def make_camera_binary_image(image, sigma, clean_bound, death_pixel_ids_list):
fig, ax = plt.subplots(1, 2, figsize=(14, 7))
geom = CameraGeometry.from_name('LSTCam-003')
disp0 = CameraDisplay(geom, ax=ax[0])
disp0.image = image
disp0.cmap = plt.cm.gnuplot2
disp0.add_colorbar(ax=ax[0])
ax[0].set_title(
"Cleaning threshold from interleaved pedestal. \n sigma = {}".format(
sigma),
fontsize=15)
disp1 = CameraDisplay(geom, ax=ax[1])
disp1.image = image
cmap = matplotlib.colors.ListedColormap(['black', 'red'])
bounds = [0, clean_bound, 2 * clean_bound]
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
disp1.cmap = cmap
disp1.add_colorbar(norm=norm,
boundaries=bounds,
ticks=[0, clean_bound, 2 * clean_bound])
disp1.set_limits_minmax(0, 2 * clean_bound)
disp1.highlight_pixels(death_pixel_ids_list, linewidth=3)
ax[1].set_title("Red pixels - above cleaning tailcut threshold",
fontsize=15)
plt.tight_layout()
plt.show()
def create(self, df, geom):
super().save()
camera = CameraDisplay(geom, ax=self.ax,
image=np.ma.zeros(2048),
cmap='viridis')
camera.add_colorbar(pad=-0.2)
camera.colorbar.set_label("Peak Time (ns)", fontsize=20)
with PdfPages(self.output_path) as pdf:
n_rows = len(df.index)
desc = "Saving image pages"
for index, row in tqdm(df.iterrows(), total=n_rows, desc=desc):
event_id = row['id']
tel = row['tel']
image = row['peak_time']
tc = row['tc']
hillas = row['hillas']
cleaned_image = np.ma.masked_array(image, mask=~tc)
cleaned_image.fill_value = 0
max_ = np.percentile(cleaned_image.compressed(), 99)
min_ = np.percentile(cleaned_image.compressed(), 1)
camera.image = cleaned_image
camera.set_limits_minmax(min_, max_)
camera.highlight_pixels(np.arange(2048), 'black', 1, 0.2)
# camera.overlay_moments_update(hillas, color='red')
self.ax.set_title("Event: {}, Tel: {}".format(event_id, tel))
self.ax.axis('off')
camera.colorbar.ax.tick_params(labelsize=30)
pdf.savefig(self.fig)
def test_camera_display_single():
""" test CameraDisplay functionality """
from ..mpl_camera import CameraDisplay
geom = CameraGeometry.from_name("LSTCam")
disp = CameraDisplay(geom)
image = np.random.normal(size=len(geom.pix_x))
disp.image = image
disp.add_colorbar()
disp.cmap = "nipy_spectral"
disp.set_limits_minmax(0, 10)
disp.set_limits_percent(95)
disp.enable_pixel_picker()
disp.highlight_pixels([1, 2, 3, 4, 5])
disp.norm = "log"
disp.norm = "symlog"
disp.cmap = "rainbow"
with pytest.raises(ValueError):
disp.image = np.ones(10)
with pytest.raises(ValueError):
disp.add_colorbar()
disp.add_ellipse(centroid=(0, 0), width=0.1, length=0.1, angle=0.1)
disp.clear_overlays()
def remove_star_and_run(self, list_of_file, max_events,
noise_pixels_id_list):
signal_place_after_clean = np.zeros(1855)
sum_ped_ev = 0
alive_ped_ev = 0
for input_file in list_of_file:
print(input_file)
r0_r1_calibrator = LSTR0Corrections(pedestal_path=None,
r1_sample_start=3,
r1_sample_end=39)
reader = LSTEventSource(input_url=input_file,
max_events=max_events)
for i, ev in enumerate(reader):
r0_r1_calibrator.calibrate(ev)
if i % 10000 == 0:
print(ev.r0.event_id)
if ev.lst.tel[1].evt.tib_masked_trigger == 32:
sum_ped_ev += 1
self.r1_dl1_calibrator(ev)
img = ev.dl1.tel[1].image
img[noise_pixels_id_list] = 0
geom = ev.inst.subarray.tel[1].camera
clean = tailcuts_clean(geom, img,
**self.cleaning_parameters)
cleaned = img.copy()
cleaned[~clean] = 0.0
signal_place_after_clean[np.where(clean == True)] += 1
if np.sum(cleaned > 0) > 0:
alive_ped_ev += 1
fig, ax = plt.subplots(figsize=(10, 8))
geom = ev.inst.subarray.tel[1].camera
disp0 = CameraDisplay(geom, ax=ax)
disp0.image = signal_place_after_clean / sum_ped_ev
disp0.highlight_pixels(noise_pixels_id_list, linewidth=3)
disp0.add_colorbar(ax=ax,
label="N times signal remain after cleaning [%]")
disp0.cmap = 'gnuplot2'
ax.set_title("{} \n {}/{}".format(
input_file.split("/")[-1][8:21], alive_ped_ev, sum_ped_ev),
fontsize=25)
print("{}/{}".format(alive_ped_ev, sum_ped_ev))
ax.set_xlabel(" ")
ax.set_ylabel(" ")
plt.tight_layout()
plt.show()
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_pixel_shapes(pix_type):
""" test CameraDisplay functionality """
from ..mpl_camera import CameraDisplay
geom = CameraGeometry.from_name("LSTCam")
geom.pix_type = pix_type
disp = CameraDisplay(geom)
image = np.random.normal(size=len(geom.pix_x))
disp.image = image
disp.add_colorbar()
disp.highlight_pixels([1, 2, 3, 4, 5])
disp.add_ellipse(centroid=(0, 0), width=0.1, length=0.1, angle=0.1)
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)
def plot_camera_display(self, image, input_file, noise_pixels_id_list,
alive_ped_ev, sum_ped_ev):
fig, ax = plt.subplots(figsize=(10, 8))
geom = CameraGeometry.from_name('LSTCam-003')
disp0 = CameraDisplay(geom, ax=ax)
disp0.image = image
disp0.highlight_pixels(noise_pixels_id_list, linewidth=3)
disp0.add_colorbar(ax=ax,
label="N times signal remain after cleaning [%]")
disp0.cmap = 'gnuplot2'
ax.set_title("{} \n {}/{}".format(
input_file.split("/")[-1][8:21], alive_ped_ev, sum_ped_ev),
fontsize=25)
print("{}/{}".format(alive_ped_ev, sum_ped_ev))
ax.set_xlabel(" ")
ax.set_ylabel(" ")
plt.tight_layout()
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')
from ctapipe.image import toymodel
from ctapipe.instrument import CameraGeometry
from ctapipe.visualization import CameraDisplay
if __name__ == '__main__':
plt.style.use('ggplot')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
geom = CameraGeometry.from_name('NectarCam')
disp = CameraDisplay(geom, ax=ax)
disp.add_colorbar()
model = toymodel.generate_2d_shower_model(
centroid=(0.05, 0.0), width=0.05, length=0.15, psi='35d'
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=1500, nsb_level_pe=5
)
disp.image = image
mask = disp.image > 10
disp.highlight_pixels(mask, linewidth=2, color='crimson')
plt.show()
def plot_event(fitter,
image,
geometry,
n_sigma=3,
init=False,
clean_mask=None,
show_ellipsis=True,
save=False,
ids=''):
"""
Plot the image of the event in the camera along with the extracted
ellipsis before or after the fitting procedure.
