
use_classifier = True
# Classifiers
if use_classifier:
classifier_files = (args.classifier_dir +
"/classifier_{mode}_{cam_id}_{classifier}.pkl.gz")
clf_file = classifier_files.format(
**{
"mode": force_mode,
"wave_args": "mixed",
"classifier": classifier_method,
"cam_id": "{cam_id}",
})
classifier = EventClassifier.load(clf_file, cam_id_list=cameras)
# Regressors
if use_regressor:
regressor_files = (args.regressor_dir +
"/regressor_{mode}_{cam_id}_{regressor}.pkl.gz")
reg_file = regressor_files.format(
**{
"mode": force_mode,
"wave_args": "mixed",
"regressor": regressor_method,
"cam_id": "{cam_id}",
})
regressor = EnergyRegressor.load(reg_file, cam_id_list=cameras)
# catch ctr-c signal to exit current loop and still display results
signal_handler = SignalHandler()
signal.signal(signal.SIGINT, signal_handler)
# Declaration of the column descriptor for the (possible) images file
class StoredImages(tb.IsDescription):
event_id = tb.Int32Col(dflt=1, pos=0)
tel_id = tb.Int16Col(dflt=1, pos=1)
dl1_phe_image = tb.Float32Col(shape=(1855), pos=2)
mc_phe_image = tb.Float32Col(shape=(1855), pos=3)
# this class defines the reconstruction parameters to keep track of
class RecoEvent(tb.IsDescription):
obs_id = tb.Int16Col(dflt=-1, pos=0)
event_id = tb.Int32Col(dflt=-1, pos=1)
NTels_trig = tb.Int16Col(dflt=0, pos=2)
NTels_reco = tb.Int16Col(dflt=0, pos=3)
NTels_reco_lst = tb.Int16Col(dflt=0, pos=4)
NTels_reco_mst = tb.Int16Col(dflt=0, pos=5)
NTels_reco_sst = tb.Int16Col(dflt=0, pos=6)
mc_energy = tb.Float32Col(dflt=np.nan, pos=7)
reco_energy = tb.Float32Col(dflt=np.nan, pos=8)
reco_alt = tb.Float32Col(dflt=np.nan, pos=9)
reco_az = tb.Float32Col(dflt=np.nan, pos=10)
offset = tb.Float32Col(dflt=np.nan, pos=11)
xi = tb.Float32Col(dflt=np.nan, pos=12)
ErrEstPos = tb.Float32Col(dflt=np.nan, pos=13)
ErrEstDir = tb.Float32Col(dflt=np.nan, pos=14)
gammaness = tb.Float32Col(dflt=np.nan, pos=15)
success = tb.BoolCol(dflt=False, pos=16)
score = tb.Float32Col(dflt=np.nan, pos=17)
h_max = tb.Float32Col(dflt=np.nan, pos=18)
reco_core_x = tb.Float32Col(dflt=np.nan, pos=19)
reco_core_y = tb.Float32Col(dflt=np.nan, pos=20)
mc_core_x = tb.Float32Col(dflt=np.nan, pos=21)
mc_core_y = tb.Float32Col(dflt=np.nan, pos=22)
reco_outfile = tb.open_file(
mode="w",
# if no outfile name is given (i.e. don't to write the event list to disk),
# need specify two "driver" arguments
**({
"filename": args.outfile
} if args.outfile else {
"filename": "no_outfile.h5",
"driver": "H5FD_CORE",
"driver_core_backing_store": False,
}))
reco_table = reco_outfile.create_table("/", "reco_events", RecoEvent)
reco_event = reco_table.row
# Create the images file only if the user want to store the images
if args.save_images is True:
images_outfile = tb.open_file("images.h5", mode="w")
images_table = {}
images_phe = {}
# Telescopes in analysis
allowed_tels = set(prod3b_tel_ids(array, site=site))
for i, filename in enumerate(filenamelist):
source = event_source(input_url=filename,
allowed_tels=allowed_tels,
max_events=args.max_events)
# loop that cleans and parametrises the images and performs the reconstruction
for (
event,
dl1_phe_image,
mc_phe_image,
n_pixel_dict,
hillas_dict,
hillas_dict_reco,
n_tels,
tot_signal,
max_signals,
n_cluster_dict,
reco_result,
impact_dict,
) in preper.prepare_event(source):
# Angular quantities
run_array_direction = event.mcheader.run_array_direction
# Angular separation between true and reco direction
xi = angular_separation(event.mc.az, event.mc.alt, reco_result.az,
reco_result.alt)
# Angular separation bewteen the center of the camera and the reco direction.
offset = angular_separation(
run_array_direction[0], # az
run_array_direction[1], # alt
reco_result.az,
reco_result.alt,
)
# Height of shower maximum
h_max = reco_result.h_max
if hillas_dict is not None:
# Estimate particle energy
if use_regressor is True:
energy_tel = np.zeros(len(hillas_dict.keys()))
weight_tel = np.zeros(len(hillas_dict.keys()))
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = event.inst.subarray.tel[tel_id].camera.cam_id
moments = hillas_dict[tel_id]
model = regressor.model_dict[cam_id]
# Features to be fed in the regressor
features_img = np.array([
np.log10(moments.intensity),
np.log10(impact_dict[tel_id].value),
moments.width.value,
moments.length.value,
h_max.value,
])
energy_tel[idx] = model.predict([features_img])
weight_tel[idx] = moments.intensity
reco_energy = np.sum(
weight_tel * energy_tel) / sum(weight_tel)
else:
reco_energy = np.nan
# Estimate particle score/gammaness
if use_classifier is True:
score_tel = np.zeros(len(hillas_dict.keys()))
gammaness_tel = np.zeros(len(hillas_dict.keys()))
weight_tel = np.zeros(len(hillas_dict.keys()))
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = event.inst.subarray.tel[tel_id].camera.cam_id
moments = hillas_dict[tel_id]
model = classifier.model_dict[cam_id]
# Features to be fed in the classifier
features_img = np.array([
np.log10(reco_energy),
moments.width.value,
moments.length.value,
moments.skewness,
moments.kurtosis,
h_max.value,
])
# Output of classifier according to type of classifier
if use_proba_for_classifier is False:
score_tel[idx] = model.decision_function(
[features_img])
else:
gammaness_tel[idx] = model.predict_proba(
[features_img])[:, 1]
# Should test other weighting strategy (e.g. power of charge, impact, etc.)
# For now, weighting a la Mars
weight_tel[idx] = np.sqrt(moments.intensity)
# Weight the final decision/proba
if use_proba_for_classifier is True:
gammaness = np.sum(
weight_tel * gammaness_tel) / sum(weight_tel)
else:
score = np.sum(
weight_tel * score_tel) / sum(weight_tel)
else:
score = np.nan
gammaness = np.nan
# Regardless if energy or gammaness is estimated, if the user
# wants to save the images of the run we do it here
# (Probably not the most efficient way, but for one file is ok)
if args.save_images is True:
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = event.inst.subarray.tel[tel_id].camera.cam_id
if cam_id not in images_phe:
images_table[cam_id] = images_outfile.create_table(
"/", "_".join(["images", cam_id]),
StoredImages)
images_phe[cam_id] = images_table[cam_id].row
shower = event.mc
mc_core_x = shower.core_x
mc_core_y = shower.core_y
reco_core_x = reco_result.core_x
reco_core_y = reco_result.core_y
alt, az = reco_result.alt, reco_result.az
# Fill table's attributes
reco_event["NTels_trig"] = len(event.dl0.tels_with_data)
reco_event["NTels_reco"] = len(hillas_dict)
reco_event["NTels_reco_lst"] = n_tels["LST_LST_LSTCam"]
reco_event["NTels_reco_mst"] = n_tels["MST_MST_NectarCam"]
reco_event["NTels_reco_sst"] = n_tels["SST"] # will change
reco_event["reco_energy"] = reco_energy
reco_event["reco_alt"] = alt.to("deg").value
reco_event["reco_az"] = az.to("deg").value
reco_event["offset"] = offset.to("deg").value
reco_event["xi"] = xi.to("deg").value
reco_event["h_max"] = h_max.to("m").value
reco_event["reco_core_x"] = reco_core_x.to("m").value
reco_event["reco_core_y"] = reco_core_y.to("m").value
reco_event["mc_core_x"] = mc_core_x.to("m").value
reco_event["mc_core_y"] = mc_core_y.to("m").value
if use_proba_for_classifier is True:
reco_event["gammaness"] = gammaness
else:
reco_event["score"] = score
reco_event["success"] = True
reco_event["ErrEstPos"] = np.nan
reco_event["ErrEstDir"] = np.nan
else:
reco_event["success"] = False
# save basic event infos
reco_event["mc_energy"] = event.mc.energy.to("TeV").value
reco_event["event_id"] = event.r1.event_id
reco_event["obs_id"] = event.r1.obs_id
if args.save_images is True:
images_phe[cam_id]["event_id"] = event.r0.event_id
images_phe[cam_id]["tel_id"] = tel_id
images_phe[cam_id]["dl1_phe_image"] = dl1_phe_image
images_phe[cam_id]["mc_phe_image"] = mc_phe_image
images_phe[cam_id].append()
# Fill table
reco_table.flush()
reco_event.append()
if signal_handler.stop:
break
if signal_handler.stop:
break
# make sure everything gets written out nicely
reco_table.flush()
if args.save_images is True:
for table in images_table.values():
table.flush()
# Add in meta-data's table?
try:
print()
evt_cutflow()
print()
img_cutflow()
except ZeroDivisionError:
pass
print("Job done!")
def __init__(
self,
config,
subarray,
cams_and_foclens,
mode,
event_cutflow=None,
image_cutflow=None,
debug=False,
):
"""Initiliaze an EventPreparer object."""
