class ManualGainSelector(GainSelector):
"""
Manually choose a gain channel.
"""
channel = traits.CaselessStrEnum(
["HIGH", "LOW"],
default_value="HIGH",
help="Which gain channel to retain").tag(config=True)
def select_channel(self, waveforms):
n_pixels = waveforms.shape[1]
return np.full(n_pixels, GainChannel[self.channel])
class HillasIntersection(Reconstructor):
"""
This class is a simple re-implementation of Hillas parameter based event
reconstruction. e.g. https://arxiv.org/abs/astro-ph/0607333
In this case the Hillas parameters are all constructed in the shared
angular ( Nominal) system. Direction reconstruction is performed by
extrapolation of the major axes of the Hillas parameters in the nominal
system and the weighted average of the crossing points is taken. Core
reconstruction is performed by performing the same procedure in the
tilted ground system.
The height of maximum is reconstructed by the projection os the image
centroid onto the shower axis, taking the weighted average of all images.
Uncertainties on the positions are provided by taking the spread of the
crossing points, however this means that no uncertainty can be provided
for multiplicity 2 events.
"""
atmosphere_profile_name = traits.CaselessStrEnum(
[
'paranal',
],
default_value="paranal",
help="name of atmosphere profile to use").tag(config=True)
weighting = traits.CaselessStrEnum(
['Konrad', 'hess'],
default_value='Konrad',
help='Weighting Method name').tag(config=True)
def __init__(self, config=None, parent=None, **kwargs):
"""
Weighting must be a function similar to the weight_konrad already implemented
"""
super().__init__(config=config, parent=parent, **kwargs)
# We need a conversion function from height above ground to depth of maximum
# To do this we need the conversion table from CORSIKA
_ = get_atmosphere_profile_functions(self.atmosphere_profile_name)
self.thickness_profile, self.altitude_profile = _
# other weighting schemes can be implemented. just add them as additional methods
if self.weighting == "Konrad":
self._weight_method = self.weight_konrad
def predict(self,
hillas_dict,
inst,
array_pointing,
telescopes_pointings=None):
"""
Parameters
----------
hillas_dict: dict
Dictionary containing Hillas parameters for all telescopes
in reconstruction
inst : ctapipe.io.InstrumentContainer
instrumental description
array_pointing: SkyCoord[AltAz]
pointing direction of the array
telescopes_pointings: dict[SkyCoord[AltAz]]
dictionary of pointing direction per each telescope
Returns
-------
ReconstructedShowerContainer:
"""
# filter warnings for missing obs time. this is needed because MC data has no obs time
warnings.filterwarnings(action='ignore',
category=MissingFrameAttributeWarning)
# stereoscopy needs at least two telescopes
if len(hillas_dict) < 2:
raise TooFewTelescopesException(
"need at least two telescopes, have {}".format(
len(hillas_dict)))
# check for np.nan or 0 width's as these screw up weights
if any(
[np.isnan(hillas_dict[tel]['width'].value)
for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==np.nan")
if any([hillas_dict[tel]['width'].value == 0 for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==0")
if telescopes_pointings is None:
telescopes_pointings = {
tel_id: array_pointing
for tel_id in hillas_dict.keys()
}
tilted_frame = TiltedGroundFrame(pointing_direction=array_pointing)
ground_positions = inst.subarray.tel_coords
grd_coord = GroundFrame(x=ground_positions.x,
y=ground_positions.y,
z=ground_positions.z)
tilt_coord = grd_coord.transform_to(tilted_frame)
tel_x = {
tel_id: tilt_coord.x[tel_id - 1]
for tel_id in list(hillas_dict.keys())
}
tel_y = {
tel_id: tilt_coord.y[tel_id - 1]
for tel_id in list(hillas_dict.keys())
}