Parameters
----------
image:
Distribution of signal for the event in number of p.e.
n_sigma: float
Multiplicative factor on the extracted width and length
used for the displayed ellipsis
init: boolean
If True, use the starting parameters for the ellipsis
If False, use the ending parameters for the ellipsis
clean_mask: boolean array
cleaning selected pixels for the Hillas parameters extraction
show_ellipsis: boolean
If True, display the ellipsis
save: bool
Save and close the figure if True, return it otherwise
ids: string
Can be used to modify the save location
Returns
-------
cam_display: `ctapipe.visualization.CameraDisplay`
Camera image using matplotlib
"""
fig, axes = plt.subplots(figsize=(10, 8))
cam_display = CameraDisplay(geometry, image, ax=axes)
cam_display.add_colorbar(ax=axes)
if init:
params = fitter.start_parameters
else:
params = fitter.end_parameters
length = n_sigma * params['length']
psi = params['psi']
if show_ellipsis:
cam_display.add_ellipse(centroid=(params['x_cm'], params['y_cm']),
width=n_sigma * params['wl'] *
params['length'],
length=length,
angle=psi,
linewidth=6,
color='r',
linestyle='--',
label=r'{} $\sigma$ contour'.format(n_sigma))
cam_display.axes.legend(loc='best')
if init and clean_mask is not None:
cam_display.highlight_pixels(clean_mask, color='r')
if save:
cam_display.axes.get_figure().savefig('event/' + ids + '_init' +
str(init) + '.png')
plt.close()
return None if save else cam_display
def nsb_rate(
baseline_histo_file, dark_histo_file, param_file, template_filename,
plot="show", plot_nsb_range=None, norm="log",
bias_resistance=1e4 * u.Ohm, cell_capacitance=5e-14 * u.Farad
):
baseline_histo = Histogram1D.load(baseline_histo_file)
dark_histo = Histogram1D.load(dark_histo_file)
baseline_shift = baseline_histo.mean()-dark_histo.mean()
n_pixel = len(DigiCam.geometry.neighbors)
pixels = np.arange(n_pixel, dtype=int)
with open(param_file) as file:
pulse_template = NormalizedPulseTemplate.load(template_filename)
pulse_area = pulse_template.integral() * u.ns
charge_to_amplitude = pulse_template.compute_charge_amplitude_ratio(7, 4)
calibration_parameters = yaml.load(file)
gain_integral = np.array(calibration_parameters['gain'])
gain_amplitude = gain_integral * charge_to_amplitude
crosstalk = np.array(calibration_parameters['mu_xt'])
rate = _compute_nsb_rate(
baseline_shift=baseline_shift, gain=gain_amplitude,
pulse_area=pulse_area, crosstalk=crosstalk,
bias_resistance=bias_resistance, cell_capacitance=cell_capacitance
)
bad_pixels = get_bad_pixels(
calib_file=param_file, nsigma_gain=5, nsigma_elecnoise=5,
dark_histo=dark_histo_file, nsigma_dark=8, plot=None, output=None
)
bad_pixels = np.unique(np.hstack(
(
bad_pixels,
pixels[rate < 0],
pixels[rate > 5 * u.GHz]
)
))
avg_matrix = _get_average_matrix_bad_pixels(DigiCam.geometry, bad_pixels)
good_pixels_mask = np.ones(n_pixel, dtype=bool)
good_pixels_mask[bad_pixels] = False
good_pixels = pixels[good_pixels_mask]
rate[bad_pixels] = avg_matrix[bad_pixels, :].dot(rate[good_pixels])
if plot is None:
return rate
fig1, ax = plt.subplots(1, 1)
display = CameraDisplay(DigiCam.geometry, ax=ax, norm=norm,
title='NSB rate [GHz]')
rate_ghz = rate.to(u.GHz).value
display.image = rate_ghz
if plot_nsb_range is None:
plot_nsb_range = (np.min(rate_ghz), np.max(rate_ghz))
display.set_limits_minmax(*plot_nsb_range)
display.add_colorbar(ax=ax)
display.highlight_pixels(bad_pixels, color='r', linewidth=2)
plt.tight_layout()
output_path = os.path.dirname(plot)
if plot == "show" or \
(output_path != "" and not os.path.isdir(output_path)):
if not plot == "show":
print('WARNING: Path ' + output_path + ' for output trigger ' +
'uniformity does not exist, displaying the plot instead.\n')
plt.show()
else:
plt.savefig(plot)
print(plot, 'created')
plt.close(fig1)
return rate
def plot_pedestals(data_file, pedestal_file, run=0, plot_file=None, tel_id=1, offset_value=400, sample_size=1000):
"""
plot pedestal quantities quantities
Parameters
----------
data_file: pedestal run
pedestal_file: file with drs4 corrections
run: run number of data to be corrected
plot_file: name of output pdf file
tel_id: id of the telescope
offset_value: baseline off_set
"""
config = {
"LSTEventSource": {
"allowed_tels": [1],
"LSTR0Corrections": {
"drs4_pedestal_path": pedestal_file,
},
}
}
# event_reader
reader = EventSource(data_file, config=Config(config), max_events=None)
t = np.linspace(2, 37, 36)
# configuration for the charge integrator
charge_config = Config(
{
"FixedWindowSum": {
"window_shift": 6,
"window_width": 12,
"peak_index": 18,
}
}
)
# declare the pedestal component
pedestal = PedestalIntegrator(
tel_id=tel_id,
time_sampling_correction_path=None,
sample_size=sample_size,
sample_duration=1000000,
charge_median_cut_outliers=[-10, 10],
charge_std_cut_outliers=[-10, 10],
charge_product="FixedWindowSum",
config=charge_config,
subarray=reader.subarray,
)
for i, event in enumerate(reader):
if tel_id != event.trigger.tels_with_trigger[0]:
raise Exception(
