def show_image(simtel_file_path, output_file_path, tel_num, event_id, channel=0, quiet=False):
# GET EVENT #############################################################
# hessio_event_source returns a Python generator that streams data from an
# EventIO/HESSIO MC data file (e.g. a standard CTA data file).
# This generator contains ctapipe.core.Container instances ("event").
#
# Parameters:
# - max_events: maximum number of events to read
# - allowed_tels: select only a subset of telescope, if None, all are read.
source = hessio_event_source(simtel_file_path, allowed_tels=[tel_num])
event = None
for ev in source:
if int(ev.dl0.event_id) == event_id:
event = ev
break
if event is None:
raise Exception("Error: event '{}' not found for telescope '{}'.".format(event_id, tel_num))
# INIT PLOT #############################################################
x, y = event.meta.pixel_pos[tel_num]
foclen = event.meta.optical_foclen[tel_num]
geom = ctapipe.io.CameraGeometry.guess(x, y, foclen)
disp = ctapipe.visualization.CameraDisplay(geom, title='CT%d' % tel_num)
disp.enable_pixel_picker()
disp.add_colorbar()
disp.axes.set_title('Telescope {:03d}, Event {:05d}'.format(tel_num, event_id))
# GET PHOTOELECTRON IMAGE (CLEAN IMAGE) #################################
disp.image = event.mc.tel[tel_num].photo_electrons
# COMPUTE hILLAS PARAMETERS #############################################
image = disp.image.copy()
#hillas = hillas_parameters(geom.pix_x.value, geom.pix_y.value, image)
hillas = hillas_parameters(geom.pix_x, geom.pix_y, image)
print("Hillas parameters:", hillas)
# PLOT ##################################################################
#disp.set_limits_minmax(0, 9000)
disp.set_limits_percent(70) # TODO
# Plot Hillas parameters
disp.overlay_moments(hillas, linewidth=3, color='yellow')
plt.savefig(output_file_path, bbox_inches='tight')
if not quiet:
plt.show()
disp.image = im
if args.hillas:
clean_mask = reco.cleaning.tailcuts_clean(geom,im,1,picture_thresh=10,boundary_thresh=5)
camera_coord = CameraFrame(x=x,y=y,z=np.zeros(x.shape)*u.m)
nom_coord = camera_coord.transform_to(NominalFrame(array_direction=[70*u.deg,0*u.deg],
pointing_direction=[70*u.deg,0*u.deg],
focal_length=tel['TelescopeTable_VersionFeb2016'][tel['TelescopeTable_VersionFeb2016']['TelID']==args.tel]['FL'][0]*u.m))
image = np.asanyarray(im * clean_mask, dtype=np.float64)
nom_x = nom_coord.x
nom_y = nom_coord.y
hillas = reco.hillas_parameters(x,y,im * clean_mask)
hillas_nom = reco.hillas_parameters(nom_x,nom_y,im * clean_mask)
print (hillas)
print (hillas_nom)
disp.image = im * clean_mask
disp.overlay_moments(hillas, color='seagreen', linewidth=3)
disp.set_limits_percent(70)
plt.pause(1.0)
if args.write:
plt.savefig('CT{:03d}_EV{:010d}.png'
.format(args.tel, event.dl0.event_id))
print("FINISHED READING DATA FILE")
# 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()
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.value, geom.pix_y.value, 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()
# 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.value, geom.pix_y.value, 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()
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()