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_hillas_overlay():
from ctapipe.visualization import CameraDisplay
disp = CameraDisplay(CameraGeometry.from_name("LSTCam"))
hillas = CameraHillasParametersContainer(x=0.1 * u.m,
y=-0.1 * u.m,
length=0.5 * u.m,
width=0.2 * u.m,
psi=90 * u.deg)
disp.overlay_moments(hillas)
def draw_several_cams(geom, ncams=4):
cmaps = ["jet", "afmhot", "terrain", "autumn"]
fig, axs = plt.subplots(
1,
ncams,
figsize=(15, 4),
)
for ii in range(ncams):
disp = CameraDisplay(
geom,
ax=axs[ii],
title="CT{}".format(ii + 1),
)
disp.cmap = cmaps[ii]
model = toymodel.Gaussian(
x=(0.2 - ii * 0.1) * u.m,
y=(-ii * 0.05) * u.m,
width=(0.05 + 0.001 * ii) * u.m,
length=(0.15 + 0.05 * ii) * u.m,
psi=ii * 20 * u.deg,
)
image, _, _ = model.generate_image(
geom,
intensity=1500,
nsb_level_pe=5,
)
mask = tailcuts_clean(
geom,
image,
picture_thresh=6 * image.mean(),
boundary_thresh=4 * image.mean(),
)
cleaned = image.copy()
cleaned[~mask] = 0
hillas = hillas_parameters(geom, cleaned)
disp.image = image
disp.add_colorbar(ax=axs[ii])
disp.set_limits_percent(95)
disp.overlay_moments(hillas, linewidth=3, color="blue")
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 = 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,
)
mask = tailcuts_clean(
geom,
image,
picture_thresh=6 * image.mean(),
boundary_thresh=4 * image.mean()
)
cleaned = image.copy()
cleaned[~mask] = 0
hillas = hillas_parameters(geom, cleaned)
disp.image = image
disp.add_colorbar(ax=axs[ii])
disp.set_limits_percent(95)
disp.overlay_moments(hillas, linewidth=3, color='blue')
def draw_several_cams(geom, ncams=4):
cmaps = ['jet', 'afmhot', 'terrain', 'autumn']
fig, axs = plt.subplots(
1, ncams, figsize=(15, 4),
)
for ii in range(ncams):
disp = 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=0.05 + 0.001 * ii,
length=0.15 + 0.05 * ii,
psi=ii * 20 * u.deg,
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom,
model.pdf,
intensity=1500,
nsb_level_pe=5,
)
mask = tailcuts_clean(
geom,
image,
picture_thresh=6 * image.mean(),
boundary_thresh=4 * image.mean()
)
cleaned = image.copy()
cleaned[~mask] = 0
hillas = hillas_parameters(geom, cleaned)
disp.image = image
disp.add_colorbar(ax=axs[ii])
disp.set_limits_percent(95)
disp.overlay_moments(hillas, linewidth=3, color='blue')
def main(filename, config_file=None):
geom = read_camera_geometries(filename)["LSTCam"]
dl1_parameters_table = Table.read(filename, path=dl1_params_lstcam_key)
images_table = Table.read(filename, path=dl1_images_lstcam_key)
dl1_table = join(
dl1_parameters_table, images_table, keys=["event_id", "tel_id", "obs_id"]
)
params_cleaning = get_cleaning_config(config_file)
selected_table = dl1_table[np.isfinite(dl1_table["intensity"])]
selected_table = dl1_table[dl1_table["intensity"] > 500]
with PdfPages("images_examples.pdf") as pp:
for ii, row in enumerate(selected_table[:10]):
h = get_hillas_container(row)
image = row["image"]
peak_time = row["peak_time"]
clean_mask = tailcuts_clean(geom, image, **params_cleaning)
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
fig.suptitle(f"event id : {row['event_id']}")
ax = axes[0]
display = CameraDisplay(geom, image, ax=ax)
display.add_colorbar(ax=ax)
ax.set_title("charges")
display.highlight_pixels(clean_mask, color="red", alpha=0.33)
display.overlay_moments(h)
ax = axes[1]
display = CameraDisplay(geom, peak_time, ax=ax)
display.highlight_pixels(clean_mask, color="red", alpha=0.2)
display.add_colorbar(ax=ax)
ax.set_title("peak time")
pp.savefig(dpi=100)
if __name__ == "__main__":
# Load the camera
geom = CameraGeometry.from_name("LSTCam")
disp = CameraDisplay(geom)
disp.add_colorbar()
# Create a fake camera image to display:
model = toymodel.Gaussian(
x=0.2 * u.m, y=0.0 * u.m, width=0.05 * u.m, length=0.15 * u.m, psi="35d"
)
image, sig, bg = model.generate_image(geom, intensity=1500, nsb_level_pe=2)
# Apply image cleaning
cleanmask = tailcuts_clean(geom, image, picture_thresh=10, boundary_thresh=5)
clean = image.copy()
clean[~cleanmask] = 0.0
# Calculate image parameters
hillas = hillas_parameters(geom, clean)
print(hillas)