f"Given wrong telescope id {tel_id}, files has id {event.trigger.tels_with_trigger[0]}"
)
are_pedestals_calculated = pedestal.calculate_pedestals(event)
if are_pedestals_calculated:
ped_data = event.mon.tel[tel_id].pedestal
break
camera_geometry = reader.subarray.tels[tel_id].camera.geometry
camera_geometry = camera_geometry.transform_to(EngineeringCameraFrame())
if are_pedestals_calculated and plot_file is not None:
with PdfPages(plot_file) as pdf:
plt.rc("font", size=15)
# first figure
fig = plt.figure(1, figsize=(12, 24))
plt.tight_layout()
n_samples = charge_config["FixedWindowSum"]["window_width"]
fig.suptitle(f"Run {run}, integration on {n_samples} samples", fontsize=25)
pad = 420
image = ped_data.charge_median
mask = ped_data.charge_median_outliers
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera_geometry)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} pedestal [ADC]', rotation=90)
plt.title(f"{channel[chan]} pedestal [ADC]")
disp.add_colorbar()
image = ped_data.charge_std
mask = ped_data.charge_std_outliers
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera_geometry)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} pedestal std [ADC]', rotation=90)
plt.title(f"{channel[chan]} pedestal std [ADC]")
disp.add_colorbar()
# histograms
for chan in np.arange(2):
mean_ped = ped_data.charge_mean[chan]
ped_std = ped_data.charge_std[chan]
# select good pixels
select = np.logical_not(mask[chan])
# fig.suptitle(f"Run {run} channel: {channel[chan]}", fontsize=25)
pad += 1
# pedestal charge
plt.subplot(pad)
plt.tight_layout()
plt.ylabel("pixels")
plt.xlabel(f"{channel[chan]} pedestal")
median = np.median(mean_ped[select])
rms = np.std(mean_ped[select])
label = f"{channel[chan]} Median {median:3.2f}, std {rms:3.2f}"
plt.hist(mean_ped[select], bins=50, label=label)
plt.legend()
pad += 1
# pedestal std
plt.subplot(pad)
plt.ylabel("pixels")
plt.xlabel(f"{channel[chan]} pedestal std")
median = np.median(ped_std[select])
rms = np.std(ped_std[select])
label = f" Median {median:3.2f}, std {rms:3.2f}"
plt.hist(ped_std[select], bins=50, label=label)
plt.legend()
plt.subplots_adjust(top=0.94, bottom=0.04, right=0.96)
pdf.savefig()
plt.close()
# event_reader
# reader = EventSource(data_file, config=Config(config), max_events=1000)
pix = 0
pad = 420
offset_value = reader.r0_r1_calibrator.offset.tel[tel_id]
# plot corrected waveforms of first 8 events
for i, ev in enumerate(reader):
for chan in np.arange(2):
if pad == 420:
# new figure
fig = plt.figure(ev.index.event_id * 1000, figsize=(12, 24))
fig.suptitle(f"Run {run}, pixel {pix}", fontsize=25)
plt.tight_layout()
pad += 1
plt.subplot(pad)
# remove samples at beginning / end of waveform
start = reader.r0_r1_calibrator.r1_sample_start.tel[tel_id]
end = reader.r0_r1_calibrator.r1_sample_end.tel[tel_id]
plt.subplots_adjust(top=0.92)
label = f"event {ev.index.event_id}, {channel[chan]}: R0"
plt.step(
t,
ev.r0.tel[tel_id].waveform[chan, pix, start:end],
color="blue",
label=label,
)
label = "baseline correction \n + dt corr + interp. spikes"
plt.step(
t,
ev.r1.tel[tel_id].waveform[chan, pix] + offset_value,
alpha=0.5,
color="green",
label=label,
)
plt.plot([0, 40], [offset_value, offset_value], "k--", label="offset")
plt.xlabel("time sample [ns]")
plt.ylabel("counts [ADC]")
plt.legend()
plt.ylim(200, 600)
if pad == 428:
pad = 420
plt.subplots_adjust(top=0.92)
pdf.savefig()
plt.close()
if i == 8:
break
elif not are_pedestals_calculated:
log.error("Not able to calculate pedestals or output pdf file not especified.")
elif plot_file is None:
log.warning("Not PDF outputfile specified.")
from ctapipe.image import toymodel
from ctapipe.io import CameraGeometry
from ctapipe.visualization import CameraDisplay
from matplotlib import pyplot as plt
if __name__ == '__main__':
plt.style.use('ggplot')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
geom = CameraGeometry.from_name('hess', 1)
disp = CameraDisplay(geom, ax=ax)
disp.add_colorbar()
model = toymodel.generate_2d_shower_model(
centroid=(0.05, 0.0), width=0.005, length=0.025, psi='35d'
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=50, nsb_level_pe=20
)
disp.image = image
mask = disp.image > 15
disp.highlight_pixels(mask, linewidth=3)
plt.show()
# ax1.set_yticks([])
ax1.set_xlabel('$x$-position / \\si{\\metre}')
ax1.set_ylabel('$y$-position / \\si{\\metre}')
ax1.set_title('')
# # ax.set_axis('off')
# mask = disp.image > 10
# disp.highlight_pixels(mask, linewidth=1, color='crimson')
waveforms = event.dl0.tel[tel_id].waveform.T
# from IPython import embed; embed()
m = (waveforms.max(axis=0) > 5) & (waveforms.max(axis=0) < 10)
selected_pixel = np.argmax(m)
mask = np.zeros_like(m).astype(np.bool)
mask[selected_pixel] = True
disp.highlight_pixels(mask, linewidth=1, color='C0', alpha=1)
t = np.arange(0, waveforms.shape[0], 1)
ax2.plot(t, waveforms[:, selected_pixel], color='C0', lw=1)
# from IPython import embed; embed()
ax2.set_xlabel('Time / \\si{\\nano\\second}')
ax2.set_ylabel('Voltage / a.u.')
