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Add configuration option to choose which cleaning to use to get training data #135

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May 11, 2021
Merged

Add configuration option to choose which cleaning to use to get training data #135

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2149393
Add configuration option to use either biggest or extended cleaning
HealthyPear May 10, 2021
6fa1a9b
Merge branch 'master' into feature-choose_image_quality_source
HealthyPear May 11, 2021
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1 change: 1 addition & 0 deletions protopipe/aux/example_config_files/analysis.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,7 @@ ImageCleaning:

# Cut for image selection
ImageSelection:
source: "extended" # biggest or extended
charge: [50., 1e10]
pixel: [3, 1e10]
ellipticity: [0.1, 0.6]
Expand Down
62 changes: 42 additions & 20 deletions protopipe/pipeline/event_preparer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -165,10 +165,19 @@ def __init__(
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"]
try:
self.image_selection_source = config["ImageSelection"]["source"]
charge_bounds = config["ImageSelection"]["charge"]
npix_bounds = config["ImageSelection"]["pixel"]
ellipticity_bounds = config["ImageSelection"]["ellipticity"]
nominal_distance_bounds = config["ImageSelection"]["nominal_distance"]
except KeyError:
# defaults for a CTAMARS-like analysis
self.image_selection_source = "extended"
charge_bounds = [50., 1.e10]
npix_bounds = [3, 1e10]
ellipticity_bounds = [0.1, 0.6]
nominal_distance_bounds = [0., 0.8]

if debug:
camera_radius(
Expand Down Expand Up @@ -541,7 +550,7 @@ def prepare_event(self, source, return_stub=True, save_images=False, debug=False
# could this go into `hillas_parameters` ...?
max_signals[tel_id] = np.max(image_extended)

except FileNotFoundError as e: # JLK, WHAT?
except FileNotFoundError as e:
print(e)
continue

Expand All @@ -551,8 +560,15 @@ def prepare_event(self, source, return_stub=True, save_images=False, debug=False

cleaned_image_is_good = True # we assume this

if self.image_selection_source == "extended":
cleaned_image_to_use = image_extended
elif self.image_selection_source == "biggest":
cleaned_image_to_use = image_biggest
else:
raise ValueError("Only supported cleanings are 'biggest' or 'extended'.")

# Apply some selection
if self.image_cutflow.cut("min pixel", image_biggest):
if self.image_cutflow.cut("min pixel", cleaned_image_to_use):
if debug:
print(
bcolors.WARNING
Expand All @@ -562,7 +578,7 @@ def prepare_event(self, source, return_stub=True, save_images=False, debug=False
good_for_reco[tel_id] = 0 # we record it as BAD
cleaned_image_is_good = False

if self.image_cutflow.cut("min charge", np.sum(image_biggest)):
if self.image_cutflow.cut("min charge", np.sum(cleaned_image_to_use)):
if debug:
print(
bcolors.WARNING
Expand Down Expand Up @@ -617,12 +633,16 @@ def prepare_event(self, source, return_stub=True, save_images=False, debug=False
# ===================================================
# PARAMETRIZED IMAGE SELECTION
# ===================================================
if self.image_selection_source == "extended":
moments_to_use = moments
else:
moments_to_use = moments_reco

# if width and/or length are zero (e.g. when there is
# only only one pixel or when all pixel are exactly
# in one row), the parametrisation
# won't be very useful: skip
if self.image_cutflow.cut("poor moments", moments_reco):
if self.image_cutflow.cut("poor moments", moments_to_use):
if debug:
print(
bcolors.WARNING
Expand All @@ -632,20 +652,20 @@ def prepare_event(self, source, return_stub=True, save_images=False, debug=False
good_for_reco[tel_id] = 0 # we record it as BAD

if self.image_cutflow.cut(
"close to the edge", moments_reco, camera.camera_name
"close to the edge", moments_to_use, camera.camera_name
):
if debug:
print(
bcolors.WARNING
+ "WARNING : out of containment radius!\n"
+ f"Camera radius = {self.camera_radius[camera.camera_name]}\n"
+ f"COG radius = {moments_reco.r}"
+ f"COG radius = {moments_to_use.r}"
+ bcolors.ENDC
)

good_for_reco[tel_id] = 0

if self.image_cutflow.cut("bad ellipticity", moments_reco):
if self.image_cutflow.cut("bad ellipticity", moments_to_use):
if debug:
print(
bcolors.WARNING
Expand Down Expand Up @@ -703,9 +723,6 @@ def prepare_event(self, source, return_stub=True, save_images=False, debug=False
# DIRECTION RECONSTRUCTION
# =============================================================

# n_tels["reco"] = len(hillas_dict_reco)
# n_tels["discri"] = len(hillas_dict)

# convert dictionary in numpy array to get a "mask"
images_status = np.asarray(list(good_for_reco.values()))
# record how many images will used for reconstruction
Expand Down Expand Up @@ -759,34 +776,39 @@ def prepare_event(self, source, return_stub=True, save_images=False, debug=False
with warnings.catch_warnings():
warnings.simplefilter("ignore")

if self.image_selection_source == "extended":
hillas_dict_to_use = hillas_dict
else:
hillas_dict_to_use = hillas_dict_reco

# use only the successfully parametrized images
# to reconstruct the direction of this event
successfull_hillas = np.where(images_status == 1)[0]
all_images = np.asarray(list(good_for_reco.keys()))
good_images = set(all_images[successfull_hillas])
good_hillas_dict_reco = {
k: v for k, v in hillas_dict_reco.items() if k in good_images
good_hillas_dict = {
k: v for k, v in hillas_dict_to_use.items() if k in good_images
}

if debug:
print(
bcolors.PURPLE
+ f"{len(good_hillas_dict_reco)} images will be "
+ f"{len(good_hillas_dict)} images will be "
+ "used to recover the shower's direction..."
+ bcolors.ENDC
)

# Reconstruction results
reco_result = self.shower_reco.predict(
good_hillas_dict_reco,
good_hillas_dict,
source.subarray,
SkyCoord(alt=alt, az=az, frame="altaz"),
None, # use the array direction
)

# Impact parameter for energy estimation (/ tel)
# Impact parameter for telescope-wise energy estimation
subarray = source.subarray
for tel_id in hillas_dict.keys():
for tel_id in hillas_dict_to_use.keys():

pos = subarray.positions[tel_id]

Expand Down
1 change: 1 addition & 0 deletions protopipe/scripts/tests/test_config_analysis_north.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,7 @@ ImageCleaning:

# Cut for image selection
ImageSelection:
source: "extended" # biggest or extended
charge: [50., 1e10]
pixel: [3, 1e10]
ellipticity: [0.1, 0.6]
Expand Down
1 change: 1 addition & 0 deletions protopipe/scripts/tests/test_config_analysis_south.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,7 @@ ImageCleaning:

# Cut for image selection
ImageSelection:
source: "extended" # biggest or extended
charge: [50., 1e10]
pixel: [3, 1e10]
ellipticity: [0.1, 0.8]
Expand Down