def image_cleaning_filter(reader, images, **opts):
"""
Filter images according to a cleaning operation.
Parameters
----------
reader: `DL1DataReader`
images
options for image cleaning
Returns
-------
mask (Array of bool)
"""
try:
from ctapipe.image import cleaning
except ImportError:
raise ImportError(
"The `ctapipe.image.cleaning` python module is required to perform cleaning operation"
)
try:
from ctapipe.instrument.camera import CameraGeometry
except ImportError:
raise ImportError(
"The `ctapipe.instrument.CameraGeometry` python module is required to perform cleaning operation"
)
geom = CameraGeometry.from_name(reader.tel_type.split("_")[-1])
def clean(img):
return cleaning.tailcuts_clean(geom, img, **opts)
clean_mask = np.apply_along_axis(clean, 1, images)
return clean_mask.any(axis=1)
def leakage_filter(reader,
images,
leakage_value=1.0,
leakage_number=2,
**opts):
"""
Filter images on leakage
Comment: An image cleaning filter is applied by default.
Parameters
----------
reader: `DL1DataReader`
images
options for image cleaning
leakage_value
leakage_number
Returns
-------
mask (Array of bool)
"""
try:
from ctapipe.image import cleaning, leakage
except ImportError:
raise ImportError(
"The `ctapipe.image.cleaning` and/or `ctapipe.image.leakage` python module is required to perform leakage operation"
)
try:
from ctapipe.instrument.camera import CameraGeometry
except ImportError:
raise ImportError(
"The `ctapipe.instrument.CameraGeometry` python module is required to perform leakage operation"
)
if leakage_number not in [1, 2]:
raise ValueError(
"The leakage_number is {}. Valid options are 1 or 2.".format(
leakage_number))
geom = CameraGeometry.from_name(reader.tel_type.split("_")[-1])
def leak(img):
cleanmask = cleaning.tailcuts_clean(geom, img, **opts)
mask = False
if any(cleanmask):
leakage_values = leakage(geom, img, cleanmask)
if hasattr(leakage_values,
"leakage{}_intensity".format(leakage_number)):
mask = (leakage_values["leakage{}_intensity".format(
leakage_number)] <= leakage_value)
elif hasattr(leakage_values,
"intensity_width_{}".format(leakage_number)):
mask = (
leakage_values["intensity_width_{}".format(leakage_number)]
<= leakage_value)
return mask
leakage_mask = np.apply_along_axis(leak, 1, images)
return leakage_mask
def test_psi_20():
from ctapipe.image import timing_parameters
# Then try a different rotation angle
grad = 2
intercept = 1
deviation = 0.1
geom = CameraGeometry.from_name("LSTCam")
psi = 20 * u.deg
hillas = HillasParametersContainer(x=0 * u.m, y=0 * u.m, psi=psi)
random = np.random.RandomState(1)
peak_time = intercept + grad * (np.cos(psi) * geom.pix_x.value
+ np.sin(psi) * geom.pix_y.value)
peak_time += random.normal(0, deviation, geom.n_pixels)
timing = timing_parameters(
geom,
image=np.ones(geom.n_pixels),
peak_time=peak_time,
hillas_parameters=hillas,
cleaning_mask=np.ones(geom.n_pixels, dtype=bool)
)
# Test we get the values we put in back out again
assert_allclose(timing.slope, grad / geom.pix_x.unit, rtol=1e-2)
assert_allclose(timing.intercept, intercept, rtol=1e-2)
assert_allclose(timing.deviation, deviation, rtol=1e-2)
def test_ignore_negative():
from ctapipe.image import timing_parameters
grad = 2.0
intercept = 1.0
deviation = 0.1
geom = CameraGeometry.from_name("LSTCam")
hillas = HillasParametersContainer(x=0 * u.m, y=0 * u.m, psi=0 * u.deg)
random = np.random.RandomState(1)
peak_time = intercept + grad * geom.pix_x.value
peak_time += random.normal(0, deviation, geom.n_pixels)
image = np.ones(geom.n_pixels)
image[5:10] = -1.0
cleaning_mask = image >= 0
timing = timing_parameters(
geom,
image,
peak_time=peak_time,
hillas_parameters=hillas,
cleaning_mask=cleaning_mask,
)
# Test we get the values we put in back out again
assert_allclose(timing.slope, grad / geom.pix_x.unit, rtol=1e-2)
assert_allclose(timing.intercept, intercept, rtol=1e-2)
assert_allclose(timing.deviation, deviation, rtol=1e-2)
def test_psi_0():
from ctapipe.image import timing_parameters
"""
Simple test that gradient fitting gives expected answers for perfect
gradient
"""
grad = 2.0
intercept = 1.0
deviation = 0.1
geom = CameraGeometry.from_name("LSTCam")
hillas = HillasParametersContainer(x=0 * u.m, y=0 * u.m, psi=0 * u.deg)
random = np.random.RandomState(1)
peak_time = intercept + grad * geom.pix_x.value
peak_time += random.normal(0, deviation, geom.n_pixels)
timing = timing_parameters(
geom,
image=np.ones(geom.n_pixels),
peak_time=peak_time,
hillas_parameters=hillas,
cleaning_mask=np.ones(geom.n_pixels, dtype=bool)
)
# Test we get the values we put in back out again
assert_allclose(timing.slope, grad / geom.pix_x.unit, rtol=1e-2)
assert_allclose(timing.intercept, intercept, rtol=1e-2)
assert_allclose(timing.deviation, deviation, rtol=1e-2)
def generate_geometry_from_camera(camera,
source_x=0. * u.mm,
source_y=0. * u.mm):
"""
Generate the SST-1M geometry from the CTS configuration
:param camera: a CTS instance
:param source_x: x coord
:param source_y: y coord
:return: the geometry for visualisation and the list of "good" pixels
"""
pix_x = []
pix_y = []
pix_id = []
for pix in camera.Pixels:
pix_x.append(pix.center[0])
pix_y.append(pix.center[1])
pix_id.append(pix.ID)
neighbors_pix = _find_neighbor_pixels(pix_x, pix_y, 30.)