ax2.set_title('')
ax2.set_xlim([0, 29])
plt.tight_layout(pad=0, rect=(0.0, 0, 1.007, 1))
plt.subplots_adjust(wspace=0.35)
plt.savefig('build/preprocessing.pdf')
with open('build/preprocessing_energy.txt', 'w') as f:
energy = event.mc.energy.to_value('TeV')
def plot_all(ped_data, ff_data, calib_data, run=0, plot_file="none"):
"""
plot camera calibration quantities
Parameters
----------
ped_data: pedestal container PedestalContainer()
ff_data: flat-field container FlatFieldContainer()
calib_data: calibration container WaveformCalibrationContainer()
"""
camera = CameraGeometry.from_name("LSTCam", 2)
# plot open pdf
if plot_file != "none":
pp = PdfPages(plot_file)
plt.rc('font', size=15)
### first figure
fig = plt.figure(1, figsize=(12, 24))
plt.tight_layout()
fig.suptitle(f"Run {run}", fontsize=25)
pad = 420
image = ff_data.charge_median
mask = ff_data.charge_median_outliers
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
#disp.axes.text(lposx, 0, f'{channel[chan]} signal charge (ADC)', rotation=90)
plt.title(f'{channel[chan]} signal charge [ADC]')
disp.add_colorbar()
image = ff_data.charge_std
mask = ff_data.charge_std_outliers
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
#disp.axes.text(lposx, 0, f'{channel[chan]} signal std [ADC]', rotation=90)
plt.title(f'{channel[chan]} signal std [ADC]')
disp.add_colorbar()
image = ped_data.charge_median
mask = ped_data.charge_median_outliers
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
#disp.axes.text(lposx, 0, f'{channel[chan]} pedestal [ADC]', rotation=90)
plt.title(f'{channel[chan]} pedestal [ADC]')
disp.add_colorbar()
image = ped_data.charge_std
mask = ped_data.charge_std_outliers
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
#disp.axes.text(lposx, 0, f'{channel[chan]} pedestal std [ADC]', rotation=90)
plt.title(f'{channel[chan]} pedestal std [ADC]')
disp.add_colorbar()
plt.subplots_adjust(top=0.92)
if plot_file != "none":
pp.savefig()
### second figure
fig = plt.figure(2, figsize=(12, 24))
plt.tight_layout()
fig.suptitle(f"Run {run}", fontsize=25)
pad = 420
# time
image = ff_data.time_median
mask = ff_data.time_median_outliers
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
#disp.axes.text(lposx, 0, f'{channel[chan]} time', rotation=90)
plt.title(f'{channel[chan]} time')
disp.add_colorbar()
image = ff_data.relative_gain_median
mask = calib_data.unusable_pixels
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
disp.highlight_pixels(mask[chan], linewidth=2)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
disp.set_limits_minmax(0.7, 1.3)
plt.title(f'{channel[chan]} relative gain')
#disp.axes.text(lposx, 0, f'{channel[chan]} relative gain', rotation=90)
disp.add_colorbar()
# pe
image = calib_data.n_pe
mask = calib_data.unusable_pixels
image = np.where(np.isnan(image), 0, image)
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.cmap = plt.cm.coolwarm
plt.title(f'{channel[chan]} photon-electrons')
#disp.axes.text(lposx, 0, f'{channel[chan]} photon-electrons', rotation=90)
disp.add_colorbar()
# pe histogram
pad += 1
plt.subplot(pad)
plt.tight_layout()
for chan in np.arange(2):
n_pe = calib_data.n_pe[chan]
# select good pixels
select = np.logical_not(mask[chan])
median = int(np.median(n_pe[select]))
rms = np.std(n_pe[select])
mymin = median - 4 * rms
mymax = median + 4 * rms
label = f"{channel[chan]} Median {median:3.2f}, std {rms:5.2f}"
plt.hist(n_pe[select],
label=label,
histtype='step',
range=(mymin, mymax),
bins=50,
stacked=True,
alpha=0.5,
fill=True)
plt.legend()
plt.xlabel(f'pe', fontsize=20)
plt.ylabel('pixels', fontsize=20)
# pe scatter plot
pad += 1
plt.subplot(pad)
plt.tight_layout()
HG = calib_data.n_pe[0]
LG = calib_data.n_pe[1]
HG = np.where(np.isnan(HG), 0, HG)
LG = np.where(np.isnan(LG), 0, LG)
mymin = np.median(LG) - 2 * np.std(LG)
mymax = np.median(LG) + 2 * np.std(LG)
plt.hist2d(LG, HG, bins=[100, 100])
plt.xlabel("LG", fontsize=20)
plt.ylabel("HG", fontsize=20)
x = np.arange(mymin, mymax)
plt.plot(x, x)
plt.ylim(mymin, mymax)
plt.xlim(mymin, mymax)
plt.subplots_adjust(top=0.92)
if plot_file != "none":
pp.savefig()
### figures 3 and 4 : histograms
for chan in np.arange(2):
n_pe = calib_data.n_pe[chan]
gain_median = ff_data.relative_gain_median[chan]
#charge_median = ff_data.charge_median[chan]
charge_mean = ff_data.charge_mean[chan]
charge_std = ff_data.charge_std[chan]
#median_ped = ped_data.charge_median[chan]
mean_ped = ped_data.charge_mean[chan]
ped_std = ped_data.charge_std[chan]
# select good pixels
select = np.logical_not(mask[chan])
fig = plt.figure(chan + 10, figsize=(12, 18))
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
fig.suptitle(f"Run {run} channel: {channel[chan]}", fontsize=25)
# charge
plt.subplot(321)
plt.tight_layout()
median = int(np.median(charge_mean[select]))
rms = np.std(charge_mean[select])
label = f"Median {median:3.2f}, std {rms:5.0f}"
plt.xlabel('charge (ADC)', fontsize=20)
plt.ylabel('pixels', fontsize=20)
plt.hist(charge_mean[select], bins=50, label=label)
plt.legend()
plt.subplot(322)
plt.tight_layout()
plt.ylabel('pixels', fontsize=20)
plt.xlabel('charge std', fontsize=20)
median = np.median(charge_std[select])
rms = np.std(charge_std[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(charge_std[select], bins=50, label=label)
plt.legend()
# pedestal charge
plt.subplot(323)
plt.tight_layout()
plt.ylabel('pixels', fontsize=20)
plt.xlabel('pedestal', fontsize=20)
median = np.median(mean_ped[select])
rms = np.std(mean_ped[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(mean_ped[select], bins=50, label=label)
plt.legend()
# pedestal std
plt.subplot(324)
plt.ylabel('pixels', fontsize=20)
plt.xlabel('pedestal std', fontsize=20)
median = np.median(ped_std[select])
rms = np.std(ped_std[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(ped_std[select], bins=50, label=label)
plt.legend()
# relative gain
plt.subplot(325)
plt.tight_layout()
plt.ylabel('pixels', fontsize=20)
plt.xlabel('relative gain', fontsize=20)
median = np.median(gain_median[select])
rms = np.std(gain_median[select])
label = f"Relative gain {median:3.2f}, std {rms:5.2f}"
plt.hist(gain_median[select], bins=50, label=label)
plt.legend()
# photon electrons
plt.subplot(326)
plt.tight_layout()
plt.ylabel('pixels', fontsize=20)
plt.xlabel('pe', fontsize=20)
median = np.median(n_pe[select])
rms = np.std(n_pe[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(n_pe[select], bins=50, label=label)
plt.legend()
plt.subplots_adjust(top=0.92)
if plot_file != "none":
pp.savefig(plt.gcf())
if plot_file != "none":
pp.close()
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.generate_2d_shower_model(
centroid=(0.2, 0.0), width=0.05, length=0.15, psi='35d'
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=1500, nsb_level_pe=3
)
# Apply image cleaning
cleanmask = tailcuts_clean(
geom, image, picture_thresh=10, boundary_thresh=5
)
# Calculate image parameters
hillas = hillas_parameters(geom[cleanmask], image[cleanmask])
# Show the camera image and overlay Hillas ellipse and clean pixels
disp.image = image
disp.highlight_pixels(cleanmask, color='crimson')
