def test_convert_geometry(cam_id, rot):
geom = CameraGeometry.from_name(cam_id)
if geom.pix_type == 'rectangular':
return # skip non-hexagonal cameras, since they don't need conversion
model = generate_2d_shower_model(centroid=(0.4, 0), width=0.01, length=0.03,
psi="25d")
_, image, _ = make_toymodel_shower_image(geom, model.pdf,
intensity=50,
nsb_level_pe=100)
hillas_0 = hillas_parameters(geom.pix_x, geom.pix_y, image)
geom2d, image2d = convert_geometry_1d_to_2d(geom, image,
geom.cam_id + str(rot),
add_rot=rot)
geom1d, image1d = convert_geometry_back(geom2d, image2d,
geom.cam_id + str(rot),
add_rot=rot)
hillas_1 = hillas_parameters(geom1d.pix_x, geom1d.pix_y, image1d)
if __name__ == "__main__":
plot_cam(geom, geom2d, geom1d, image, image2d, image1d)
plt.tight_layout()
plt.pause(.1)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_convert_geometry(cam_id, rot):
geom = CameraGeometry.from_name(cam_id)
if geom.pix_type=='rectangular':
return # skip non-hexagonal cameras, since they don't need conversion
model = generate_2d_shower_model(centroid=(0.4, 0), width=0.01, length=0.03,
psi="25d")
_,image,_ = make_toymodel_shower_image(geom, model.pdf,
intensity=50,
nsb_level_pe=100)
hillas_0 = hillas_parameters(geom.pix_x, geom.pix_y, image)
geom2d, image2d = convert_geometry_1d_to_2d(geom, image,
geom.cam_id+str(rot),
add_rot=-2)
geom1d, image1d = convert_geometry_back(geom2d, image2d,
geom.cam_id+str(rot),
add_rot=rot)
hillas_1 = hillas_parameters(geom1d.pix_x, geom1d.pix_y, image1d)
if __name__ == "__main__":
plot_cam(geom, geom2d, geom1d, image, image2d, image1d)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_convert_geometry_mock(cam_id, rot):
"""here we use a different key for the back conversion to trigger the mock conversion
"""
geom = CameraGeometry.from_name(cam_id)
image = create_mock_image(geom)
hillas_0 = hillas_parameters(geom, image)
if geom.pix_type == 'hexagonal':
convert_geometry_1d_to_2d = convert_geometry_hex1d_to_rect2d
convert_geometry_back = convert_geometry_rect2d_back_to_hexe1d
geom2d, image2d = convert_geometry_1d_to_2d(geom, image, key=None,
add_rot=rot)
geom1d, image1d = convert_geometry_back(geom2d, image2d,
"_".join([geom.cam_id,
str(rot), "mock"]),
add_rot=rot)
else:
# originally rectangular geometries don't need a buffer and therefore no mock
# conversion
return
hillas_1 = hillas_parameters(geom, image1d)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_convert_geometry_mock(cam_id, rot):
"""here we use a different key for the back conversion to trigger the mock conversion
"""
geom = CameraGeometry.from_name(cam_id)
image = create_mock_image(geom)
hillas_0 = hillas_parameters(geom, image)
if geom.pix_type == "hexagonal":
convert_geometry_1d_to_2d = convert_geometry_hex1d_to_rect2d
convert_geometry_back = convert_geometry_rect2d_back_to_hexe1d
geom2d, image2d = convert_geometry_1d_to_2d(geom,
image,
key=None,
add_rot=rot)
geom1d, image1d = convert_geometry_back(
geom2d,
image2d,
"_".join([geom.cam_id, str(rot), "mock"]),
add_rot=rot)
else:
# originally rectangular geometries don't need a buffer and therefore no mock
# conversion
return
hillas_1 = hillas_parameters(geom, image1d)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_hillas_container():
geom, image = create_sample_image_zeros(psi="0d")
params = hillas_parameters(geom, image)
assert isinstance(params, CameraHillasParametersContainer)
geom.frame = CameraFrame(focal_length=28 * u.m)
geom_telescope_frame = geom.transform_to(TelescopeFrame())
params = hillas_parameters(geom_telescope_frame, image)
assert isinstance(params, HillasParametersContainer)
def test_hillas_masked_array():
geom, image, clean_mask = create_sample_image(psi='0d')
image_zeros = image.copy()
image_zeros[~clean_mask] = 0
hillas_zeros = hillas_parameters(geom, image_zeros)
image_masked = np.ma.masked_array(image, mask=~clean_mask)
hillas_masked = hillas_parameters(geom, image_masked)
compare_hillas(hillas_zeros, hillas_masked)
def test_hillas_masked_array():
geom, image, clean_mask = create_sample_image(psi='0d')
image_zeros = image.copy()
image_zeros[~clean_mask] = 0
hillas_zeros = hillas_parameters(geom, image_zeros)
image_masked = np.ma.masked_array(image, mask=~clean_mask)
hillas_masked = hillas_parameters(geom, image_masked)
compare_hillas(hillas_zeros, hillas_masked)
def test_hillas_selected():
"""
test Hillas-parameter routines on a sample image with selected values
against a sample image with masked values set tozero
"""
geom, image = create_sample_image_zeros()
geom_selected, image_selected = create_sample_image_selected_pixel()
results = hillas_parameters(geom, image)
results_selected = hillas_parameters(geom_selected, image_selected)
compare_hillas(results, results_selected)
def test_hillas_selected():
"""
test Hillas-parameter routines on a sample image with selected values
against a sample image with masked values set tozero
"""
geom, image = create_sample_image_zeros()
geom_selected, image_selected = create_sample_image_selected_pixel()
results = hillas_parameters(geom, image)
results_selected = hillas_parameters(geom_selected, image_selected)
compare_hillas(results, results_selected)
def test_convert_geometry(cam_id, rot):
geom = CameraGeometry.from_name(cam_id)
model = generate_2d_shower_model(centroid=(0.4, 0),
width=0.01,
length=0.03,
psi="25d")
_, image, _ = make_toymodel_shower_image(geom,
model.pdf,
intensity=50,
nsb_level_pe=100)
hillas_0 = hillas_parameters(geom, image)
if geom.pix_type == 'hexagonal':
convert_geometry_1d_to_2d = convert_geometry_hex1d_to_rect2d
convert_geometry_back = convert_geometry_rect2d_back_to_hexe1d
geom2d, image2d = convert_geometry_1d_to_2d(geom,
image,
geom.cam_id + str(rot),
add_rot=rot)
geom1d, image1d = convert_geometry_back(geom2d,
image2d,
geom.cam_id + str(rot),
add_rot=rot)