# Cleaning for reconstruction
self.cleaner_reco = ImageCleaner( # for reconstruction
config=config["ImageCleaning"]["biggest"],
cameras=cams_and_foclens.keys(),
mode=mode,
)
# Cleaning for energy/score estimation
# Add possibility to force energy/score cleaning with tailcut analysis
force_mode = mode
try:
if config["General"]["force_tailcut_for_extended_cleaning"] is True:
force_mode = config["General"]["force_mode"]
print("> Activate force-mode for cleaning!!!!")
except:
pass # force_mode = mode
self.cleaner_extended = ImageCleaner( # for energy/score estimation
config=config["ImageCleaning"]["extended"],
cameras=cams_and_foclens.keys(),
mode=force_mode,
)
# Image book keeping
self.image_cutflow = image_cutflow or CutFlow("ImageCutFlow")
# Add quality cuts on images
charge_bounds = config["ImageSelection"]["charge"]
npix_bounds = config["ImageSelection"]["pixel"]
ellipticity_bounds = config["ImageSelection"]["ellipticity"]
nominal_distance_bounds = config["ImageSelection"]["nominal_distance"]
if debug:
camera_radius(cams_and_foclens,
"all") # Display all registered camera radii
# Radii in meters from CTA-MARS
# self.camera_radius = {
# cam_id: camera_radius(cams_and_foclens, cam_id)
# for cam_id in cams_and_foclens.keys()
# }
# Radii in degrees from CTA-MARS
self.camera_radius = {
cam_id: CTAMARS_radii(cam_id)
for cam_id in cams_and_foclens.keys()
}
self.image_cutflow.set_cuts(
OrderedDict([
("noCuts", None),
("min pixel", lambda s: np.count_nonzero(s) < npix_bounds[0]),
("min charge", lambda x: x < charge_bounds[0]),
(
"poor moments",
lambda m: m.width <= 0 or m.length <= 0 or np.isnan(
m.width) or np.isnan(m.length),
),
(
"bad ellipticity",
lambda m: (m.width / m.length) < ellipticity_bounds[0] or
(m.width / m.length) > ellipticity_bounds[-1],
),
(
"close to the edge",
lambda m, cam_id: m.r.value >
(nominal_distance_bounds[-1] * self.camera_radius[cam_id]),
), # in meter
]))
# Configuration for the camera calibrator
cfg = Config()
extractor = TwoPassWindowSum(config=cfg, subarray=subarray)
# Get the name of the image extractor in order to adapt some options
# specific to TwoPassWindowSum later on
self.extractorName = list(extractor.get_current_config().items())[0][0]
self.calib = CameraCalibrator(
config=cfg,
image_extractor=extractor,
subarray=subarray,
)
# Reconstruction
self.shower_reco = MyHillasReconstructor()
# Event book keeping
self.event_cutflow = event_cutflow or CutFlow("EventCutFlow")
# Add cuts on events
self.min_ntel = config["Reconstruction"]["min_tel"]
self.event_cutflow.set_cuts(
OrderedDict([
("noCuts", None),
("min2Tels trig", lambda x: x < self.min_ntel),
("min2Tels reco", lambda x: x < self.min_ntel),
("direction nan", lambda x: x.is_valid is False),
]))
def main():
# your favourite units here
energy_unit = u.TeV
angle_unit = u.deg
dist_unit = u.m
parser = make_argparser()
parser.add_argument(
'-o',
'--outfile',
type=str,
help="if given, write output file with reconstruction results")
parser.add_argument('--plot_c',
action='store_true',
help="plot camera-wise displays")
group = parser.add_mutually_exclusive_group()
group.add_argument('--proton',
action='store_true',
help="do protons instead of gammas")
group.add_argument('--electron',
action='store_true',
help="do electrons instead of gammas")
args = parser.parse_args()
if args.infile_list:
filenamelist = []
for f in args.infile_list:
filenamelist += glob("{}/{}".format(args.indir, f))
elif args.proton:
filenamelist = glob("{}/proton/*gz".format(args.indir))
channel = "proton"
elif args.electron:
filenamelist = glob("{}/electron/*gz".format(args.indir))
channel = "electron"
elif args.gamma:
filenamelist = glob("{}/gamma/*gz".format(args.indir))
channel = "gamma"
else:
raise ValueError("don't know which input to use...")
filenamelist.sort()
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
else:
print("found {} files".format(len(filenamelist)))
tel_phi = {}
tel_theta = {}
# keeping track of events and where they were rejected
Eventcutflow = CutFlow("EventCutFlow")
Imagecutflow = CutFlow("ImageCutFlow")
# takes care of image cleaning
cleaner = ImageCleaner(mode=args.mode,
cutflow=Imagecutflow,
wavelet_options=args.raw,
skip_edge_events=args.skip_edge_events,
island_cleaning=True)
# the class that does the shower reconstruction
shower_reco = HillasReconstructor()
shower_max_estimator = ShowerMaxEstimator("paranal")
preper = EventPreparer(
cleaner=cleaner,
hillas_parameters=hillas_parameters,
shower_reco=shower_reco,
event_cutflow=Eventcutflow,
image_cutflow=Imagecutflow,
# event/image cuts:
allowed_cam_ids=[], # means: all
min_ntel=3,
min_charge=args.min_charge,
min_pixel=3)
# a signal handler to abort the event loop but still do the post-processing
signal_handler = SignalHandler()
signal.signal(signal.SIGINT, signal_handler)
try:
# this class defines the reconstruction parameters to keep track of
class RecoEvent(tb.IsDescription):
NTels_trigg = tb.Int16Col(dflt=1, pos=0)
NTels_clean = tb.Int16Col(dflt=1, pos=1)
EnMC = tb.Float32Col(dflt=1, pos=2)
xi = tb.Float32Col(dflt=1, pos=3)
DeltaR = tb.Float32Col(dflt=1, pos=4)
ErrEstPos = tb.Float32Col(dflt=1, pos=5)
ErrEstDir = tb.Float32Col(dflt=1, pos=6)
h_max = tb.Float32Col(dflt=1, pos=7)
reco_outfile = tb.open_file(
args.outfile,
mode="w",
# if we don't want to write the event list to disk, need to add more arguments
**({} if args.store else {
"driver": "H5FD_CORE",
"driver_core_backing_store": False
}))
reco_table = reco_outfile.create_table("/", "reco_event", RecoEvent)
reco_event = reco_table.row
except:
reco_event = RecoEvent()
print("no pytables installed?")
# ## ####### ####### ########
# ## ## ## ## ## ## ##
# ## ## ## ## ## ## ##
# ## ## ## ## ## ########
# ## ## ## ## ## ##
# ## ## ## ## ## ##
# ######## ####### ####### ##
cam_id_map = {}
# define here which telescopes to loop over
allowed_tels = None
# allowed_tels = prod3b_tel_ids("L+F+D")
for i, filename in enumerate(filenamelist[:args.last]):
print("file: {i} filename = {filename}".format(i=i, filename=filename))
source = hessio_event_source(filename,
allowed_tels=allowed_tels,
max_events=args.max_events)
# loop that cleans and parametrises the images and performs the reconstruction
for (event, hillas_dict, n_tels, tot_signal, max_signal, pos_fit,
dir_fit, h_max, err_est_pos,
err_est_dir) in preper.prepare_event(source):
shower = event.mc
org_alt = u.Quantity(shower.alt).to(u.deg)
org_az = u.Quantity(shower.az).to(u.deg)
if org_az > 180 * u.deg:
org_az -= 360 * u.deg
org_the = alt_to_theta(org_alt)
org_phi = az_to_phi(org_az)
if org_phi > 180 * u.deg:
org_phi -= 360 * u.deg
if org_phi < -180 * u.deg:
org_phi += 360 * u.deg
shower_org = linalg.set_phi_theta(org_phi, org_the)
shower_core = convert_astropy_array([shower.core_x, shower.core_y])
xi = linalg.angle(dir_fit, shower_org).to(angle_unit)
diff = linalg.length(pos_fit[:2] - shower_core)
# print some performance
print()
print("xi = {:4.3f}".format(xi))
print("pos = {:4.3f}".format(diff))
print("h_max reco: {:4.3f}".format(h_max.to(u.km)))
print("err_est_dir: {:4.3f}".format(err_est_dir.to(angle_unit)))
print("err_est_pos: {:4.3f}".format(err_est_pos))
try:
# store the reconstruction data in the PyTable
reco_event["NTels_trigg"] = n_tels["tot"]
reco_event["NTels_clean"] = len(shower_reco.circles)
reco_event["EnMC"] = event.mc.energy / energy_unit
reco_event["xi"] = xi / angle_unit
reco_event["DeltaR"] = diff / dist_unit
reco_event["ErrEstPos"] = err_est_pos / dist_unit
reco_event["ErrEstDir"] = err_est_dir / angle_unit
reco_event["h_max"] = h_max / dist_unit
reco_event.append()
reco_table.flush()
print()
print("xi res (68-percentile) = {:4.3f} {}".format(
np.percentile(reco_table.cols.xi, 68), angle_unit))
print("core res (68-percentile) = {:4.3f} {}".format(
np.percentile(reco_table.cols.DeltaR, 68), dist_unit))
print("h_max (median) = {:4.3f} {}".format(
np.percentile(reco_table.cols.h_max, 50), dist_unit))
except NoPyTables:
pass
if args.plot_c:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.gca(projection='3d')
for c in shower_reco.circles.values():
points = [
c.pos + t * c.a * u.km for t in np.linspace(0, 15, 3)
]
ax.plot(*np.array(points).T,
linewidth=np.sqrt(c.weight) / 10)
ax.scatter(*c.pos[:, None].value, s=np.sqrt(c.weight))
plt.xlabel("x")
plt.ylabel("y")
plt.pause(.1)
# this plots
# • the MC shower core
# • the reconstructed shower core
# • the used telescopes
# • and the trace of the Hillas plane on the ground
plt.figure()
for tel_id, c in shower_reco.circles.items():
plt.scatter(c.pos[0], c.pos[1], s=np.sqrt(c.weight))
plt.gca().annotate(tel_id,
(c.pos[0].value, c.pos[1].value))
plt.plot([
c.pos[0].value - 500 * c.norm[1],
c.pos[0].value + 500 * c.norm[1]
], [
c.pos[1].value + 500 * c.norm[0],
c.pos[1].value - 500 * c.norm[0]
],
linewidth=np.sqrt(c.weight) / 10)
plt.scatter(*pos_fit[:2],
c="black",
marker="*",
label="fitted")
plt.scatter(*shower_core[:2],
c="black",
marker="P",
label="MC")
plt.legend()
plt.xlabel("x")
plt.ylabel("y")
plt.xlim(-1400, 1400)
plt.ylim(-1400, 1400)
plt.show()
if signal_handler.stop: break
if signal_handler.stop: break
print("\n" + "=" * 35 + "\n")
print("xi res (68-percentile) = {:4.3f} {}".format(
np.percentile(reco_table.cols.xi, 68), angle_unit))
print("core res (68-percentile) = {:4.3f} {}".format(
np.percentile(reco_table.cols.DeltaR, 68), dist_unit))
print("h_max (median) = {:4.3f} {}".format(
np.percentile(reco_table.cols.h_max, 50), dist_unit))
# print the cutflows for telescopes and camera images
print("\n\n")
Eventcutflow("min2Tels trig")
print()
Imagecutflow(sort_column=1)
# if we don't want to plot anything, we can exit now
if not args.plot:
return
# ######## ## ####### ######## ######
# ## ## ## ## ## ## ## ##
# ## ## ## ## ## ## ##
# ######## ## ## ## ## ######
# ## ## ## ## ## ##
# ## ## ## ## ## ## ##
# ## ######## ####### ## ######
plt.figure()
plt.hist(reco_table.cols.h_max, bins=np.linspace(000, 15000, 51, True))
plt.title(channel)
plt.xlabel("h_max reco")
plt.pause(.1)
figure = plt.figure()
xi_edges = np.linspace(0, 5, 20)
plt.hist(reco_table.cols.xi, bins=xi_edges, log=True)
plt.xlabel(r"$\xi$ / deg")
if args.write:
save_fig('{}/reco_xi_{}'.format(args.plots_dir, args.mode))
plt.pause(.1)
plt.figure()
plt.hist(reco_table.cols.ErrEstDir[:], bins=np.linspace(0, 20, 50))
plt.title(channel)
plt.xlabel("beta")
plt.pause(.1)
plt.figure()
plt.hist(np.log10(reco_table.cols.xi[:] / reco_table.cols.ErrEstDir[:]),
bins=50)
plt.title(channel)
plt.xlabel("log_10(xi / beta)")
plt.pause(.1)
# convert the xi-list into a dict with the number of used telescopes as keys
xi_vs_tel = {}
for xi, ntel in zip(reco_table.cols.xi, reco_table.cols.NTels_clean):
if ntel not in xi_vs_tel:
xi_vs_tel[ntel] = [xi]
else:
xi_vs_tel[ntel].append(xi)
print(args.mode)
for ntel, xis in sorted(xi_vs_tel.items()):
print("NTel: {} -- median xi: {}".format(ntel, np.median(xis)))
# print("histogram:", np.histogram(xis, bins=xi_edges))
# create a list of energy bin-edges and -centres for violin plots
Energy_edges = np.linspace(2, 8, 13)
Energy_centres = (Energy_edges[1:] + Energy_edges[:-1]) / 2.