disp.overlay_moments(hillas, color='cyan', linewidth=3)
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))
def plot_all(ped_data, ff_data, calib_data, run=0, plot_file=None):
"""
plot camera calibration quantities
Parameters
----------
ped_data: pedestal container PedestalContainer()
ff_data: flat-field container FlatFieldContainer()
calib_data: calibration container WaveformCalibrationContainer()
run: run number
plot_file: name of the output PDF file. No file is produced if name is not provided
"""
# read geometry
camera = load_camera_geometry()
camera = camera.transform_to(EngineeringCameraFrame())
# plot open pdf
if plot_file is not None:
with PdfPages(plot_file) as pdf:
plt.rc("font", size=15)
# first figure
fig = plt.figure(1, figsize=(12, 24))
plt.tight_layout()
fig.suptitle(f"Run {run}", fontsize=25)
pad = 420
image = ff_data.charge_median
mask = ff_data.charge_median_outliers
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} signal charge (ADC)', rotation=90)
plt.title(f"{channel[chan]} signal charge [ADC]")
disp.add_colorbar()
image = ff_data.charge_std
mask = ff_data.charge_std_outliers
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} signal std [ADC]', rotation=90)
plt.title(f"{channel[chan]} signal std [ADC]")
disp.add_colorbar()
image = ped_data.charge_median
mask = ped_data.charge_median_outliers
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} pedestal [ADC]', rotation=90)
plt.title(f"{channel[chan]} pedestal [ADC]")
disp.add_colorbar()
image = ped_data.charge_std
mask = ped_data.charge_std_outliers
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} pedestal std [ADC]', rotation=90)
plt.title(f"{channel[chan]} pedestal std [ADC]")
disp.add_colorbar()
plt.subplots_adjust(top=0.92)
pdf.savefig()
plt.close()
# second figure
fig = plt.figure(2, figsize=(12, 24))
plt.tight_layout()
fig.suptitle(f"Run {run}", fontsize=25)
pad = 420
# time
image = ff_data.time_median
mask = ff_data.time_median_outliers
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} time', rotation=90)
plt.title(f"{channel[chan]} time")
disp.add_colorbar()
image = ff_data.relative_gain_median
mask = calib_data.unusable_pixels
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
disp.highlight_pixels(mask[chan], linewidth=2)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
disp.set_limits_minmax(0.7, 1.3)
plt.title(f"{channel[chan]} relative signal")
# disp.axes.text(lposx, 0, f'{channel[chan]} relative gain', rotation=90)
disp.add_colorbar()
# pe
image = calib_data.n_pe
mask = calib_data.unusable_pixels
image = np.where(np.isnan(image), 0, image)
for chan in np.arange(2):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.cmap = plt.cm.coolwarm
plt.title(f"{channel[chan]} photon-electrons")
# disp.axes.text(lposx, 0, f'{channel[chan]} photon-electrons', rotation=90)
disp.add_colorbar()
# pe histogram
pad += 1
plt.subplot(pad)
plt.tight_layout()
for chan in np.arange(2):
n_pe = calib_data.n_pe[chan]
# select good pixels
select = np.logical_not(mask[chan])
median = int(np.median(n_pe[select]))
rms = np.std(n_pe[select])
mymin = median - 4 * rms
mymax = median + 4 * rms
label = f"{channel[chan]} Median {median:3.2f}, std {rms:5.2f}"
plt.hist(
n_pe[select],
label=label,
histtype="step",
range=(mymin, mymax),
bins=50,
stacked=True,
alpha=0.5,
fill=True,
)
plt.legend()
plt.xlabel("pe", fontsize=20)
plt.ylabel("pixels", fontsize=20)
# pe scatter plot
pad += 1
plt.subplot(pad)
plt.tight_layout()
HG = calib_data.n_pe[0]
LG = calib_data.n_pe[1]
HG = np.where(np.isnan(HG), 0, HG)
LG = np.where(np.isnan(LG), 0, LG)
mymin = np.median(LG) - 2 * np.std(LG)
mymax = np.median(LG) + 2 * np.std(LG)
plt.hist2d(LG, HG, bins=[100, 100])
plt.xlabel("LG", fontsize=20)
plt.ylabel("HG", fontsize=20)
x = np.arange(mymin, mymax)
plt.plot(x, x)
plt.ylim(mymin, mymax)
plt.xlim(mymin, mymax)
plt.subplots_adjust(top=0.92)
pdf.savefig()
plt.close()
# figures 3 and 4: histograms
for chan in np.arange(2):
n_pe = calib_data.n_pe[chan]
gain_median = ff_data.relative_gain_median[chan]
charge_median = ff_data.charge_median[chan]
#charge_mean = ff_data.charge_mean[chan]
charge_std = ff_data.charge_std[chan]
n_ff = ff_data.n_events
median_ped = ped_data.charge_median[chan]
#mean_ped = ped_data.charge_mean[chan]
ped_std = ped_data.charge_std[chan]
n_ped = ped_data.n_events
dc_to_pe = calib_data.dc_to_pe[chan]
time_correction = calib_data.time_correction[chan]
# select good pixels
select = np.logical_not(mask[chan])
fig = plt.figure(chan + 10, figsize=(12, 24))
fig.tight_layout(rect=[0, 0.0, 1, 0.95])
fig.suptitle(f"Run {run} channel: {channel[chan]}", fontsize=25)
# charge
plt.subplot(421)
plt.title(f"FF sample of {n_ff} events")
plt.tight_layout()
median = int(np.median(charge_median[select]))
rms = np.std(charge_median[select])
label = f"Median {median:3.2f}, std {rms:5.0f}"
plt.xlabel("charge (ADC)", fontsize=20)
plt.ylabel("pixels", fontsize=20)
plt.hist(charge_median[select], bins=50, label=label)
plt.legend()
plt.subplot(422)
plt.tight_layout()
plt.ylabel("pixels", fontsize=20)
plt.xlabel("charge std", fontsize=20)
median = np.median(charge_std[select])
rms = np.std(charge_std[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(charge_std[select], bins=50, label=label)
plt.legend()
# pedestal charge
plt.subplot(423)
plt.tight_layout()
plt.title(f"pedestal sample of {n_ped} events")
plt.ylabel("pixels", fontsize=20)
plt.xlabel("pedestal", fontsize=20)
median = np.median(median_ped[select])
rms = np.std(median_ped[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(median_ped[select], bins=50, label=label)
plt.legend()
# pedestal std
plt.subplot(424)
plt.ylabel("pixels", fontsize=20)
plt.xlabel("pedestal std", fontsize=20)
median = np.median(ped_std[select])
rms = np.std(ped_std[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(ped_std[select], bins=50, label=label)
plt.legend()
# relative gain
plt.subplot(425)
plt.tight_layout()
plt.ylabel("pixels", fontsize=20)
plt.xlabel("relative signal", fontsize=20)
median = np.median(gain_median[select])
rms = np.std(gain_median[select])
label = f"Relative gain {median:3.2f}, std {rms:5.2f}"
plt.hist(gain_median[select], bins=50, label=label)
plt.legend()
# photon electrons
plt.subplot(426)
plt.tight_layout()
plt.ylabel("pixels", fontsize=20)
plt.xlabel("time corrections [ns]", fontsize=20)
median = np.median(time_correction[select])
rms = np.std(time_correction[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(time_correction[select].value, bins=50, label=label)
plt.legend()
plt.subplots_adjust(top=0.92)
# photon electrons
plt.subplot(427)
plt.tight_layout()
plt.ylabel("pixels", fontsize=20)
plt.xlabel("pe", fontsize=20)
median = np.median(n_pe[select])
rms = np.std(n_pe[select])
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(n_pe[select], bins=50, label=label)
plt.legend()
plt.subplots_adjust(top=0.92)
# gain
plt.subplot(428)
plt.tight_layout()
plt.ylabel("pixels", fontsize=20)
plt.xlabel("flat-fielded gain [ADC/pe]", fontsize=20)
denominator = dc_to_pe[select]
numerator = 1.
