def display_event(event, geoms):
"""an extremely inefficient display. It creates new instances of
CameraDisplay for every event and every camera, and also new axes
for each event. It's hacked, but it works
"""
print("Displaying... please wait (this is an inefficient implementation)")
global fig
ntels = len(event.r0.tels_with_data)
fig.clear()
plt.suptitle("EVENT {}".format(event.r0.event_id))
disps = []
for ii, tel_id in enumerate(event.r0.tels_with_data):
print("\t draw cam {}...".format(tel_id))
nn = int(ceil(sqrt(ntels)))
ax = plt.subplot(nn, nn, ii + 1)
x, y = event.inst.pixel_pos[tel_id]
geom = geoms[tel_id]
disp = CameraDisplay(geom, ax=ax, title="CT{0}".format(tel_id))
disp.pixels.set_antialiaseds(False)
disp.autoupdate = False
disp.cmap = 'afmhot'
chan = 0
signals = event.r0.tel[tel_id].adc_sums[chan].astype(float)
signals -= signals.mean()
disp.image = signals
disp.set_limits_percent(95)
disp.add_colorbar()
disps.append(disp)
return disps
def Simple_CamPlot(image, geom, pix_ids, title=""):
disp1 = CameraDisplay(geom, title=title)
disp1.add_colorbar()
disp1.set_limits_minmax(zmin=image.min() * 0.98, zmax=image.max() * 1.02)
blankam1 = np.zeros(1855)
blankam1[pix_ids] = image
disp1.image = blankam1
plt.show()
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 create(self, df, geom):
count = np.stack(df['tc']).sum(0)
camera = CameraDisplay(geom, ax=self.ax,
image=count,
cmap='viridis')
camera.add_colorbar()
camera.colorbar.set_label("Count")
self.ax.set_title("Pixel Hits after Tailcuts For Run")
self.ax.axis('off')
def plot(self, df, n_frames, geom, output_path, title):
camera = CameraDisplay(geom,
ax=self.ax_camera,
image=np.zeros(2048),
cmap='viridis')
camera.add_colorbar()
camera.colorbar.set_label("Amplitude (p.e.)")
self.fig.suptitle(title + " - " + self.description)
# Create animation
interval = 25 # Fast
# interval = 100 # Slow
def animation_generator():
for index, row in df.iterrows():
event_id = row['event_id']
images = row['images']
tc = row['tc']
# max_ = np.percentile(event.max(), 60)
# camera.image = image
# camera.set_limits_minmax(min_, max_)
tc_2d = np.ones(images.shape, dtype=np.bool) * tc[None, :]
cleaned_events = np.ma.masked_array(images, mask=~tc_2d)
max_ = cleaned_events.max() # np.percentile(dl1, 99.9)
if max_ < 6:
max_ = 6
min_ = np.percentile(images, 0.1)
camera.set_limits_minmax(min_, max_)
self.ax_camera.set_title("Event: {}".format(event_id))
for s in images:
camera.image = s
yield
source = animation_generator()
self.log.info("Output: {}".format(output_path))
with tqdm(total=n_frames, desc="Creating animation") as pbar:
def animate(_):
pbar.update(1)
next(source)
anim = animation.FuncAnimation(self.fig,
animate,
frames=n_frames - 1,
interval=interval)
anim.save(output_path)
self.log.info("Created animation: {}".format(output_path))
def create(self, image, coords, title):
camera = CameraDisplay(coords.geom, ax=self.ax,
image=image,
cmap='viridis')
camera.add_colorbar()
camera.colorbar.set_label("Residual RMS (p.e.)", fontsize=20)
camera.image = image
camera.colorbar.ax.tick_params(labelsize=30)
self.fig.suptitle("Jupiter RMS ON-OFF")
self.ax.set_title(title)
self.ax.axis('off')
def create(self, image, label, title):
camera = CameraDisplay(get_geometry(),
ax=self.ax,
image=image,
cmap='viridis')
camera.add_colorbar()
camera.colorbar.set_label(label, fontsize=20)
camera.image = image
camera.colorbar.ax.tick_params(labelsize=30)
# self.ax.set_title(title)
self.ax.axis('off')
def plot_muon_event(ax, geom, image, centroid, ringrad_camcoord, ringrad_inner,
ringrad_outer, event_id):
"""
Function to plot single muon events
Parameters
----------
image
ax : `matplotlib.pyplot.axis`
geom : CameraGeometry
centroid : `float`
Centroid of the muon ring
ringrad_camcoord : `float`
Ring radius in camera coordinates
ringrad_inner : `float`
Inner ring radius in camera coordinates
ringrad_outer : `float`
Outer ring radius in camera coordinates
event_id : `int`
ID of the analyzed event
Returns
-------
ax : `matplotlib.pyplot.axis`
"""
disp0 = CameraDisplay(geom, ax=ax)
disp0.image = image
disp0.cmap = 'viridis'
disp0.add_colorbar(ax=ax)
disp0.add_ellipse(centroid,
ringrad_camcoord.value,
ringrad_camcoord.value,
0.,
0.,
color="red")
disp0.add_ellipse(centroid,
ringrad_inner.value,
ringrad_inner.value,
0.,
0.,
color="magenta")
disp0.add_ellipse(centroid,
ringrad_outer.value,
ringrad_outer.value,
0.,
0.,
color="magenta")
ax.set_title(f"Event {event_id}")
return ax
def Simple_AxCamPlot(image, geom, pix_ids, title=""):
f, ax = plt.subplots()
disp1 = CameraDisplay(geom, title=title, ax=ax)
disp1.add_colorbar(ax=ax)
# ~ disp1.set_limits_minmax(zmin=image.min()*0.98,zmax=image.max()*1.02)
disp1.set_limits_minmax(zmin=image.min(), zmax=image.max())
blankam1 = np.zeros(1855)
blankam1[pix_ids] = image
disp1.image = blankam1
ax.set_ylim(-0.65, 0.65)
ax.set_xlim(-0.6, 0.6)
return ax
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 start(self):
# n_events = self.reader.num_events
source = self.reader.read()
desc = "Looping through file"
for event in tqdm(source, desc=desc): #, total=n_events):
ev = event.count
self.calibrator.calibrate(event)
for tel_id in event.r0.tels_with_data:
geom = self.geometry.get_camera(tel_id)
nom_geom = self.geometry.get_nominal(tel_id)
image = event.dl1.tel[tel_id].image[0]
# Cleaning
cuts = self.tail_cut[geom.cam_id]
tc = tailcuts_clean(geom, image, *cuts)
if not tc.any():
# self.log.warning('No image')
continue
# cleaned = np.ma.masked_array(image, mask=~tc)
cleaned = image * tc
# Hillas
try:
hillas = hillas_parameters(nom_geom, cleaned)
except HillasParameterizationError:
# self.log.warning('HillasParameterizationError')
continue
# embed()
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
camera = CameraDisplay(nom_geom, ax=ax, image=image, cmap='viridis')
camera.add_colorbar()
cen_x = u.Quantity(hillas.cen_x).value
cen_y = u.Quantity(hillas.cen_y).value
length = u.Quantity(hillas.length).value
width = u.Quantity(hillas.width).value
print(cen_x, cen_y, length, width)
camera.add_ellipse(centroid=(cen_x, cen_y),
length=length * 2,
width=width * 2,
angle=hillas.psi.rad)
plt.show()
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),
)
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 plot_event_with_shower_position_seperate_events(self, event1, event2):
x, y, alt, az = self.transform_to_telescope_xy_altaz_from_event(event1)
x = x.to_value(u.m)
y = y.to_value(u.m)
# Cameras
m1_cam = copy.deepcopy(event1.inst.subarray.tel[1].camera)
m2_cam = copy.deepcopy(event2.inst.subarray.tel[2].camera)
m1_cam.rotate(-19.1*u.deg)#(-90+70.9)
m2_cam.rotate((-90+70.9)*u.deg)
# Charge images
m1_event_image = event1.dl1.tel[1].image