# convert the xi-list in to an energy-binned dict with the bin centre as keys
xi_vs_energy = {}
for en, xi in zip(reco_table.cols.EnMC, reco_table.cols.xi):
# get the bin number this event belongs into
sbin = np.digitize(np.log10(en), Energy_edges) - 1
# the central value of the bin is the key for the dictionary
if Energy_centres[sbin] not in xi_vs_energy:
xi_vs_energy[Energy_centres[sbin]] = [xi]
else:
xi_vs_energy[Energy_centres[sbin]] += [xi]
# plotting the angular error as violin plots with binning in
# number of telescopes and shower energy
figure = plt.figure()
plt.subplot(211)
plt.violinplot([np.log10(a) for a in xi_vs_tel.values()],
[a for a in xi_vs_tel.keys()],
points=60,
widths=.75,
showextrema=False,
showmedians=True)
plt.xlabel("Number of Telescopes")
plt.ylabel(r"log($\xi$ / deg)")
plt.ylim(-3, 2)
plt.grid()
plt.subplot(212)
plt.violinplot([np.log10(a) for a in xi_vs_energy.values()],
[a for a in xi_vs_energy.keys()],
points=60,
widths=(Energy_edges[1] - Energy_edges[0]) / 1.5,
showextrema=False,
showmedians=True)
plt.xlabel(r"log(Energy / GeV)")
plt.ylabel(r"log($\xi$ / deg)")
plt.ylim(-3, 2)
plt.grid()
plt.tight_layout()
if args.write:
save_fig('{}/reco_xi_vs_E_NTel_{}'.format(args.plots_dir, args.mode))
plt.pause(.1)
# convert the diffs-list into a dict with the number of used telescopes as keys
diff_vs_tel = {}
for diff, ntel in zip(reco_table.cols.DeltaR, reco_table.cols.NTels_clean):
if ntel not in diff_vs_tel:
diff_vs_tel[ntel] = [diff]
else:
diff_vs_tel[ntel].append(diff)
# convert the diffs-list in to an energy-binned dict with the bin centre as keys
diff_vs_energy = {}
for en, diff in zip(reco_table.cols.EnMC, reco_table.cols.DeltaR):
# get the bin number this event belongs into
sbin = np.digitize(np.log10(en), Energy_edges) - 1
# the central value of the bin is the key for the dictionary
if Energy_centres[sbin] not in diff_vs_energy:
diff_vs_energy[Energy_centres[sbin]] = [diff]
else:
diff_vs_energy[Energy_centres[sbin]] += [diff]
# plotting the core position error as violin plots with binning in
# number of telescopes an shower energy
plt.figure()
plt.subplot(211)
plt.violinplot([np.log10(a) for a in diff_vs_tel.values()],
[a for a in diff_vs_tel.keys()],
points=60,
widths=.75,
showextrema=False,
showmedians=True)
plt.xlabel("Number of Telescopes")
plt.ylabel(r"log($\Delta R$ / m)")
plt.grid()
plt.subplot(212)
plt.violinplot([np.log10(a) for a in diff_vs_energy.values()],
[a for a in diff_vs_energy.keys()],
points=60,
widths=(Energy_edges[1] - Energy_edges[0]) / 1.5,
showextrema=False,
showmedians=True)
plt.xlabel(r"log(Energy / GeV)")
plt.ylabel(r"log($\Delta R$ / m)")
plt.grid()
plt.tight_layout()
if args.write:
save_fig('{}/reco_dist_vs_E_NTel_{}'.format(args.plots_dir, args.mode))
plt.show()
fig = classifier.show_importances(ClassifierFeatures._fields)
fig.set_size_inches(15, 10)
for ax in fig.axes:
plt.sca(ax)
plt.xticks(rotation=45)
for label in ax.get_xmajorticklabels():
label.set_horizontalalignment("right")
plt.subplots_adjust(top=0.9, bottom=0.135, left=0.034, right=0.98,
hspace=0.478, wspace=0.08)
plt.pause(.1)
# do some cross-validation now
if args.check:
# keeping track of events and where they were rejected
Eventcutflow = CutFlow("EventCutFlow")
Imagecutflow = CutFlow("ImageCutFlow")
# takes care of image cleaning
cleaner = ImageCleaner(mode=args.mode, cutflow=Imagecutflow,
wavelet_options=args.raw,
skip_edge_events=False, island_cleaning=True)
# the class that does the shower reconstruction
shower_reco = HillasReconstructor()
preper = EventPreparer(
cleaner=cleaner, shower_reco=shower_reco,
event_cutflow=Eventcutflow, image_cutflow=Imagecutflow,
# event/image cuts:
allowed_cam_ids=[], # [] or None means: all
min_ntel=2,
def main():
# Argument parser
parser = make_argparser()
parser.add_argument(
"--debug",
action="store_true",
help="Print debugging information",
)
parser.add_argument(
"--save_images",
action="store_true",
help="Save also all images",
)
parser.add_argument(
"--estimate_energy",
type=str2bool,
default=False,
help="Estimate the events' energy with a regressor from\
protopipe.scripts.build_model",
)
parser.add_argument("--regressor_dir",
type=str,
default="./",
help="regressors directory")
args = parser.parse_args()
# Read configuration file
cfg = load_config(args.config_file)
try: # If the user didn't specify a site and/or and array...
site = cfg["General"]["site"]
array = cfg["General"]["array"]
except KeyError: # ...raise an error and exit.
print("\033[91m ERROR: make sure that both 'site' and 'array' are "
"specified in the analysis configuration file! \033[0m")
exit()
if args.infile_list:
filenamelist = []
for f in args.infile_list:
filenamelist += glob("{}/{}".format(args.indir, f))
filenamelist.sort()
else:
raise ValueError("don't know which input to use...")
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
else:
print("found {} files".format(len(filenamelist)))
# Get the IDs of the involved telescopes and associated cameras together
# with the equivalent focal lengths from the first event
allowed_tels, cams_and_foclens, subarray = prod3b_array(
filenamelist[0], site, array)
# keeping track of events and where they were rejected
evt_cutflow = CutFlow("EventCutFlow")
img_cutflow = CutFlow("ImageCutFlow")
preper = EventPreparer(
config=cfg,
subarray=subarray,
cams_and_foclens=cams_and_foclens,
mode=args.mode,
event_cutflow=evt_cutflow,
image_cutflow=img_cutflow,
)
# catch ctr-c signal to exit current loop and still display results
signal_handler = SignalHandler()
signal.signal(signal.SIGINT, signal_handler)
# Regressor method
regressor_method = cfg["EnergyRegressor"]["method_name"]
# wrapper for the scikit-learn regressor
if args.estimate_energy is True:
regressor_files = (args.regressor_dir +
"/regressor_{mode}_{cam_id}_{regressor}.pkl.gz")
reg_file = regressor_files.format(
**{
"mode": args.mode,
"wave_args": "mixed", # ToDo, control
"regressor": regressor_method,
"cam_id": "{cam_id}",
})
regressor = EnergyRegressor.load(reg_file,
cam_id_list=cams_and_foclens.keys())