gain = np.divide(numerator, denominator, out=np.zeros_like(denominator), where=denominator != 0)
median = np.median(gain)
rms = np.std(gain)
label = f"Median {median:3.2f}, std {rms:3.2f}"
plt.hist(gain, bins=50, label=label)
plt.legend()
plt.subplots_adjust(top=0.92)
pdf.savefig(plt.gcf())
plt.close()
from ctapipe.instrument import CameraGeometry
from ctapipe.visualization import CameraDisplay
if __name__ == '__main__':
plt.style.use('ggplot')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
geom = CameraGeometry.from_name('NectarCam')
disp = CameraDisplay(geom, ax=ax)
disp.add_colorbar()
model = toymodel.generate_2d_shower_model(centroid=(0.05, 0.0),
width=0.05,
length=0.15,
psi='35d')
image, sig, bg = toymodel.make_toymodel_shower_image(geom,
model.pdf,
intensity=1500,
nsb_level_pe=5)
disp.image = image
mask = disp.image > 10
disp.highlight_pixels(mask, linewidth=2, color='crimson')
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 nsb_rate(
files, aux_basepath, dark_histo_file, param_file, template_filename,
output=None, plot="show", plot_nsb_range=None, norm="log",
plot_baselines=False, disable_bar=False, max_events=None, n_skip=10,
stars=True,
bias_resistance=1e4 * u.Ohm, cell_capacitance=5e-14 * u.Farad
):
files = np.atleast_1d(files)
if len(files) == 1 and not files[0].endswith('.fz'):
table = Table.read(files[0])[:max_events]
data = dict(table)
data['nsb_rate'] = np.array(data['nsb_rate']) * u.GHz
else:
dark_histo = Histogram1D.load(dark_histo_file)
n_pixel = len(DigiCam.geometry.neighbors)
pixels = np.arange(n_pixel, dtype=int)
with open(param_file) as file:
pulse_template = NormalizedPulseTemplate.load(template_filename)
pulse_area = pulse_template.integral() * u.ns
charge_to_amplitude = pulse_template.compute_charge_amplitude_ratio(7, 4)
calibration_parameters = yaml.load(file)
gain_integral = np.array(calibration_parameters['gain'])
gain_amplitude = gain_integral * charge_to_amplitude
crosstalk = np.array(calibration_parameters['mu_xt'])
events = calibration_event_stream(files, max_events=max_events,
disable_bar=disable_bar)
events = add_slow_data_calibration(
events, basepath=aux_basepath,
aux_services=('DriveSystem', )
)
data = {
"baseline": [],
"nsb_rate": [],
"good_pixels_mask": [],
"timestamp": [],
"event_id": [],
"az": [],
"el": [],
}
bad_pixels = get_bad_pixels(
calib_file=param_file, nsigma_gain=5, nsigma_elecnoise=5,
dark_histo=dark_histo_file, nsigma_dark=8, plot=None, output=None
)
events_skipped = 0
for event in events:
if event.event_type.INTERNAL not in event.event_type:
continue
events_skipped += 1
if events_skipped < n_skip:
continue
events_skipped = 0
data['baseline'].append(event.data.digicam_baseline)
baseline_shift = event.data.digicam_baseline - dark_histo.mean()
rate = _compute_nsb_rate(
baseline_shift=baseline_shift, gain=gain_amplitude,
pulse_area=pulse_area, crosstalk=crosstalk,
bias_resistance=bias_resistance, cell_capacitance=cell_capacitance
)
bad_pixels_event = np.unique(np.hstack(
(
bad_pixels,
pixels[rate < 0],
pixels[rate > 5 * u.GHz]
)
))
avg_matrix = _get_average_matrix_bad_pixels(
DigiCam.geometry, bad_pixels_event
)
good_pixels_mask = np.ones(n_pixel, dtype=bool)
good_pixels_mask[bad_pixels_event] = False
good_pixels = pixels[good_pixels_mask]
rate[bad_pixels_event] = avg_matrix[bad_pixels_event, :].dot(
rate[good_pixels]
)
data['good_pixels_mask'].append(good_pixels_mask)
data['timestamp'].append(event.data.local_time)
data['event_id'].append(event.event_id)
data['nsb_rate'].append(rate)
data['az'].append(event.slow_data.DriveSystem.current_position_az)
data['el'].append(event.slow_data.DriveSystem.current_position_el)
data['nsb_rate'] = np.array(data['nsb_rate']) * u.GHz
if output is not None:
table = Table(data)
if os.path.isfile(output):
os.remove(output)
table.write(output, format='fits')
time_obs = Time(
np.array(data['timestamp'], dtype=np.float64) * 1e-9,
format='unix'
)
if plot_baselines:
fig2, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 8), dpi=50)
baseline_std = np.std(data['baseline'], axis=0)
ax1.hist(baseline_std, 100)
ax1.set_xlabel('std(baseline) [LSB]')
# pixels_shown = np.arange(len(baseline_std))[baseline_std > 10]
pixels_shown = [834,]
ax2.plot_date(
time_obs.to_datetime(),
data['baseline'][:, pixels_shown],
'-'
)
ax2.set_xlabel('time')
ax2.set_ylabel('baseline [LSB]')
plt.tight_layout()
plt.show()
plt.close(fig2)
az_obs = np.array(data['az']) * u.deg
el_obs = np.array(data['el']) * u.deg
n_event = len(data['timestamp'])
fig1, ax = plt.subplots(1, 1, figsize=(16, 12), dpi=50)
date = datetime.fromtimestamp(data['timestamp'][0]*1e-9)
date_str = date.strftime("%H:%M:%S")
display = CameraDisplay(
DigiCam.geometry, ax=ax, norm=norm,
title='NSB rate [GHz], t=' + date_str
)
rate_ghz = np.array(data['nsb_rate'][0].to(u.GHz).value)
display.image = rate_ghz
if plot_nsb_range is None:
min_range_rate = np.max([np.min(rate_ghz), 50e-3])
plot_nsb_range = (min_range_rate, np.max(rate_ghz))
display.set_limits_minmax(*plot_nsb_range)
display.add_colorbar(ax=ax)
bad_pixels = np.arange(
len(data['good_pixels_mask'][0])
)[~data['good_pixels_mask'][0]]
display.highlight_pixels(bad_pixels, color='r', linewidth=2)
display.axes.set_xlim([-500., 500.])
display.axes.set_ylim([-500., 500.])