m2_event_image = event2.dl1.tel[2].image
# Peak position maps
#m1_event_times = b5["M1"].dl1.tel[1].peakpos
#m2_event_times = b5["M2"].dl1.tel[2].peakpos
fig1, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15,10))
# M1 charge map
disp1 = CameraDisplay(m1_cam, m1_event_image, ax=ax1, title="MAGIC 1 image")
disp1.add_colorbar(ax=ax1)
# M2 charge map
disp2 = CameraDisplay(m2_cam, m2_event_image, ax=ax2, title="MAGIC 2 image")
disp2.add_colorbar(ax=ax2)
ax1.plot(x, y, marker='*', color='red')
ax1.annotate(
s="Peter", xy=(x, y), xytext=(5, 5),
textcoords='offset points', color='red',
)
#ax1.scatter(cam.pix_x, cam.pix_y, marker="o", s=10, c='red')
ax2.plot(x, y, marker='*', color='red')
ax2.annotate(
s="Peter", xy=(x, y), xytext=(5, 5),
textcoords='offset points', color='red',
)
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 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 display_array_camera(image, camera_geometry, axes=None, **kwargs):
"""
Display the image of an event
Parameters
----------
image: array_like
axes: matplotlib.pyplot.axes
Returns
-------
d1: ctapipe.visualization.CameraDisplay
"""
if axes is None:
fig, axes = plt.subplots(figsize=(10, 8))
d1 = CameraDisplay(camera_geometry, image, ax=axes, **kwargs)
d1.add_colorbar(ax=axes)
return d1
def plot_all(data_file, legend='camera average'):
mean_charge_all, std_charge_all, mean_t_all, std_t_all, true_pe = \
load_data(data_file)
mean_t_100pe = np.array(
[np.interp(100, true_pe[:, i], mean_t_all[:, i]) for i in range(1296)])
std_t_100pe = np.array(
[np.interp(100, true_pe[:, i], std_t_all[:, i]) for i in range(1296)])
fig, axes = plt.subplots(2, 3, figsize=(24, 18))
plot_zone(true_pe,
mean_charge_all,
[np.logspace(.5, 2.75, 101),
np.logspace(-.3, 2.8, 101)],
axes[0, 0],
legend,
yscale='log')
axes[0, 0].loglog([0.1, 1000], [0.1, 1000], 'k--')
axes[0, 0].set_ylabel('mean charge reco. [p.e]')
plot_zone(true_pe,
std_charge_all,
[np.logspace(.5, 2.75, 101),
np.logspace(-0.5, 1.5, 101)],
axes[0, 1],
legend,
yscale='log')
axes[0, 1].loglog([0.1, 1000], np.sqrt([0.1, 1000]), 'k--')
axes[0, 1].set_ylabel('std charge reco. [p.e]')
plot_resol(data_file, legend=legend, ax=axes[0, 2])
plot_offset(data_file, legend=legend, ax=axes[1, 0])
display = CameraDisplay(DigiCam.geometry,
ax=axes[1, 1],
title='timing offset (at 100 p.e) [ns]')
display.image = mean_t_100pe - np.nanmean(mean_t_100pe)
display.set_limits_minmax(-2, 2)
display.add_colorbar(ax=axes[1, 1])
display = CameraDisplay(DigiCam.geometry,
ax=axes[1, 2],
title='timing resolution (at 100 p.e.) [ns]')
display.image = std_t_100pe
display.set_limits_minmax(0.1, 0.3)
display.add_colorbar(ax=axes[1, 2])
plt.tight_layout()
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 trigger_uniformity(files,
plot="show",
event_types=None,
disable_bar=False):
events = event_stream(files, disable_bar=disable_bar)
if event_types is not None:
events = filter_event_types(events, flags=event_types)
# patxh matrix is a bool of size n_patch x n_pixel
patch_matrix = compute_patch_matrix(camera=DigiCam)
n_patch, n_pixel = patch_matrix.shape
top7 = np.zeros([n_patch], dtype=np.float32)
n_event = 0
for event in events:
n_event += 1
tel = event.r0.tels_with_data[0]
top7 += np.sum(event.r0.tel[tel].trigger_output_patch7, axis=1)
patches_rate = top7 / n_event
pixels_rate = patches_rate.reshape([1, -1]).dot(patch_matrix).flatten()
print('pixels_rate from', np.min(pixels_rate), 'to', np.max(pixels_rate),
'trigger/event')
if plot is None:
return pixels_rate
fig1 = plt.figure()
ax = plt.gca()
display = CameraDisplay(DigiCam.geometry,
ax=ax,
title='Trigger uniformity')
display.add_colorbar()
display.image = pixels_rate
output_path = os.path.dirname(plot)
if plot == "show" or 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)
plt.close(fig1)
return pixels_rate
def start(self):
geom = None
imsum = None
disp = None
for data in hessio_event_source(self.infile,
allowed_tels=self._selected_tels,
max_events=self.max_events):
self.calibrator.calibrate(data)
if geom is None:
x, y = data.inst.pixel_pos[self._base_tel]
flen = data.inst.optical_foclen[self._base_tel]
geom = CameraGeometry.guess(x, y, flen)
imsum = np.zeros(shape=x.shape, dtype=np.float)
disp = CameraDisplay(geom, title=geom.cam_id)
disp.add_colorbar()
disp.cmap = 'viridis'
if len(data.dl0.tels_with_data) <= 2:
continue
imsum[:] = 0
for telid in data.dl0.tels_with_data:
imsum += data.dl1.tel[telid].image[0]
self.log.info("event={} ntels={} energy={}" \
.format(data.r0.event_id,
len(data.dl0.tels_with_data),
data.mc.energy))
disp.image = imsum
plt.pause(0.1)
if self.output_suffix is not "":
filename = "{:020d}{}".format(data.r0.event_id,
self.output_suffix)
self.log.info("saving: '{}'".format(filename))
plt.savefig(filename)
def start(self):
geom = None
imsum = None
disp = None
for data in hessio_event_source(self.infile,
allowed_tels=self._selected_tels,
max_events=self.max_events):
self.calibrator.calibrate(data)
if geom is None:
x, y = data.inst.pixel_pos[self._base_tel]
flen = data.inst.optical_foclen[self._base_tel]
geom = CameraGeometry.guess(x, y, flen)
imsum = np.zeros(shape=x.shape, dtype=np.float)
disp = CameraDisplay(geom, title=geom.cam_id)
disp.add_colorbar()
disp.cmap = 'viridis'
if len(data.dl0.tels_with_data) <= 2:
continue
imsum[:] = 0
for telid in data.dl0.tels_with_data:
imsum += data.dl1.tel[telid].image[0]
self.log.info("event={} ntels={} energy={}" \
.format(data.r0.event_id,
len(data.dl0.tels_with_data),
data.mc.energy))
disp.image = imsum
plt.pause(0.1)
if self.output_suffix is not "":
filename = "{:020d}{}".format(data.r0.event_id,
self.output_suffix)
self.log.info("saving: '{}'".format(filename))
plt.savefig(filename)
def start(self):
geom = None
imsum = None
disp = None
for event in self.reader:
self.calibrator(event)
if geom is None:
geom = event.inst.subarray.tel[self._base_tel].camera
imsum = np.zeros(shape=geom.pix_x.shape, dtype=np.float)
disp = CameraDisplay(geom, title=geom.cam_id)
disp.add_colorbar()
disp.cmap = 'viridis'
if len(event.dl0.tels_with_data) <= 2:
continue
imsum[:] = 0
for telid in event.dl0.tels_with_data:
imsum += event.dl1.tel[telid].image[0]
self.log.info(
"event={} ntels={} energy={}".format(
event.r0.event_id, len(event.dl0.tels_with_data),
event.mc.energy
)
)
disp.image = imsum
plt.pause(0.1)
if self.output_suffix is not "":
filename = "{:020d}{}".format(
event.r0.event_id, self.output_suffix
)
self.log.info(f"saving: '{filename}'")
plt.savefig(filename)
def plot_residual(fitter, image, geometry, save=False, ids=''):
"""
Plot the residuals image- spatial_model in the camera after fitting
Parameters
----------
image:
Distribution of signal for the event in number of p.e.