# Show the camera image and overlay Hillas ellipse and clean pixels
disp.image = image
disp.cmap = "inferno"
disp.highlight_pixels(cleanmask, color="crimson")
disp.overlay_moments(hillas, color="cyan", linewidth=1)
plt.show()
psi='35d')
image, sig, bg = toymodel.make_toymodel_shower_image(geom, model.pdf,
intensity=50,
nsb_level_pe=1000)
# Apply image cleaning
cleanmask = tailcuts_clean(geom, image, picture_thresh=200,
boundary_thresh=100)
clean = image.copy()
clean[~cleanmask] = 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 and clean pixels
disp.image = image
disp.cmap = 'PuOr'
disp.highlight_pixels(cleanmask, color='black')
disp.overlay_moments(hillas, color='cyan', 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()
def main():
args = parser.parse_args()
event_generator = fact_event_generator(
args.inputfile,
args.drsfile,
allowed_triggers={4},
)
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_axes([0, 0, 0.4, 1])
ax1.set_axis_off()
divider = make_axes_locatable(ax1)
cax1 = divider.append_axes('right', size="5%", pad=0.05)
ax2 = fig.add_axes([0.5, 0.0, 0.4, 1])
ax2.set_axis_off()
divider = make_axes_locatable(ax2)
cax2 = divider.append_axes('right', size="5%", pad=0.05)
geom = CameraGeometry.from_name('FACT')
disp1 = CameraDisplay(geom, ax=ax1)
disp1.add_colorbar(cax=cax1, label='Photons')
disp2 = CameraDisplay(geom, ax=ax2)
disp2.add_colorbar(cax=cax2, label='ArrivalTime')
ax1.set_title('Photons')
ax2.set_title('Peak Position')
for e in event_generator:
dl1_calibrator.calibrate(e)
image = e.dl1.tel[0].image[0]
cleaning_mask = tailcuts_clean(geom, image, 5, 3.5)
if sum(cleaning_mask) < 15:
continue
hillas_container = hillas_parameters(
geom.pix_x[cleaning_mask],
geom.pix_y[cleaning_mask],
image[cleaning_mask],
)
disp1.overlay_moments(hillas_container,
linewidth=1.5,
color='c',
with_label=False)
disp1.highlight_pixels(cleaning_mask)
disp1.image = e.dl1.tel[0].image[0]
disp2.image = e.dl1.tel[0].peakpos[0]
for disp in (disp1, disp2):
disp.highlight_pixels(cleaning_mask, color='r', linewidth=1.5)
fig.suptitle('FACT Event {}'.format(e.trig.gps_time.iso))
plt.pause(0.01)
input('Press enter for next event')
# get a single image
for i, img in enumerate(source):
# Pick a (random) good looking image
if i > 147:
break
im = img.dl1.tel[0].image
print(img)
# Apply image cleaning
cleanmask = tailcuts_clean(
geom,
im,
picture_thresh=30,
boundary_thresh=5,
min_number_picture_neighbors=0,
)
if sum(cleanmask) == 0:
pass
else:
# Calculate image parameters
hillas = hillas_parameters(geom[cleanmask], im[cleanmask])
# Show the camera image and overlay Hillas ellipse and clean pixels
disp.image = im
disp.highlight_pixels(cleanmask, color="crimson")
disp.overlay_moments(hillas, color="red", linewidth=2)
plt.savefig("build/hillas_example.png", dpi=200)
def start(self):
disp = None
for event in tqdm(self.event_source,
desc='Tel{}'.format(self.tel),
total=self.event_source.max_events,
disable=~self.progress):
self.log.debug(event.trig)
self.log.debug("Energy: {}".format(event.mc.energy))
self.calibrator.calibrate(event)
if disp is None:
geom = event.inst.subarray.tel[self.tel].camera
self.log.info(geom)
disp = CameraDisplay(geom)
# disp.enable_pixel_picker()
disp.add_colorbar()
if self.display:
plt.show(block=False)
# display the event
disp.axes.set_title('CT{:03d} ({}), event {:06d}'.format(
self.tel, geom.cam_id, event.r0.event_id))
if self.samples:
# display time-varying event
data = event.dl0.tel[self.tel].pe_samples[self.channel]
for ii in range(data.shape[1]):
disp.image = data[:, ii]
disp.set_limits_percent(70)
plt.suptitle("Sample {:03d}".format(ii))
if self.display:
plt.pause(self.delay)
if self.write:
plt.savefig('CT{:03d}_EV{:10d}_S{:02d}.png'.format(
self.tel, event.r0.event_id, ii))
else:
# display integrated event:
im = event.dl1.tel[self.tel].image[self.channel]
if self.clean:
mask = tailcuts_clean(geom,
im,
picture_thresh=10,
boundary_thresh=7)
im[~mask] = 0.0
disp.image = im
if self.hillas:
try:
ellipses = disp.axes.findobj(Ellipse)
if len(ellipses) > 0:
ellipses[0].remove()
params = hillas_parameters(geom, image=im)