else:
if geom.cam_id == "ASTRICam":
convert_geometry_1d_to_2d = astri_to_2d_array
convert_geometry_back = array_2d_to_astri
elif geom.cam_id == "CHEC":
convert_geometry_1d_to_2d = chec_to_2d_array
convert_geometry_back = array_2d_to_chec
else:
print("camera {geom.cam_id} not implemented")
return
image2d = convert_geometry_1d_to_2d(image)
image1d = convert_geometry_back(image2d)
hillas_1 = hillas_parameters(geom, image1d)
# if __name__ == "__main__":
# plot_cam(geom, geom2d, geom1d, image, image2d, image1d)
# plt.tight_layout()
# plt.pause(.1)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_array_draw():
filename = get_dataset("gamma_test.simtel.gz")
cam_geom = {}
source = hessio_event_source(filename, max_events=2)
r1 = HESSIOR1Calibrator()
dl0 = CameraDL0Reducer()
calibrator = CameraDL1Calibrator()
for event in source:
array_pointing = SkyCoord(
event.mcheader.run_array_direction[1] * u.rad,
event.mcheader.run_array_direction[0] * u.rad,
frame=AltAz)
# array_view = ArrayPlotter(instrument=event.inst,
# system=TiltedGroundFrame(
# pointing_direction=array_pointing))
hillas_dict = {}
r1.calibrate(event)
dl0.reduce(event)
calibrator.calibrate(event) # calibrate the events
# store MC pointing direction for the array
for tel_id in event.dl0.tels_with_data:
pmt_signal = event.dl1.tel[tel_id].image[0]
geom = deepcopy(event.inst.subarray.tel[tel_id].camera)
fl = event.inst.subarray.tel[tel_id].optics.equivalent_focal_length
# Transform the pixels positions into nominal coordinates
camera_coord = CameraFrame(x=geom.pix_x,
y=geom.pix_y,
z=np.zeros(geom.pix_x.shape) * u.m,
focal_length=fl,
rotation=90 * u.deg - geom.cam_rotation)
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=array_pointing,
pointing_direction=array_pointing))
geom.pix_x = nom_coord.x
geom.pix_y = nom_coord.y
mask = tailcuts_clean(geom,
pmt_signal,
picture_thresh=10.,
boundary_thresh=5.)
try:
moments = hillas_parameters(geom, pmt_signal * mask)
hillas_dict[tel_id] = moments
nom_coord = NominalPlotter(hillas_parameters=hillas_dict,
draw_axes=True)
nom_coord.draw_array()
except HillasParameterizationError as e:
print(e)
continue
def test_FitGammaHillas():
'''
a test of the complete fit procedure on one event including:
• tailcut cleaning
• hillas parametrisation
• GreatCircle creation
• direction fit
• position fit
in the end, proper units in the output are asserted '''
filename = get_dataset("gamma_test.simtel.gz")
fit = HillasReconstructor()
cam_geom = {}
tel_phi = {}
tel_theta = {}
source = hessio_event_source(filename)
for event in source:
hillas_dict = {}
for tel_id in event.dl0.tels_with_data:
if tel_id not in cam_geom:
cam_geom[tel_id] = CameraGeometry.guess(
event.inst.pixel_pos[tel_id][0],
event.inst.pixel_pos[tel_id][1],
event.inst.optical_foclen[tel_id])
tel_phi[tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad
tel_theta[tel_id] = (np.pi/2-event.mc.tel[tel_id].altitude_raw)*u.rad
pmt_signal = event.r0.tel[tel_id].adc_sums[0]
mask = tailcuts_clean(cam_geom[tel_id], pmt_signal,
picture_thresh=10., boundary_thresh=5.)
pmt_signal[mask == 0] = 0
try:
moments = hillas_parameters(event.inst.pixel_pos[tel_id][0],
event.inst.pixel_pos[tel_id][1],
pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2: continue
fit_result = fit.predict(hillas_dict, event.inst, tel_phi, tel_theta)
print(fit_result)
fit_result.alt.to(u.deg)
fit_result.az.to(u.deg)
fit_result.core_x.to(u.m)
assert fit_result.is_valid
return
def test_straight_line_width_0():
''' Test that hillas_parameters.width is 0 for a straight line of pixels '''
# three pixels in a straight line
long = np.array([0, 1, 2]) * 0.01
trans = np.zeros(len(long))
pix_id = np.arange(len(long))
np.random.seed(0)
for dx in (-1, 0, 1):
for dy in (-1, 0, 1):
for psi in np.linspace(0, np.pi, 20):
x = dx + np.cos(psi) * long + np.sin(psi) * trans
y = dy - np.sin(psi) * long + np.cos(psi) * trans
geom = CameraGeometry(
camera_name='testcam',
pix_id=pix_id,
pix_x=x * u.m,
pix_y=y * u.m,
pix_type='hexagonal',
pix_area=1 * u.m**2,
)
img = np.random.poisson(5, size=len(long))
result = hillas_parameters(geom, img)
assert result.width.value == 0
def hillas(event):
hillas_dict = {}
tel_phi = {}
tel_theta = {}
for tel in event:
tel_id = tel['tel_id']
tel_phi[tel_id] = 0. * u.deg
tel_theta[tel_id] = 20. * u.deg
pmt_signal = tel['adc_sums'][0]
# print(pmt_signal)
inst = broadcastInst.value
pix_x = inst.pixel_pos[tel_id][0]
pix_y = inst.pixel_pos[tel_id][1]
foc = inst.optical_foclen[tel_id]
cam_geom = CameraGeometry.guess(pix_x, pix_y, foc)
mask = tailcuts_clean(cam_geom,
pmt_signal,
1,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask == 0] = 0
try:
moments = hillas_parameters(cam_geom.pix_x, cam_geom.pix_y,
pmt_signal)
hillas_dict[tel['tel_id']] = moments
except HillasParameterizationError as e:
print(e)
return (hillas_dict, tel_phi, tel_theta)
def test_straight_line_width_0():
""" Test that hillas_parameters.width is 0 for a straight line of pixels """
# three pixels in a straight line
long = np.array([0, 1, 2]) * 0.01
trans = np.zeros(len(long))
pix_id = np.arange(len(long))
rng = np.random.default_rng(0)
for dx in (-1, 0, 1):
for dy in (-1, 0, 1):
for psi in np.linspace(0, np.pi, 20):
x = dx + np.cos(psi) * long + np.sin(psi) * trans
y = dy - np.sin(psi) * long + np.cos(psi) * trans
geom = CameraGeometry(
camera_name="testcam",
pix_id=pix_id,
pix_x=x * u.m,
pix_y=y * u.m,
pix_type="hexagonal",
pix_area=1 * u.m**2,
)
img = rng.poisson(5, size=len(long))
result = hillas_parameters(geom, img)
assert result.width.value == 0
assert np.isnan(result.width_uncertainty.value)
def draw_several_cams(geom, ncams=4):
cmaps = ['jet', 'afmhot', 'terrain', 'autumn']
fig, axs = plt.subplots(1, ncams, figsize=(15, 4), sharey=True, sharex=True)
for ii in range(ncams):
disp = visualization.CameraDisplay(