nom_frame = NominalFrame(origin=array_pointing)
hillas_dict_mod = copy.deepcopy(hillas_dict)
for tel_id, hillas in hillas_dict_mod.items():
# prevent from using rads instead of meters as inputs
assert hillas.x.to(u.m).unit == u.Unit('m')
focal_length = inst.subarray.tel[
tel_id].optics.equivalent_focal_length
camera_frame = CameraFrame(
telescope_pointing=telescopes_pointings[tel_id],
focal_length=focal_length,
)
cog_coords = SkyCoord(x=hillas.x, y=hillas.y, frame=camera_frame)
cog_coords_nom = cog_coords.transform_to(nom_frame)
hillas.x = cog_coords_nom.delta_alt
hillas.y = cog_coords_nom.delta_az
src_x, src_y, err_x, err_y = self.reconstruct_nominal(hillas_dict_mod)
core_x, core_y, core_err_x, core_err_y = self.reconstruct_tilted(
hillas_dict_mod, tel_x, tel_y)
err_x *= u.rad
err_y *= u.rad
nom = SkyCoord(delta_az=src_x * u.rad,
delta_alt=src_y * u.rad,
frame=nom_frame)
# nom = sky_pos.transform_to(nom_frame)
sky_pos = nom.transform_to(array_pointing.frame)
result = ReconstructedShowerContainer()
result.alt = sky_pos.altaz.alt.to(u.rad)
result.az = sky_pos.altaz.az.to(u.rad)
tilt = SkyCoord(
x=core_x * u.m,
y=core_y * u.m,
frame=tilted_frame,
)
grd = project_to_ground(tilt)
result.core_x = grd.x
result.core_y = grd.y
x_max = self.reconstruct_xmax(
nom.delta_az,
nom.delta_alt,
tilt.x,
tilt.y,
hillas_dict_mod,
tel_x,
tel_y,
90 * u.deg - array_pointing.alt,
)
result.core_uncert = np.sqrt(core_err_x**2 + core_err_y**2) * u.m
result.tel_ids = [h for h in hillas_dict_mod.keys()]
result.average_intensity = np.mean(
[h.intensity for h in hillas_dict_mod.values()])
result.is_valid = True
src_error = np.sqrt(err_x**2 + err_y**2)
result.alt_uncert = src_error.to(u.rad)
result.az_uncert = src_error.to(u.rad)
result.h_max = x_max
result.h_max_uncert = np.nan
result.goodness_of_fit = np.nan
return result
def reconstruct_nominal(self, hillas_parameters):
"""
Perform event reconstruction by simple Hillas parameter intersection
in the nominal system
Parameters
----------
hillas_parameters: dict
Hillas parameter objects
Returns
-------
Reconstructed event position in the horizon system
"""
if len(hillas_parameters) < 2:
return None # Throw away events with < 2 images
# Find all pairs of Hillas parameters
combos = itertools.combinations(list(hillas_parameters.values()), 2)
hillas_pairs = list(combos)
# Copy parameters we need to a numpy array to speed things up
h1 = list(
map(
lambda h: [
h[0].psi.to_value(u.rad), h[0].x.to_value(u.rad), h[0].y.
to_value(u.rad), h[0].intensity
], hillas_pairs))
h1 = np.array(h1)
h1 = np.transpose(h1)
h2 = list(
map(
lambda h: [
h[1].psi.to_value(u.rad), h[1].x.to_value(u.rad), h[1].y.
to_value(u.rad), h[1].intensity
], hillas_pairs))
h2 = np.array(h2)
h2 = np.transpose(h2)
# Perform intersection
sx, sy = self.intersect_lines(h1[1], h1[2], h1[0], h2[1], h2[2], h2[0])
# Weight by chosen method
weight = self._weight_method(h1[3], h2[3])
# And sin of interception angle
weight *= self.weight_sin(h1[0], h2[0])
# Make weighted average of all possible pairs
x_pos = np.average(sx, weights=weight)
y_pos = np.average(sy, weights=weight)
var_x = np.average((sx - x_pos)**2, weights=weight)
var_y = np.average((sy - y_pos)**2, weights=weight)
return x_pos, y_pos, np.sqrt(var_x), np.sqrt(var_y)
def reconstruct_tilted(self, hillas_parameters, tel_x, tel_y):
"""
Core position reconstruction by image axis intersection in the tilted
system
Parameters
----------
hillas_parameters: dict
Hillas parameter objects
tel_x: dict
Telescope X positions, tilted system
tel_y: dict
Telescope Y positions, tilted system
Returns
-------
(float, float, float, float):
core position X, core position Y, core uncertainty X,
core uncertainty X
"""
if len(hillas_parameters) < 2:
return None # Throw away events with < 2 images
hill_list = list()
tx = list()
ty = list()
# Need to loop here as dict is unordered
for tel in hillas_parameters.keys():
hill_list.append(hillas_parameters[tel])
tx.append(tel_x[tel])
ty.append(tel_y[tel])
# Find all pairs of Hillas parameters
hillas_pairs = list(itertools.combinations(hill_list, 2))
tel_x = list(itertools.combinations(tx, 2))
tel_y = list(itertools.combinations(ty, 2))
tx = np.zeros((len(tel_x), 2))
ty = np.zeros((len(tel_y), 2))
for i, _ in enumerate(tel_x):
tx[i][0], tx[i][1] = tel_x[i][0].to_value(
u.m), tel_x[i][1].to_value(u.m)
ty[i][0], ty[i][1] = tel_y[i][0].to_value(
u.m), tel_y[i][1].to_value(u.m)
tel_x = np.array(tx)
tel_y = np.array(ty)
# Copy parameters we need to a numpy array to speed things up
hillas1 = map(lambda h: [h[0].psi.to_value(u.rad), h[0].intensity],
hillas_pairs)
hillas1 = np.array(list(hillas1))
hillas1 = np.transpose(hillas1)
hillas2 = map(lambda h: [h[1].psi.to_value(u.rad), h[1].intensity],
hillas_pairs)
hillas2 = np.array(list(hillas2))
hillas2 = np.transpose(hillas2)
# Perform intersection
crossing_x, crossing_y = self.intersect_lines(tel_x[:, 0], tel_y[:, 0],
hillas1[0], tel_x[:, 1],
tel_y[:, 1], hillas2[0])
# Weight by chosen method
weight = self._weight_method(hillas1[1], hillas2[1])
# And sin of interception angle
weight *= self.weight_sin(hillas1[0], hillas2[0])
# Make weighted average of all possible pairs
x_pos = np.average(crossing_x, weights=weight)
y_pos = np.average(crossing_y, weights=weight)
var_x = np.average((crossing_x - x_pos)**2, weights=weight)
var_y = np.average((crossing_y - y_pos)**2, weights=weight)
return x_pos, y_pos, np.sqrt(var_x), np.sqrt(var_y)
def reconstruct_xmax(self, source_x, source_y, core_x, core_y,
hillas_parameters, tel_x, tel_y, zen):
"""
Geometrical depth of shower maximum reconstruction, assuming the shower
maximum lies at the image centroid
Parameters
----------
source_x: float
Source X position in nominal system
source_y: float
Source Y position in nominal system
core_x: float
Core X position in nominal system
core_y: float
Core Y position in nominal system
hillas_parameters: dict
Dictionary of hillas parameters objects
tel_x: dict
Dictionary of telescope X positions in tilted frame
tel_y: dict
Dictionary of telescope Y positions in tilted frame
zen: float
Zenith angle of shower
Returns
-------
float:
Estimated depth of shower maximum
"""
cog_x = list()
cog_y = list()
amp = list()
tx = list()
ty = list()
# Loops over telescopes in event
for tel in hillas_parameters.keys():
cog_x.append(hillas_parameters[tel].x.to_value(u.rad))
cog_y.append(hillas_parameters[tel].y.to_value(u.rad))
amp.append(hillas_parameters[tel].intensity)
tx.append(tel_x[tel].to_value(u.m))
ty.append(tel_y[tel].to_value(u.m))
height = get_shower_height(source_x.to_value(u.rad),
source_y.to_value(u.rad), np.array(cog_x),
np.array(cog_y), core_x.to_value(u.m),
core_y.to_value(u.m), np.array(tx),
np.array(ty))
weight = np.array(amp)
mean_height = np.sum(height * weight) / np.sum(weight)
# This value is height above telescope in the tilted system,
# we should convert to height above ground
mean_height *= np.cos(zen)
# Add on the height of the detector above sea level
mean_height += 2100 # TODO: replace with instrument info
if mean_height > 100000 or np.isnan(mean_height):
mean_height = 100000
mean_height *= u.m
# Lookup this height in the depth tables, the convert Hmax to Xmax
# x_max = self.thickness_profile(mean_height.to(u.km))
# Convert to slant depth
# x_max /= np.cos(zen)
return mean_height
@staticmethod
def intersect_lines(xp1, yp1, phi1, xp2, yp2, phi2):
"""
Perform intersection of two lines. This code is borrowed from read_hess.