plt.tight_layout()
if stars is True:
stars_az, stars_alt, stars_pmag = get_stars_in_fov(
az_obs[0], el_obs[0], time_obs
)
stars_x, stars_y = transform_azel_to_xy(
stars_az, stars_alt, az_obs, el_obs
)
point_stars = []
for index_star in range(len(stars_pmag)):
point_star, = ax.plot(
stars_x[index_star, 0],
stars_y[index_star, 0],
'ok',
ms=20-2*stars_pmag[index_star],
mew=3,
mfc='None'
)
point_stars.append(point_star)
def update(i, display):
print('frame', i, '/', len(data['timestamp']))
display.image = data['nsb_rate'][i].to(u.GHz).value
date = datetime.fromtimestamp(data['timestamp'][i] * 1e-9)
date_str = date.strftime("%H:%M:%S")
display.axes.set_title('NSB rate [GHz], t=' + date_str)
bad_pixels = np.arange(
len(data['good_pixels_mask'][i])
)[~data['good_pixels_mask'][i]]
display.highlight_pixels(
bad_pixels, color='r', linewidth=2
)
if stars is True:
for index_star in range(len(stars_pmag)):
point_stars[index_star].set_xdata(
stars_x[index_star, i]
)
point_stars[index_star].set_ydata(
stars_y[index_star, i]
)
anim = FuncAnimation(
fig1,
update,
frames=len(data['timestamp']),
interval=20,
fargs=(display, )
)
Writer = animation.writers['ffmpeg']
writer = Writer(fps=50, metadata=dict(artist='Y. Renier'),
bitrate=4000, codec='h263p')
output_path = os.path.dirname(plot)
if plot == "show" or \
(output_path != "" and not os.path.isdir(output_path)):
if not plot == "show":
print('WARNING: Path ' + output_path + ' for output trigger ' +
'uniformity does not exist, displaying the plot instead.\n')
display.enable_pixel_picker()
plt.show()
else:
anim.save(plot, writer=writer)
print(plot, 'created')
plt.close(fig1)
def plot_pedestals(data_file,
pedestal_file,
run=0,
plot_file="none",
tel_id=1,
offset_value=400):
"""
plot pedestal quantities quantities
Parameters
----------
data_file: pedestal run
pedestal_file: file with drs4 corrections
run: run number of data to be corrected
plot_file: name of output pdf file
tel_id: id of the telescope
offset_value: baseline off_set
"""
# plot open pdf
if plot_file != "none":
pp = PdfPages(plot_file)
plt.rc('font', size=15)
# r0 calibrator
r0_calib = LSTR0Corrections(pedestal_path=pedestal_file,
offset=offset_value,
tel_id=tel_id)
# event_reader
reader = event_source(data_file, max_events=1000)
t = np.linspace(2, 37, 36)
# configuration for the charge integrator
charge_config = Config(
{"FixedWindowSum": {
"window_start": 12,
"window_width": 12,
}})
# declare the pedestal component
pedestal = PedestalIntegrator(tel_id=tel_id,
sample_size=1000,
sample_duration=1000000,
charge_median_cut_outliers=[-10, 10],
charge_std_cut_outliers=[-10, 10],
charge_product="FixedWindowSum",
config=charge_config,
subarray=reader.subarray)
for i, event in enumerate(reader):
if tel_id != event.r0.tels_with_data[0]:
raise Exception(
f"Given wrong telescope id {tel_id}, files has id {event.r0.tels_with_data[0]}"
)
# move from R0 to R1
r0_calib.calibrate(event)
ok = pedestal.calculate_pedestals(event)
if ok:
ped_data = event.mon.tel[tel_id].pedestal
break
camera_geometry = reader.subarray.tels[tel_id].camera.geometry
camera_geometry = camera_geometry.transform_to(EngineeringCameraFrame())
# plot open pdf
if plot_file != "none":
pp = PdfPages(plot_file)
plt.rc('font', size=15)
### first figure
fig = plt.figure(1, figsize=(12, 24))
plt.tight_layout()
n_samples = charge_config["FixedWindowSum"]['window_width']
fig.suptitle(f"Run {run}, integration on {n_samples} samples", fontsize=25)
pad = 420
image = ped_data.charge_median
mask = ped_data.charge_median_outliers
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera_geometry)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} pedestal [ADC]', rotation=90)
plt.title(f'{channel[chan]} pedestal [ADC]')
disp.add_colorbar()
image = ped_data.charge_std
mask = ped_data.charge_std_outliers
for chan in (np.arange(2)):
pad += 1
plt.subplot(pad)
plt.tight_layout()
disp = CameraDisplay(camera_geometry)
mymin = np.median(image[chan]) - 2 * np.std(image[chan])
mymax = np.median(image[chan]) + 2 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
disp.highlight_pixels(mask[chan], linewidth=2)
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
# disp.axes.text(lposx, 0, f'{channel[chan]} pedestal std [ADC]', rotation=90)
plt.title(f'{channel[chan]} pedestal std [ADC]')
disp.add_colorbar()
### histograms
for chan in np.arange(2):
mean_ped = ped_data.charge_mean[chan]
ped_std = ped_data.charge_std[chan]
# select good pixels
select = np.logical_not(mask[chan])
#fig.suptitle(f"Run {run} channel: {channel[chan]}", fontsize=25)
pad += 1
# pedestal charge
plt.subplot(pad)
plt.tight_layout()
plt.ylabel('pixels')
plt.xlabel(f'{channel[chan]} pedestal')
median = np.median(mean_ped[select])
rms = np.std(mean_ped[select])
label = f"{channel[chan]} Median {median:3.2f}, std {rms:3.2f}"
plt.hist(mean_ped[select], bins=50, label=label)
plt.legend()
pad += 1
# pedestal std
plt.subplot(pad)
plt.ylabel('pixels')
plt.xlabel(f'{channel[chan]} pedestal std')
median = np.median(ped_std[select])
rms = np.std(ped_std[select])
label = f" Median {median:3.2f}, std {rms:3.2f}"
plt.hist(ped_std[select], bins=50, label=label)
plt.legend()
plt.subplots_adjust(top=0.94)
if plot_file != "none":
pp.savefig()
pix = 0
pad = 420
# plot corrected waveforms of first 8 events
for i, ev in enumerate(reader):
for chan in np.arange(2):
if pad == 420:
# new figure
fig = plt.figure(ev.index.event_id, figsize=(12, 24))
fig.suptitle(f"Run {run}, pixel {pix}", fontsize=25)
plt.tight_layout()
pad += 1
plt.subplot(pad)
plt.subplots_adjust(top=0.92)
label = f"event {ev.index.event_id}, {channel[chan]}: R0"
plt.step(t,
ev.r0.tel[tel_id].waveform[chan, pix, 2:38],
color="blue",
label=label)
r0_calib.subtract_pedestal(ev, tel_id)