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
"""
params = fitter.end_parameters
rl = 1 + params['rl'] if params['rl'] >= 0 else 1 / (1 - params['rl'])
mu = asygaussian2d(params['charge'] * geometry.pix_area.to_value(u.m**2),
geometry.pix_x.value, geometry.pix_y.value,
params['x_cm'], params['y_cm'],
params['wl'] * params['length'], params['length'],
params['psi'], rl)
residual = image - mu
fig, axes = plt.subplots(figsize=(10, 8))
cam_display = CameraDisplay(geometry, residual, ax=axes)
cam_display.add_colorbar(ax=axes)
if save:
cam_display.axes.get_figure().savefig('event/' + ids +
'_residuals.png')
plt.close()
return None if save else cam_display
def start(self):
geom = None
imsum = None
disp = None
for event in self.reader:
self.calibrator.calibrate(event)
if geom is None:
geom = event.inst.subarray.tel[self._base_tel].camera
imsum = np.zeros(shape=geom.pix_x.shape, dtype=np.float)
disp = CameraDisplay(geom, title=geom.cam_id)
disp.add_colorbar()
disp.cmap = 'viridis'
if len(event.dl0.tels_with_data) <= 2:
continue
imsum[:] = 0
for telid in event.dl0.tels_with_data:
imsum += event.dl1.tel[telid].image[0]
self.log.info(
"event={} ntels={} energy={}".format(
event.r0.event_id, len(event.dl0.tels_with_data),
event.mc.energy
)
)
disp.image = imsum
plt.pause(0.1)
if self.output_suffix is not "":
filename = "{:020d}{}".format(
event.r0.event_id, self.output_suffix
)
self.log.info(f"saving: '{filename}'")
plt.savefig(filename)
def start(self):
geom = None
imsum = None
disp = None
for event in self.reader:
self.calibrator(event)
if geom is None:
geom = self.reader.subarray.tel[self._base_tel].camera.geometry
imsum = np.zeros(shape=geom.pix_x.shape, dtype=np.float64)
disp = CameraDisplay(geom, title=geom.camera_name)
disp.add_colorbar()
disp.cmap = "viridis"
if len(event.dl0.tel.keys()) <= 2:
continue
imsum[:] = 0
for telid in event.dl0.tel.keys():
imsum += event.dl1.tel[telid].image
self.log.info(
"event={} ntels={} energy={}".format(
event.index.event_id,
len(event.dl0.tel.keys()),
event.simulation.shower.energy,
)
)
disp.image = imsum
plt.pause(0.1)
if self.output_suffix != "":
filename = "{:020d}{}".format(event.index.event_id, self.output_suffix)
self.log.info(f"saving: '{filename}'")
plt.savefig(filename)
def plot_allparam_map(df):
nc = 4
nr = 2
f,axs = plt.subplots(ncols=nc,nrows=nr,figsize=(12,6))
# ~ table = Table.read('./NewNectarCam.camgeom.fits.gz')
# ~ geom = CameraGeometry.from_table(table)
# ~ geom.rotate(10.3*u.deg)
geom = CameraGeometry.from_name("NectarCam-002")
for ii,key in enumerate(df):
# ~ for ii,key in enumerate(['Light I', 'ped mean', 'ped width', 'res', 'Mu2', 'gain']):
ax = axs[ii%nr,ii//nr%nc]
blankam = np.zeros(1855)
blankam[pix_ids]=df[key]
disp = CameraDisplay(geom,title=key,ax=ax)
disp.add_colorbar(ax=ax)
disp.set_limits_minmax(zmin=np.min(df[key])*.99,zmax=np.max(df[key])*1.01)
disp.image = blankam
ax.set_ylim(-0.8,0.8)
ax.set_xlim(-0.5,0.4)
plt.show()
return
def transform_and_clean_hex_samples(pmt_samples, cam_geom):
# rotate all samples in the image to a rectangular image
rot_geom, rot_samples = convert_geometry_1d_to_2d(
cam_geom, pmt_samples, cam_geom.cam_id)
print("rot samples.shape:", rot_samples.shape)
# rotate the samples back to hex image
unrot_geom, unrot_samples = convert_geometry_back(rot_geom, rot_samples,
cam_geom.cam_id)
global fig
global cb1, ax1
global cb2, ax2
global cb3, ax3
if fig is None:
fig = plt.figure(figsize=(10, 10))
else:
fig.delaxes(ax1)
fig.delaxes(ax2)
fig.delaxes(ax3)
cb1.remove()
cb2.remove()
cb3.remove()
ax1 = fig.add_subplot(221)
disp1 = CameraDisplay(rot_geom, image=np.sum(rot_samples, axis=-1), ax=ax1)
plt.gca().set_aspect('equal', adjustable='box')
plt.title("rotated image")
disp1.cmap = plt.cm.inferno
disp1.add_colorbar()
cb1 = disp1.colorbar
ax2 = fig.add_subplot(222)
disp2 = CameraDisplay(cam_geom, image=np.sum(pmt_samples, axis=-1), ax=ax2)
plt.gca().set_aspect('equal', adjustable='box')
plt.title("original image")
disp2.cmap = plt.cm.inferno
disp2.add_colorbar()
cb2 = disp2.colorbar
ax3 = fig.add_subplot(223)
disp3 = CameraDisplay(unrot_geom, image=np.sum(unrot_samples, axis=-1), ax=ax3)
plt.gca().set_aspect('equal', adjustable='box')
plt.title("de-rotated image")
disp3.cmap = plt.cm.inferno
disp3.add_colorbar()
cb3 = disp3.colorbar
plt.pause(.1)
response = input("press return to continue")
if response != "":
exit()
class ImagePlotter(Component):
display = Bool(True,
help='Display the photoelectron images on-screen as they '
'are produced.').tag(config=True)
output_path = Unicode(None,
allow_none=True,
help='Output path for the pdf containing all the '
'images. Set to None for no saved '
'output.').tag(config=True)
def __init__(self, config=None, parent=None, **kwargs):
"""
Plotter for camera images.