geom,
ax=axs[ii],
title="CT{}".format(ii + 1),
)
disp.cmap = cmaps[ii]
model = toymodel.generate_2d_shower_model(
centroid=(0.2 - ii * 0.1, -ii * 0.05),
width=0.005 + 0.001 * ii,
length=0.1 + 0.05 * ii,
psi=ii * 20 * u.deg,
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom,
model.pdf,
intensity=50,
nsb_level_pe=1000,
)
clean = image.copy()
clean[image <= 3.0 * image.mean()] = 0.0
hillas = hillas_parameters(geom.pix_x, geom.pix_y, clean)
disp.image = image
disp.add_colorbar(ax=axs[ii])
disp.set_limits_percent(95)
disp.overlay_moments(hillas, linewidth=3, color='blue')
def test_FitGammaHillas():
'''
a test of the complete fit procedure on one event including:
• tailcut cleaning
• hillas parametrisation
• GreatCircle creation
• direction fit
• position fit
in the end, proper units in the output are asserted '''
filename = get_path("gamma_test.simtel.gz")
fit = FitGammaHillas()
cam_geom = {}
tel_phi = {}
tel_theta = {}
source = hessio_event_source(filename)
for event in source:
hillas_dict = {}
for tel_id in event.dl0.tels_with_data:
if tel_id not in cam_geom:
cam_geom[tel_id] = CameraGeometry.guess(
event.inst.pixel_pos[tel_id][0],
event.inst.pixel_pos[tel_id][1],
event.inst.optical_foclen[tel_id])
tel_phi[tel_id] = 0.*u.deg
tel_theta[tel_id] = 20.*u.deg
pmt_signal = event.dl0.tel[tel_id].adc_sums[0]
mask = tailcuts_clean(cam_geom[tel_id], pmt_signal, 1,
picture_thresh=10., boundary_thresh=5.)
pmt_signal[mask == 0] = 0
try:
moments = hillas_parameters(event.inst.pixel_pos[tel_id][0],
event.inst.pixel_pos[tel_id][1],
pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2: continue
fit_result = fit.predict(hillas_dict, event.inst, tel_phi, tel_theta)
print(fit_result)
fit_result.alt.to(u.deg)
fit_result.az.to(u.deg)
fit_result.core_x.to(u.m)
assert fit_result.is_valid
return
def main():
paths = [
"/Volumes/gct-jason/astri_onsky_archive/d2019-05-15_simulations/proton/run1_dl1.h5",
]
df_list = []
for ipath, path in enumerate(paths):
with DL1Reader(path) as reader:
n_events = reader.get_metadata()['n_events']
mapping = reader.get_mapping()
geom = get_ctapipe_camera_geometry(mapping, plate_scale=37.56e-3)
desc = "Looping over events"
it = reader.iterate_over_events()
for df in tqdm(it, total=n_events, desc=desc):
iev = df['iev'].values[0]
image = df['photons'].values
time = df['pulse_time'].values
mask = obtain_cleaning_mask(geom, image, time)
if not mask.any():
continue
image_m = image[mask]
time_m = time[mask]
geom_m = geom[mask]
try:
hillas = hillas_parameters(geom_m, image_m)
except HillasParameterizationError:
continue
# timing_parameters(geom_m, image_m, time_m, hillas)
gt0 = image_m > 0
pix_x = geom_m.pix_x[gt0]
pix_y = geom_m.pix_y[gt0]
peakpos = time_m[gt0]
intensity = image_m[gt0]
longi, trans = camera_to_shower_coordinates(
pix_x, pix_y, hillas.x, hillas.y, hillas.psi)
longi = longi.value
trans = trans.value
# df_list.append(pd.DataFrame(dict(
# ipath=ipath,
# iev=iev,
# longi=longi,
# peakpos=peakpos,
# )))
p_relation = RelationPlotter()
p_relation.plot(longi, peakpos, intensity)
p_relation.save(
get_plot(
f"d190524_time_gradient/relation/i{ipath}_e{iev}.pdf"))
def test_reconstruction():
"""
a test of the complete fit procedure on one event including:
• tailcut cleaning
• hillas parametrisation
• HillasPlane creation
• direction fit
• position fit
in the end, proper units in the output are asserted """
filename = get_dataset_path("gamma_test.simtel.gz")
fit = HillasReconstructor()
tel_azimuth = {}
tel_altitude = {}
source = EventSourceFactory.produce(
input_url=filename,
product='HESSIOEventSource',
)
for event in source:
hillas_dict = {}
for tel_id in event.dl0.tels_with_data:
geom = event.inst.subarray.tel[tel_id].camera
tel_azimuth[tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad
tel_altitude[tel_id] = event.mc.tel[tel_id].altitude_raw * u.rad
pmt_signal = event.r0.tel[tel_id].image[0]
mask = tailcuts_clean(geom,
pmt_signal,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask == 0] = 0
try:
moments = hillas_parameters(geom, pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2:
continue
fit_result = fit.predict(hillas_dict, event.inst, tel_azimuth,
tel_altitude)
print(fit_result)
fit_result.alt.to(u.deg)
fit_result.az.to(u.deg)
fit_result.core_x.to(u.m)
assert fit_result.is_valid
return
def test_width_0():
geom, image, clean_mask = create_sample_image("30d")
hillas = hillas_parameters(geom[clean_mask], image[clean_mask])
hillas.width = 0 * u.m
conc = concentration_parameters(geom, image, hillas)
assert conc.core == 0
def test_array_draw():
filename = get_dataset("gamma_test.simtel.gz")
cam_geom = {}
source = hessio_event_source(filename, max_events=2)
r1 = HessioR1Calibrator(None, None)
dl0 = CameraDL0Reducer(None, None)
calibrator = CameraDL1Calibrator(None, None)
for event in source:
array_pointing = SkyCoord(event.mcheader.run_array_direction[1] * u.rad,
event.mcheader.run_array_direction[0] * u.rad,
frame=AltAz)
# array_view = ArrayPlotter(instrument=event.inst,
# system=TiltedGroundFrame(
# pointing_direction=array_pointing))
hillas_dict = {}
r1.calibrate(event)
dl0.reduce(event)
calibrator.calibrate(event) # calibrate the events
# store MC pointing direction for the array
for tel_id in event.dl0.tels_with_data:
pmt_signal = event.dl1.tel[tel_id].image[0]
geom = event.inst.subarray.tel[tel_id].camera
fl = event.inst.subarray.tel[tel_id].optics.effective_focal_length
# Transform the pixels positions into nominal coordinates
camera_coord = CameraFrame(x=geom.pix_x, y=geom.pix_y,
z=np.zeros(geom.pix_x.shape) * u.m,
focal_length=fl,
rotation=90 * u.deg - geom.cam_rotation)
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=array_pointing,
pointing_direction=array_pointing))
mask = tailcuts_clean(geom, pmt_signal,
picture_thresh=10., boundary_thresh=5.)