geom = CameraGeometry(0,
pix_id,
pix_x * u.mm - source_x,
pix_y * u.mm - source_y,
np.ones(1296) * 400.,
pix_type='hexagonal',
neighbors=neighbors_pix)
return geom
def test_leakage():
from ctapipe.image.leakage import leakage
geom = CameraGeometry.from_name('LSTCam')
img = np.ones(geom.n_pixels)
mask = np.ones(len(geom), dtype=bool)
l = leakage(geom, img, mask)
ratio1 = np.sum(geom.get_border_pixel_mask(1)) / geom.n_pixels
ratio2 = np.sum(geom.get_border_pixel_mask(2)) / geom.n_pixels
assert l.intensity_width_1 == ratio1
assert l.intensity_width_2 == ratio2
assert l.pixels_width_1 == ratio1
assert l.pixels_width_2 == ratio2
def test_leakage():
from ctapipe.image.leakage import leakage
geom = CameraGeometry.from_name('LSTCam')
img = np.ones(geom.n_pixels)
mask = np.ones(len(geom), dtype=bool)
l = leakage(geom, img, mask)
ratio1 = np.sum(geom.get_border_pixel_mask(1)) / geom.n_pixels
ratio2 = np.sum(geom.get_border_pixel_mask(2)) / geom.n_pixels
assert l.leakage1_intensity == ratio1
assert l.leakage2_intensity == ratio2
assert l.leakage1_pixel == ratio1
assert l.leakage2_pixel == ratio2
def image_intensity_after_cleaning_filter(reader,
images,
i_min=-np.inf,
i_max=np.inf,
**opts):
"""
Filter images on intensity (in pe) after cleaning
Parameters
----------
reader: `DL1DataReader`
images
options for image cleaning
i_min
i_max
Returns
-------
mask (Array of bool)
"""
try:
from ctapipe.image import cleaning
except ImportError:
raise ImportError(
"The `ctapipe.image.cleaning` python module is required to perform cleaning operation"
)
try:
from ctapipe.instrument.camera import CameraGeometry
except ImportError:
raise ImportError(
"The `ctapipe.instrument.CameraGeometry` python module is required to perform cleaning operation"
)
geom = CameraGeometry.from_name(reader.tel_type.split("_")[-1])
def int_after_clean(img):
cleanmask = cleaning.tailcuts_clean(geom, img, **opts)
clean = img.copy()
clean[~cleanmask] = 0.0
amps = np.sum(clean)
return (i_min < amps) & (amps < i_max)
int_mask = np.apply_along_axis(int_after_clean, 1, images)
return int_mask
def test_hash():
types = ["LST", "MST", "SST"]
names = ["LST", "MST", "SST-1M"]
cameras = ["LSTCam", "FlashCam", "DigiCam"]
telescopes = []
for name, type, camera in zip(names, types, cameras):
for i in range(3):
telescopes.append(
TelescopeDescription(name=name,
tel_type=type,
optics=OpticsDescription.from_name(name),
camera=CameraGeometry.from_name(camera)))
assert len(telescopes) == 9
assert len(set(telescopes)) == 3
def initialise_plots(self):
# first load in a very nasty way the camera geometry
pix_x, pix_y, pix_id = [], [], []
pixels = self.cts.camera.Pixels
pixelspresent = list(self.cts.pixel_to_led['DC'].keys())
for pix in pixels:
if pix.ID in pixelspresent:
pix_x.append(pix.center[0])
pix_y.append(pix.center[1])
pix_id.append(pix.ID)
else:
pix_x.append(-200.)