Parameters
----------
config : traitlets.loader.Config
Configuration specified by config file or cmdline arguments.
Used to set traitlet values.
Set to None if no configuration to pass.
tool : ctapipe.core.Tool
Tool executable that is calling this component.
Passes the correct logger to the component.
Set to None if no Tool to pass.
kwargs
"""
super().__init__(config=config, parent=parent, **kwargs)
self._current_tel = None
self.c_intensity = None
self.c_pulse_time = None
self.cb_intensity = None
self.cb_pulse_time = None
self.pdf = None
self._init_figure()
def _init_figure(self):
self.fig = plt.figure(figsize=(16, 7))
self.ax_intensity = self.fig.add_subplot(1, 2, 1)
self.ax_pulse_time = self.fig.add_subplot(1, 2, 2)
if self.output_path:
self.log.info(f"Creating PDF: {self.output_path}")
self.pdf = PdfPages(self.output_path)
@staticmethod
def get_geometry(event, telid):
return event.inst.subarray.tel[telid].camera
def plot(self, event, telid):
chan = 0
image = event.dl1.tel[telid].image[chan]
pulse_time = event.dl1.tel[telid].pulse_time[chan]
if self._current_tel != telid:
self._current_tel = telid
self.ax_intensity.cla()
self.ax_pulse_time.cla()
# Redraw camera
geom = self.get_geometry(event, telid)
self.c_intensity = CameraDisplay(geom, ax=self.ax_intensity)
self.c_pulse_time = CameraDisplay(geom, ax=self.ax_pulse_time)
tmaxmin = event.dl0.tel[telid].waveform.shape[2]
t_chargemax = pulse_time[image.argmax()]
cmap_time = colors.LinearSegmentedColormap.from_list(
'cmap_t', [(0 / tmaxmin, 'darkgreen'),
(0.6 * t_chargemax / tmaxmin, 'green'),
(t_chargemax / tmaxmin, 'yellow'),
(1.4 * t_chargemax / tmaxmin, 'blue'),
(1, 'darkblue')])
self.c_pulse_time.pixels.set_cmap(cmap_time)
if not self.cb_intensity:
self.c_intensity.add_colorbar(ax=self.ax_intensity,
label='Intensity (p.e.)')
self.cb_intensity = self.c_intensity.colorbar
else:
self.c_intensity.colorbar = self.cb_intensity
self.c_intensity.update(True)
if not self.cb_pulse_time:
self.c_pulse_time.add_colorbar(ax=self.ax_pulse_time,
label='Pulse Time (ns)')
self.cb_pulse_time = self.c_pulse_time.colorbar
else:
self.c_pulse_time.colorbar = self.cb_pulse_time
self.c_pulse_time.update(True)
self.c_intensity.image = image
if pulse_time is not None:
self.c_pulse_time.image = pulse_time
self.fig.suptitle("Event_index={} Event_id={} Telescope={}".format(
event.count, event.r0.event_id, telid))
if self.display:
plt.pause(0.001)
if self.pdf is not None:
self.pdf.savefig(self.fig)
def finish(self):
if self.pdf is not None:
self.log.info("Closing PDF")
self.pdf.close()
# load the camera
tel = TelescopeDescription.from_name("SST-1M", "DigiCam")
print(tel, tel.optics.effective_focal_length)
geom = tel.camera
# poor-man's coordinate transform from telscope to camera frame (it's
# better to use ctapipe.coordiantes when they are stable)
scale = tel.optics.effective_focal_length.to(geom.pix_x.unit).value
fov = np.deg2rad(4.0)
maxwid = np.deg2rad(0.01)
maxlen = np.deg2rad(0.03)
disp = CameraDisplay(geom, ax=ax)
disp.cmap = plt.cm.terrain
disp.add_colorbar(ax=ax)
def update(frame):
centroid = np.random.uniform(-fov, fov, size=2) * scale
width = np.random.uniform(0, maxwid) * scale
length = np.random.uniform(0, maxlen) * scale + width
angle = np.random.uniform(0, 360)
intens = np.random.exponential(2) * 50
model = toymodel.generate_2d_shower_model(
centroid=centroid,
width=width,
length=length,
psi=angle * u.deg,
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom,
from matplotlib import pyplot as plt
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=0.005,
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)
def transform_and_clean_hex_image(pmt_signal, cam_geom, photo_electrons):
start_time = time.time()
colors = cm.inferno(pmt_signal/max(pmt_signal))
new_geom, new_signal = convert_geometry_1d_to_2d(
cam_geom, pmt_signal, cam_geom.cam_id)
print("rot_signal", np.count_nonzero(np.isnan(new_signal)))
square_mask = new_geom.mask
cleaned_img = wavelet_transform(new_signal,
raw_option_string=args.raw)
unrot_img = cleaned_img[square_mask]
unrot_colors = cm.inferno(unrot_img/max(unrot_img))
cleaned_img_ik = kill_isolpix(cleaned_img, threshold=.5)
unrot_img_ik = cleaned_img_ik[square_mask]
unrot_colors_ik = cm.inferno(unrot_img_ik/max(unrot_img_ik))
square_image_add_noise = np.copy(new_signal)
square_image_add_noise[~square_mask] = \
np.random.normal(0.13, 5.77, np.count_nonzero(~square_mask))
square_image_add_noise_cleaned = wavelet_transform(square_image_add_noise,
raw_option_string=args.raw)
square_image_add_noise_cleaned_ik = kill_isolpix(square_image_add_noise_cleaned,
threshold=1.5)
unrot_geom, unrot_noised_signal = convert_geometry_back(
new_geom, square_image_add_noise_cleaned_ik, cam_geom.cam_id)
end_time = time.time()
print(end_time - start_time)
global fig
global cb1, ax1
global cb2, ax2
global cb3, ax3
global cb4, ax4
global cb5, ax5
global cb6, ax6
global cb7, ax7
global cb8, ax8
global cb9, ax9
if fig is None:
fig = plt.figure(figsize=(10, 10))
else:
fig.delaxes(ax1)
fig.delaxes(ax2)
fig.delaxes(ax3)
fig.delaxes(ax4)
fig.delaxes(ax5)
fig.delaxes(ax6)
fig.delaxes(ax7)
fig.delaxes(ax8)
fig.delaxes(ax9)
cb1.remove()
cb2.remove()
cb3.remove()
cb4.remove()
cb5.remove()
cb6.remove()
cb7.remove()
cb8.remove()
cb9.remove()
ax1 = fig.add_subplot(333)
disp1 = CameraDisplay(cam_geom, image=photo_electrons, ax=ax1)
plt.gca().set_aspect('equal', adjustable='box')
plt.title("photo-electron image")
disp1.cmap = plt.cm.inferno
disp1.add_colorbar()
cb1 = disp1.colorbar
ax2 = fig.add_subplot(336)
disp2 = CameraDisplay(cam_geom, image=pmt_signal, ax=ax2)
plt.gca().set_aspect('equal', adjustable='box')
disp2.cmap = plt.cm.inferno
disp2.add_colorbar()
cb2 = disp2.colorbar
plt.title("noisy image")
ax3 = fig.add_subplot(331)
plt.imshow(new_signal, interpolation='none', cmap=cm.inferno,
origin='lower')
plt.gca().set_aspect('equal', adjustable='box')
plt.title("noisy, slanted image")
cb3 = plt.colorbar()
ax4 = fig.add_subplot(334)
plt.imshow(cleaned_img, interpolation='none', cmap=cm.inferno,
origin='lower')
plt.gca().set_aspect('equal', adjustable='box')
plt.title("cleaned, slanted image, islands not killed")
cb4 = plt.colorbar()
ax4.set_axis_off()
ax5 = fig.add_subplot(337)
plt.imshow(np.sqrt(cleaned_img_ik), interpolation='none', cmap=cm.inferno,
origin='lower')
plt.gca().set_aspect('equal', adjustable='box')
plt.title("cleaned, slanted image, islands killed")
cb5 = plt.colorbar()
ax5.set_axis_off()
#
ax6 = fig.add_subplot(332)
plt.imshow(square_image_add_noise, interpolation='none', cmap=cm.inferno,
origin='lower')
plt.gca().set_aspect('equal', adjustable='box')
plt.title("slanted image, noise added")
cb6 = plt.colorbar()
ax6.set_axis_off()
#
ax7 = fig.add_subplot(335)
plt.imshow(np.sqrt(square_image_add_noise_cleaned), interpolation='none',
cmap=cm.inferno,
origin='lower')
plt.gca().set_aspect('equal', adjustable='box')
plt.title("slanted image, noise added, cleaned")
cb7 = plt.colorbar()
ax7.set_axis_off()
ax8 = fig.add_subplot(338)
plt.imshow(square_image_add_noise_cleaned_ik, interpolation='none',
cmap=cm.inferno,
origin='lower')
plt.gca().set_aspect('equal', adjustable='box')
plt.title("slanted image, noise added, cleaned, islands killed")
cb8 = plt.colorbar()
ax8.set_axis_off()
try:
ax9 = fig.add_subplot(339)
disp9 = CameraDisplay(unrot_geom, image=unrot_noised_signal,
ax=ax9)
plt.gca().set_aspect('equal', adjustable='box')
plt.title("cleaned, original geometry, islands killed")
disp9.cmap = plt.cm.inferno
disp9.add_colorbar()
cb9 = disp9.colorbar
except:
pass
plt.suptitle(cam_geom.cam_id)
plt.subplots_adjust(top=0.94, bottom=.08, left=0, right=.96, hspace=.41, wspace=.08)
plt.pause(.1)
response = input("press return to continue")
if response != "":
exit()
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 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))
bad_pixels = {'color': 'black', 'alpha': 0.1}
cw = cm.coolwarm
cw.set_bad(**bad_pixels)
vi = cm.viridis
vi.set_bad(**bad_pixels)
p = "build/simtel-output.zst"
f = SimTelFile(p)
s = SimTelEventSource(p)
geom = s.subarray.tel[1].camera.geometry
fig, (ax_im, ax_t) = plt.subplots(ncols=2, figsize=(8, 4))
disp_im = CameraDisplay(geom, ax=ax_im, cmap=vi)
disp_im.add_colorbar()
disp_t = CameraDisplay(geom, ax=ax_t, cmap=cw)
disp_t.add_colorbar()
fig.tight_layout()
fig.show()
def plot(pe):
image = pe['photoelectrons']
image[image == 0] = np.nan
time = np.empty_like(image)
mask = pe['pixel_id']
time[:] = np.nan
time[mask] = pe['time'] - np.mean(pe['time'])
class ImagePlotter(Component):
name = 'ImagePlotter'
display = Bool(False,
help='Display the photoelectron images on-screen as they '
'are produced.').tag(config=True)
output_path = Unicode(None, allow_none=True,
help='Output path for the pdf containing all the '
'images. Set to None for no saved '
'output.').tag(config=True)
def __init__(self, config, tool, **kwargs):
"""
Plotter for camera images.
Parameters
----------
config : traitlets.loader.Config
Configuration specified by config file or cmdline arguments.
Used to set traitlet values.
Set to None if no configuration to pass.
tool : ctapipe.core.Tool
Tool executable that is calling this component.
Passes the correct logger to the component.
Set to None if no Tool to pass.
kwargs
"""
super().__init__(config=config, parent=tool, **kwargs)
self._current_tel = None
self.c_intensity = None
self.c_peakpos = None
self.cb_intensity = None
self.cb_peakpos = None
self.pdf = None
self._init_figure()
def _init_figure(self):
self.fig = plt.figure(figsize=(16, 7))
self.ax_intensity = self.fig.add_subplot(1, 2, 1)
self.ax_peakpos = self.fig.add_subplot(1, 2, 2)
if self.output_path:
self.log.info("Creating PDF: {}".format(self.output_path))
self.pdf = PdfPages(self.output_path)
def get_geometry(self, event, telid):
return event.inst.subarray.tel[telid].camera
def plot(self, event, telid):
chan = 0
image = event.dl1.tel[telid].image[chan]
peakpos = event.dl1.tel[telid].peakpos[chan]
if self._current_tel != telid:
self._current_tel = telid
self.ax_intensity.cla()
self.ax_peakpos.cla()
# Redraw camera
geom = self.get_geometry(event, telid)
self.c_intensity = CameraDisplay(geom, cmap=plt.cm.viridis,
ax=self.ax_intensity)
self.c_peakpos = CameraDisplay(geom, cmap=plt.cm.viridis,
ax=self.ax_peakpos)
tmaxmin = event.dl0.tel[telid].pe_samples.shape[2]
t_chargemax = peakpos[image.argmax()]
cmap_time = colors.LinearSegmentedColormap.from_list(
'cmap_t', [(0 / tmaxmin, 'darkgreen'),
(0.6 * t_chargemax / tmaxmin, 'green'),
(t_chargemax / tmaxmin, 'yellow'),
(1.4 * t_chargemax / tmaxmin, 'blue'),
(1, 'darkblue')])
self.c_peakpos.pixels.set_cmap(cmap_time)
if not self.cb_intensity:
self.c_intensity.add_colorbar(ax=self.ax_intensity,
label='Intensity (p.e.)')
self.cb_intensity = self.c_intensity.colorbar
else:
self.c_intensity.colorbar = self.cb_intensity
self.c_intensity.update(True)
if not self.cb_peakpos:
self.c_peakpos.add_colorbar(ax=self.ax_peakpos,
label='Peakpos (ns)')
self.cb_peakpos = self.c_peakpos.colorbar
else:
self.c_peakpos.colorbar = self.cb_peakpos
self.c_peakpos.update(True)
self.c_intensity.image = image
if peakpos is not None:
self.c_peakpos.image = peakpos
self.fig.suptitle("Event_index={} Event_id={} Telescope={}"
.format(event.count, event.r0.event_id, telid))
if self.display:
plt.pause(0.001)
if self.pdf is not None:
self.pdf.savefig(self.fig)
def finish(self):
if self.pdf is not None:
self.log.info("Closing PDF")
self.pdf.close()
def plot(event, telid, chan, extractor_name):
# Extract required images
dl0 = event.dl0.tel[telid].waveform[chan]
t_pe = event.mc.tel[telid].photo_electron_image
dl1 = event.dl1.tel[telid].image[chan]
max_time = np.unravel_index(np.argmax(dl0), dl0.shape)[1]
max_charges = np.max(dl0, axis=1)
max_pix = int(np.argmax(max_charges))
min_pix = int(np.argmin(max_charges))
geom = event.inst.subarray.tel[telid].camera
nei = geom.neighbors
# Get Neighbours
max_pixel_nei = nei[max_pix]
min_pixel_nei = nei[min_pix]
# Draw figures
ax_max_nei = {}
ax_min_nei = {}
fig_waveforms = plt.figure(figsize=(18, 9))
fig_waveforms.subplots_adjust(hspace=.5)
fig_camera = plt.figure(figsize=(15, 12))
ax_max_pix = fig_waveforms.add_subplot(4, 2, 1)
ax_min_pix = fig_waveforms.add_subplot(4, 2, 2)
ax_max_nei[0] = fig_waveforms.add_subplot(4, 2, 3)
ax_min_nei[0] = fig_waveforms.add_subplot(4, 2, 4)
ax_max_nei[1] = fig_waveforms.add_subplot(4, 2, 5)
ax_min_nei[1] = fig_waveforms.add_subplot(4, 2, 6)
ax_max_nei[2] = fig_waveforms.add_subplot(4, 2, 7)
ax_min_nei[2] = fig_waveforms.add_subplot(4, 2, 8)
ax_img_nei = fig_camera.add_subplot(2, 2, 1)
ax_img_max = fig_camera.add_subplot(2, 2, 2)
ax_img_true = fig_camera.add_subplot(2, 2, 3)
ax_img_cal = fig_camera.add_subplot(2, 2, 4)
# Draw max pixel traces
ax_max_pix.plot(dl0[max_pix])
ax_max_pix.set_xlabel("Time (ns)")
ax_max_pix.set_ylabel("DL0 Samples (ADC)")
ax_max_pix.set_title(
f'(Max) Pixel: {max_pix}, True: {t_pe[max_pix]}, '
f'Measured = {dl1[max_pix]:.3f}'
)
max_ylim = ax_max_pix.get_ylim()
for i, ax in ax_max_nei.items():
if len(max_pixel_nei) > i:
pix = max_pixel_nei[i]
ax.plot(dl0[pix])
ax.set_xlabel("Time (ns)")
ax.set_ylabel("DL0 Samples (ADC)")
ax.set_title(
"(Max Nei) Pixel: {}, True: {}, Measured = {:.3f}"
.format(pix, t_pe[pix], dl1[pix])
)
ax.set_ylim(max_ylim)
# Draw min pixel traces
ax_min_pix.plot(dl0[min_pix])
ax_min_pix.set_xlabel("Time (ns)")
ax_min_pix.set_ylabel("DL0 Samples (ADC)")
ax_min_pix.set_title(
f'(Min) Pixel: {min_pix}, True: {t_pe[min_pix]}, '
f'Measured = {dl1[min_pix]:.3f}'
)
ax_min_pix.set_ylim(max_ylim)
for i, ax in ax_min_nei.items():
if len(min_pixel_nei) > i:
pix = min_pixel_nei[i]
ax.plot(dl0[pix])
ax.set_xlabel("Time (ns)")
ax.set_ylabel("DL0 Samples (ADC)")
ax.set_title(
f'(Min Nei) Pixel: {pix}, True: {t_pe[pix]}, '
f'Measured = {dl1[pix]:.3f}'
)
ax.set_ylim(max_ylim)
# Draw cameras
nei_camera = np.zeros_like(max_charges, dtype=np.int)
nei_camera[min_pixel_nei] = 2
nei_camera[min_pix] = 1
nei_camera[max_pixel_nei] = 3
nei_camera[max_pix] = 4
camera = CameraDisplay(geom, ax=ax_img_nei)
camera.image = nei_camera
ax_img_nei.set_title("Neighbour Map")
ax_img_nei.annotate(
f"Pixel: {max_pix}",
xy=(geom.pix_x.value[max_pix], geom.pix_y.value[max_pix]),
xycoords='data',
xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
ax_img_nei.annotate(
f"Pixel: {min_pix}",
xy=(geom.pix_x.value[min_pix], geom.pix_y.value[min_pix]),
xycoords='data',
xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
camera = CameraDisplay(geom, ax=ax_img_max)
camera.image = dl0[:, max_time]
camera.add_colorbar(ax=ax_img_max, label="DL0 Samples (ADC)")
ax_img_max.set_title(f"Max Timeslice (T = {max_time})")
ax_img_max.annotate(
f"Pixel: {max_pix}",
xy=(geom.pix_x.value[max_pix], geom.pix_y.value[max_pix]),
xycoords='data',
xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
ax_img_max.annotate(
f"Pixel: {min_pix}",
xy=(geom.pix_x.value[min_pix], geom.pix_y.value[min_pix]),
xycoords='data',
xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
camera = CameraDisplay(geom, ax=ax_img_true)
camera.image = t_pe
camera.add_colorbar(ax=ax_img_true, label="True Charge (p.e.)")
ax_img_true.set_title("True Charge")
ax_img_true.annotate(
f"Pixel: {max_pix}",
xy=(geom.pix_x.value[max_pix], geom.pix_y.value[max_pix]),
xycoords='data',
xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
ax_img_true.annotate(
f"Pixel: {min_pix}",
xy=(geom.pix_x.value[min_pix], geom.pix_y.value[min_pix]),
xycoords='data',
xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
camera = CameraDisplay(geom, ax=ax_img_cal)
camera.image = dl1
camera.add_colorbar(ax=ax_img_cal, label="Calib Charge (Photo-electrons)")
ax_img_cal.set_title(f"Charge (integrator={extractor_name})")
ax_img_cal.annotate(
f"Pixel: {max_pix}",
xy=(geom.pix_x.value[max_pix], geom.pix_y.value[max_pix]),
xycoords='data',
xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
ax_img_cal.annotate(
f"Pixel: {min_pix}",
xy=(geom.pix_x.value[min_pix], geom.pix_y.value[min_pix]),
xycoords='data',
xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2, alpha=0.4),
horizontalalignment='left',
verticalalignment='top'
)
fig_waveforms.suptitle(f"Integrator = {extractor_name}")
fig_camera.suptitle(f"Camera = {geom.cam_id}")
plt.show()
"""Draw lines between a pixel and its neighbors"""
neigh = geom.neighbors[pixel_index] # neighbor indices (not pixel ids)
x, y = geom.pix_x[pixel_index].value, geom.pix_y[pixel_index].value
for nn in neigh:
nx, ny = geom.pix_x[nn].value, geom.pix_y[nn].value
plt.plot([x, nx], [y, ny], color=color, **kwargs)
if __name__ == '__main__':
# Load the camera
geom = CameraGeometry.from_name("LSTCam")
disp = CameraDisplay(geom)
disp.set_limits_minmax(0, 300)
disp.add_colorbar()
# Create a fake camera image to display:
model = toymodel.generate_2d_shower_model(centroid=(0.2, 0.0),
width=0.01,
length=0.1,
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()
if __name__ == '__main__':
plt.style.use("ggplot")
fig, ax = plt.subplots()
# load the camera
tel = TelescopeDescription.from_name("SST-1M", "DigiCam")
geom = tel.camera
fov = 0.3
maxwid = 0.05
maxlen = 0.1
disp = CameraDisplay(geom, ax=ax)
disp.cmap = 'inferno'
disp.add_colorbar(ax=ax)
def update(frame):
x, y = np.random.uniform(-fov, fov, size=2)
width = np.random.uniform(0.01, maxwid)
length = np.random.uniform(width, maxlen)
angle = np.random.uniform(0, 180)
intens = width * length * (5e4 + 1e5 * np.random.exponential(2))
model = toymodel.Gaussian(
x=x * u.m,
y=y * u.m,
width=width * u.m,
length=length * u.m,
psi=angle * u.deg,
)
def test_convert_geometry():
filename = get_path("gamma_test.simtel.gz")
cam_geom = {}
source = hessio_event_source(filename)
# testing a few images just for the sake of being thorough
counter = 5
for event in source:
for tel_id in event.dl0.tels_with_data:
if tel_id not in cam_geom:
cam_geom[tel_id] = CameraGeometry.guess(
event.inst.pixel_pos[tel_id][0],
event.inst.pixel_pos[tel_id][1],
event.inst.optical_foclen[tel_id])
# we want to test conversion of hex to rectangular pixel grid
if cam_geom[tel_id].pix_type is not "hexagonal":
continue
print(tel_id, cam_geom[tel_id].pix_type)
pmt_signal = apply_mc_calibration(
#event.dl0.tel[tel_id].adc_samples[0],
event.dl0.tel[tel_id].adc_sums[0],
event.mc.tel[tel_id].dc_to_pe[0],
event.mc.tel[tel_id].pedestal[0])
new_geom, new_signal = convert_geometry_1d_to_2d(
cam_geom[tel_id], pmt_signal, cam_geom[tel_id].cam_id, add_rot=-2)
unrot_geom, unrot_signal = convert_geometry_back(
new_geom, new_signal, cam_geom[tel_id].cam_id,
event.inst.optical_foclen[tel_id], add_rot=4)
# if run as main, do some plotting
if __name__ == "__main__":
fig = plt.figure()
plt.style.use('seaborn-talk')
ax1 = fig.add_subplot(131)
disp1 = CameraDisplay(cam_geom[tel_id],
image=np.sum(pmt_signal, axis=1)
if pmt_signal.shape[-1] == 25 else pmt_signal,
ax=ax1)
disp1.cmap = plt.cm.hot
disp1.add_colorbar()
plt.title("original geometry")
ax2 = fig.add_subplot(132)
disp2 = CameraDisplay(new_geom,
image=np.sum(new_signal, axis=2)
if new_signal.shape[-1] == 25 else new_signal,
ax=ax2)
disp2.cmap = plt.cm.hot
disp2.add_colorbar()
plt.title("slanted geometry")
ax3 = fig.add_subplot(133)
disp3 = CameraDisplay(unrot_geom, image=np.sum(unrot_signal, axis=1)
if unrot_signal.shape[-1] == 25 else unrot_signal,
ax=ax3)
disp3.cmap = plt.cm.hot
disp3.add_colorbar()
plt.title("geometry converted back to hex")
plt.show()
# do some tailcuts cleaning
mask1 = tailcuts_clean(cam_geom[tel_id], pmt_signal, 1,
picture_thresh=10.,
boundary_thresh=5.)
mask2 = tailcuts_clean(unrot_geom, unrot_signal, 1,
picture_thresh=10.,
boundary_thresh=5.)
pmt_signal[mask1==False] = 0
unrot_signal[mask2==False] = 0
'''
testing back and forth conversion on hillas parameters... '''
try:
moments1 = hillas_parameters(cam_geom[tel_id].pix_x,
cam_geom[tel_id].pix_y,
pmt_signal)
moments2 = hillas_parameters(unrot_geom.pix_x,
unrot_geom.pix_y,
unrot_signal)
except (HillasParameterizationError, AssertionError) as e:
'''
we don't want this test to fail because the hillas code
threw an error '''
print(e)
counter -= 1
if counter < 0:
return
else:
continue
'''
test if the hillas parameters from the original geometry and the
forth-and-back rotated geometry are close '''
assert np.allclose(
[moments1.length.value, moments1.width.value,
moments1.phi.value],
[moments2.length.value, moments2.width.value,
moments2.phi.value],
rtol=1e-2, atol=1e-2)
counter -= 1
if counter < 0:
return
def plot(self, input_file, event, telid, chan, extractor_name, nei):
# Extract required images
dl0 = event.dl0.tel[telid].adc_samples[chan]
t_pe = event.mc.tel[telid].photo_electron_image
dl1 = event.dl1.tel[telid].image[chan]
max_time = np.unravel_index(np.argmax(dl0), dl0.shape)[1]
max_charges = np.max(dl0, axis=1)
max_pix = int(np.argmax(max_charges))
min_pix = int(np.argmin(max_charges))
geom = CameraGeometry.guess(*event.inst.pixel_pos[telid],
event.inst.optical_foclen[telid])
# Get Neighbours
max_pixel_nei = nei[max_pix]
min_pixel_nei = nei[min_pix]
# Get Windows
windows = event.dl1.tel[telid].extracted_samples[chan]
length = np.sum(windows, axis=1)
start = np.argmax(windows, axis=1)
end = start + length
# Draw figures
ax_max_nei = {}
ax_min_nei = {}