disp.overlay_moments(params,
color='pink',
lw=3,
with_label=False)
except HillasParameterizationError:
pass
if self.display:
plt.pause(self.delay)
if self.write:
plt.savefig('CT{:03d}_EV{:010d}.png'.format(
self.tel, event.r0.event_id))
self.log.info("FINISHED READING DATA FILE")
if disp is None:
self.log.warning(
'No events for tel {} were found in {}. Try a '
'different EventIO file or another telescope'.format(
self.tel, self.infile), )
pass
boundary_thresh=boundary,
picture_thresh=picture,
min_number_picture_neighbors=min_num_neighbors)
cleaned_image = image.copy()
cleaned_image[~clean] = 0.0
hillas_c = hillas_parameters(camera[clean], image[clean])
hillas_containers[telescope_id] = hillas_c
if len(hillas_containers) < 2:
print("break")
length = np.linspace(-1, 1, 30)
length = u.Quantity(length, u.m, copy=False)
d3 = CameraDisplay(camera, ax=axs[2])
d3.image = cleaned_image
d3.axes.scatter(0, 0, color="red", s=50)
d3.overlay_moments(hillas_containers[telescope_id],
color="red",
with_label=False,
lw=2,
keep_old=True)
x = hillas_containers[telescope_id].x + length * np.cos(
hillas_containers[telescope_id].psi)
y = hillas_containers[telescope_id].y + length * np.sin(
hillas_containers[telescope_id].psi)
d3.axes.plot(x, y)
plt.show()
def start(self):
# Get first event information
first_event = self.reader.get_event(0)
n_pixels = first_event.inst.num_pixels[0]
n_samples = first_event.r0.tel[0].num_samples
pos = first_event.inst.pixel_pos[0]
foclen = first_event.inst.optical_foclen[0]
geom = CameraGeometry.guess(*pos, foclen)
# Setup Output
output_dir = self.reader.output_directory
title = self.reader.filename
title = title[:title.find("_")]
# Prepare Output
if not exists(output_dir):
self.log.info("Creating directory: {}".format(output_dir))
makedirs(output_dir)
output_path = join(output_dir, title + "_events.pdf")
# Setup plot
fig = plt.figure(figsize=(10, 10))
ax_camera = fig.add_subplot(1, 1, 1)
fig.patch.set_visible(False)
ax_camera.axis('off')
camera = CameraDisplay(geom,
ax=ax_camera,
image=np.zeros(2048),
cmap='viridis')
camera.add_colorbar()
cb = camera.colorbar
camera.colorbar.set_label("Amplitude (p.e.)")
fig.suptitle(title)
source = self.reader.read()
desc = "Looping through file"
with PdfPages(output_path) as pdf:
for event in tqdm(source, desc=desc):
ev = event.count
event_id = event.r0.event_id
self.r1.calibrate(event)
self.dl0.reduce(event)
self.dl1.calibrate(event)
for t in event.r0.tels_with_data:
dl1 = event.dl1.tel[t].image[0]
# Cleaning
tc = tailcuts_clean(geom, dl1, 20, 10)
if not tc.any():
continue
cleaned_dl1 = np.ma.masked_array(dl1, mask=~tc)
try:
# hillas = hillas_parameters(*pos, cleaned_tc)
hillas = hillas_parameters_4(*pos, cleaned_dl1)
except HillasParameterizationError:
continue
ax_camera.cla()
camera = CameraDisplay(geom,
ax=ax_camera,
image=np.zeros(2048),
cmap='viridis')
camera.colorbar = cb
camera.image = dl1
max_ = cleaned_dl1.max() # np.percentile(dl1, 99.9)
min_ = np.percentile(dl1, 0.1)
camera.set_limits_minmax(min_, max_)
camera.highlight_pixels(tc, 'white')
camera.overlay_moments(hillas, color='red')
camera.update(True)
ax_camera.set_title("Event: {}".format(event_id))
ax_camera.axis('off')
pdf.savefig(fig)
self.log.info("Created images: {}".format(output_path))
# ax2 = fig.add_subplot(222)
ax2 = fig.add_subplot(131)
disp2 = CameraDisplay(camera, image=pmt_signal, ax=ax2)
disp2.cmap = plt.cm.inferno
disp2.add_colorbar(label="signal")
plt.title("calibrated noisy image")
# ax3 = fig.add_subplot(223)
ax3 = fig.add_subplot(132)
disp3 = CameraDisplay(new_geom_t,
image=np.sqrt(pmt_signal_t),
ax=ax3)
disp3.cmap = plt.cm.inferno
disp3.add_colorbar(label="sqrt(signal)")
disp3.overlay_moments(hillas['t'],
color='seagreen',
linewidth=3)
plt.title(
"tailcut cleaned ({},{}) ; alpha = {:4.3f}".format(
Cleaner['t'].tail_thresholds[new_geom_t.cam_id][0],
Cleaner['t'].tail_thresholds[new_geom_t.cam_id][1],
alpha['t']))
# ax4 = fig.add_subplot(224)
ax4 = fig.add_subplot(133)
disp4 = CameraDisplay(
new_geom_w,
image=np.sqrt(
np.sum(pmt_signal_w, axis=1) if pmt_signal_w.