pix_y.append(-200.)
pix_id.append(pix.ID)
pix_x = list(pix_x)
pix_y = list(pix_y)
pix_id = list(pix_id)
neighbors_pix = find_neighbor_pixels(pix_x, pix_y, 30.)
geom = CameraGeometry(0,
pix_id,
pix_x * u.mm,
pix_y * u.mm,
np.ones((1296)) * 400.,
'hexagonal',
neighbors=neighbors_pix)
plt.figure(0, figsize=(20, 6))
self.plots = []
plt.subplot(1, 2, 1)
self.plots.append(
visualization.CameraDisplay(geom,
title='AC status',
norm='lin',
cmap='coolwarm'))
self.plots[-1].add_colorbar()
self.plots[-1].image = np.multiply(self.ac_status, self.ac_level)
plt.subplot(1, 2, 2)
self.plots.append(
visualization.CameraDisplay(geom,
title='DC status',
norm='lin',
cmap='coolwarm'))
self.plots[-1].add_colorbar()
self.plots[-1].image = np.multiply(self.dc_status, self.dc_level)
def test_ignore_negative():
grad = 2.0
intercept = 1.0
geom = CameraGeometry.from_name("LSTCam")
hillas = HillasParametersContainer(x=0 * u.m, y=0 * u.m, psi=0 * u.deg)
image = np.ones(geom.n_pixels)
image[5:10] = -1.0
timing = timing_parameters(
geom,
image,
peakpos=intercept + grad * geom.pix_x.value,
hillas_parameters=hillas,
)
# Test we get the values we put in back out again
assert_allclose(timing.slope, grad / geom.pix_x.unit)
assert_allclose(timing.intercept, intercept)
def test_psi_20():
# Then try a different rotation angle
grad = 2
intercept = 1
geom = CameraGeometry.from_name("LSTCam")
psi = 20 * u.deg
hillas = HillasParametersContainer(x=0 * u.m, y=0 * u.m, psi=psi)
timing = timing_parameters(
geom,
image=np.ones(geom.n_pixels),
peakpos=intercept + grad * (np.cos(psi) * geom.pix_x.value
+ np.sin(psi) * geom.pix_y.value),
hillas_parameters=hillas,
)
# Test we get the values we put in back out again
assert_allclose(timing.slope, grad / geom.pix_x.unit)
assert_allclose(timing.intercept, intercept)
def test_psi_0():
"""
Simple test that gradient fitting gives expected answers for perfect
gradient
"""
grad = 2.0
intercept = 1.0
geom = CameraGeometry.from_name("LSTCam")
hillas = HillasParametersContainer(x=0 * u.m, y=0 * u.m, psi=0 * u.deg)
timing = timing_parameters(
geom,
image=np.ones(geom.n_pixels),
peakpos=intercept + grad * geom.pix_x.value,
hillas_parameters=hillas,
)
# Test we get the values we put in back out again
assert_allclose(timing.slope, grad / geom.pix_x.unit)
assert_allclose(timing.intercept, intercept)
def generate_geometry(camera):
"""
Generate the SST-1M geometry from the CTS configuration
:param cts: a CTS instance
:param available_board: which board per sector are available (dict)
:return: the geometry for visualisation and the list of "good" pixels
"""
pix_x = []
pix_y = []
pix_id = []
for pix in camera.Pixels:
pix_x.append(pix.center[0])
pix_y.append(pix.center[1])
pix_id.append(pix.ID)
neighbors_pix = _find_neighbor_pixels(pix_x, pix_y, 30.)