label = "+ pedestal substraction"
plt.step(t,
ev.r1.tel[tel_id].waveform[chan, pix, 2:38],
color="red",
alpha=0.5,
label=label)
r0_calib.time_lapse_corr(ev, tel_id)
r0_calib.interpolate_spikes(ev, tel_id)
label = "+ dt corr + interp. spikes"
plt.step(t,
ev.r1.tel[tel_id].waveform[chan, pix, 2:38],
alpha=0.5,
color="green",
label=label)
plt.plot([0, 40], [offset_value, offset_value],
'k--',
label="offset")
plt.xlabel("time sample [ns]")
plt.ylabel("counts [ADC]")
plt.legend()
plt.ylim([-50, 500])
if plot_file != "none" and pad == 428:
pad = 420
plt.subplots_adjust(top=0.92)
pp.savefig()
if i == 8:
break
if plot_file != "none":
pp.close()
def finish(self):
"""
write fit results in h5 file and the check-plots in pdf file
"""
gain = np.ma.array(self.fit_parameters.T[0], mask=self.fit_error.T)
quadratic_term = np.ma.array(self.fit_parameters.T[1], mask=self.fit_error.T)
# give to the badly fitted pixel a median value for the B term
median_quadratic_term = np.ma.median(quadratic_term, axis=0)
fill_array = np.ones((constants.N_PIXELS, constants.N_GAINS)) * median_quadratic_term
quadratic_term_corrected = np.ma.filled(quadratic_term, fill_array)
with h5py.File(self.output_path, 'w') as hf:
hf.create_dataset('gain', data=gain.T)
hf.create_dataset('B_term', data=quadratic_term_corrected.T)
hf.create_dataset('covariance_matrix', data=self.fit_cov_matrix)
hf.create_dataset('bad_fit_mask', data=self.fit_error)
# remember the camera median and the variance per run
channel = ["HG", "LG"]
for chan in [0, 1]:
if self.signal[chan] is not None:
hf.create_dataset(f'median_signal_{channel[chan]}', data=np.median(self.signal[chan], axis=0))
hf.create_dataset(f'median_variance_{channel[chan]}', data=np.median(self.variance[chan], axis=0))
hf.create_dataset(f'runs_{channel[chan]}', data=self.selected_runs[chan])
hf.create_dataset('runs', data=self.run_list)
hf.create_dataset('sub_run', data=self.sub_run)
# plot open pdf
with PdfPages(self.plot_path) as pdf:
plt.rc("font", size=15)
for chan in self.gain_channels:
# plot the used runs and their median camera charge
fig = plt.figure((chan + 1), figsize=(8, 20))
fig.suptitle(f"{channel[chan]} channel", fontsize=25)
ax = plt.subplot(2, 1, 1)
ax.grid(True)
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(plt.MultipleLocator(1))
plt.plot(np.median(self.signal[chan], axis=0), self.selected_runs[chan], "o")
plt.xlabel(r'$\mathrm{\overline{Q}-\overline{ped}}$ [ADC]')
plt.ylabel(r'Runs used in the fit')
plt.subplot(2, 1, 2)
camera = load_camera_geometry()
camera = camera.transform_to(EngineeringCameraFrame())
disp = CameraDisplay(camera)
image = self.fit_parameters.T[1].T * 100
mymin = np.median(image[chan]) - 3 * np.std(image[chan])
mymax = np.median(image[chan]) + 3 * np.std(image[chan])
disp.set_limits_minmax(mymin, mymax)
mask = np.where(self.fit_error[chan] == 1)[0]
disp.highlight_pixels(mask, linewidth=2.5, color="green")
disp.image = image[chan]
disp.cmap = plt.cm.coolwarm
plt.title(f"{channel[chan]} Fitted B values [%]")
disp.add_colorbar()
plt.tight_layout()
pdf.savefig()
# plot the fit results and residuals for four arbitrary pixels
fig = plt.figure((chan + 1) * 10, figsize=(11, 22))
fig.suptitle(f"{channel[chan]} channel", fontsize=25)
pad = 0
for pix in [0, 600, 1200, 1800]:
pad += 1
plt.subplot(4, 2, pad)
plt.grid(which='minor')
mask = self.unusable_pixels[chan][pix]
sig = np.ma.array(self.signal[chan][pix], mask=mask).compressed()
var = np.ma.array(self.variance[chan][pix], mask=mask).compressed()
popt = self.fit_parameters[chan, pix]
# plot points
plt.plot(sig, var, 'o', color="C0")
# plot fit
min_x = min(1000, np.min(sig) * 0.9)
max_x = max(10000, np.max(sig) * 1.1)
x = np.arange(np.min(sig), np.max(sig))
plt.plot(x, quadratic_fit(x, *popt), '--', color="C1",
label=f'Pixel {pix}:\ng={popt[0]:5.2f} [ADC/pe] , B={popt[1]:5.3f}')
plt.xlim(min_x, max_x)
plt.xlabel('Q-ped [ADC]')
plt.ylabel(r'$\mathrm{\sigma_Q^2-\sigma_{ped}^2}$ [$ADC^2$]')
plt.xscale('log')
plt.yscale('log')
plt.legend()
# plot residuals
pad += 1
plt.subplot(4, 2, pad)
plt.grid(which='both', axis='both')
popt = self.fit_parameters[chan, pix]
plt.plot(sig, (quadratic_fit(sig, *popt) - var) / var * 100, 'o', color="C0")
plt.xlim(min_x, max_x)
plt.xscale('log')
plt.ylabel('fit residuals %')
plt.xlabel('Q-ped [ADC]')
plt.hlines(0, 0, np.max(sig), linestyle='dashed', color="black")
plt.tight_layout()
pdf.savefig()
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()
import astropy.units as u
import numpy as np
np.random.seed(1337)
geom = CameraGeometry.from_name('FACT')
geom = geom.transform_to(EngineeringCameraFrame())
model = SkewedGaussian(
width=0.01 * u.m,
length=0.03 * u.m,
x=0.14 * u.m,
y=0.07 * u.m,
skewness=0.05,
psi=30 * u.deg,
)
img, signal, noise = model.generate_image(geom, 1000, nsb_level_pe=2)
fig = plt.figure(figsize=(5, 5))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_axis_off()
disp = CameraDisplay(geom, image=img, ax=ax, cmap='inferno')
disp.highlight_pixels(geom.get_border_pixel_mask(2), color='C0')
ax.set_title('')
fig.savefig('build/leakage.pdf', bbox_inches='tight')
from ctapipe.instrument import CameraGeometry
from ctapipe.visualization import CameraDisplay
if __name__ == '__main__':
plt.style.use('ggplot')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
geom = CameraGeometry.from_name('NectarCam')
disp = CameraDisplay(geom, ax=ax)
disp.add_colorbar()
model = toymodel.generate_2d_shower_model(centroid=(0.05, 0.0),
width=0.005,
length=0.025,
psi='35d')
image, sig, bg = toymodel.make_toymodel_shower_image(geom,
model.pdf,
intensity=50,
nsb_level_pe=20)
disp.image = image
mask = disp.image > 15
disp.highlight_pixels(mask, linewidth=3)
plt.show()
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)