shape[-1] == 25 else pmt_signal_w),
ax=ax4)
class EventViewer():
def __init__(
self,
event_stream,
camera=DigiCam,
scale='lin',
limits_colormap=None,
limits_readout=None,
time_bin_start=0,
pixel_id_start=0
):
matplotlib.figure.autolayout = False
self.first_call = True
self.event_stream = event_stream
self.scale = scale
if limits_colormap is not None:
self.limits_colormap = limits_colormap
else:
self.limits_colormap = [-np.inf, np.inf]
self.limits_readout = limits_readout
self.time_bin = time_bin_start
self.pixel_id = pixel_id_start
self.mask_pixels = False
self.hillas = False
self.event_clicked_on = EventClicked(pixel_start=self.pixel_id)
self.camera = camera
self.n_pixels = len(self.camera.Pixels)
self.cluster_matrix = np.zeros(
(len(self.camera.Clusters_7), len(self.camera.Clusters_7))
)
for cluster in self.camera.Clusters_7:
for patch in cluster.patchesID:
self.cluster_matrix[cluster.ID, patch] = 1
self.event_id = None
self.r0_container = None
self.r1_container = None
self.dl0_container = None
self.dl1_container = None
self.dl2_container = None
self.trigger_output = None
self.trigger_input = None
self.trigger_patch = None
self.n_samples = None
self.adc_samples = None
self.nsb = [np.nan] * self.n_pixels
self.gain_drop = [np.nan] * self.n_pixels
self.baseline = [np.nan] * self.n_pixels
self.std = [np.nan] * self.n_pixels
self.flag = None
self.readout_view_types = [
'raw', 'baseline substracted', 'photon', 'trigger input',
'trigger output', 'cluster 7', 'reconstructed charge'
]
self.readout_view_type = 'raw'
self.camera_view_types = ['sum', 'std', 'mean', 'max', 'time']
self.camera_view_type = 'std'
self.figure = plt.figure(figsize=(20, 10))
self.axis_readout = self.figure.add_subplot(122)
self.axis_camera = self.figure.add_subplot(121)
self.axis_camera.axis('off')
self.axis_readout.set_xlabel('t [ns]')
self.axis_readout.set_ylabel('[ADC]')
self.axis_readout.legend(loc='upper right')
self.axis_readout.yaxis.set_major_formatter(FormatStrFormatter('%d'))
self.axis_readout.yaxis.set_major_locator(
MaxNLocator(integer=True, nbins=10)
)
self.trace_readout = None
self.trace_time_plot, = self.axis_readout.plot(
np.array([self.time_bin, self.time_bin]) * 4,
np.ones(2),
color='k',
linestyle='--'
)
self.camera_visu = CameraDisplay(
self.camera.geometry,
ax=self.axis_camera,
title='',
norm=self.scale,
cmap='viridis',
allow_pick=True
)
self.camera_visu.image = np.zeros(self.n_pixels)
self.camera_visu.cmap.set_bad(color='k')
self.camera_visu.add_colorbar(
orientation='horizontal', pad=0.03, fraction=0.05, shrink=.85
)
self.camera_visu.colorbar.set_label('[LSB]')
self.camera_visu.axes.get_xaxis().set_visible(False)
self.camera_visu.axes.get_yaxis().set_visible(False)
self.camera_visu.on_pixel_clicked = self.draw_readout
self.camera_visu.pixels.set_snap(False) # snap cursor to pixel center
# Buttons
self.axis_next_event_button = self.figure.add_axes(
[0.35, 0.9, 0.15, 0.07], zorder=np.inf
)
self.axis_next_camera_view_button = self.figure.add_axes(
[0., 0.85, 0.1, 0.15], zorder=np.inf
)
self.axis_next_view_type_button = self.figure.add_axes(
[0., 0.18, 0.1, 0.15], zorder=np.inf
)
self.axis_check_button = self.figure.add_axes(
[0.35, 0.18, 0.1, 0.1], zorder=np.inf
)
self.axis_next_camera_view_button.axis('off')
self.axis_next_view_type_button.axis('off')
self.button_next_event = Button(self.axis_next_event_button, 'Next')
self.radio_button_next_camera_view = RadioButtons(
self.axis_next_camera_view_button,
self.camera_view_types,
active=self.camera_view_types.index(self.camera_view_type)
)
self.radio_button_next_view_type = RadioButtons(
self.axis_next_view_type_button,
self.readout_view_types,
active=self.readout_view_types.index(self.readout_view_type)
)
self.check_button = CheckButtons(
self.axis_check_button, ('mask', 'hillas'),
(self.mask_pixels, self.hillas)
)
self.radio_button_next_view_type.set_active(