try:
moments = hillas_parameters(nom_coord.x,
nom_coord.y,
pmt_signal * mask)
hillas_dict[tel_id] = moments
nom_coord = NominalPlotter(hillas_parameters=hillas_dict,
draw_axes=True)
nom_coord.draw_array()
except HillasParameterizationError as e:
print(e)
continue
def test_concentration():
geom, image, clean_mask = create_sample_image('30d')
hillas = hillas_parameters(geom[clean_mask], image[clean_mask])
conc = concentration(geom, image, hillas)
assert 0.1 <= conc.concentration_cog <= 0.3
assert 0.05 <= conc.concentration_pixel <= 0.2
assert 0.3 <= conc.concentration_core <= 0.6
def test_concentration():
geom, image, clean_mask = create_sample_image("30d")
hillas = hillas_parameters(geom[clean_mask], image[clean_mask])
conc = concentration(geom, image, hillas)
assert 0.1 <= conc.cog <= 0.3
assert 0.05 <= conc.pixel <= 0.2
assert 0.3 <= conc.core <= 0.6
def test_FitGammaHillas():
'''
a test of the complete fit procedure on one event including:
• tailcut cleaning
• hillas parametrisation
• GreatCircle creation
• direction fit
• position fit
in the end, proper units in the output are asserted '''
filename = get_dataset("gamma_test.simtel.gz")
fit = HillasReconstructor()
tel_phi = {}
tel_theta = {}
source = hessio_event_source(filename)
for event in source:
hillas_dict = {}
for tel_id in event.dl0.tels_with_data:
geom = event.inst.subarray.tel[tel_id].camera
tel_phi[tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad
tel_theta[tel_id] = (np.pi / 2 -
event.mc.tel[tel_id].altitude_raw) * u.rad
pmt_signal = event.r0.tel[tel_id].image[0]
mask = tailcuts_clean(geom,
pmt_signal,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask == 0] = 0
try:
moments = hillas_parameters(geom, pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2: continue
fit_result = fit.predict(hillas_dict, event.inst, tel_phi, tel_theta)
print(fit_result)
fit_result.alt.to(u.deg)
fit_result.az.to(u.deg)
fit_result.core_x.to(u.m)
assert fit_result.is_valid
return
def plot_event(event, reco, pdf):
cams = [
event.inst.subarray.tels[i].camera for i in event.r0.tels_with_data
]
cams = [c for c in cams if c.cam_id in allowed_cameras]
n_tels = len(cams)
p = 1
params = {}
pointing_azimuth = {}
pointing_altitude = {}
for telescope_id, dl1 in event.dl1.tel.items():
camera = event.inst.subarray.tels[telescope_id].camera
if camera.cam_id not in allowed_cameras:
continue
nn = int(np.ceil(np.sqrt(n_tels)))
ax = plt.subplot(nn, nn, p)
p += 1
boundary_thresh, picture_thresh = cleaning_level[camera.cam_id]
mask = tailcuts_clean(camera,
dl1.image[0],
boundary_thresh=boundary_thresh,
picture_thresh=picture_thresh,
min_number_picture_neighbors=1)
#
if mask.sum() < 3: # only two pixel remaining. No luck anyways.
continue
h = hillas_parameters(
camera[mask],
dl1.image[0, mask],
)
disp = CameraDisplay(camera, ax=ax, title="CT{0}".format(telescope_id))
disp.pixels.set_antialiaseds(False)
disp.autoupdate = False
disp.add_colorbar()
# Show the camera image and overlay Hillas ellipse and clean pixels
disp.image = dl1.image[0]
disp.cmap = 'viridis'
disp.highlight_pixels(mask, color='white')
disp.overlay_moments(h, color='red', linewidth=5)
pointing_azimuth[
telescope_id] = event.mc.tel[telescope_id].azimuth_raw * u.rad
pointing_altitude[
telescope_id] = event.mc.tel[telescope_id].altitude_raw * u.rad
params[telescope_id] = h
return reco.predict(params, event.inst, pointing_altitude,
pointing_azimuth)
def test_convert_geometry(cam_id, rot):
geom = CameraGeometry.from_name(cam_id)
image = create_mock_image(geom)
hillas_0 = hillas_parameters(geom, image)
if geom.pix_type == "hexagonal":
convert_geometry_1d_to_2d = convert_geometry_hex1d_to_rect2d
convert_geometry_back = convert_geometry_rect2d_back_to_hexe1d
geom2d, image2d = convert_geometry_1d_to_2d(geom,
image,
geom.cam_id + str(rot),
add_rot=rot)
geom1d, image1d = convert_geometry_back(geom2d,
image2d,
geom.cam_id + str(rot),
add_rot=rot)
else:
if geom.cam_id == "ASTRICam":
convert_geometry_1d_to_2d = astri_to_2d_array
convert_geometry_back = array_2d_to_astri
elif geom.cam_id == "CHEC":
convert_geometry_1d_to_2d = chec_to_2d_array
convert_geometry_back = array_2d_to_chec
else:
print("camera {geom.cam_id} not implemented")
return
image2d = convert_geometry_1d_to_2d(image)
image1d = convert_geometry_back(image2d)
hillas_1 = hillas_parameters(geom, image1d)
# if __name__ == "__main__":
# plot_cam(geom, geom2d, geom1d, image, image2d, image1d)
# plt.tight_layout()
# plt.pause(.1)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_no_pixels_near_cog():
geom, image, clean_mask = create_sample_image("30d")
hillas = hillas_parameters(geom[clean_mask], image[clean_mask])
# remove pixels close to cog from the cleaning pixels
clean_mask &= ((geom.pix_x - hillas.x)**2 +
(geom.pix_y - hillas.y)**2) >= (2 * geom.pixel_width**2)
conc = concentration_parameters(geom[clean_mask], image[clean_mask],
hillas)
assert conc.cog == 0