# COLUMN DESCRIPTOR AS DICTIONARY
# Column descriptor for the file containing output training data."""
DataTrainingOutput = dict(
# ======================================================================
# ARRAY
obs_id=tb.Int16Col(dflt=1, pos=0),
event_id=tb.Int32Col(dflt=1, pos=1),
tel_id=tb.Int16Col(dflt=1, pos=2),
N_LST=tb.Int16Col(dflt=1, pos=3),
N_MST=tb.Int16Col(dflt=1, pos=4),
N_SST=tb.Int16Col(dflt=1, pos=5),
n_tel_reco=tb.FloatCol(dflt=1, pos=6),
n_tel_discri=tb.FloatCol(dflt=1, pos=7),
# ======================================================================
# DL1
hillas_intensity_reco=tb.Float32Col(dflt=1, pos=8),
hillas_intensity=tb.Float32Col(dflt=1, pos=9),
hillas_x_reco=tb.Float32Col(dflt=1, pos=10),
hillas_y_reco=tb.Float32Col(dflt=1, pos=11),
hillas_x=tb.Float32Col(dflt=1, pos=12),
hillas_y=tb.Float32Col(dflt=1, pos=13),
hillas_r_reco=tb.Float32Col(dflt=1, pos=14),
hillas_r=tb.Float32Col(dflt=1, pos=15),
hillas_phi_reco=tb.Float32Col(dflt=1, pos=16),
hillas_phi=tb.Float32Col(dflt=1, pos=17),
hillas_length_reco=tb.Float32Col(dflt=1, pos=18),
hillas_length=tb.Float32Col(dflt=1, pos=19),
hillas_width_reco=tb.Float32Col(dflt=1, pos=20),
hillas_width=tb.Float32Col(dflt=1, pos=21),
hillas_psi_reco=tb.Float32Col(dflt=1, pos=22),
hillas_psi=tb.Float32Col(dflt=1, pos=23),
hillas_skewness_reco=tb.Float32Col(dflt=1, pos=24),
hillas_skewness=tb.Float32Col(dflt=1, pos=25),
hillas_kurtosis=tb.Float32Col(dflt=1, pos=26),
hillas_kurtosis_reco=tb.Float32Col(dflt=1, pos=27),
leakage_intensity_width_1_reco=tb.Float32Col(dflt=np.nan, pos=28),
leakage_intensity_width_2_reco=tb.Float32Col(dflt=np.nan, pos=29),
leakage_intensity_width_1=tb.Float32Col(dflt=np.nan, pos=30),
leakage_intensity_width_2=tb.Float32Col(dflt=np.nan, pos=31),
# The following are missing from current ctapipe DL1 output
# Not sure if it's worth to add them
hillas_ellipticity_reco=tb.FloatCol(dflt=1, pos=32),
hillas_ellipticity=tb.FloatCol(dflt=1, pos=33),
max_signal_cam=tb.Float32Col(dflt=1, pos=34),
pixels=tb.Int16Col(dflt=1, pos=35),
clusters=tb.Int16Col(dflt=-1, pos=36),
# ======================================================================
# DL2 - DIRECTION RECONSTRUCTION
impact_dist=tb.Float32Col(dflt=1, pos=37),
h_max=tb.Float32Col(dflt=1, pos=38),
alt=tb.Float32Col(dflt=np.nan, pos=39),
az=tb.Float32Col(dflt=np.nan, pos=40),
err_est_pos=tb.Float32Col(dflt=1, pos=41),
err_est_dir=tb.Float32Col(dflt=1, pos=42),
xi=tb.Float32Col(dflt=np.nan, pos=43),
offset=tb.Float32Col(dflt=np.nan, pos=44),
mc_core_x=tb.FloatCol(dflt=1, pos=45),
mc_core_y=tb.FloatCol(dflt=1, pos=46),
reco_core_x=tb.FloatCol(dflt=1, pos=47),
reco_core_y=tb.FloatCol(dflt=1, pos=48),
mc_h_first_int=tb.FloatCol(dflt=1, pos=49),
mc_x_max=tb.Float32Col(dflt=np.nan, pos=50),
is_valid=tb.BoolCol(dflt=False, pos=51),
good_image=tb.Int16Col(dflt=1, pos=52),
# ======================================================================
# DL2 - ENERGY ESTIMATION
true_energy=tb.FloatCol(dflt=1, pos=53),
reco_energy=tb.FloatCol(dflt=np.nan, pos=54),
reco_energy_tel=tb.Float32Col(dflt=np.nan, pos=55),
# ======================================================================
# DL1 IMAGES
# this is optional data saved by the user
# since these data declarations require to know how many pixels
# each saved image will have,
# we add them later on, right before creating the table
# We list them here for reference
# true_image=tb.Float32Col(shape=(1855), pos=56),
# reco_image=tb.Float32Col(shape=(1855), pos=57),
# cleaning_mask_reco=tb.BoolCol(shape=(1855), pos=58), # not in ctapipe
)
outfile = tb.open_file(args.outfile, mode="w")
outTable = {}
outData = {}
for i, filename in enumerate(filenamelist):
print("file: {} filename = {}".format(i, filename))
source = event_source(input_url=filename,
allowed_tels=allowed_tels,
max_events=args.max_events)
# loop that cleans and parametrises the images and performs the
# reconstruction for each event
for (
event,
reco_image,
cleaning_mask_reco,
cleaning_mask_clusters,
true_image,
n_pixel_dict,
hillas_dict,
hillas_dict_reco,
leakage_dict,
n_tels,
max_signals,
n_cluster_dict,
reco_result,
impact_dict,
good_event,
good_for_reco,
) in preper.prepare_event(source,
save_images=args.save_images,
debug=args.debug):
# Angular quantities
run_array_direction = event.mcheader.run_array_direction
if good_event:
xi = angular_separation(event.mc.az, event.mc.alt,
reco_result.az, reco_result.alt)
offset = angular_separation(
run_array_direction[0], # az
run_array_direction[1], # alt
reco_result.az,
reco_result.alt,
)
# Impact parameter
reco_core_x = reco_result.core_x
reco_core_y = reco_result.core_y
# Height of shower maximum
h_max = reco_result.h_max
# Todo add conversion in number of radiation length,
# need an atmosphere profile
is_valid = True
else: # something went wrong and the shower's reconstruction failed
xi = np.nan * u.deg
offset = np.nan * u.deg
reco_core_x = np.nan * u.m
reco_core_y = np.nan * u.m
h_max = np.nan * u.m
reco_result.alt = np.nan * u.deg
reco_result.az = np.nan * u.deg
is_valid = False
reco_energy = np.nan
reco_energy_tel = dict()
# Not optimal at all, two loop on tel!!!
# For energy estimation
# Estimate energy only if the shower was reconstructed
if (args.estimate_energy is True) and is_valid:
weight_tel = np.zeros(len(hillas_dict.keys()))
energy_tel = np.zeros(len(hillas_dict.keys()))
for idx, tel_id in enumerate(hillas_dict.keys()):
# use only images that survived cleaning and
# parametrization
if not good_for_reco[tel_id]:
# bad images will get an undetermined energy
# this is a per-telescope energy
# NOT the estimated energy for the shower
reco_energy_tel[tel_id] = np.nan
continue
cam_id = source.subarray.tel[tel_id].camera.camera_name
moments = hillas_dict[tel_id]
model = regressor.model_dict[cam_id]
features_img = np.array([
np.log10(moments.intensity),
np.log10(impact_dict[tel_id].value),
moments.width.value,
moments.length.value,
h_max.value,
])
energy_tel[idx] = model.predict([features_img])
weight_tel[idx] = moments.intensity
reco_energy_tel[tel_id] = energy_tel[idx]
reco_energy = np.sum(weight_tel * energy_tel) / sum(weight_tel)
else:
for idx, tel_id in enumerate(hillas_dict.keys()):
reco_energy_tel[tel_id] = np.nan
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = source.subarray.tel[tel_id].camera.camera_name
if cam_id not in outData:
if args.save_images is True:
# we define and save images content here, to make it
# adaptive to different cameras
n_pixels = source.subarray.tel[
tel_id].camera.geometry.n_pixels
DataTrainingOutput["true_image"] = tb.Float32Col(
shape=(n_pixels), pos=56)
DataTrainingOutput["reco_image"] = tb.Float32Col(
shape=(n_pixels), pos=57)
DataTrainingOutput["cleaning_mask_reco"] = tb.BoolCol(
shape=(n_pixels), pos=58) # not in ctapipe
DataTrainingOutput[
"cleaning_mask_clusters"] = tb.BoolCol(
shape=(n_pixels), pos=58) # not in ctapipe
outTable[cam_id] = outfile.create_table(
"/",
cam_id,
DataTrainingOutput,
)
outData[cam_id] = outTable[cam_id].row
moments = hillas_dict[tel_id]
ellipticity = moments.width / moments.length
# Write to file also the Hillas parameters that have been used
# to calculate reco_results
moments_reco = hillas_dict_reco[tel_id]
ellipticity_reco = moments_reco.width / moments_reco.length
outData[cam_id]["good_image"] = good_for_reco[tel_id]
outData[cam_id]["is_valid"] = is_valid
outData[cam_id]["impact_dist"] = impact_dict[tel_id].to(
"m").value
outData[cam_id]["max_signal_cam"] = max_signals[tel_id]
outData[cam_id]["hillas_intensity"] = moments.intensity
outData[cam_id]["N_LST"] = n_tels["LST_LST_LSTCam"]
outData[cam_id]["N_MST"] = (n_tels["MST_MST_NectarCam"] +
n_tels["MST_MST_FlashCam"] +
n_tels["MST_SCT_SCTCam"])
outData[cam_id]["N_SST"] = (n_tels["SST_1M_DigiCam"] +
n_tels["SST_ASTRI_ASTRICam"] +
n_tels["SST_GCT_CHEC"])
outData[cam_id]["hillas_width"] = moments.width.to("deg").value
outData[cam_id]["hillas_length"] = moments.length.to(
"deg").value
outData[cam_id]["hillas_psi"] = moments.psi.to("deg").value
outData[cam_id]["hillas_skewness"] = moments.skewness
outData[cam_id]["hillas_kurtosis"] = moments.kurtosis
outData[cam_id]["h_max"] = h_max.to("m").value
outData[cam_id]["err_est_pos"] = np.nan
outData[cam_id]["err_est_dir"] = np.nan
outData[cam_id]["true_energy"] = event.mc.energy.to(
"TeV").value
outData[cam_id]["hillas_x"] = moments.x.to("deg").value
outData[cam_id]["hillas_y"] = moments.y.to("deg").value
outData[cam_id]["hillas_phi"] = moments.phi.to("deg").value
outData[cam_id]["hillas_r"] = moments.r.to("deg").value
outData[cam_id]["pixels"] = n_pixel_dict[tel_id]
outData[cam_id]["obs_id"] = event.index.obs_id
outData[cam_id]["event_id"] = event.index.event_id
outData[cam_id]["tel_id"] = tel_id
outData[cam_id]["xi"] = xi.to("deg").value
outData[cam_id]["reco_energy"] = reco_energy
outData[cam_id]["hillas_ellipticity"] = ellipticity.value
outData[cam_id]["clusters"] = n_cluster_dict[tel_id]
outData[cam_id]["n_tel_discri"] = n_tels["GOOD images"]
outData[cam_id]["mc_core_x"] = event.mc.core_x.to("m").value