self.readout_view_types.index(self.readout_view_type)
)
self.radio_button_next_camera_view.set_active(
self.camera_view_types.index(self.camera_view_type)
)
def next(self, event=None, step=1):
for i, event in zip(range(step), self.event_stream):
pass
telescope_id = event.r0.tels_with_data[0]
self.event_id = event.r0.tel[telescope_id].camera_event_number
self.r0_container = event.r0.tel[telescope_id]
self.r1_container = event.r1.tel[telescope_id]
self.dl0_container = event.dl0.tel[telescope_id]
self.dl1_container = event.dl1.tel[telescope_id]
self.dl2_container = event.dl2
self.adc_samples = self.r0_container.adc_samples
if hasattr(self.r0_container, 'trigger_output_patch7'):
self.trigger_output = self.r0_container.trigger_output_patch7
if hasattr(self.r0_container, 'trigger_input_traces'):
self.trigger_input = self.r0_container.trigger_input_traces
self.n_samples = self.adc_samples.shape[1]
if self.trace_readout is None:
self.trace_readout, = self.axis_readout.step(
np.arange(self.n_samples) * 4,
np.ones(self.n_samples),
where='mid'
)
try:
if np.isnan(self.r0_container.digicam_baseline).all():
self.baseline = self.r0_container.baseline
else:
self.baseline = self.r0_container.digicam_baseline
zero_image = np.zeros((self.n_pixels, self.n_samples))
if self.r0_container.standard_deviation is not None:
self.std = self.r0_container.standard_deviation
else:
self.std = np.nan * zero_image
if self.r0_container.camera_event_type is not None:
self.flag = self.r0_container.camera_event_type
else:
self.flag = np.nan
if self.r1_container.nsb is not None:
self.nsb = self.r1_container.nsb
else:
self.nsb = np.nan * zero_image
if self.r1_container.gain_drop is not None:
self.gain_drop = self.r1_container.gain_drop
else:
self.gain_drop = np.nan * zero_image
except:
pass
if self.first_call:
self.first_call = False
self.update()
def update(self):
self.draw_readout(self.pixel_id)
self.draw_camera()
self.button_next_event.label.set_text(
'Next : current event {}'.format(self.event_id))
def draw(self):
self.next()
self.button_next_event.on_clicked(self.next)
self.radio_button_next_camera_view.on_clicked(self.next_camera_view)
self.radio_button_next_view_type.on_clicked(self.next_view_type)
self.check_button.on_clicked(self.draw_on_camera)
self.figure.canvas.mpl_connect('key_press_event', self.press)
self.camera_visu._on_pick(self.event_clicked_on)
plt.show()
def draw_camera(self, plot_hillas=False):
image = self.compute_image()
if image is None:
print("Warning: unable to compute image in draw_camera()")
return
self.camera_visu.image = image
if plot_hillas:
self.camera_visu.overlay_moments(self.dl2_container.shower)
def draw_readout(self, pixel):
trace = self.compute_trace()
if trace is None:
print('WARNING: unable to compute trace in draw_readout()')
return
y = trace[pixel]
if self.limits_readout is not None:
limits_y = self.limits_readout
else:
limits_y = [np.min(y), np.max(y) + 10]
self.pixel_id = pixel
self.event_clicked_on.ind[-1] = self.pixel_id
self.trace_readout.set_ydata(y)
legend = ''
try:
legend += ' flag = {},'.format(self.flag)
except:
pass
legend += ' pixel = {},'.format(self.pixel_id)
legend += ' bin = {} \n'.format(self.time_bin)
try:
legend += ' B = {:0.2f} [LSB],'.format(
self.baseline[self.pixel_id]
)
except:
pass
try:
legend += ' $\sigma = $ {:0.2f} [LSB] \n'.format(
self.std[self.pixel_id]
)
except:
pass
try:
legend += ' $G_{{drop}} = $ {:0.2f},'.format(
self.gain_drop[self.pixel_id]
)
legend += ' $f_{{nsb}} = $ {:0.2f} [GHz]'.format(
self.nsb[self.pixel_id]
)
except:
pass
self.trace_readout.set_label(legend)
self.trace_time_plot.set_ydata(limits_y)
self.trace_time_plot.set_xdata(self.time_bin * 4)
self.axis_readout.set_ylim(limits_y)
self.axis_readout.legend(loc='upper right')
if self.readout_view_type in ['photon', 'reconstructed charge']:
self.axis_readout.set_ylabel('[p.e.]')