def test_skewness():
np.random.seed(42)
geom = CameraGeometry.from_name('LSTCam')
width = 0.03 * u.m
length = 0.15 * u.m
intensity = 2500
xs = u.Quantity([0.5, 0.5, -0.5, -0.5], u.m)
ys = u.Quantity([0.5, -0.5, 0.5, -0.5], u.m)
psis = Angle([-90, -45, 0, 45, 90], unit='deg')
skews = [0, 0.3, 0.6]
for x, y, psi, skew in itertools.product(xs, ys, psis, skews):
# make a toymodel shower model
model = toymodel.SkewedGaussian(
x=x,
y=y,
width=width,
length=length,
psi=psi,
skewness=skew,
)
_, signal, _ = model.generate_image(
geom,
intensity=intensity,
nsb_level_pe=5,
)
result = hillas_parameters(geom, signal)
assert quantity_approx(result.x, x, rel=0.1)
assert quantity_approx(result.y, y, rel=0.1)
assert quantity_approx(result.width, width, rel=0.1)
assert quantity_approx(result.length, length, rel=0.1)
psi_same = result.psi.to_value(u.deg) == approx(psi.deg, abs=3)
psi_opposite = abs(result.psi.to_value(u.deg) - psi.deg) == approx(
180.0, abs=3)
assert psi_same or psi_opposite
# if we have delta the other way around, we get a negative sign for skewness
# skewness is quite imprecise, maybe we could improve this somehow
if psi_same:
assert result.skewness == approx(skew, abs=0.3)
else:
assert result.skewness == approx(-skew, abs=0.3)
assert signal.sum() == result.intensity
def test_reconstruction():
"""
a test of the complete fit procedure on one event including:
• tailcut cleaning
• hillas parametrisation
• HillasPlane creation
• direction fit
• position fit
in the end, proper units in the output are asserted """
filename = get_dataset_path("gamma_test.simtel.gz")
fit = HillasReconstructor()
tel_azimuth = {}
tel_altitude = {}
source = EventSourceFactory.produce(input_url=filename)
for event in source:
hillas_dict = {}
for tel_id in event.dl0.tels_with_data:
geom = event.inst.subarray.tel[tel_id].camera
tel_azimuth[tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad
tel_altitude[tel_id] = event.mc.tel[tel_id].altitude_raw * u.rad
pmt_signal = event.r0.tel[tel_id].image[0]
mask = tailcuts_clean(geom, pmt_signal,
picture_thresh=10., boundary_thresh=5.)
pmt_signal[mask == 0] = 0
try:
moments = hillas_parameters(geom, pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2:
continue
fit_result = fit.predict(hillas_dict, event.inst, tel_azimuth, tel_altitude)
print(fit_result)
fit_result.alt.to(u.deg)
fit_result.az.to(u.deg)
fit_result.core_x.to(u.m)
assert fit_result.is_valid
def test_convert_geometry(cam_id, rot):
geom = CameraGeometry.from_name(cam_id)
image = create_mock_image(geom)
hillas_0 = hillas_parameters(geom, image)
if geom.pix_type == 'hexagonal':
convert_geometry_1d_to_2d = convert_geometry_hex1d_to_rect2d
convert_geometry_back = convert_geometry_rect2d_back_to_hexe1d
geom2d, image2d = convert_geometry_1d_to_2d(geom, image,
geom.cam_id + str(rot),
add_rot=rot)
geom1d, image1d = convert_geometry_back(geom2d, image2d,
geom.cam_id + str(rot),
add_rot=rot)
else:
if geom.cam_id == "ASTRICam":
convert_geometry_1d_to_2d = astri_to_2d_array
convert_geometry_back = array_2d_to_astri
elif geom.cam_id == "CHEC":
convert_geometry_1d_to_2d = chec_to_2d_array
convert_geometry_back = array_2d_to_chec
else:
print("camera {geom.cam_id} not implemented")
return
image2d = convert_geometry_1d_to_2d(image)
image1d = convert_geometry_back(image2d)
hillas_1 = hillas_parameters(geom, image1d)
# if __name__ == "__main__":
# plot_cam(geom, geom2d, geom1d, image, image2d, image1d)
# plt.tight_layout()
# plt.pause(.1)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_with_toy():
np.random.seed(42)
geom = CameraGeometry.from_name("LSTCam")
width = 0.03 * u.m
length = 0.15 * u.m
width_uncertainty = 0.00094 * u.m
length_uncertainty = 0.00465 * u.m
intensity = 500
xs = u.Quantity([0.5, 0.5, -0.5, -0.5], u.m)
ys = u.Quantity([0.5, -0.5, 0.5, -0.5], u.m)
psis = Angle([-90, -45, 0, 45, 90], unit="deg")
for x, y in zip(xs, ys):
for psi in psis:
# make a toymodel shower model
model = toymodel.Gaussian(
x=x,
y=y,
width=width,
length=length,
psi=psi,
)
image, signal, noise = model.generate_image(
geom,
intensity=intensity,
nsb_level_pe=5,
)
result = hillas_parameters(geom, signal)
assert u.isclose(result.x, x, rtol=0.1)
assert u.isclose(result.y, y, rtol=0.1)
assert u.isclose(result.width, width, rtol=0.1)
assert u.isclose(result.width_uncertainty,
width_uncertainty,
rtol=0.4)
assert u.isclose(result.length, length, rtol=0.1)
assert u.isclose(result.length_uncertainty,
length_uncertainty,
rtol=0.4)
assert (result.psi.to_value(u.deg) == approx(
psi.deg, abs=2)) or abs(result.psi.to_value(u.deg) -
psi.deg) == approx(180.0, abs=2)
assert signal.sum() == result.intensity
def test_convert_geometry_mock(cam_id, rot):
"""here we use a different key for the back conversion to trigger the mock conversion
"""
geom = CameraGeometry.from_name(cam_id)
model = generate_2d_shower_model(centroid=(0.4, 0),
width=0.01,
length=0.03,
psi="25d")
_, image, _ = make_toymodel_shower_image(geom,
model.pdf,
intensity=50,
nsb_level_pe=100)
hillas_0 = hillas_parameters(geom, image)
if geom.pix_type == 'hexagonal':
convert_geometry_1d_to_2d = convert_geometry_hex1d_to_rect2d
convert_geometry_back = convert_geometry_rect2d_back_to_hexe1d
geom2d, image2d = convert_geometry_1d_to_2d(geom,
image,
key=None,
add_rot=rot)
geom1d, image1d = convert_geometry_back(
geom2d,
image2d,
"_".join([geom.cam_id, str(rot), "mock"]),
add_rot=rot)
else:
# originally rectangular geometries don't need a buffer and therefore no mock
# conversion
return
hillas_1 = hillas_parameters(geom, image1d)
assert np.abs(hillas_1.phi - hillas_0.phi).deg < 1.0
def test_FitGammaHillas():
filename = get_path("gamma_test.simtel.gz")
fit = FitGammaHillas()
fit.setup_geometry(*load_hessio(filename),
phi=180 * u.deg,
theta=20 * u.deg)
tel_geom = {}
source = hessio_event_source(filename)
for event in source:
hillas_dict = {}
for tel_id in set(event.trig.tels_with_trigger) & set(
event.dl0.tels_with_data):
if tel_id not in tel_geom:
tel_geom[tel_id] = CameraGeometry.guess(
fit.cameras(tel_id)['PixX'].to(u.m),
fit.cameras(tel_id)['PixY'].to(u.m),
fit.telescopes['FL'][tel_id - 1] * u.m)
pmt_signal = event.dl0.tel[tel_id].adc_sums[0]
mask = tailcuts_clean(tel_geom[tel_id],
pmt_signal,
1,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask is False] = 0
try:
moments, moms2 = hillas_parameters(
fit.cameras(tel_id)['PixX'],
fit.cameras(tel_id)['PixY'], pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2: continue
fit_result = fit.predict(hillas_dict)
print(fit_result)
assert fit_result
return
def start(self):
# n_events = self.reader.num_events
source = self.reader.read()
desc = "Looping through file"
for event in tqdm(source, desc=desc): #, total=n_events):
ev = event.count
self.calibrator.calibrate(event)
for tel_id in event.r0.tels_with_data:
geom = self.geometry.get_camera(tel_id)
nom_geom = self.geometry.get_nominal(tel_id)
image = event.dl1.tel[tel_id].image[0]
# Cleaning
cuts = self.tail_cut[geom.cam_id]
tc = tailcuts_clean(geom, image, *cuts)
if not tc.any():
# self.log.warning('No image')
continue
# cleaned = np.ma.masked_array(image, mask=~tc)
cleaned = image * tc
# Hillas
try:
hillas = hillas_parameters(nom_geom, cleaned)
except HillasParameterizationError:
# self.log.warning('HillasParameterizationError')
continue
# embed()
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
camera = CameraDisplay(nom_geom, ax=ax, image=image, cmap='viridis')
camera.add_colorbar()
cen_x = u.Quantity(hillas.cen_x).value
cen_y = u.Quantity(hillas.cen_y).value
length = u.Quantity(hillas.length).value
width = u.Quantity(hillas.width).value
print(cen_x, cen_y, length, width)
camera.add_ellipse(centroid=(cen_x, cen_y),
length=length * 2,
width=width * 2,
angle=hillas.psi.rad)
plt.show()
def test_skewness():
np.random.seed(42)
geom = CameraGeometry.from_name('LSTCam')
width = 0.03 * u.m
length = 0.15 * u.m
intensity = 2500
xs = u.Quantity([0.5, 0.5, -0.5, -0.5], u.m)
ys = u.Quantity([0.5, -0.5, 0.5, -0.5], u.m)
psis = Angle([-90, -45, 0, 45, 90], unit='deg')
skews = [0, 0.3, 0.6]
for x, y, psi, skew in itertools.product(xs, ys, psis, skews):
# make a toymodel shower model
model = toymodel.SkewedGaussian(
x=x, y=y,
width=width,
length=length,
psi=psi,
skewness=skew,
)
_, signal, _ = model.generate_image(
geom, intensity=intensity, nsb_level_pe=5,
)
result = hillas_parameters(geom, signal)
assert quantity_approx(result.x, x, rel=0.1)
assert quantity_approx(result.y, y, rel=0.1)
assert quantity_approx(result.width, width, rel=0.1)
assert quantity_approx(result.length, length, rel=0.1)
psi_same = result.psi.to_value(u.deg) == approx(psi.deg, abs=3)
psi_opposite = abs(result.psi.to_value(u.deg) - psi.deg) == approx(180.0, abs=3)
assert psi_same or psi_opposite
# if we have delta the other way around, we get a negative sign for skewness
# skewness is quite imprecise, maybe we could improve this somehow
if psi_same:
assert result.skewness == approx(skew, abs=0.3)
else:
assert result.skewness == approx(-skew, abs=0.3)
assert signal.sum() == result.intensity
def hillas_parameters(geom, image):
try:
return hillas.hillas_parameters(geom, image)
except hillas.HillasParameterizationError:
unit = geom.pix_x.unit
return hillas.MomentParameters(
size=0.,
cen_x=np.nan * unit,
cen_y=np.nan * unit,
length=np.nan * unit,
width=np.nan * unit,
r=np.nan * unit,
phi=hillas.Angle(np.nan * u.rad),
psi=hillas.Angle(np.nan * u.rad),
miss=np.nan * unit,
skewness=None,
kurtosis=None,
)
def test_FitGammaHillas():
filename = get_path("gamma_test.simtel.gz")
fit = FitGammaHillas()
fit.setup_geometry(*load_hessio(filename), phi=180 * u.deg, theta=20 * u.deg)
tel_geom = {}
source = hessio_event_source(filename)
for event in source:
hillas_dict = {}
for tel_id in set(event.trig.tels_with_trigger) & set(event.dl0.tels_with_data):
