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 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 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()
def start(self):
geom = None
imsum = None
disp = None
for data in hessio_event_source(self.infile,
allowed_tels=self._selected_tels,
max_events=None):
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.r0.tels_with_data) <= 2:
continue
imsum[:] = 0
for telid in data.r0.tels_with_data:
imsum += data.r0.tel[telid].adc_sums[0]
self.log.info("event={} ntels={} energy={}" \
.format(data.r0.event_id,
len(data.r0.tels_with_data),
data.mc.energy))
disp.image = imsum
plt.pause(0.1)
def draw_camera(self, tel, data, axes=None):
"""
Draw a camera image using the correct geometry.
Parameters
----------
tel : int
The telescope you want drawn.
data : `np.array`
1D array with length equal to npix.
axes : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one.
Returns
-------
`ctapipe.visualization.CameraDisplay`
"""
geom = self.get_geometry(tel)
axes = axes if axes is not None else plt.gca()
camera = CameraDisplay(geom, ax=axes)
camera.image = data
camera.cmap = plt.cm.viridis
# camera.add_colorbar(ax=axes, label="Amplitude (ADC)")
# camera.set_limits_percent(95) # autoscale
return camera
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
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 camera_image(input_file, ax):
first_event = input_file.get_event(0)
geom = CameraGeometry.guess(*first_event.meta.pixel_pos[telid],
first_event.meta.optical_foclen[telid])
camera = CameraDisplay(geom, ax=ax)
camera.cmap = plt.cm.viridis
import time
def update(iframe, source):
data = next(source)
print(iframe)
# data = np.zeros((2048,128))
# data[iframe,0] = iframe
camera.image = data
return camera.pixels,
source = get_data(input_file)
anim = FuncAnimation(fig,
update,
fargs=[source],
blit=True,
frames=1000,
interval=1,
repeat=False)
plt.show()
def draw_camera(self, tel, data, axes=None):
"""
Draw a camera image using the correct geometry.
Parameters
----------
tel : int
The telescope you want drawn.
data : `np.array`
1D array with length equal to npix.
axes : `matplotlib.axes.Axes`
A matplotlib axes object to plot on, or None to create a new one.
Returns
-------
`ctapipe.visualization.CameraDisplay`
"""
geom = self.get_geometry(tel)
axes = axes if axes is not None else plt.gca()
camera = CameraDisplay(geom, ax=axes)
camera.image = data
camera.cmap = plt.cm.viridis
# camera.add_colorbar(ax=axes, label="Amplitude (ADC)")
# camera.set_limits_percent(95) # autoscale
return camera
def display_event(event):
"""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)
geom = event.inst.subarray.tel[tel_id].camera
disp = CameraDisplay(geom, ax=ax, title="CT{0}".format(tel_id))
disp.pixels.set_antialiaseds(False)
disp.autoupdate = False
disp.cmap = random.choice(cmaps)
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 plot_muon_event(ax, geom, image, centroid, ringrad_camcoord,
ringrad_inner, ringrad_outer, event_id):
"""
Paramenters
---------
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 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 make_camera_image(image):
plt.figure(figsize=(6, 6))
geom = CameraGeometry.from_name('LSTCam-003')
disp = CameraDisplay(geom)
disp.image = image
disp.cmap = plt.cm.gnuplot2
disp.add_colorbar()
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 _display_camera_animation(self):
#plt.style.use("ggplot")
fig = plt.figure(num="ctapipe Camera Demo", figsize=(7, 7))
ax = plt.subplot(111)
# load the camera
geom = CameraGeometry.from_name(self.camera)
disp = CameraDisplay(geom, ax=ax, autoupdate=True, )
disp.cmap = plt.cm.terrain
def update(frame):
centroid = np.random.uniform(-0.5, 0.5, size=2)
width = np.random.uniform(0, 0.01)
length = np.random.uniform(0, 0.03) + width
angle = np.random.uniform(0, 360)
intens = np.random.exponential(2) * 50
model = toymodel.generate_2d_shower_model(centroid=centroid,
width=width,
length=length,
psi=angle * u.deg)
image, sig, bg = toymodel.make_toymodel_shower_image(geom, model.pdf,
intensity=intens,
nsb_level_pe=5000)
# alternate between cleaned and raw images
if self._counter == self.cleanframes:
plt.suptitle("Image Cleaning ON")
self.imclean = True
if self._counter == self.cleanframes*2:
plt.suptitle("Image Cleaning OFF")
self.imclean = False
self._counter = 0
if self.imclean:
cleanmask = tailcuts_clean(geom, image/80.0)
for ii in range(3):
dilate(geom, cleanmask)
image[cleanmask == 0] = 0 # zero noise pixels
self.log.debug("count = {}, image sum={} max={}"
.format(self._counter, image.sum(), image.max()))
disp.image = image
if self.autoscale:
disp.set_limits_percent(95)
else:
disp.set_limits_minmax(-100, 4000)
disp.axes.figure.canvas.draw()
self._counter += 1
return [ax,]
self.anim = FuncAnimation(fig, update, interval=self.delay,
blit=self.blit)
plt.show()
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 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 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 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 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 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 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
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()
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 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')
#Calculate source position
mcAlt = hdu_list[1].data.field(4)[i]
mcAz = hdu_list[1].data.field(5)[i]
mcAlttel = hdu_list[1].data.field(19)[i]
mcAztel = hdu_list[1].data.field(20)[i]
srcpos = Disp.calc_CamSourcePos([mcAlt], [mcAz], [mcAlttel], [mcAztel],
focal_length)
Source_X = srcpos[0]
Source_Y = srcpos[1]
cen_x = hdu_list[1].data.field(16)[i]
cen_y = hdu_list[1].data.field(17)[i]
disp = Disp.calc_DISP(Source_X, Source_Y, cen_x, cen_y)
display = CameraDisplay(geom)
display.add_colorbar()
image = hdu_list[2].data[i]
display.image = image
display.cmap = 'CMRmap'
plt.plot([Source_X], [Source_Y], marker='o', markersize=10, color="green")
plt.plot([cen_x], [cen_y], marker='x', markersize=10, color="blue")
plt.plot([Source_X, cen_x], [Source_Y, cen_y], '-', color="red")
plt.show()
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()
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')
def _display_camera_animation(self):
# plt.style.use("ggplot")
fig = plt.figure(num="ctapipe Camera Demo", figsize=(7, 7))
ax = plt.subplot(111)
# load the camera
tel = TelescopeDescription.from_name(optics_name=self.optics,
camera_name=self.camera)
geom = tel.camera
# poor-man's coordinate transform from telscope to camera frame (it's
# better to use ctapipe.coordiantes when they are stable)
scale = tel.optics.effective_focal_length.to(geom.pix_x.unit).value
fov = np.deg2rad(4.0)
maxwid = np.deg2rad(0.01)
maxlen = np.deg2rad(0.03)
disp = CameraDisplay(geom,
ax=ax,
autoupdate=True,
title="{}, f={}".format(
tel, tel.optics.effective_focal_length))
disp.cmap = plt.cm.terrain
def update(frame):
centroid = np.random.uniform(-fov, fov, size=2) * scale
width = np.random.uniform(0, maxwid) * scale
length = np.random.uniform(0, maxlen) * scale + width
angle = np.random.uniform(0, 360)
intens = np.random.exponential(2) * 50
model = toymodel.generate_2d_shower_model(centroid=centroid,
width=width,
length=length,
psi=angle * u.deg)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=intens, nsb_level_pe=5000)
# alternate between cleaned and raw images
if self._counter == self.cleanframes:
plt.suptitle("Image Cleaning ON")
self.imclean = True
if self._counter == self.cleanframes * 2:
plt.suptitle("Image Cleaning OFF")
self.imclean = False
self._counter = 0
if self.imclean:
cleanmask = tailcuts_clean(geom, image / 80.0)
for ii in range(3):
dilate(geom, cleanmask)
image[cleanmask == 0] = 0 # zero noise pixels
self.log.debug("count = {}, image sum={} max={}".format(
self._counter, image.sum(), image.max()))
disp.image = image
if self.autoscale:
disp.set_limits_percent(95)
else:
disp.set_limits_minmax(-100, 4000)
disp.axes.figure.canvas.draw()
self._counter += 1
return [
ax,
]
self.anim = FuncAnimation(fig,
update,
interval=self.delay,
blit=self.blit)
plt.show()
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 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()
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 check_interleave_pedestal_cleaning(path_list, calib_time_file, calib_file, max_events=10000):
signal_place_after_clean = np.zeros(1855)
sum_ped_ev = 0
alive_ped_ev = 0
for path in path_list:
print(path)
r0_r1_calibrator = LSTR0Corrections(pedestal_path=None,
r1_sample_start=3,
r1_sample_end=39)
r1_dl1_calibrator = LSTCameraCalibrator(calibration_path=calib_file,
time_calibration_path=calib_time_file,
extractor_product="LocalPeakWindowSum",
config=charge_config,
allowed_tels=[1])
reader = LSTEventSource(input_url=path, 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
r1_dl1_calibrator(ev)
img = ev.dl1.tel[1].image
geom = ev.inst.subarray.tel[1].camera
clean = tailcuts_clean(
geom,
img,
picture_thresh=6,
boundary_thresh=3,
min_number_picture_neighbors=1,
keep_isolated_pixels=False
)
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=(8, 8))
geom = ev.inst.subarray.tel[1].camera
disp0 = CameraDisplay(geom, ax=ax)