else:
self.axis_readout.set_ylabel('[LSB]')
def compute_trace(self):
image = None
if self.readout_view_type in self.readout_view_types:
if self.readout_view_type == 'raw':
image = self.adc_samples
elif (
self.readout_view_type == 'trigger output' and
self.trigger_output is not None
):
image = np.array(
[
self.trigger_output[pixel.patch]
for pixel in self.camera.Pixels
]
)
elif (
self.readout_view_type == 'trigger input' and
self.trigger_input is not None
):
image = np.array(
[
self.trigger_input[pixel.patch]
for pixel in self.camera.Pixels
]
)
elif (
self.readout_view_type == 'cluster 7' and
self.trigger_input is not None
):
trigger_input_patch = np.dot(
self.cluster_matrix, self.trigger_input
)
image = np.array(
[
trigger_input_patch[pixel.patch]
for pixel in self.camera.Pixels
]
)
elif (
self.readout_view_type == 'photon' and
self.dl1_container.pe_samples_trace is not None
):
image = self.dl1_container.pe_samples_trace
elif (
self.readout_view_type == 'baseline substracted' and
self.r1_container.adc_samples is not None
):
image = self.adc_samples - self.baseline[:, np.newaxis]
elif (
self.readout_view_type == 'reconstructed charge' and
self.dl1_container.time_bin is not None or
self.dl1_container.pe_samples is not None
):
image = np.zeros((self.n_pixels, self.n_samples))
time_bins = self.dl1_container.time_bin
image[time_bins] = self.dl1_container.pe_samples
else:
image = np.zeros((self.n_pixels, self.n_samples))
if image is None:
print('WARNING: unexpected error in compute_trace()',
'with readout_view_type=', self.readout_view_type)
else:
print('WARNING: requested view type (', self.readout_view_type,
'is not in the available view_types:',
self.readout_view_types)
return image
def next_camera_view(self, camera_view, event=None):
self.camera_view_type = camera_view
if self.readout_view_type in ['photon', 'reconstructed charge']:
self.camera_visu.colorbar.set_label('[p.e.]')
else:
self.camera_visu.colorbar.set_label('[LSB]')
self.update()
def next_view_type(self, view_type, event=None):
self.readout_view_type = view_type
if view_type in ['photon', 'reconstructed charge']:
self.camera_visu.colorbar.set_label('[p.e.]')
else:
self.camera_visu.colorbar.set_label('[LSB]')
self.update()
def draw_on_camera(self, to_draw_on, event=None):
if to_draw_on == 'hillas':
if self.hillas:
self.hillas = False
else:
self.hillas = True
if to_draw_on == 'mask':
if self.mask_pixels:
self.mask_pixels = False
else:
self.mask_pixels = True
self.update()
def set_time(self, time):
if time < self.n_samples and time >= 0:
self.time_bin = time
self.update()
def set_pixel(self, pixel_id):
if pixel_id < self.n_samples and pixel_id >= 0:
self.pixel_id = pixel_id
self.update()
def compute_image(self):
image = self.compute_trace()
if image is None:
print('WARNING: unable to compute trace in compute_image()')
return None
if self.camera_view_type in self.camera_view_types:
if self.camera_view_type == 'mean':
self.image = np.mean(image, axis=1)
elif self.camera_view_type == 'std':
self.image = np.std(image, axis=1)
elif self.camera_view_type == 'max':
self.image = np.max(image, axis=1)
elif self.camera_view_type == 'sum':
self.image = np.sum(image, axis=1)
elif self.camera_view_type == 'time':
self.image = image[:, self.time_bin]
else:
print('Cannot compute for camera type : %s' %
self.camera_view_type)
if self.limits_colormap is not None:
mask = (self.image >= self.limits_colormap[0])
if not self.limits_colormap[1] == np.inf:
image[(self.image > self.limits_colormap[1])] = \
self.limits_colormap[1]
if self.mask_pixels:
mask = mask * self.dl1_container.cleaning_mask
if self.hillas:
self.camera_visu.overlay_moments(
self.dl2_container.shower, color='r', linewidth=4
)
else:
self.camera_visu.clear_overlays()