outData[cam_id]["mc_core_y"] = event.mc.core_y.to("m").value
outData[cam_id]["reco_core_x"] = reco_core_x.to("m").value
outData[cam_id]["reco_core_y"] = reco_core_y.to("m").value
outData[cam_id]["mc_h_first_int"] = event.mc.h_first_int.to(
"m").value
outData[cam_id]["offset"] = offset.to("deg").value
outData[cam_id]["mc_x_max"] = event.mc.x_max.value # g / cm2
outData[cam_id]["alt"] = reco_result.alt.to("deg").value
outData[cam_id]["az"] = reco_result.az.to("deg").value
outData[cam_id]["reco_energy_tel"] = reco_energy_tel[tel_id]
# Variables from hillas_dist_reco
outData[cam_id]["n_tel_reco"] = n_tels["GOOD images"]
outData[cam_id]["hillas_x_reco"] = moments_reco.x.to(
"deg").value
outData[cam_id]["hillas_y_reco"] = moments_reco.y.to(
"deg").value
outData[cam_id]["hillas_phi_reco"] = moments_reco.phi.to(
"deg").value
outData[cam_id][
"hillas_ellipticity_reco"] = ellipticity_reco.value
outData[cam_id]["hillas_r_reco"] = moments_reco.r.to(
"deg").value
outData[cam_id]["hillas_skewness_reco"] = moments_reco.skewness
outData[cam_id]["hillas_kurtosis_reco"] = moments_reco.kurtosis
outData[cam_id]["hillas_width_reco"] = moments_reco.width.to(
"deg").value
outData[cam_id]["hillas_length_reco"] = moments_reco.length.to(
"deg").value
outData[cam_id]["hillas_psi_reco"] = moments_reco.psi.to(
"deg").value
outData[cam_id][
"hillas_intensity_reco"] = moments_reco.intensity
outData[cam_id][
"leakage_intensity_width_1_reco"] = leakage_dict[tel_id][
"leak1_reco"]
outData[cam_id][
"leakage_intensity_width_2_reco"] = leakage_dict[tel_id][
"leak2_reco"]
outData[cam_id]["leakage_intensity_width_1"] = leakage_dict[
tel_id]["leak1"]
outData[cam_id]["leakage_intensity_width_2"] = leakage_dict[
tel_id]["leak2"]
# =======================
# IMAGES INFORMATION
# =======================
if args.save_images is True:
# we define and save images content here, to make it
# adaptive to different cameras
outData[cam_id]["true_image"] = true_image[tel_id]
outData[cam_id]["reco_image"] = reco_image[tel_id]
outData[cam_id]["cleaning_mask_reco"] = cleaning_mask_reco[
tel_id]
outData[cam_id][
"cleaning_mask_clusters"] = cleaning_mask_clusters[
tel_id]
# =======================
outData[cam_id].append()
if signal_handler.stop:
break
if signal_handler.stop:
break
# make sure that all the events are properly stored
for table in outTable.values():
table.flush()
print(bcolors.BOLD +
"\n\n==================================================\n" +
"Statistical summary of processed events and images\n" +
"==================================================\n"
# + bcolors.ENDC
)
evt_cutflow()
# Catch specific cases
triggered_events = evt_cutflow.cuts["min2Tels trig"][1]
reconstructed_events = evt_cutflow.cuts["min2Tels reco"][1]
if triggered_events == 0:
print("\033[93mWARNING: No events have been triggered"
" by the selected telescopes! \033[0m")
else:
print("\n")
img_cutflow()
if reconstructed_events == 0:
print("\033[93m WARNING: None of the triggered events have been "
"properly reconstructed by the selected telescopes!\n"
"DL1 file will be empty! \033[0m")
print(bcolors.ENDC)
def main():
# your favourite units here
energy_unit = u.TeV
angle_unit = u.deg
dist_unit = u.m
agree_threshold = .5
min_tel = 3
parser = make_argparser()
parser.add_argument('--classifier', type=str,
default='data/classifier_pickle/classifier'
'_{mode}_{cam_id}_{classifier}.pkl')
parser.add_argument('--regressor', type=str,
default='data/classifier_pickle/regressor'
'_{mode}_{cam_id}_{regressor}.pkl')
parser.add_argument('-o', '--outfile', type=str, default="",
help="location to write the classified events to.")
parser.add_argument('--wave_dir', type=str, default=None,
help="directory where to find mr_filter. "
"if not set look in $PATH")
parser.add_argument('--wave_temp_dir', type=str, default='/tmp/', help="directory "
"where mr_filter to store the temporary fits files")
group = parser.add_mutually_exclusive_group()
group.add_argument('--proton', action='store_true',
help="do protons instead of gammas")
group.add_argument('--electron', action='store_true',
help="do electrons instead of gammas")
args = parser.parse_args()
if args.infile_list:
filenamelist = []
for f in args.infile_list:
filenamelist += glob("{}/{}".format(args.indir, f))
filenamelist.sort()
elif args.proton:
filenamelist = sorted(glob("{}/proton/*gz".format(args.indir)))
elif args.electron:
filenamelist = glob("{}/electron/*gz".format(args.indir))
channel = "electron"
else:
filenamelist = sorted(glob("{}/gamma/*gz".format(args.indir)))
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
# keeping track of events and where they were rejected
Eventcutflow = CutFlow("EventCutFlow")
Imagecutflow = CutFlow("ImageCutFlow")
# takes care of image cleaning
cleaner = ImageCleaner(mode=args.mode, cutflow=Imagecutflow,
wavelet_options=args.raw,
skip_edge_events=False, island_cleaning=True)
# the class that does the shower reconstruction
shower_reco = HillasReconstructor()
preper = EventPreparer(
cleaner=cleaner, hillas_parameters=hillas_parameters,
shower_reco=shower_reco,
event_cutflow=Eventcutflow, image_cutflow=Imagecutflow,
# event/image cuts:
allowed_cam_ids=[],
min_ntel=2, min_charge=args.min_charge, min_pixel=3)
# wrapper for the scikit-learn classifier
classifier = EventClassifier.load(
args.classifier.format(**{
"mode": args.mode,
"wave_args": "mixed",
"classifier": 'RandomForestClassifier',
"cam_id": "{cam_id}"}),
cam_id_list=args.cam_ids)
# wrapper for the scikit-learn regressor
regressor = EnergyRegressor.load(
args.regressor.format(**{
"mode": args.mode,
"wave_args": "mixed",
"regressor": "RandomForestRegressor",
"cam_id": "{cam_id}"}),
cam_id_list=args.cam_ids)
ClassifierFeatures = namedtuple(
"ClassifierFeatures", (
"impact_dist",
"sum_signal_evt",
"max_signal_cam",
"sum_signal_cam",
"N_LST",
"N_MST",
"N_SST",
"width",
"length",
"skewness",
"kurtosis",
"h_max",
"err_est_pos",
"err_est_dir"))
EnergyFeatures = namedtuple(
"EnergyFeatures", (
"impact_dist",
"sum_signal_evt",
"max_signal_cam",
"sum_signal_cam",
"N_LST",
"N_MST",
"N_SST",
"width",
"length",
"skewness",
"kurtosis",
"h_max",
"err_est_pos",
"err_est_dir"))
# catch ctr-c signal to exit current loop and still display results
signal_handler = SignalHandler()
signal.signal(signal.SIGINT, signal_handler)
# this class defines the reconstruction parameters to keep track of
class RecoEvent(tb.IsDescription):
Run_ID = tb.Int16Col(dflt=-1, pos=0)
Event_ID = tb.Int16Col(dflt=-1, pos=1)
NTels_trig = tb.Int16Col(dflt=0, pos=0)
NTels_reco = tb.Int16Col(dflt=0, pos=1)
NTels_reco_lst = tb.Int16Col(dflt=0, pos=2)
NTels_reco_mst = tb.Int16Col(dflt=0, pos=3)
NTels_reco_sst = tb.Int16Col(dflt=0, pos=4)
MC_Energy = tb.Float32Col(dflt=np.nan, pos=5)
reco_Energy = tb.Float32Col(dflt=np.nan, pos=6)
reco_phi = tb.Float32Col(dflt=np.nan, pos=7)
reco_theta = tb.Float32Col(dflt=np.nan, pos=8)
off_angle = tb.Float32Col(dflt=np.nan, pos=9)
xi = tb.Float32Col(dflt=np.nan, pos=10)
DeltaR = tb.Float32Col(dflt=np.nan, pos=11)
ErrEstPos = tb.Float32Col(dflt=np.nan, pos=12)
ErrEstDir = tb.Float32Col(dflt=np.nan, pos=13)
gammaness = tb.Float32Col(dflt=np.nan, pos=14)
channel = "gamma" if "gamma" in " ".join(filenamelist) else "proton"
reco_outfile = tb.open_file(
mode="w",
# if no outfile name is given (i.e. don't to write the event list to disk),
# need specify two "driver" arguments
**({"filename": args.outfile} if args.outfile else
{"filename": "no_outfile.h5",
"driver": "H5FD_CORE", "driver_core_backing_store": False}))
reco_table = reco_outfile.create_table("/", "reco_events", RecoEvent)
reco_event = reco_table.row
allowed_tels = None # all telescopes
allowed_tels = prod3b_tel_ids("L+N+D")
for i, filename in enumerate(filenamelist[:args.last]):
# print(f"file: {i} filename = {filename}")
source = hessio_event_source(filename,
allowed_tels=allowed_tels,
max_events=args.max_events)
# loop that cleans and parametrises the images and performs the reconstruction
for (event, hillas_dict, n_tels,
tot_signal, max_signals, pos_fit, dir_fit, h_max,
err_est_pos, err_est_dir) in preper.prepare_event(source, True):
# now prepare the features for the classifier
cls_features_evt = {}
reg_features_evt = {}
if hillas_dict is not None:
for tel_id in hillas_dict.keys():
Imagecutflow.count("pre-features")
tel_pos = np.array(event.inst.tel_pos[tel_id][:2]) * u.m
moments = hillas_dict[tel_id]
impact_dist = linalg.length(tel_pos - pos_fit)
cls_features_tel = ClassifierFeatures(
impact_dist=impact_dist / u.m,
sum_signal_evt=tot_signal,
max_signal_cam=max_signals[tel_id],
sum_signal_cam=moments.size,
N_LST=n_tels["LST"],
N_MST=n_tels["MST"],
N_SST=n_tels["SST"],
width=moments.width / u.m,
length=moments.length / u.m,
skewness=moments.skewness,
kurtosis=moments.kurtosis,
h_max=h_max / u.m,
err_est_pos=err_est_pos / u.m,
err_est_dir=err_est_dir / u.deg
)
reg_features_tel = EnergyFeatures(
impact_dist=impact_dist / u.m,
sum_signal_evt=tot_signal,
max_signal_cam=max_signals[tel_id],
sum_signal_cam=moments.size,