if tel_id not in tel_geom:
tel_geom[tel_id] = CameraGeometry.guess(
fit.cameras(tel_id)["PixX"].to(u.m),
fit.cameras(tel_id)["PixY"].to(u.m),
fit.telescopes["FL"][tel_id - 1] * u.m,
)
pmt_signal = event.dl0.tel[tel_id].adc_sums[0]
mask = tailcuts_clean(tel_geom[tel_id], pmt_signal, 1, picture_thresh=10.0, boundary_thresh=5.0)
pmt_signal[mask is False] = 0
try:
moments, moms2 = hillas_parameters(fit.cameras(tel_id)["PixX"], fit.cameras(tel_id)["PixY"], pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2:
continue
fit_result = fit.predict(hillas_dict)
print(fit_result)
assert fit_result
return
def test_with_toy():
np.random.seed(42)
geom = CameraGeometry.from_name('LSTCam')
width = 0.03
length = 0.15
intensity = 500
xs = (0.5, 0.5, -0.5, -0.5)
ys = (0.5, -0.5, 0.5, -0.5)
psis = Angle([-90, -45, 0, 45, 90], unit='deg')
for x, y in zip(xs, ys):
for psi in psis:
# make a toymodel shower model
model = toymodel.generate_2d_shower_model(
centroid=(x, y),
width=width,
length=length,
psi=psi,
)
image, signal, noise = toymodel.make_toymodel_shower_image(
geom,
model.pdf,
intensity=intensity,
nsb_level_pe=5,
)
result = hillas_parameters(geom, signal)
assert result.x.to_value(u.m) == approx(x, rel=0.1)
assert result.y.to_value(u.m) == approx(y, rel=0.1)
assert result.width.to_value(u.m) == approx(width, rel=0.1)
assert result.length.to_value(u.m) == approx(length, rel=0.1)
assert ((result.psi.to_value(u.deg) == approx(psi.deg, abs=2))
or abs(result.psi.to_value(u.deg) - psi.deg) == approx(
180.0, abs=2))
assert signal.sum() == result.intensity
def test_with_toy():
np.random.seed(42)
geom = CameraGeometry.from_name('LSTCam')
width = 0.03 * u.m
length = 0.15 * u.m
intensity = 500
xs = u.Quantity([0.5, 0.5, -0.5, -0.5], u.m)
ys = u.Quantity([0.5, -0.5, 0.5, -0.5], u.m)
psis = Angle([-90, -45, 0, 45, 90], unit='deg')
for x, y in zip(xs, ys):
for psi in psis:
# make a toymodel shower model
model = toymodel.Gaussian(
x=x, y=y,
width=width, length=length,
psi=psi,
)
image, signal, noise = model.generate_image(
geom, intensity=intensity, nsb_level_pe=5,
)
result = hillas_parameters(geom, signal)
assert quantity_approx(result.x, x, rel=0.1)
assert quantity_approx(result.y, y, rel=0.1)
assert quantity_approx(result.width, width, rel=0.1)
assert quantity_approx(result.length, length, rel=0.1)
assert (
(result.psi.to_value(u.deg) == approx(psi.deg, abs=2))
or abs(result.psi.to_value(u.deg) - psi.deg) == approx(180.0, abs=2)
)
assert signal.sum() == result.intensity
def test_single_pixel():
x = y = np.arange(3)
x, y = np.meshgrid(x, y)
geom = CameraGeometry(
camera_name="testcam",
pix_id=np.arange(9),
pix_x=x.ravel() * u.cm,
pix_y=y.ravel() * u.cm,
pix_type="rectangular",
pix_area=1 * u.cm**2,
)
image = np.zeros((3, 3))
image[1, 1] = 10
image = image.ravel()
hillas = hillas_parameters(geom, image)
assert hillas.length.value == 0
assert hillas.width.value == 0
assert np.isnan(hillas.psi)
def test_convert_geometry():
filename = get_path("gamma_test.simtel.gz")
cam_geom = {}
source = hessio_event_source(filename)
# testing a few images just for the sake of being thorough
counter = 5
for event in source:
for tel_id in event.dl0.tels_with_data:
if tel_id not in cam_geom:
cam_geom[tel_id] = CameraGeometry.guess(
event.inst.pixel_pos[tel_id][0],
event.inst.pixel_pos[tel_id][1],
event.inst.optical_foclen[tel_id])
# we want to test conversion of hex to rectangular pixel grid
if cam_geom[tel_id].pix_type is not "hexagonal":
continue
print(tel_id, cam_geom[tel_id].pix_type)
pmt_signal = apply_mc_calibration(
#event.dl0.tel[tel_id].adc_samples[0],
event.dl0.tel[tel_id].adc_sums[0],
event.mc.tel[tel_id].dc_to_pe[0],
event.mc.tel[tel_id].pedestal[0])
new_geom, new_signal = convert_geometry_1d_to_2d(
cam_geom[tel_id], pmt_signal, cam_geom[tel_id].cam_id, add_rot=-2)
unrot_geom, unrot_signal = convert_geometry_back(
new_geom, new_signal, cam_geom[tel_id].cam_id,
event.inst.optical_foclen[tel_id], add_rot=4)
# if run as main, do some plotting
if __name__ == "__main__":
fig = plt.figure()
plt.style.use('seaborn-talk')
ax1 = fig.add_subplot(131)
disp1 = CameraDisplay(cam_geom[tel_id],
image=np.sum(pmt_signal, axis=1)
if pmt_signal.shape[-1] == 25 else pmt_signal,
ax=ax1)
disp1.cmap = plt.cm.hot
disp1.add_colorbar()
plt.title("original geometry")
ax2 = fig.add_subplot(132)
disp2 = CameraDisplay(new_geom,
image=np.sum(new_signal, axis=2)
if new_signal.shape[-1] == 25 else new_signal,
ax=ax2)
disp2.cmap = plt.cm.hot
disp2.add_colorbar()
plt.title("slanted geometry")
ax3 = fig.add_subplot(133)
disp3 = CameraDisplay(unrot_geom, image=np.sum(unrot_signal, axis=1)
if unrot_signal.shape[-1] == 25 else unrot_signal,
ax=ax3)
disp3.cmap = plt.cm.hot
disp3.add_colorbar()
plt.title("geometry converted back to hex")
plt.show()
# do some tailcuts cleaning
mask1 = tailcuts_clean(cam_geom[tel_id], pmt_signal, 1,
picture_thresh=10.,
boundary_thresh=5.)