fig_waveforms = plt.figure(figsize=(18, 9))
fig_waveforms.subplots_adjust(hspace=.5)
fig_camera = plt.figure(figsize=(15, 12))
ax_max_pix = fig_waveforms.add_subplot(4, 2, 1)
ax_min_pix = fig_waveforms.add_subplot(4, 2, 2)
ax_max_nei[0] = fig_waveforms.add_subplot(4, 2, 3)
ax_min_nei[0] = fig_waveforms.add_subplot(4, 2, 4)
ax_max_nei[1] = fig_waveforms.add_subplot(4, 2, 5)
ax_min_nei[1] = fig_waveforms.add_subplot(4, 2, 6)
ax_max_nei[2] = fig_waveforms.add_subplot(4, 2, 7)
ax_min_nei[2] = fig_waveforms.add_subplot(4, 2, 8)
ax_img_nei = fig_camera.add_subplot(2, 2, 1)
ax_img_max = fig_camera.add_subplot(2, 2, 2)
ax_img_true = fig_camera.add_subplot(2, 2, 3)
ax_img_cal = fig_camera.add_subplot(2, 2, 4)
# Draw max pixel traces
ax_max_pix.plot(dl0[max_pix])
ax_max_pix.set_xlabel("Time (ns)")
ax_max_pix.set_ylabel("DL0 Samples (ADC)")
ax_max_pix.set_title("(Max) Pixel: {}, True: {}, Measured = {:.3f}"
.format(max_pix, t_pe[max_pix], dl1[max_pix]))
max_ylim = ax_max_pix.get_ylim()
ax_max_pix.plot([start[max_pix], start[max_pix]],
ax_max_pix.get_ylim(), color='r', alpha=1)
ax_max_pix.plot([end[max_pix], end[max_pix]],
ax_max_pix.get_ylim(), color='r', alpha=1)
for i, ax in ax_max_nei.items():
if len(max_pixel_nei) > i:
pix = max_pixel_nei[i]
ax.plot(dl0[pix])
ax.set_xlabel("Time (ns)")
ax.set_ylabel("DL0 Samples (ADC)")
ax.set_title("(Max Nei) Pixel: {}, True: {}, Measured = {:.3f}"
.format(pix, t_pe[pix], dl1[pix]))
ax.set_ylim(max_ylim)
ax.plot([start[pix], start[pix]],
ax.get_ylim(), color='r', alpha=1)
ax.plot([end[pix], end[pix]],
ax.get_ylim(), color='r', alpha=1)
# Draw min pixel traces
ax_min_pix.plot(dl0[min_pix])
ax_min_pix.set_xlabel("Time (ns)")
ax_min_pix.set_ylabel("DL0 Samples (ADC)")
ax_min_pix.set_title("(Min) Pixel: {}, True: {}, Measured = {:.3f}"
.format(min_pix, t_pe[min_pix], dl1[min_pix]))
ax_min_pix.set_ylim(max_ylim)
ax_min_pix.plot([start[min_pix], start[min_pix]],
ax_min_pix.get_ylim(), color='r', alpha=1)
ax_min_pix.plot([end[min_pix], end[min_pix]],
ax_min_pix.get_ylim(), color='r', alpha=1)
for i, ax in ax_min_nei.items():
if len(min_pixel_nei) > i:
pix = min_pixel_nei[i]
ax.plot(dl0[pix])
ax.set_xlabel("Time (ns)")
ax.set_ylabel("DL0 Samples (ADC)")
ax.set_title("(Min Nei) Pixel: {}, True: {}, Measured = {:.3f}"
.format(pix, t_pe[pix], dl1[pix]))
ax.set_ylim(max_ylim)
ax.plot([start[pix], start[pix]],
ax.get_ylim(), color='r', alpha=1)
ax.plot([end[pix], end[pix]],
ax.get_ylim(), color='r', alpha=1)
# Draw cameras
nei_camera = np.zeros_like(max_charges, dtype=np.int)
nei_camera[min_pixel_nei] = 2
nei_camera[min_pix] = 1
nei_camera[max_pixel_nei] = 3
nei_camera[max_pix] = 4
camera = CameraDisplay(geom, ax=ax_img_nei)
camera.image = nei_camera
camera.cmap = plt.cm.viridis
ax_img_nei.set_title("Neighbour Map")
ax_img_nei.annotate("Pixel: {}".format(max_pix),
xy=(geom.pix_x.value[max_pix],
geom.pix_y.value[max_pix]),
xycoords='data', xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
ax_img_nei.annotate("Pixel: {}".format(min_pix),
xy=(geom.pix_x.value[min_pix],
geom.pix_y.value[min_pix]),
xycoords='data', xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
camera = CameraDisplay(geom, ax=ax_img_max)
camera.image = dl0[:, max_time]
camera.cmap = plt.cm.viridis
camera.add_colorbar(ax=ax_img_max, label="DL0 Samples (ADC)")
ax_img_max.set_title("Max Timeslice (T = {})".format(max_time))
ax_img_max.annotate("Pixel: {}".format(max_pix),
xy=(geom.pix_x.value[max_pix],
geom.pix_y.value[max_pix]),
xycoords='data', xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
ax_img_max.annotate("Pixel: {}".format(min_pix),
xy=(geom.pix_x.value[min_pix],
geom.pix_y.value[min_pix]),
xycoords='data', xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
camera = CameraDisplay(geom, ax=ax_img_true)
camera.image = t_pe
camera.cmap = plt.cm.viridis
camera.add_colorbar(ax=ax_img_true, label="True Charge (p.e.)")
ax_img_true.set_title("True Charge")
ax_img_true.annotate("Pixel: {}".format(max_pix),
xy=(geom.pix_x.value[max_pix],
geom.pix_y.value[max_pix]),
xycoords='data', xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
ax_img_true.annotate("Pixel: {}".format(min_pix),
xy=(geom.pix_x.value[min_pix],
geom.pix_y.value[min_pix]),
xycoords='data', xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
camera = CameraDisplay(geom, ax=ax_img_cal)
camera.image = dl1
camera.cmap = plt.cm.viridis
camera.add_colorbar(ax=ax_img_cal,
label="Calib Charge (Photo-electrons)")
ax_img_cal.set_title("Charge (integrator={})".format(extractor_name))
ax_img_cal.annotate("Pixel: {}".format(max_pix),
xy=(geom.pix_x.value[max_pix],
geom.pix_y.value[max_pix]),
xycoords='data', xytext=(0.05, 0.98),
textcoords='axes fraction',
arrowprops=dict(facecolor='red', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
ax_img_cal.annotate("Pixel: {}".format(min_pix),
xy=(geom.pix_x.value[min_pix],
geom.pix_y.value[min_pix]),
xycoords='data', xytext=(0.05, 0.94),
textcoords='axes fraction',
arrowprops=dict(facecolor='orange', width=2,
alpha=0.4),
horizontalalignment='left',
verticalalignment='top')
fig_waveforms.suptitle("Integrator = {}".format(extractor_name))
fig_camera.suptitle("Camera = {}".format(geom.cam_id))
waveform_output_name = "e{}_t{}_c{}_extractor{}_waveform.pdf"\
.format(event.count, telid, chan, extractor_name)
camera_output_name = "e{}_t{}_c{}_extractor{}_camera.pdf"\
.format(event.count, telid, chan, extractor_name)
output_dir = self.output_dir
if output_dir is None:
output_dir = input_file.output_directory
output_dir = os.path.join(output_dir, self.name)
if not os.path.exists(output_dir):
self.log.info("Creating directory: {}".format(output_dir))
os.makedirs(output_dir)
waveform_output_path = os.path.join(output_dir, waveform_output_name)
self.log.info("Saving: {}".format(waveform_output_path))
fig_waveforms.savefig(waveform_output_path, format='pdf',
bbox_inches='tight')
camera_output_path = os.path.join(output_dir, camera_output_name)
self.log.info("Saving: {}".format(camera_output_path))
fig_camera.savefig(camera_output_path, format='pdf',
bbox_inches='tight')