N_LST=n_tels["LST"],
N_MST=n_tels["MST"],
N_SST=n_tels["SST"],
width=moments.width / u.m,
length=moments.length / u.m,
skewness=moments.skewness,
kurtosis=moments.kurtosis,
h_max=h_max / u.m,
err_est_pos=err_est_pos / u.m,
err_est_dir=err_est_dir / u.deg
)
if np.isnan(cls_features_tel).any() or np.isnan(reg_features_tel).any():
continue
Imagecutflow.count("features nan")
cam_id = event.inst.subarray.tel[tel_id].camera.cam_id
try:
reg_features_evt[cam_id] += [reg_features_tel]
cls_features_evt[cam_id] += [cls_features_tel]
except KeyError:
reg_features_evt[cam_id] = [reg_features_tel]
cls_features_evt[cam_id] = [cls_features_tel]
# save basic event infos
reco_event["MC_Energy"] = event.mc.energy.to(energy_unit).value
reco_event["Event_ID"] = event.r1.event_id
reco_event["Run_ID"] = event.r1.run_id
if cls_features_evt and reg_features_evt:
predict_energ = regressor.predict_by_event([reg_features_evt])["mean"][0]
predict_proba = classifier.predict_proba_by_event([cls_features_evt])
gammaness = predict_proba[0, 0]
# the MC direction of origin of the simulated particle
shower = event.mc
shower_core = np.array([shower.core_x / u.m, shower.core_y / u.m]) * u.m
shower_org = linalg.set_phi_theta(shower.az + 90 * u.deg,
90. * u.deg - shower.alt)
# and how the reconstructed direction compares to that
xi = linalg.angle(dir_fit, shower_org)
phi, theta = linalg.get_phi_theta(dir_fit)
phi = (phi if phi > 0 else phi + 360 * u.deg)
DeltaR = linalg.length(pos_fit[:2] - shower_core)
# TODO: replace with actual array pointing direction
array_pointing = linalg.set_phi_theta(0 * u.deg, 20. * u.deg)
# angular offset between the reconstructed direction and the array
# pointing
off_angle = linalg.angle(dir_fit, array_pointing)
reco_event["NTels_trig"] = len(event.dl0.tels_with_data)
reco_event["NTels_reco"] = len(hillas_dict)
reco_event["NTels_reco_lst"] = n_tels["LST"]
reco_event["NTels_reco_mst"] = n_tels["MST"]
reco_event["NTels_reco_sst"] = n_tels["SST"]
reco_event["reco_Energy"] = predict_energ.to(energy_unit).value
reco_event["reco_phi"] = phi / angle_unit
reco_event["reco_theta"] = theta / angle_unit
reco_event["off_angle"] = off_angle / angle_unit
reco_event["xi"] = xi / angle_unit
reco_event["DeltaR"] = DeltaR / dist_unit
reco_event["ErrEstPos"] = err_est_pos / dist_unit
reco_event["ErrEstDir"] = err_est_dir / angle_unit
reco_event["gammaness"] = gammaness
reco_event.append()
reco_table.flush()
if signal_handler.stop:
break
if signal_handler.stop:
break
try:
print()
Eventcutflow()
print()
Imagecutflow()
# do some simple event selection
# and print the corresponding selection efficiency
N_selected = len([x for x in reco_table.where(
"""(NTels_reco > min_tel) & (gammaness > agree_threshold)""")])
N_total = len(reco_table)
print("\nfraction selected events:")
print("{} / {} = {} %".format(N_selected, N_total, N_selected / N_total * 100))
except ZeroDivisionError:
pass
print("\nlength filenamelist:", len(filenamelist[:args.last]))
# do some plotting if so desired
if args.plot:
gammaness = [x['gammaness'] for x in reco_table]
NTels_rec = [x['NTels_reco'] for x in reco_table]
NTel_bins = np.arange(np.min(NTels_rec), np.max(NTels_rec) + 2) - .5
NTels_rec_lst = [x['NTels_reco_lst'] for x in reco_table]
NTels_rec_mst = [x['NTels_reco_mst'] for x in reco_table]
NTels_rec_sst = [x['NTels_reco_sst'] for x in reco_table]
reco_energy = np.array([x['reco_Energy'] for x in reco_table])
mc_energy = np.array([x['MC_Energy'] for x in reco_table])
fig = plt.figure(figsize=(15, 5))
plt.suptitle(" ** ".join([args.mode, "protons" if args.proton else "gamma"]))
plt.subplots_adjust(left=0.05, right=0.97, hspace=0.39, wspace=0.2)
ax = plt.subplot(131)
histo = np.histogram2d(NTels_rec, gammaness,
bins=(NTel_bins, np.linspace(0, 1, 11)))[0].T
histo_normed = histo / histo.max(axis=0)
im = ax.imshow(histo_normed, interpolation='none', origin='lower',
aspect='auto',
# extent=(*NTel_bins[[0, -1]], 0, 1),
cmap=plt.cm.inferno)
ax.set_xlabel("NTels")
ax.set_ylabel("drifted gammaness")
plt.title("Total Number of Telescopes")
# next subplot
ax = plt.subplot(132)
histo = np.histogram2d(NTels_rec_sst, gammaness,
bins=(NTel_bins, np.linspace(0, 1, 11)))[0].T
histo_normed = histo / histo.max(axis=0)
im = ax.imshow(histo_normed, interpolation='none', origin='lower',
aspect='auto',
# extent=(*NTel_bins[[0, -1]], 0, 1),
cmap=plt.cm.inferno)
ax.set_xlabel("NTels")
plt.setp(ax.get_yticklabels(), visible=False)
plt.title("Number of SSTs")
# next subplot
ax = plt.subplot(133)
histo = np.histogram2d(NTels_rec_mst, gammaness,
bins=(NTel_bins, np.linspace(0, 1, 11)))[0].T
histo_normed = histo / histo.max(axis=0)
im = ax.imshow(histo_normed, interpolation='none', origin='lower',
aspect='auto',
# extent=(*NTel_bins[[0, -1]], 0, 1),
cmap=plt.cm.inferno)
cb = fig.colorbar(im, ax=ax)
ax.set_xlabel("NTels")
plt.setp(ax.get_yticklabels(), visible=False)
plt.title("Number of MSTs")
plt.subplots_adjust(wspace=0.05)
# plot the energy migration matrix
plt.figure()
plt.hist2d(np.log10(reco_energy), np.log10(mc_energy), bins=20,
cmap=plt.cm.inferno)
plt.xlabel("E_MC / TeV")
plt.ylabel("E_rec / TeV")
plt.colorbar()
plt.show()
elif args.proton:
filenamelist = glob("{}/proton/*gz".format(args.indir))
channel = "proton"
elif args.electron:
filenamelist = glob("{}/electron/*gz".format(args.indir))
channel = "electron"
else:
filenamelist = glob("{}/gamma/*gz".format(args.indir))
channel = "gamma"
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
# keeping track of events and where they were rejected
Eventcutflow = CutFlow("EventCutFlow")
Imagecutflow = CutFlow("ImageCutFlow")
# takes care of image cleaning
cleaner = ImageCleaner(mode=args.mode, cutflow=Imagecutflow,
wavelet_options=args.raw,
skip_edge_events=False, island_cleaning=True)
# the class that does the shower reconstruction
shower_reco = HillasReconstructor()
preper = EventPreparer(
cleaner=cleaner,
hillas_parameters=hillas_parameters,
shower_reco=shower_reco,
event_cutflow=Eventcutflow, image_cutflow=Imagecutflow,
class SimpleEventWriter(Tool):
name = 'ctapipe-simple-event-writer'
description = Unicode(__doc__)
infile = Unicode(help='input file to read', default='').tag(config=True)
outfile = Unicode(help='output file name',
default_value='output.h5').tag(config=True)
progress = Bool(help='display progress bar',
default_value=True).tag(config=True)
aliases = Dict({
'infile': 'EventSource.input_url',
'outfile': 'SimpleEventWriter.outfile',
'max-events': 'EventSource.max_events',
'progress': 'SimpleEventWriter.progress'
})
classes = List([EventSource, CameraCalibrator, CutFlow])
def setup(self):
self.log.info('Configure EventSource...')
self.event_source = self.add_component(
EventSource.from_config(config=self.config, parent=self))
self.calibrator = self.add_component(CameraCalibrator(parent=self))
self.writer = self.add_component(
HDF5TableWriter(filename=self.outfile,
group_name='image_infos',
overwrite=True))
# Define Pre-selection for images
preselcuts = self.config['Preselect']
self.image_cutflow = CutFlow('Image preselection')
self.image_cutflow.set_cuts(
dict(no_sel=None,
n_pixel=lambda s: np.count_nonzero(s) < preselcuts['n_pixel'][
'min'],
image_amplitude=lambda q: q < preselcuts['image_amplitude'][
'min']))
# Define Pre-selection for events
self.event_cutflow = CutFlow('Event preselection')
self.event_cutflow.set_cuts(dict(no_sel=None))
def start(self):
self.log.info('Loop on events...')
for event in tqdm(self.event_source,
desc='EventWriter',
total=self.event_source.max_events,
disable=~self.progress):
self.event_cutflow.count('no_sel')
self.calibrator(event)
for tel_id in event.dl0.tels_with_data:
self.image_cutflow.count('no_sel')
camera = event.inst.subarray.tel[tel_id].camera
dl1_tel = event.dl1.tel[tel_id]
# Image cleaning
image = dl1_tel.image # Waiting for automatic gain selection
mask = tailcuts_clean(camera,
image,
picture_thresh=10,
boundary_thresh=5)
cleaned = image.copy()
cleaned[~mask] = 0
# Preselection cuts
if self.image_cutflow.cut('n_pixel', cleaned):
continue
if self.image_cutflow.cut('image_amplitude', np.sum(cleaned)):
continue
# Image parametrisation
params = hillas_parameters(camera, cleaned)
# Save Ids, MC infos and Hillas informations
self.writer.write(camera.cam_id, [event.r0, event.mc, params])
def finish(self):
self.log.info('End of job.')
self.image_cutflow()
self.event_cutflow()
self.writer.close()
def main():
# Argument parser
parser = make_argparser()
parser.add_argument(
"--estimate_energy",
type=str2bool,
default=False,
help="Make estimation of energy",
)
parser.add_argument("--regressor_dir",
type=str,
default="./",
help="regressors directory")
args = parser.parse_args()
# Read configuration file
cfg = load_config(args.config_file)
# Read site layout
site = cfg["General"]["site"]
array = cfg["General"]["array"]
if args.infile_list:
filenamelist = []
for f in args.infile_list:
filenamelist += glob("{}/{}".format(args.indir, f))
filenamelist.sort()
else:
raise ValueError("don't know which input to use...")
