def test_fit_core():
'''
creating some great circles pointing in different directions (two north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1]*u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
'''
creating the fit class and setting the the great circle member '''
fit = FitGammaHillas()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
''' performing the position fit with a seed that is quite far away '''
pos_fit = fit.fit_core([100, 1000]*u.m)
print("position test fit:", pos_fit)
''' the result should be close to the origin of the coordinate system '''
np.testing.assert_allclose(pos_fit/u.m, [0, 0], atol=1e-3)
def test_FitGammaHillas():
'''
a test on one event of the complete fit procedure 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] = 180.*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 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 reco(single_telescope_data):
fit = FitGammaHillas()
inst = broadcastInst.value
hillas_dict, tel_phi, tel_theta = single_telescope_data
fit_result = fit.predict(hillas_dict, inst, tel_phi, tel_theta)
return fit_result
def test_fit_core():
'''
creating some great circles pointing in different directions (two north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1] * u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
'''
creating the fit class and setting the the great circle member '''
fit = FitGammaHillas()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
''' performing the position fit with a seed that is quite far away '''
pos_fit = fit.fit_core([100, 1000] * u.m)
print("position test fit:", pos_fit)
''' the result should be close to the origin of the coordinate system '''
np.testing.assert_allclose(pos_fit / u.m, [0, 0], atol=1e-3)
def test_fit_core():
'''
creating some great circles pointing in different directions (two north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1] * u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = FitGammaHillas()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the position fit with the minimisation algorithm
# and a seed that is quite far away
pos_fit_minimise = fit.fit_core_minimise([100, 1000] * u.m)
print("position fit test minimise:", pos_fit_minimise)
print()
# performing the position fit with the geometric algorithm
pos_fit_crosses, err_est_pos_fit_crosses = fit.fit_core_crosses()
print("position fit test crosses:", pos_fit_crosses)
print("error estimate:", err_est_pos_fit_crosses)
print()
# the results should be close to the origin of the coordinate system
np.testing.assert_allclose(pos_fit_minimise / u.m, [0, 0], atol=1e-3)
np.testing.assert_allclose(pos_fit_crosses / u.m, [0, 0], atol=1e-3)
def test_fit_origin():
'''
creating some great circles pointing in different directions (two north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1] * u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = FitGammaHillas()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the direction fit with the minimisation algorithm
# and a seed that is perpendicular to the up direction
dir_fit_minimise = fit.fit_origin_minimise((0.1, 0.1, 1))
print("direction fit test minimise:", dir_fit_minimise)
print()
# performing the direction fit with the geometric algorithm
dir_fit_crosses = fit.fit_origin_crosses()[0]
print("direction fit test crosses:", dir_fit_crosses)
print()
# the results should be close to the direction straight up
# np.testing.assert_allclose(dir_fit_minimise, [0, 0, 1], atol=1e-1)
np.testing.assert_allclose(dir_fit_crosses, [0, 0, 1], atol=1e-3)
def test_fit_origin():
'''
creating some great circles pointing in different directions (two north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1]*u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = FitGammaHillas()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the direction fit with the minimisation algorithm
# and a seed that is perpendicular to the up direction
dir_fit_minimise = fit.fit_origin_minimise((0.1, 0.1, 1))
print("direction fit test minimise:", dir_fit_minimise)
# performing the direction fit with the geometric algorithm
dir_fit_crosses = fit.fit_origin_crosses()[0]
print("direction fit test crosses:", dir_fit_crosses)
# the results should be close to the direction straight up
# np.testing.assert_allclose(dir_fit_minimise, [0, 0, 1], atol=1e-1)
np.testing.assert_allclose(dir_fit_crosses, [0, 0, 1], atol=1e-3)
def test_fit_core():
'''
creating some great circles pointing in different directions (two north-south,
two east-west) and that have a slight position errors (+- 0.1 m in one of the four
cardinal directions '''
circle1 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle1.pos = [0, 0.1]*u.m
circle1.trace = [1, 0, 0]
circle2 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle2.pos = [0.1, 0] * u.m
circle2.trace = [0, 1, 0]
circle3 = GreatCircle([[1, 0, 0], [0, 0, 1]])
circle3.pos = [0, -.1] * u.m
circle3.trace = [1, 0, 0]
circle4 = GreatCircle([[0, 1, 0], [0, 0, 1]])
circle4.pos = [-.1, 0] * u.m
circle4.trace = [0, 1, 0]
# creating the fit class and setting the the great circle member
fit = FitGammaHillas()
fit.circles = {1: circle1, 2: circle2, 3: circle3, 4: circle4}
# performing the position fit with the minimisation algorithm
# and a seed that is quite far away
pos_fit_minimise = fit.fit_core_minimise([100, 1000]*u.m)
print("position fit test minimise:", pos_fit_minimise)
# performing the position fit with the geometric algorithm
pos_fit_crosses = fit.fit_core_crosses()
print("position fit test crosses:", pos_fit_crosses)
# the results should be close to the origin of the coordinate system
np.testing.assert_allclose(pos_fit_minimise/u.m, [0, 0], atol=1e-3)
np.testing.assert_allclose(pos_fit_crosses/u.m, [0, 0], atol=1e-3)
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 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