Parameters
----------
xp1: ndarray
X position of first image
yp1: ndarray
Y position of first image
phi1: ndarray
Rotation angle of first image
xp2: ndarray
X position of second image
yp2: ndarray
Y position of second image
phi2: ndarray
Rotation angle of second image
Returns
-------
ndarray of x and y crossing points for all pairs
"""
sin_1 = np.sin(phi1)
cos_1 = np.cos(phi1)
a1 = sin_1
b1 = -1 * cos_1
c1 = yp1 * cos_1 - xp1 * sin_1
sin_2 = np.sin(phi2)
cos_2 = np.cos(phi2)
a2 = sin_2
b2 = -1 * cos_2
c2 = yp2 * cos_2 - xp2 * sin_2
det_ab = (a1 * b2 - a2 * b1)
det_bc = (b1 * c2 - b2 * c1)
det_ca = (c1 * a2 - c2 * a1)
# if math.fabs(det_ab) < 1e-14 : # /* parallel */
# return 0,0
xs = det_bc / det_ab
ys = det_ca / det_ab
return xs, ys
@staticmethod
def weight_konrad(p1, p2):
return (p1 * p2) / (p1 + p2)
@staticmethod
def weight_sin(phi1, phi2):
return np.abs(np.sin(phi1 - phi2))
class CameraDemo(Tool):
name = u"ctapipe-camdemo"
description = "Display fake events in a demo camera"
delay = traits.Int(50, help="Frame delay in ms", min=20).tag(config=True)
cleanframes = traits.Int(100,
help="Number of frames between turning on "
"cleaning",
min=0).tag(config=True)
autoscale = traits.Bool(False, help='scale each frame to max if '
'True').tag(config=True)
blit = traits.Bool(False,
help='use blit operation to draw on screen ('
'much faster but may cause some draw '
'artifacts)').tag(config=True)
camera = traits.CaselessStrEnum(
CameraGeometry.get_known_camera_names(),
default_value='NectarCam',
help='Name of camera to display').tag(config=True)
optics = traits.CaselessStrEnum(
OpticsDescription.get_known_optics_names(),
default_value='MST',
help='Telescope optics description name').tag(config=True)
aliases = traits.Dict({
'delay': 'CameraDemo.delay',
'cleanframes': 'CameraDemo.cleanframes',
'autoscale': 'CameraDemo.autoscale',
'blit': 'CameraDemo.blit',
'camera': 'CameraDemo.camera',
'optics': 'CameraDemo.optics',
})
def __init__(self):
super().__init__()
self._counter = 0
self.imclean = False
def start(self):
self.log.info("Starting CameraDisplay for {}".format(self.camera))
self._display_camera_animation()
def _display_camera_animation(self):
# plt.style.use("ggplot")
fig = plt.figure(num="ctapipe Camera Demo", figsize=(7, 7))
ax = plt.subplot(111)
# load the camera
tel = TelescopeDescription.from_name(optics_name=self.optics,
camera_name=self.camera)
geom = tel.camera
# poor-man's coordinate transform from telscope to camera frame (it's
# better to use ctapipe.coordiantes when they are stable)
scale = tel.optics.effective_focal_length.to(geom.pix_x.unit).value
fov = np.deg2rad(4.0)
maxwid = np.deg2rad(0.01)
maxlen = np.deg2rad(0.03)
disp = CameraDisplay(geom,
ax=ax,
autoupdate=True,
title="{}, f={}".format(
tel, tel.optics.effective_focal_length))
disp.cmap = plt.cm.terrain
def update(frame):
centroid = np.random.uniform(-fov, fov, size=2) * scale
width = np.random.uniform(0, maxwid) * scale
length = np.random.uniform(0, maxlen) * scale + width
angle = np.random.uniform(0, 360)
intens = np.random.exponential(2) * 50
model = toymodel.generate_2d_shower_model(centroid=centroid,
width=width,
length=length,
psi=angle * u.deg)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=intens, nsb_level_pe=5000)
# alternate between cleaned and raw images
if self._counter == self.cleanframes:
plt.suptitle("Image Cleaning ON")
self.imclean = True
if self._counter == self.cleanframes * 2:
plt.suptitle("Image Cleaning OFF")
self.imclean = False
self._counter = 0
if self.imclean:
cleanmask = tailcuts_clean(geom, image / 80.0)
for ii in range(3):
dilate(geom, cleanmask)
image[cleanmask == 0] = 0 # zero noise pixels
self.log.debug("count = {}, image sum={} max={}".format(
self._counter, image.sum(), image.max()))
disp.image = image
if self.autoscale:
disp.set_limits_percent(95)
else:
disp.set_limits_minmax(-100, 4000)
disp.axes.figure.canvas.draw()
self._counter += 1
return [
ax,
]
self.anim = FuncAnimation(fig,
update,
interval=self.delay,
blit=self.blit)
plt.show()