if not filenamelist:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
else:
print("found {} files".format(len(filenamelist)))
# keeping track of events and where they were rejected
evt_cutflow = CutFlow("EventCutFlow")
img_cutflow = CutFlow("ImageCutFlow")
preper = EventPreparer(config=cfg,
mode=args.mode,
event_cutflow=evt_cutflow,
image_cutflow=img_cutflow)
# catch ctr-c signal to exit current loop and still display results
signal_handler = SignalHandler()
signal.signal(signal.SIGINT, signal_handler)
# Regressor method
regressor_method = cfg["EnergyRegressor"]["method_name"]
# wrapper for the scikit-learn regressor
if args.estimate_energy is True:
# regressor_files = args.regressor_dir + "/regressor_{mode}_{cam_id}_{regressor}.pkl.gz"
regressor_files = (args.regressor_dir +
"/regressor_{mode}_{cam_id}_{regressor}.pkl.gz")
reg_file = regressor_files.format(
**{
"mode": args.mode,
"wave_args": "mixed", # ToDo, control
"regressor": regressor_method,
"cam_id": "{cam_id}",
})
# from IPython import embed
# embed()
regressor = EnergyRegressor.load(reg_file, cam_id_list=args.cam_ids)
class EventFeatures(tb.IsDescription):
impact_dist = tb.Float32Col(dflt=1, pos=0)
sum_signal_evt = tb.Float32Col(dflt=1, pos=1)
max_signal_cam = tb.Float32Col(dflt=1, pos=2)
sum_signal_cam = tb.Float32Col(dflt=1, pos=3)
N_LST = tb.Int16Col(dflt=1, pos=4)
N_MST = tb.Int16Col(dflt=1, pos=5)
N_SST = tb.Int16Col(dflt=1, pos=6)
width = tb.Float32Col(dflt=1, pos=7)
length = tb.Float32Col(dflt=1, pos=8)
skewness = tb.Float32Col(dflt=1, pos=9)
kurtosis = tb.Float32Col(dflt=1, pos=10)
h_max = tb.Float32Col(dflt=1, pos=11)
err_est_pos = tb.Float32Col(dflt=1, pos=12)
err_est_dir = tb.Float32Col(dflt=1, pos=13)
mc_energy = tb.FloatCol(dflt=1, pos=14)
local_distance = tb.Float32Col(dflt=1, pos=15)
n_pixel = tb.Int16Col(dflt=1, pos=16)
n_cluster = tb.Int16Col(dflt=-1, pos=17)
obs_id = tb.Int16Col(dflt=1, pos=18)
event_id = tb.Int32Col(dflt=1, pos=19)
tel_id = tb.Int16Col(dflt=1, pos=20)
xi = tb.Float32Col(dflt=np.nan, pos=21)
reco_energy = tb.FloatCol(dflt=np.nan, pos=22)
ellipticity = tb.FloatCol(dflt=1, pos=23)
n_tel_reco = tb.FloatCol(dflt=1, pos=24)
n_tel_discri = tb.FloatCol(dflt=1, pos=25)
mc_core_x = tb.FloatCol(dflt=1, pos=26)
mc_core_y = tb.FloatCol(dflt=1, pos=27)
reco_core_x = tb.FloatCol(dflt=1, pos=28)
reco_core_y = tb.FloatCol(dflt=1, pos=29)
mc_h_first_int = tb.FloatCol(dflt=1, pos=30)
offset = tb.Float32Col(dflt=np.nan, pos=31)
mc_x_max = tb.Float32Col(dflt=np.nan, pos=31)
alt = tb.Float32Col(dflt=np.nan, pos=33)
az = tb.Float32Col(dflt=np.nan, pos=34)
reco_energy_tel = tb.Float32Col(dflt=np.nan, pos=35)
# from hillas_reco
ellipticity_reco = tb.FloatCol(dflt=1, pos=36)
local_distance_reco = tb.Float32Col(dflt=1, pos=37)
skewness_reco = tb.Float32Col(dflt=1, pos=38)
kurtosis_reco = tb.Float32Col(dflt=1, pos=39)
width_reco = tb.Float32Col(dflt=1, pos=40)
length_reco = tb.Float32Col(dflt=1, pos=41)
psi = tb.Float32Col(dflt=1, pos=42)
psi_reco = tb.Float32Col(dflt=1, pos=43)
sum_signal_cam_reco = tb.Float32Col(dflt=1, pos=44)
feature_outfile = tb.open_file(args.outfile, mode="w")
feature_table = {}
feature_events = {}
# Telescopes in analysis
allowed_tels = set(prod3b_tel_ids(array, site=site))
for i, filename in enumerate(filenamelist):
print("file: {} filename = {}".format(i, filename))
source = event_source(input_url=filename,
allowed_tels=allowed_tels,
max_events=args.max_events)
# loop that cleans and parametrises the images and performs the reconstruction
# for each event
for (
event,
n_pixel_dict,
hillas_dict,
hillas_dict_reco,
n_tels,
tot_signal,
max_signals,
n_cluster_dict,
reco_result,
impact_dict,
) in preper.prepare_event(source):
# Angular quantities
run_array_direction = event.mcheader.run_array_direction
xi = angular_separation(event.mc.az, event.mc.alt, reco_result.az,
reco_result.alt)
offset = angular_separation(
run_array_direction[0], # az
run_array_direction[1], # alt
reco_result.az,
reco_result.alt,
)
# Impact parameter
reco_core_x = reco_result.core_x
reco_core_y = reco_result.core_y
# Height of shower maximum
h_max = reco_result.h_max
# Todo add conversion in number of radiation length, need an atmosphere profile
reco_energy = np.nan
reco_energy_tel = dict()
# Not optimal at all, two loop on tel!!!
# For energy estimation
if args.estimate_energy is True:
weight_tel = np.zeros(len(hillas_dict.keys()))
energy_tel = np.zeros(len(hillas_dict.keys()))
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = event.inst.subarray.tel[tel_id].camera.cam_id
moments = hillas_dict[tel_id]
model = regressor.model_dict[cam_id]
features_img = np.array([
np.log10(moments.intensity),
np.log10(impact_dict[tel_id].value),
moments.width.value,
moments.length.value,
h_max.value,
])
energy_tel[idx] = model.predict([features_img])
weight_tel[idx] = moments.intensity
reco_energy_tel[tel_id] = energy_tel[idx]
reco_energy = np.sum(weight_tel * energy_tel) / sum(weight_tel)
else:
for idx, tel_id in enumerate(hillas_dict.keys()):
reco_energy_tel[tel_id] = np.nan
for idx, tel_id in enumerate(hillas_dict.keys()):
cam_id = event.inst.subarray.tel[tel_id].camera.cam_id
if cam_id not in feature_events:
feature_table[cam_id] = feature_outfile.create_table(
"/", "_".join(["feature_events", cam_id]),
EventFeatures)
feature_events[cam_id] = feature_table[cam_id].row
moments = hillas_dict[tel_id]
ellipticity = moments.width / moments.length
# Write to file also the Hillas parameters that have been used
# to calculate reco_results
moments_reco = hillas_dict_reco[tel_id]
ellipticity_reco = moments_reco.width / moments_reco.length
feature_events[cam_id]["impact_dist"] = (
impact_dict[tel_id].to("m").value)
feature_events[cam_id]["sum_signal_evt"] = tot_signal
feature_events[cam_id]["max_signal_cam"] = max_signals[tel_id]
feature_events[cam_id]["sum_signal_cam"] = moments.intensity
feature_events[cam_id]["N_LST"] = n_tels["LST"]
feature_events[cam_id]["N_MST"] = n_tels["MST"]
feature_events[cam_id]["N_SST"] = n_tels["SST"]
feature_events[cam_id]["width"] = moments.width.to("m").value
feature_events[cam_id]["length"] = moments.length.to("m").value
feature_events[cam_id]["psi"] = moments.psi.to("deg").value
feature_events[cam_id]["skewness"] = moments.skewness
feature_events[cam_id]["kurtosis"] = moments.kurtosis
feature_events[cam_id]["h_max"] = h_max.to("m").value
feature_events[cam_id]["err_est_pos"] = np.nan
feature_events[cam_id]["err_est_dir"] = np.nan
feature_events[cam_id]["mc_energy"] = event.mc.energy.to(
"TeV").value
feature_events[cam_id]["local_distance"] = moments.r.to(
"m").value
feature_events[cam_id]["n_pixel"] = n_pixel_dict[tel_id]
feature_events[cam_id]["obs_id"] = event.r0.obs_id
feature_events[cam_id]["event_id"] = event.r0.event_id
feature_events[cam_id]["tel_id"] = tel_id
feature_events[cam_id]["xi"] = xi.to("deg").value
feature_events[cam_id]["reco_energy"] = reco_energy
feature_events[cam_id]["ellipticity"] = ellipticity.value
feature_events[cam_id]["n_cluster"] = n_cluster_dict[tel_id]
feature_events[cam_id]["n_tel_reco"] = n_tels["reco"]
feature_events[cam_id]["n_tel_discri"] = n_tels["discri"]
feature_events[cam_id]["mc_core_x"] = event.mc.core_x.to(
"m").value
feature_events[cam_id]["mc_core_y"] = event.mc.core_y.to(
"m").value
feature_events[cam_id]["reco_core_x"] = reco_core_x.to(
"m").value
feature_events[cam_id]["reco_core_y"] = reco_core_y.to(
"m").value
feature_events[cam_id][
"mc_h_first_int"] = event.mc.h_first_int.to("m").value
feature_events[cam_id]["offset"] = offset.to("deg").value
feature_events[cam_id][
"mc_x_max"] = event.mc.x_max.value # g / cm2
feature_events[cam_id]["alt"] = reco_result.alt.to("deg").value
feature_events[cam_id]["az"] = reco_result.az.to("deg").value
feature_events[cam_id]["reco_energy_tel"] = reco_energy_tel[
tel_id]
# Variables from hillas_dist_reco
feature_events[cam_id][
"ellipticity_reco"] = ellipticity_reco.value
feature_events[cam_id][
"local_distance_reco"] = moments_reco.r.to("m").value
feature_events[cam_id]["skewness_reco"] = moments_reco.skewness
feature_events[cam_id]["kurtosis_reco"] = moments_reco.kurtosis
feature_events[cam_id]["width_reco"] = moments_reco.width.to(
"m").value
feature_events[cam_id]["length_reco"] = moments_reco.length.to(
"m").value
feature_events[cam_id]["psi_reco"] = moments_reco.psi.to(
"deg").value
feature_events[cam_id][
"sum_signal_cam_reco"] = moments_reco.intensity
feature_events[cam_id].append()
if signal_handler.stop:
break
if signal_handler.stop:
break
# make sure that all the events are properly stored
for table in feature_table.values():
table.flush()
img_cutflow()
evt_cutflow()
if args.proton:
filenamelist = glob("{}/proton/*gz".format(args.indir))
elif args.electron:
filenamelist = glob("{}/electron/*gz".format(args.indir))
else:
filenamelist = glob("{}/gamma/*gz".format(args.indir))
if len(filenamelist) == 0:
print("no files found; check indir: {}".format(args.indir))
exit(-1)
tel_phi = {}
tel_theta = {}
tel_orientation = (tel_phi, tel_theta)
Eventcutflow = CutFlow("EventCutFlow")
Imagecutflow = CutFlow("ImageCutFlow")
# pass in config and self if part of a Tool
calib = CameraCalibrator(None, None)
# use this in the selection of the gain channels
np_true_false = np.array([[True], [False]])
island_cleaning = True
skip_edge_events = args.skip_edge_events
Cleaner = {
"w":
ImageCleaner(mode="wave",
cutflow=Imagecutflow,
skip_edge_events=skip_edge_events,
def __init__(self, mode="wave", dilate=False, island_cleaning=True,
skip_edge_events=True, edge_width=1,
cutflow=CutFlow("ImageCleaner"),
wavelet_options=None,
tmp_files_directory='/dev/shm/', mrfilter_directory=None):
self.mode = mode
self.edge_width = edge_width
self.cutflow = cutflow
if skip_edge_events:
self.reject_edge_event = reject_edge_event
else:
self.reject_edge_event = lambda *a, **b: False
self.cutflow.add_cut("edge event", self.reject_edge_event)
if mode in [None, "none", "None"]:
self.clean = self.clean_none
elif mode.startswith("wave"):
self.clean = self.clean_wave
self.wavelet_cleaning = \
lambda *arg, **kwargs: WaveletTransform().clean_image(
*arg, **kwargs,
kill_isolated_pixels=island_cleaning,
tmp_files_directory=tmp_files_directory,
mrfilter_directory=mrfilter_directory)
self.island_threshold = 1.5
# command line parameters for the mr_filter call
self.wavelet_options = \
{"ASTRICam": wavelet_options or "-K -C1 -m3 -s2,2,3,3 -n4",
"DigiCam": wavelet_options or "-K -C1 -m3 -s6.274,2.629,7.755,0.076 -n4",
"FlashCam": wavelet_options or "-K -C1 -m3 -s4,4,5,4 -n4",
"NectarCam": wavelet_options or
"-K -C1 -m3 -s13.013,2.549,6.559,1.412 -n4",
"LSTCam": wavelet_options or
"-K -C1 -m3 -s23.343,2.490,-2.856,-0.719 -n4",
# WARNING: DUMMY VALUES
"CHEC": wavelet_options or "-K -C1 -m3 -s2,2,3,3 -n4"
}
# camera models for noise injection
self.noise_model = \
{"ASTRICam": EmpDist(cdf.ASTRI_CDF_FILE),
"DigiCam": EmpDist(cdf.DIGICAM_CDF_FILE),
"FlashCam": EmpDist(cdf.FLASHCAM_CDF_FILE),
"NectarCam": EmpDist(cdf.NECTARCAM_CDF_FILE),
"LSTCam": EmpDist(cdf.LSTCAM_CDF_FILE),
# WARNING: DUMMY FILE
"CHEC": EmpDist(cdf.DIGICAM_CDF_FILE)}
elif mode.startswith("tail"):
self.clean = self.clean_tail
self.tail_thresholds = \
{"ASTRICam": (5, 7), # (5, 10)?