mask2 = tailcuts_clean(unrot_geom, unrot_signal, 1,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask1==False] = 0
unrot_signal[mask2==False] = 0
'''
testing back and forth conversion on hillas parameters... '''
try:
moments1 = hillas_parameters(cam_geom[tel_id].pix_x,
cam_geom[tel_id].pix_y,
pmt_signal)
moments2 = hillas_parameters(unrot_geom.pix_x,
unrot_geom.pix_y,
unrot_signal)
except (HillasParameterizationError, AssertionError) as e:
'''
we don't want this test to fail because the hillas code
threw an error '''
print(e)
counter -= 1
if counter < 0:
return
else:
continue
'''
test if the hillas parameters from the original geometry and the
forth-and-back rotated geometry are close '''
assert np.allclose(
[moments1.length.value, moments1.width.value,
moments1.phi.value],
[moments2.length.value, moments2.width.value,
moments2.phi.value],
rtol=1e-2, atol=1e-2)
counter -= 1
if counter < 0:
return
def test_reconstructors(reconstructors):
"""
a test of the complete fit procedure on one event including:
• tailcut cleaning
• hillas parametrisation
• HillasPlane creation
• direction fit
• position fit
in the end, proper units in the output are asserted """
filename = get_dataset_path("gamma_test_large.simtel.gz")
source = event_source(filename, max_events=10)
horizon_frame = AltAz()
# record how many events were reconstructed by each reconstructor
reconstructed_events = np.zeros((len(reconstructors)))
for event in source:
array_pointing = SkyCoord(az=event.mc.az,
alt=event.mc.alt,
frame=horizon_frame)
hillas_dict = {}
telescope_pointings = {}
for tel_id in event.dl0.tels_with_data:
geom = source.subarray.tel[tel_id].camera.geometry
telescope_pointings[tel_id] = SkyCoord(
alt=event.pointing.tel[tel_id].altitude,
az=event.pointing.tel[tel_id].azimuth,
frame=horizon_frame)
pmt_signal = event.r0.tel[tel_id].waveform[0].sum(axis=1)
mask = tailcuts_clean(geom,
pmt_signal,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask == 0] = 0
try:
moments = hillas_parameters(geom, pmt_signal)
hillas_dict[tel_id] = moments
except HillasParameterizationError as e:
print(e)
continue
if len(hillas_dict) < 2:
continue
for count, reco_method in enumerate(reconstructors):
reconstructed_events[count] += 1
reconstructor = reco_method()
reconstructor_out = reconstructor.predict(hillas_dict,
source.subarray,
array_pointing,
telescope_pointings)
reconstructor_out.alt.to(u.deg)
reconstructor_out.az.to(u.deg)
reconstructor_out.core_x.to(u.m)
assert reconstructor_out.is_valid
np.testing.assert_array_less(np.zeros_like(reconstructed_events),
reconstructed_events)
def test_convert_geometry():
filename = get_path("gamma_test.simtel.gz")
cam_geom = {}
source = hessio_event_source(filename)
# testing a few images just for the sake of being thorough
counter = 5
for event in source:
for tel_id in event.dl0.tels_with_data:
if tel_id not in cam_geom:
cam_geom[tel_id] = CameraGeometry.guess(
event.inst.pixel_pos[tel_id][0],
event.inst.pixel_pos[tel_id][1],
event.inst.optical_foclen[tel_id])
# we want to test conversion of hex to rectangular pixel grid
if cam_geom[tel_id].pix_type is not "hexagonal":
continue
print(tel_id, cam_geom[tel_id].pix_type)
pmt_signal = apply_mc_calibration(
#event.dl0.tel[tel_id].adc_samples[0],
event.dl0.tel[tel_id].adc_sums[0],
event.mc.tel[tel_id].dc_to_pe[0],
event.mc.tel[tel_id].pedestal[0])
new_geom, new_signal = convert_geometry_1d_to_2d(
cam_geom[tel_id],
pmt_signal,
cam_geom[tel_id].cam_id,
add_rot=-2)
unrot_geom, unrot_signal = convert_geometry_back(
new_geom,
new_signal,
cam_geom[tel_id].cam_id,
event.inst.optical_foclen[tel_id],
add_rot=4)
# if run as main, do some plotting
if __name__ == "__main__":
fig = plt.figure()
plt.style.use('seaborn-talk')
ax1 = fig.add_subplot(131)
disp1 = CameraDisplay(
cam_geom[tel_id],
image=np.sum(pmt_signal, axis=1)
if pmt_signal.shape[-1] == 25 else pmt_signal,
ax=ax1)
disp1.cmap = plt.cm.hot
disp1.add_colorbar()
plt.title("original geometry")
ax2 = fig.add_subplot(132)
disp2 = CameraDisplay(
new_geom,
image=np.sum(new_signal, axis=2)
if new_signal.shape[-1] == 25 else new_signal,
ax=ax2)
disp2.cmap = plt.cm.hot
disp2.add_colorbar()
plt.title("slanted geometry")
ax3 = fig.add_subplot(133)
disp3 = CameraDisplay(
unrot_geom,
image=np.sum(unrot_signal, axis=1)
if unrot_signal.shape[-1] == 25 else unrot_signal,
ax=ax3)
disp3.cmap = plt.cm.hot
disp3.add_colorbar()
plt.title("geometry converted back to hex")
plt.show()
# do some tailcuts cleaning
mask1 = tailcuts_clean(cam_geom[tel_id],
pmt_signal,
1,
picture_thresh=10.,
boundary_thresh=5.)
mask2 = tailcuts_clean(unrot_geom,
unrot_signal,
1,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask1 == False] = 0
unrot_signal[mask2 == False] = 0
'''
testing back and forth conversion on hillas parameters... '''
try:
moments1 = hillas_parameters(cam_geom[tel_id].pix_x,
cam_geom[tel_id].pix_y,
pmt_signal)
moments2 = hillas_parameters(unrot_geom.pix_x,
unrot_geom.pix_y, unrot_signal)
except (HillasParameterizationError, AssertionError) as e:
'''
we don't want this test to fail because the hillas code
threw an error '''
print(e)
counter -= 1
if counter < 0:
return
else:
continue
'''
test if the hillas parameters from the original geometry and the
forth-and-back rotated geometry are close '''
assert np.allclose([
moments1.length.value, moments1.width.value, moments1.phi.value
], [
moments2.length.value, moments2.width.value, moments2.phi.value
],
rtol=1e-2,
atol=1e-2)
counter -= 1
if counter < 0:
return
def test_hillas_container():
geom, image = create_sample_image_zeros(psi='0d')
params = hillas_parameters(geom, image)
assert isinstance(params, HillasParametersContainer)
def test_hillas_failure():
geom, image = create_sample_image_zeros(psi='0d')
blank_image = zeros_like(image)
with pytest.raises(HillasParameterizationError):
hillas_parameters(geom, blank_image)
# Create a fake camera image to display:
model = mock.generate_2d_shower_model(centroid=(0.2, 0.0),
width=0.01,
length=0.1,
psi='35d')
image, sig, bg = mock.make_mock_shower_image(geom, model.pdf,
intensity=50,
nsb_level_pe=1000)
# Apply really stupid image cleaning (single threshold):
clean = image.copy()
clean[image <= 3.0 * image.mean()] = 0.0
# Calculate image parameters
hillas = hillas_parameters(geom.pix_x, geom.pix_y, clean)
print(hillas)
# Show the camera image and overlay Hillas ellipse
disp.image = image
disp.overlay_moments(hillas, color='seagreen', linewidth=3)
# Draw the neighbors of pixel 100 in red, and the neighbor-neighbors in
# green
for ii in geom.neighbors[130]:
draw_neighbors(geom, ii, color='green')
draw_neighbors(geom, 130, color='cyan', lw=2)
plt.show()