geom = CameraGeometry(cam_id=0, pix_id=pix_id, pix_x=pix_x * u.mm, pix_y=pix_y * u.mm,
pix_area=np.ones(1296) * 482.41, neighbors=neighbors_pix, pix_type='hexagonal')
return geom, pix_id
def test_hash():
from ctapipe.instrument.telescope import TelescopeDescription
from ctapipe.instrument.optics import OpticsDescription
from ctapipe.instrument.camera import CameraGeometry
types = ['LST', 'MST', 'SST']
names = ['LST', 'MST', 'SST-1M']
cameras = ['LSTCam', 'FlashCam', 'DigiCam']
telescopes = []
for name, type, camera in zip(names, types, cameras):
for i in range(3):
telescopes.append(
TelescopeDescription(name=name,
type=type,
optics=OpticsDescription.from_name(name),
camera=CameraGeometry.from_name(camera)))
assert len(telescopes) == 9
assert len(set(telescopes)) == 3
def _build_subarray_info(self, run):
"""
constructs a SubarrayDescription object from the info in an
MCRun
Parameters
----------
run: MCRun object
Returns
-------
SubarrayDescription :
instrumental information
"""
subarray = SubarrayDescription("MonteCarloArray")
runHeader = run.root.RunHeader
tabFocalTel = runHeader.tabFocalTel.read()
tabPosTelX = runHeader.tabPosTelX.read()
tabPosTelY = runHeader.tabPosTelY.read()
tabPosTelZ = runHeader.tabPosTelZ.read()
tabPoslXYZ = np.ascontiguousarray(
np.vstack((tabPosTelX, tabPosTelY, tabPosTelZ)).T)
'''
# Correspance HiPeData.Telscope.Type and camera name
# 0 LSTCam, 1 NectarCam, 2 FlashCam, 3 SCTCam,
# 4 ASTRICam, 5 DigiCam, 6 CHEC
'''
mapping_camera = {
0: 'LSTCam',
1: 'NectarCam',
2: 'FlashCam',
3: 'SCTCam',
4: 'ASTRICam',
5: 'DigiCam',
6: 'CHEC'
}
mapping_telName = {
0: 'LST',
1: 'MST',
2: 'MST',
3: 'MST',
4: 'SST-ASTRI',
5: 'SST-1M',
6: 'SST-2M'
}
for telNode in self.run.walk_nodes('/Tel', 'Group'):
try:
telType = uint64(telNode.telType.read())
telIndex = uint64(telNode.telIndex.read())
telId = uint64(telNode.telId.read())
cameraName = mapping_camera[telType]
telName = mapping_telName[telType]
camera = CameraGeometry.from_name(cameraName)
camera.cam_id = cameraName
foclen = tabFocalTel[telIndex] * u.m
tel_pos = tabPoslXYZ[telIndex] * u.m
camera.pix_x = telNode.tabPixelX.read() * u.m
camera.pix_y = telNode.tabPixelY.read() * u.m
#camera.rotate(-90.0*u.deg)
optic = OpticsDescription.from_name(telName)
optic.equivalent_focal_length = foclen
telescope_description = TelescopeDescription(telName,
telName,
optics=optic,
camera=camera)
#tel.optics.mirror_area = mirror_area
#tel.optics.num_mirror_tiles = num_tiles
subarray.tels[telId] = telescope_description
subarray.positions[telId] = tel_pos
except tables.exceptions.NoSuchNodeError as e:
pass
return subarray
calib.calibrate(event)
hillas_params = {}
subarray = event.inst.subarray
for tel_id in event.dl0.tels_with_data:
# telescope pointing direction
point_azimuth[tel_id] = event.mc.tel[tel_id].azimuth_raw * u.rad
point_altitude[tel_id] = (np.pi / 2 - event.mc.tel[tel_id].altitude_raw) * u.rad
# print(point_azimuth,point_altitude)
# Camera Geometry required for hillas parametrization
pix_x = subarray.tel[tel_id].camera.pix_x
pix_y = subarray.tel[tel_id].camera.pix_y
foclen = subarray.tel[tel_id].optics.equivalent_focal_length
camgeom = CameraGeometry.guess(pix_x, pix_y, foclen)
# note the [0] is for channel 0 which is high-gain channel
image = event.dl1.tel[tel_id].image[0]
# Cleaning of the image
cleaned_image = image
# create a clean mask of pixels above the threshold
cleanmask = tailcuts_clean(camgeom, image, picture_thresh=10, boundary_thresh=5)
# set all rejected pixels to zero
cleaned_image[~cleanmask] = 0
# Calulate hillas parameters
# It fails for empty pixels
try:
hillas_params[tel_id] = hillas_parameters(camgeom, cleaned_image)
cameras = ["LSTCam", "FlashCam", "DigiCam"]
telescopes = []
for name, type, camera in zip(names, types, cameras):
for i in range(3):
telescopes.append(
TelescopeDescription(name=name,
tel_type=type,
optics=OpticsDescription.from_name(name),
camera=CameraGeometry.from_name(camera)))
assert len(telescopes) == 9
assert len(set(telescopes)) == 3
OPTICS_NAMES = OpticsDescription.get_known_optics_names()
CAMERA_NAMES = CameraGeometry.get_known_camera_names()
@pytest.mark.parametrize("camera_name", CAMERA_NAMES)
@pytest.mark.parametrize("optics_name", OPTICS_NAMES)
def test_telescope_from_name(optics_name, camera_name):
""" Check we can construct all telescopes from their names """
tel = TelescopeDescription.from_name(optics_name, camera_name)
assert optics_name in str(tel)
assert camera_name in str(tel)
assert tel.camera.pix_x.shape[0] > 0
assert tel.optics.equivalent_focal_length.to("m") > 0
assert tel.type in ["MST", "SST", "LST", "UNKNOWN"]