"FlashCam": (12, 15),
"LSTCam": (5, 10), # ?? (3, 6) for Abelardo...
# ASWG Zeuthen talk by Abelardo Moralejo:
"NectarCam": (4, 8),
# "FlashCam": (4, 8), # there is some scaling missing?
"DigiCam": (3, 6),
"CHEC": (2, 4),
"SCTCam": (1.5, 3)}
self.island_threshold = 1.5
self.dilate = dilate
else:
raise UnknownMode(
'cleaning mode "{}" not found'.format(mode))
if island_cleaning:
self.island_cleaning = kill_isolpix
else:
# just a pass-through that does nothing
# (saves an if-statement in every `clean` call)
self.island_cleaning = lambda x, *args, **kw: x
def __init__(self, config, mode, event_cutflow=None, image_cutflow=None):
"""Initiliaze an EventPreparer object."""
# Cleaning for reconstruction
self.cleaner_reco = ImageCleaner( # for reconstruction
config=config["ImageCleaning"]["biggest"],
mode=mode)
# Cleaning for energy/score estimation
# Add possibility to force energy/score cleaning with tailcut analysis
force_mode = mode
try:
if config["General"]["force_tailcut_for_extended_cleaning"] is True:
force_mode = config["General"]["force_mode"]
print("> Activate force-mode for cleaning!!!!")
except:
pass # force_mode = mode
self.cleaner_extended = ImageCleaner( # for energy/score estimation
config=config["ImageCleaning"]["extended"],
mode=force_mode)
# Image book keeping
self.image_cutflow = image_cutflow or CutFlow("ImageCutFlow")
# Add quality cuts on images
charge_bounds = config["ImageSelection"]["charge"]
npix_bounds = config["ImageSelection"]["pixel"]
ellipticity_bounds = config["ImageSelection"]["ellipticity"]
nominal_distance_bounds = config["ImageSelection"]["nominal_distance"]
self.camera_radius = {
"LSTCam": 1.126,
"NectarCam": 1.126,
} # Average between max(xpix) and max(ypix), in meters
self.image_cutflow.set_cuts(
OrderedDict([
("noCuts", None),
("min pixel", lambda s: np.count_nonzero(s) < npix_bounds[0]),
("min charge", lambda x: x < charge_bounds[0]),
# ("poor moments", lambda m: m.width <= 0 or m.length <= 0 or np.isnan(m.width) or np.isnan(m.length)),
# TBC, maybe we loose events without nan conditions
("poor moments", lambda m: m.width <= 0 or m.length <= 0),
(
"bad ellipticity",
lambda m: (m.width / m.length) < ellipticity_bounds[0] or
(m.width / m.length) > ellipticity_bounds[-1],
),
# ("close to the edge", lambda m, cam_id: m.r.value > (nominal_distance_bounds[-1] * 1.12949101073069946))
# in meter
(
"close to the edge",
lambda m, cam_id: m.r.value >
(nominal_distance_bounds[-1] * self.camera_radius[cam_id]),
), # in meter
]))
# configuration for the camera calibrator
# modifies the integration window to be more like in MARS
# JLK, only for LST!!!!
cfg = Config()
cfg["ChargeExtractorFactory"]["window_width"] = 5
cfg["ChargeExtractorFactory"]["window_shift"] = 2
extractor = LocalPeakWindowSum(config=cfg)
self.calib = CameraCalibrator(config=cfg, image_extractor=extractor)
# Reconstruction
self.shower_reco = HillasReconstructor()
# Event book keeping
self.event_cutflow = event_cutflow or CutFlow("EventCutFlow")
# Add cuts on events
min_ntel = config["Reconstruction"]["min_tel"]
self.event_cutflow.set_cuts(
OrderedDict([
("noCuts", None),
("min2Tels trig", lambda x: x < min_ntel),
("min2Tels reco", lambda x: x < min_ntel),
("direction nan", lambda x: x.is_valid == False),
]))
def test_CutFlow():
flow = CutFlow("TestFlow")
# set_cut and add_cut a aliases
flow.set_cut("smaller5", lambda x: x < 5)
flow.add_cut("smaller3", lambda x: x < 3)
for i in range(2, 6):
flow.count("noCuts")
# .keep counts if the function returns True,
# i.e. when we "keep" the event
if flow.keep("smaller5", i):
# .cut counts if the function returns False,
# i.e. when we do NOT "cut" the event
if flow.cut("smaller3", i):
pass
else:
# do something else that could fail or be rejected
try:
assert i == 3
flow.count("something")
except:
pass
t = flow(sort_column=1)
assert np.all(t["selected Events"] == [4, 3, 2, 1])
with raises(UndefinedCutException):
flow.cut("undefined", 5)
with raises(PureCountingCutException):
flow.cut("noCuts")
class SimpleEventWriter(Tool):
name = 'ctapipe-simple-event-writer'
description = Unicode(__doc__)
infile = Unicode(help='input file to read', default='').tag(config=True)
outfile = Unicode(help='output file name', default_value='output.h5').tag(config=True)
progress = Bool(help='display progress bar', default_value=True).tag(config=True)
aliases = Dict({
'infile': 'EventSourceFactory.input_url',
'outfile': 'SimpleEventWriter.outfile',
'max-events': 'EventSourceFactory.max_events',
'progress': 'SimpleEventWriter.progress'
})
classes = List([EventSourceFactory, CameraCalibrator, CutFlow])
def setup(self):
self.log.info('Configure EventSourceFactory...')
self.event_source = EventSourceFactory.produce(
config=self.config, tool=self, product='SimTelEventSource'
)
self.event_source.allowed_tels = self.config['Analysis']['allowed_tels']
self.calibrator = CameraCalibrator(
config=self.config, tool=self, eventsource=self.event_source
)
self.writer = HDF5TableWriter(
filename=self.outfile, group_name='image_infos', overwrite=True
)
# Define Pre-selection for images
preselcuts = self.config['Preselect']
self.image_cutflow = CutFlow('Image preselection')
self.image_cutflow.set_cuts(dict(
no_sel=None,
n_pixel=lambda s: np.count_nonzero(s) < preselcuts['n_pixel']['min'],
image_amplitude=lambda q: q < preselcuts['image_amplitude']['min']
))
# Define Pre-selection for events
self.event_cutflow = CutFlow('Event preselection')
self.event_cutflow.set_cuts(dict(
no_sel=None
))
def start(self):
self.log.info('Loop on events...')
for event in tqdm(
self.event_source,
desc='EventWriter',
total=self.event_source.max_events,
disable=~self.progress):
self.event_cutflow.count('no_sel')
self.calibrator.calibrate(event)
for tel_id in event.dl0.tels_with_data:
self.image_cutflow.count('no_sel')
camera = event.inst.subarray.tel[tel_id].camera
dl1_tel = event.dl1.tel[tel_id]
# Image cleaning
image = dl1_tel.image[0] # Waiting for automatic gain selection
mask = tailcuts_clean(camera, image, picture_thresh=10, boundary_thresh=5)
cleaned = image.copy()
cleaned[~mask] = 0
# Preselection cuts
if self.image_cutflow.cut('n_pixel', cleaned):
continue
if self.image_cutflow.cut('image_amplitude', np.sum(cleaned)):
continue
# Image parametrisation
params = hillas_parameters(camera, cleaned)
# Save Ids, MC infos and Hillas informations
self.writer.write(camera.cam_id, [event.r0, event.mc, params])
def finish(self):
self.log.info('End of job.')
self.image_cutflow()
self.event_cutflow()
self.writer.close()