return np.ma.masked_where(~mask, self.image)
def press(self, event):
sys.stdout.flush()
if event.key == 'enter':
self.next()
if event.key == 'right':
self.set_time(self.time_bin + 1)
if event.key == 'left':
self.set_time(self.time_bin - 1)
if event.key == '+':
self.set_pixel(self.pixel_id + 1)
if event.key == '-':
self.set_pixel(self.pixel_id - 1)
if event.key == 'h':
self.axis_next_event_button.set_visible(False)
if event.key == 'v':
self.axis_next_event_button.set_visible(True)
self.update()
def start(self):
disp = None
for event in tqdm(
self.event_source,
desc=f"Tel{self.tel}",
total=self.event_source.max_events,
disable=~self.progress,
):
self.log.debug(event.trigger)
self.log.debug(f"Energy: {event.simulation.shower.energy}")
self.calibrator(event)
if disp is None:
geom = self.event_source.subarray.tel[self.tel].camera.geometry
self.log.info(geom)
disp = CameraDisplay(geom)
# disp.enable_pixel_picker()
disp.add_colorbar()
if self.display:
plt.show(block=False)
# display the event
disp.axes.set_title("CT{:03d} ({}), event {:06d}".format(
self.tel, geom.camera_name, event.index.event_id))
if self.samples:
# display time-varying event
data = event.dl0.tel[self.tel].waveform
for ii in range(data.shape[1]):
disp.image = data[:, ii]
disp.set_limits_percent(70)
plt.suptitle(f"Sample {ii:03d}")
if self.display:
plt.pause(self.delay)
if self.write:
plt.savefig(
f"CT{self.tel:03d}_EV{event.index.event_id:10d}"
f"_S{ii:02d}.png")
else:
# display integrated event:
im = event.dl1.tel[self.tel].image
if self.clean:
mask = tailcuts_clean(geom,
im,
picture_thresh=10,
boundary_thresh=7)
im[~mask] = 0.0
disp.image = im
if self.hillas:
try:
ellipses = disp.axes.findobj(Ellipse)
if len(ellipses) > 0:
ellipses[0].remove()
params = hillas_parameters(geom, image=im)
disp.overlay_moments(params,
color="pink",
lw=3,
with_label=False)
except HillasParameterizationError:
pass
if self.display:
plt.pause(self.delay)
if self.write:
plt.savefig(
f"CT{self.tel:03d}_EV{event.index.event_id:010d}.png")
self.log.info("FINISHED READING DATA FILE")
if disp is None:
self.log.warning(
"No events for tel {} were found in {}. Try a "
"different EventIO file or another telescope".format(
self.tel, self.infile))
def start(self):
disp = None
for event in tqdm(self.source,
desc='Tel{}'.format(self.tel),
total=self.reader.max_events,
disable=~self.progress):
self.log.debug(event.trig)
self.log.debug("Energy: {}".format(event.mc.energy))
self.calibrator.calibrate(event)
if disp is None:
x, y = event.inst.pixel_pos[self.tel]
focal_len = event.inst.optical_foclen[self.tel]
geom = CameraGeometry.guess(x, y, focal_len)
self.log.info(geom)
disp = CameraDisplay(geom)
# disp.enable_pixel_picker()
disp.add_colorbar()
if self.display:
plt.show(block=False)
# display the event
disp.axes.set_title('CT{:03d} ({}), event {:06d}'.format(
self.tel, geom.cam_id, event.r0.event_id)
)
if self.samples:
# display time-varying event
data = event.dl0.tel[self.tel].pe_samples[self.channel]
for ii in range(data.shape[1]):
disp.image = data[:, ii]
disp.set_limits_percent(70)
plt.suptitle("Sample {:03d}".format(ii))
if self.display:
plt.pause(self.delay)
if self.write:
plt.savefig('CT{:03d}_EV{:10d}_S{:02d}.png'
.format(self.tel, event.r0.event_id, ii))
else:
# display integrated event:
im = event.dl1.tel[self.tel].image[self.channel]
if self.clean:
mask = tailcuts_clean(geom, im, picture_thresh=10,
boundary_thresh=7)
im[~mask] = 0.0
disp.image = im
if self.hillas:
try:
ellipses = disp.axes.findobj(Ellipse)
if len(ellipses) > 0:
ellipses[0].remove()
params = hillas_parameters(pix_x=geom.pix_x,
pix_y=geom.pix_y, image=im)
disp.overlay_moments(params, color='pink', lw=3,
with_label=False)
except HillasParameterizationError:
pass
if self.display:
plt.pause(self.delay)
if self.write:
plt.savefig('CT{:03d}_EV{:010d}.png'
.format(self.tel, event.r0.event_id))
self.log.info("FINISHED READING DATA FILE")
if disp is None:
self.log.warning('No events for tel {} were found in {}. Try a '
'different EventIO file or another telescope'
.format(self.tel, self.infile),
)
pass
def main(dl1_hipecta_filename, dl1_lstchain_filename):
geom = CameraGeometry.from_name('LSTCam')
dl1_hipecta = tables.open_file(dl1_hipecta_filename)
dl1_lstchain = tables.open_file(dl1_lstchain_filename)
with tables.open_file(dl1_hipecta_filename) as dl1_hipecta:
hipecta_images = Table(dl1_hipecta.root.dl1.Tel_1.calib_pic.read())
hipecta_parameters = Table(dl1_hipecta.root.dl1.Tel_1.parameters.read())
with tables.open_file(dl1_lstchain_filename) as dl1_lstchain:
simu_table = Table(dl1_lstchain.root.dl1.event.simulation.LST_LSTCam.read())
lstchain_images = Table(dl1_lstchain.root[dl1_images_lstcam_key].read())
hipecta = join(hipecta_images, hipecta_parameters, keys='event_id')
lstchain_table = hstack([lstchain_images, simu_table], join_type='exact')
lstchain_table.rename_column('tel_id_1', 'tel_id')
lstchain_table.remove_column('tel_id_2')
mega_table = join(lstchain_table[lstchain_table['tel_id']==1],
hipecta,
uniq_col_name='{table_name}_{col_name}',
table_names = ['lstchain', 'hipecta'],
keys='event_id'
)
selected_table = mega_table[:30]
params_cleaning = dict(picture_thresh=6,
boundary_thresh=3,
keep_isolated_pixels=False,
min_number_picture_neighbors=2)
lstchain_cleaning = np.apply_along_axis(tailcuts_clean_teltype, selected_table['image'], **params_cleaning)
selected_table.add_column(Column(lstchain_cleaning, dtype=int), name='lstchain_clean_mask')
with PdfPages(f'compare_lstchain_hipecta_images_{date.today()}.pdf') as pp:
for ii, row in enumerate(selected_table[:10]):
print(f"{ii}. event id : {row['event_id']}")
# print(row)
h = get_hillas_container(row)
image_lstchain = row['image']
image_hipecta = row['signal']
clean_mask_ctapipe_on_lstchain = row['lstchain_clean_mask']
clean_mask_ctapipe_on_hipecta = tailcuts_clean(geom, image_hipecta, **params_cleaning)
clean_mask_hipecta = row['clean_mask'].astype(bool)
fig, axes = plt.subplots(2,3, figsize=(12,6))
# axes[0,2].remove()
display = CameraDisplay(geom, image_lstchain, ax=axes[0,0])
display.add_colorbar(ax=axes[0,0])
axes[0,0].set_title('lstchain image')
display = CameraDisplay(geom, clean_mask_ctapipe_on_lstchain, ax=axes[0,1])
# display.add_colorbar(ax=axes[0,1])
display.highlight_pixels(clean_mask_ctapipe_on_lstchain.astype(bool), color='red')
axes[0,1].set_title('lstchain clean mask')
display = CameraDisplay(geom, image_hipecta, ax=axes[1,0])
display.add_colorbar(ax=axes[1,0])
axes[1,0].set_title('hipecta image')
display = CameraDisplay(geom, clean_mask_hipecta, ax=axes[1,1])
# display.add_colorbar(ax=axes[1,1])
display.highlight_pixels(clean_mask_ctapipe_on_hipecta, color='red')
axes[1,1].set_title('hipecta clean mask')
axes[1,1].text(0.88,0.88,s='cleaning mask\nfrom ctapipe',color='red')
axes[1,1].text(-1.5, 0.88, s=f'n_islands={row["n_islands"]}', color='black')
display.overlay_moments(h)
display = CameraDisplay(geom, row['photo_electron_image'], ax=axes[0,2])
display.add_colorbar(ax=axes[0,2])
axes[0,2].set_title('true pe image')
display.highlight_pixels(clean_mask_ctapipe_on_lstchain.astype(bool), color='red')
axes[0,2].text(0.88, 0.88, s='cleaning mask\nfrom ctapipe', color='red')
display = CameraDisplay(geom, row['photo_electron_image'], ax=axes[1,2])
display.add_colorbar(ax=axes[1,2])
axes[1,2].set_title('true pe image')
display.highlight_pixels(clean_mask_hipecta, color='red')
axes[1,2].text(0.88,0.88,s='cleaning mask\nfrom hipecta',color='red')
plt.tight_layout()
pp.savefig(dpi=100)