class CameraDemo(Tool):
name = "ctapipe-camdemo"
description = "Display fake events in a demo camera"
delay = traits.Int(50, help="Frame delay in ms", min=20).tag(config=True)
cleanframes = traits.Int(20, help="Number of frames between turning on "
"cleaning", min=0).tag(config=True)
autoscale = traits.Bool(False, help='scale each frame to max if '
'True').tag(config=True)
blit = traits.Bool(False, help='use blit operation to draw on screen ('
'much faster but may cause some draw '
'artifacts)').tag(config=True)
camera = traits.CaselessStrEnum(
CameraDescription.get_known_camera_names(),
default_value='NectarCam',
help='Name of camera to display').tag(config=True)
optics = traits.CaselessStrEnum(
OpticsDescription.get_known_optics_names(),
default_value='MST',
help='Telescope optics description name'
).tag(config=True)
num_events = traits.Int(0, help='events to show before exiting (0 for '
'unlimited)').tag(config=True)
display = traits.Bool(True, "enable or disable display (for "
"testing)").tag(config=True)
aliases = traits.Dict({
'delay': 'CameraDemo.delay',
'cleanframes': 'CameraDemo.cleanframes',
'autoscale': 'CameraDemo.autoscale',
'blit': 'CameraDemo.blit',
'camera': 'CameraDemo.camera',
'optics': 'CameraDemo.optics',
'num-events': 'CameraDemo.num_events'
})
def __init__(self):
super().__init__()
self._counter = 0
self.imclean = False
def start(self):
self.log.info(f"Starting CameraDisplay for {self.camera}")
self._display_camera_animation()
def _display_camera_animation(self):
# plt.style.use("ggplot")
fig = plt.figure(num="ctapipe Camera Demo", figsize=(7, 7))
ax = plt.subplot(111)
# load the camera
tel = TelescopeDescription.from_name(optics_name=self.optics,
camera_name=self.camera)
geom = tel.camera.geometry
# poor-man's coordinate transform from telscope to camera frame (it's
# better to use ctapipe.coordiantes when they are stable)
foclen = tel.optics.equivalent_focal_length.to(geom.pix_x.unit).value
fov = np.deg2rad(4.0)
scale = foclen
minwid = np.deg2rad(0.1)
maxwid = np.deg2rad(0.3)
maxlen = np.deg2rad(0.5)
self.log.debug(f"scale={scale} m, wid=({minwid}-{maxwid})")
disp = CameraDisplay(
geom, ax=ax, autoupdate=True,
title=f"{tel}, f={tel.optics.equivalent_focal_length}"
)
disp.cmap = plt.cm.terrain
def update(frame):
x, y = np.random.uniform(-fov, fov, size=2) * scale
width = np.random.uniform(0, maxwid - minwid) * scale + minwid
length = np.random.uniform(0, maxlen) * scale + width
angle = np.random.uniform(0, 360)
intens = np.random.exponential(2) * 500
model = toymodel.Gaussian(
x=x * u.m,
y=y * u.m,
width=width * u.m,
length=length * u.m,
psi=angle * u.deg,
)
self.log.debug(
"Frame=%d width=%03f length=%03f intens=%03d",
frame, width, length, intens
)
image, _, _ = model.generate_image(
geom,
intensity=intens,
nsb_level_pe=3,
)
# alternate between cleaned and raw images
if self._counter == self.cleanframes:
plt.suptitle("Image Cleaning ON")
self.imclean = True
if self._counter == self.cleanframes * 2:
plt.suptitle("Image Cleaning OFF")
self.imclean = False
self._counter = 0
disp.clear_overlays()
if self.imclean:
cleanmask = tailcuts_clean(geom, image,
picture_thresh=10.0,
boundary_thresh=5.0)
for ii in range(2):
dilate(geom, cleanmask)
image[cleanmask == 0] = 0 # zero noise pixels
try:
hillas = hillas_parameters(geom, image)
disp.overlay_moments(hillas, with_label=False,
color='red', alpha=0.7,
linewidth=2, linestyle='dashed')
except HillasParameterizationError:
disp.clear_overlays()
pass
self.log.debug("Frame=%d image_sum=%.3f max=%.3f",
self._counter, image.sum(), image.max())
disp.image = image
if self.autoscale:
disp.set_limits_percent(95)
else:
disp.set_limits_minmax(-5, 200)
disp.axes.figure.canvas.draw()
self._counter += 1
return [ax, ]
frames = None if self.num_events == 0 else self.num_events
repeat = True if self.num_events == 0 else False
self.log.info(f"Running for {frames} frames")
self.anim = FuncAnimation(fig, update,
interval=self.delay,
frames=frames,
repeat=repeat,
blit=self.blit)
if self.display:
plt.show()