disp0.image = signal_place_after_clean/sum_ped_ev
disp0.add_colorbar(ax=ax, label="N times signal remain after cleaning")
disp0.cmap = 'gnuplot2'
ax.set_title("{} \n {}/{}".format(path.split("/")[-1][8:21], alive_ped_ev, sum_ped_ev))
print(path.split("/")[-1][8:21])
print("{}/{}".format(alive_ped_ev, sum_ped_ev))
ax.set_xlabel(" ")
ax.set_ylabel(" ")
plt.tight_layout()
plt.show()
return signal_place_after_clean, sum_ped_ev
fig, ax = plt.subplots()
# 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(
import numpy as np
from matplotlib import pyplot as plt
from ctapipe.instrument import CameraDescription, CameraGeometry
from ctapipe.visualization import CameraDisplay
if __name__ == "__main__":
plt.style.use("bmh")
camera_names = CameraDescription.get_known_camera_names()
n_tels = len(camera_names)
n_rows = np.trunc(np.sqrt(n_tels)).astype(int)
n_cols = np.ceil(n_tels / n_rows).astype(int)
plt.figure(figsize=(15, 6))
for ii, name in enumerate(sorted(camera_names)):
print("plotting", name)
geom = CameraGeometry.from_name(name)
ax = plt.subplot(n_rows, n_cols, ii + 1)
disp = CameraDisplay(geom)
disp.image = np.random.uniform(size=geom.pix_id.shape)
disp.cmap = "viridis"
plt.xlabel("")
plt.ylabel("")
plt.tight_layout()
plt.show()
def _display_camera_animation(self):
# plt.style.use("ggplot")
fig = plt.figure(num="ctapipe Camera Demo", figsize=(7, 7))
ax = plt.subplot(111)
# load the camera
tel = TelescopeDescription.from_name(optics_name=self.optics,
camera_name=self.camera)
geom = tel.camera
# poor-man's coordinate transform from telscope to camera frame (it's
# better to use ctapipe.coordiantes when they are stable)
foclen = tel.optics.equivalent_focal_length.to(geom.pix_x.unit).value
fov = np.deg2rad(4.0)
scale = foclen
minwid = np.deg2rad(0.1)
maxwid = np.deg2rad(0.3)
maxlen = np.deg2rad(0.5)
self.log.debug("scale={} m, wid=({}-{})".format(scale, minwid, maxwid))
disp = CameraDisplay(
geom, ax=ax, autoupdate=True,
title="{}, f={}".format(tel, tel.optics.equivalent_focal_length)
)
disp.cmap = plt.cm.terrain
def update(frame):
centroid = np.random.uniform(-fov, fov, size=2) * scale
width = np.random.uniform(0, maxwid-minwid) * scale + minwid
length = np.random.uniform(0, maxlen) * scale + width
angle = np.random.uniform(0, 360)
intens = np.random.exponential(2) * 500
model = toymodel.generate_2d_shower_model(centroid=centroid,
width=width,
length=length,
psi=angle * u.deg)
self.log.debug(
"Frame=%d width=%03f length=%03f intens=%03d",
frame, width, length, intens
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom,
model.pdf,
intensity=intens,
nsb_level_pe=3,
)
# alternate between cleaned and raw images
if self._counter == self.cleanframes:
plt.suptitle("Image Cleaning ON")
self.imclean = True
if self._counter == self.cleanframes * 2:
plt.suptitle("Image Cleaning OFF")
self.imclean = False
self._counter = 0
disp.clear_overlays()
if self.imclean:
cleanmask = tailcuts_clean(geom, image,
picture_thresh=10.0,
boundary_thresh=5.0)
for ii in range(2):
dilate(geom, cleanmask)
image[cleanmask == 0] = 0 # zero noise pixels
try:
hillas = hillas_parameters(geom, image)
disp.overlay_moments(hillas, with_label=False,
color='red', alpha=0.7,
linewidth=2, linestyle='dashed')
except HillasParameterizationError:
disp.clear_overlays()
pass
self.log.debug("Frame=%d image_sum=%.3f max=%.3f",
self._counter, image.sum(), image.max())
disp.image = image
if self.autoscale:
disp.set_limits_percent(95)
else:
disp.set_limits_minmax(-5, 200)
disp.axes.figure.canvas.draw()
self._counter += 1
return [ax, ]
frames = None if self.num_events == 0 else self.num_events
repeat = True if self.num_events == 0 else False
self.log.info("Running for {} frames".format(frames))
self.anim = FuncAnimation(fig, update,
interval=self.delay,
frames=frames,
repeat=repeat,
blit=self.blit)
if self.display:
plt.show()
fig, ax = plt.subplots()
# 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(
inferno = plt.get_cmap('inferno')
inferno.set_bad('gray')
rdbu = plt.get_cmap('RdBu_r')
rdbu.set_bad('gray')
for i in range(2):
fig, axs = plt.subplots(1, 2, figsize=(5, 2), constrained_layout=True)
if i == 1:
clean = tailcuts_clean(cam, img, 9, 3)
img[~clean] = np.nan
time[~clean] = np.nan
disp = CameraDisplay(cam, ax=axs[0])
disp.image = img
disp.cmap = inferno
disp.add_colorbar(ax=axs[0])
disp.set_limits_minmax(0, 45)
disp.pixels.set_rasterized(True)
disp2 = CameraDisplay(cam, ax=axs[1])
disp2.image = time
disp2.cmap = rdbu
disp2.set_limits_minmax(10, 40)
disp2.add_colorbar(ax=axs[1])
disp2.pixels.set_rasterized(True)
axs[0].set_title('\# Photons')
axs[1].set_title('Time / ns')
for ax in axs:
