def plot_window_trans_perpix(window_number):
"""
Plot of the mean value of trans_norm_cher per pixel
:param window_number: 1 or 2 to choose between the first or second window
:return: plot the camera pixels with the mean value of trans_norm_cher for each of the pixels
"""
pix_trans_cher = np.loadtxt(
"data/mean_transmittance_per_pixel_{0}_wdw.txt".format(
num[window_number - 1]),
usecols=1,
skiprows=1,
unpack=True)
fig = plt.figure()
ax = plt.gca()
ax.set_alpha(0)
CameraDisplay(DigiCam.geometry, pix_trans_cher, cmap='viridis',
title='').highlight_pixels(range(1296),
color='k',
linewidth=0.2)
CameraDisplay(
DigiCam.geometry, pix_trans_cher, cmap='viridis',
title='').add_colorbar(label="Transmittance $\otimes$ norm\_cher")
plt.annotate("mean: {0:.2f}\%".format(np.mean(pix_trans_cher) * 100.),
xy=(-430, 490),
xycoords="data",
va="center",
ha="center",
bbox=dict(boxstyle="round", fc="w", ec="silver"))
plt.xlim(-550, 550)
plt.ylim(-550, 550)
fig.savefig('{0}/trans_values_perpixel_{1}wdw.pdf'.format(
folder[window_number - 1], num[window_number - 1]))
plt.show()
def create(self, image, coords, title, coeff, fitter):
df = fitter.get_curve(coords.xi_mg, coords.yi_mg, coeff)
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)
amplitude, x0, y0, sigma, offset = coeff
radius = fitter.find_containment_radius(coords, coeff, 0.8)
radius_deg = radius * coords.degperm
x70 = x0 + radius
y70 = y0
z = offset + amplitude * np.exp(
- ((((x70 - x0) ** 2) / (2 * sigma ** 2)) +
(((y70 - y0) ** 2) / (2 * sigma ** 2))))
CS = self.ax.contour(coords.xi_mg, coords.yi_mg, df,
linewidths=0.5, colors='r', levels=[z])
text = "80% Containment \n({:.3} degrees)".format(radius_deg)
self.ax.text(x70, y70, text, color='r')
self.fig.suptitle("Jupiter RMS ON-OFF")
self.ax.set_title(title)
self.ax.axis([-0.06, 0.03, -0.06, 0.03])
# self.ax.axis('off')
minor_locator = AutoMinorLocator(10)
self.ax.xaxis.set_minor_locator(minor_locator)
self.ax.yaxis.set_minor_locator(minor_locator)
def plot_window_trans_perpix(fname):
""" IMPORTANT
display mean value on the camera
Plot of the mean value of trans_norm_cher per pixel
:param window_number: 1 or 2 to choose between the first or second window
:return: plot the camera pixels with the mean value of trans_norm_cher for each of the pixels
"""
ave_irradiance_per_pixel = np.loadtxt(fname=fname,
usecols=1,
skiprows=1,
unpack=True)
fig = plt.figure()
ax = plt.gca()
ax.set_alpha(0)
CameraDisplay(DigiCam.geometry,
ave_irradiance_per_pixel,
cmap='viridis',
title='').highlight_pixels(range(1296), color='k', linewidth=0.2)
CameraDisplay(DigiCam.geometry,
ave_irradiance_per_pixel,
cmap='viridis', title='').add_colorbar(label="I [A]")
# plt.annotate("mean: {0:.2f}\%".format(np.mean(ave_irradiance_per_pixel)*100.),
# xy=(-430, 490),
# xycoords="data",
# va="center",
# ha="center",
# bbox=dict(boxstyle="round", fc="w", ec="silver"))
plt.xlim(-550, 550)
plt.ylim(-550, 550)
return fig, ax
def plot_model(fitter, geometry, save=False, ids=''):
"""
Create a CameraDisplay object showing the spatial model fitted to
the current event
Parameters
-------
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)
fig, axes = plt.subplots(figsize=(10, 8))
cam_display = CameraDisplay(geometry, mu, ax=axes)
cam_display.add_colorbar(ax=axes)
if save:
cam_display.axes.get_figure().savefig('event/' + ids + '_model.png')
plt.close()
return None if save else cam_display
def go(self):
fig = plt.figure()
ax = fig.add_subplot(111)
disp = None
source = hessio_event_source(self.filename, requested_event=24)
for event in source:
self.calib.calibrate(event)
for i in range(50):
ipix = np.random.randint(0, 2048)
samp = event.dl0.tel[1]['pe_samples'][0][ipix]
# plt.plot(range(len(samp)),samp)
plt.show()
if disp is None:
geom = event.inst.subarray.tel[1].camera
disp = CameraDisplay(geom)
# disp.enable_pixel_picker()
disp.add_colorbar()
plt.show(block=False)
#
im = event.dl1.tel[1].image[0]
mask = tailcuts_clean(geom,
im,
picture_thresh=10,
boundary_thresh=5)
im[~mask] = 0.0
maxpe = max(event.dl1.tel[1].image[0])
disp.image = im
print(np.mean(im), '+/-', np.std(im))
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 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 plot_camera(self): #TPA
fig2 = plt.figure(2, figsize=(7, 7))
ax2 = fig2.add_subplot(111)
# print(self.geom)
disp = CameraDisplay(self.geom)
disp.add_colorbar()
image = [1] * 2048
disp.image = image
def get_observation_parameters(charge: np.array,
peak: np.array,
cam_name: str,
cutflow: CutFlow,
boundary_threshold: float = None,
picture_threshold: float = None,
min_neighbours: float = None,
plot: bool = False,
cut: bool = True):
"""
:param charge: Charge image
:param peak: Peak time image
:param cam_name: Camera name. e.g. FlashCam, ASTRICam, etc.
:param cutflow: Cutflow for selection
:param boundary_threshold: (Optional) Cleaning parameter: boundary threshold
:param picture_threshold: (Optional) Cleaning parameter: picture threshold
:param min_neighbours: (Optional) Cleaning parameter: minimum neighbours
:param plot: If True, for each observation a plot will be shown (Default: False)
:param cut: If true, tight else loose
:return: hillas containers, leakage container, number of islands, island IDs, timing container, timing gradient
"""
charge_biggest, mask = clean_charge(charge, cam_name, boundary_threshold,
picture_threshold, min_neighbours)
camera = get_camera(cam_name)
geometry = camera.geometry
charge_biggest, camera_biggest, n_islands = mask_from_biggest_island(
charge, geometry, mask)
if cut:
if cutflow.cut(CFO_MIN_PIXEL, charge_biggest):
return
if cutflow.cut(CFO_MIN_CHARGE, np.sum(charge_biggest)):
return
if cutflow.cut(CFO_NEGATIVE_CHARGE, charge_biggest):
return
leakage_c = leakage(geometry, charge, mask)
if plot:
_, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)
CameraDisplay(geometry, charge, ax=ax1).add_colorbar()
CameraDisplay(camera_biggest, charge_biggest, ax=ax2).add_colorbar()
plt.show()
moments = hillas_parameters(camera_biggest, charge_biggest)
if cut:
if cutflow.cut(CFO_CLOSE_EDGE, moments, camera.camera_name):
return
if cutflow.cut(CFO_BAD_ELLIP, moments):
return
if cutflow.cut(CFO_POOR_MOMENTS, moments):
return
timing_c = timing_parameters(geometry, charge, peak, moments, mask)
time_gradient = timing_c.slope.value if geometry.camera_name != 'ASTRICam' else moments.skewness
return moments, leakage_c, timing_c, time_gradient, n_islands
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, ax=self.ax_intensity)
self.c_peakpos = CameraDisplay(geom, ax=self.ax_peakpos)
tmaxmin = event.dl0.tel[telid].waveform.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 plot(self, event, telid):
image = event.dl1.tel[telid].image
peak_time = event.dl1.tel[telid].peak_time
print("plot", image.shape, peak_time.shape)
if self._current_tel != telid:
self._current_tel = telid
self.ax_intensity.cla()
self.ax_peak_time.cla()
# Redraw camera
geom = self.subarray.tel[telid].camera.geometry
self.c_intensity = CameraDisplay(geom, ax=self.ax_intensity)
self.c_peak_time = CameraDisplay(geom, ax=self.ax_peak_time)
if (peak_time != 0.0).all():
tmaxmin = event.dl0.tel[telid].waveform.shape[1]
t_chargemax = peak_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_peak_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_peak_time:
self.c_peak_time.add_colorbar(ax=self.ax_peak_time,
label="Pulse Time (ns)")
self.cb_peak_time = self.c_peak_time.colorbar
else:
self.c_peak_time.colorbar = self.cb_peak_time
self.c_peak_time.update(True)
self.c_intensity.image = image
if peak_time is not None:
self.c_peak_time.image = peak_time
self.fig.suptitle("Event_index={} Event_id={} Telescope={}".format(
event.count, event.index.event_id, telid))
if self.display:
plt.pause(0.001)
if self.pdf is not None:
self.pdf.savefig(self.fig)
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 plot_cam(geom, geom2d, geom1d, image, image2d, image1d):
# plt.viridis()
plt.figure(figsize=(12, 4))
ax = plt.subplot(1, 3, 1)
CameraDisplay(geom, image=image).add_colorbar()
plt.subplot(1, 3, 2, sharex=ax, sharey=ax)
CameraDisplay(geom2d, image=image2d).add_colorbar()
plt.subplot(1, 3, 3, sharex=ax, sharey=ax)
CameraDisplay(geom1d, image=image1d).add_colorbar()
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 create(self, df, geom):
super().save()
camera = CameraDisplay(geom, ax=self.ax,
image=np.ma.zeros(2048),
cmap='viridis')
camera.add_colorbar(pad=-0.2)
camera.colorbar.set_label("Peak Time (ns)", fontsize=20)
with PdfPages(self.output_path) as pdf:
n_rows = len(df.index)
desc = "Saving image pages"
for index, row in tqdm(df.iterrows(), total=n_rows, desc=desc):
event_id = row['id']
tel = row['tel']
image = row['peak_time']
tc = row['tc']
hillas = row['hillas']
cleaned_image = np.ma.masked_array(image, mask=~tc)
cleaned_image.fill_value = 0
max_ = np.percentile(cleaned_image.compressed(), 99)
min_ = np.percentile(cleaned_image.compressed(), 1)
camera.image = cleaned_image
camera.set_limits_minmax(min_, max_)
camera.highlight_pixels(np.arange(2048), 'black', 1, 0.2)
# camera.overlay_moments_update(hillas, color='red')
self.ax.set_title("Event: {}, Tel: {}".format(event_id, tel))
self.ax.axis('off')
camera.colorbar.ax.tick_params(labelsize=30)
pdf.savefig(self.fig)
def 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 test_hillas_overlay():
from ctapipe.visualization import CameraDisplay
disp = CameraDisplay(CameraGeometry.from_name("LSTCam"))
hillas = CameraHillasParametersContainer(x=0.1 * u.m,
y=-0.1 * u.m,
length=0.5 * u.m,
width=0.2 * u.m,
psi=90 * u.deg)
disp.overlay_moments(hillas)
def 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 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 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, 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(self, event, telid):
image = event.dl1.tel[telid].image
peak_time = event.dl1.tel[telid].peak_time
if self._current_tel != telid:
self._current_tel = telid
self.ax_intensity.cla()
self.ax_peak_time.cla()
# Redraw camera
geom = self.subarray.tel[telid].camera.geometry
self.c_intensity = CameraDisplay(geom, ax=self.ax_intensity)
time_cmap = copy(plt.get_cmap("RdBu_r"))
time_cmap.set_under("gray")
time_cmap.set_over("gray")
self.c_peak_time = CameraDisplay(geom,
ax=self.ax_peak_time,
cmap=time_cmap)
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_peak_time:
self.c_peak_time.add_colorbar(ax=self.ax_peak_time,
label="Pulse Time (ns)")
self.cb_peak_time = self.c_peak_time.colorbar
else:
self.c_peak_time.colorbar = self.cb_peak_time
self.c_peak_time.update(True)
self.c_intensity.image = image
self.c_peak_time.image = peak_time
# center around brightes pixel, show 10ns total
t_chargemax = peak_time[image.argmax()]
self.c_peak_time.set_limits_minmax(t_chargemax - 5, t_chargemax + 5)
self.fig.suptitle("Event_index={} Event_id={} Telescope={}".format(
event.count, event.index.event_id, telid))
if self.display:
plt.pause(0.001)
if self.pdf is not None:
self.pdf.savefig(self.fig)
def display_dl1_event(event, camera_geometry, tel_id=1, axes=None, **kwargs):
"""
Display a DL1 event (image and pulse time map) side by side
Parameters
----------
event: ctapipe event
tel_id: int
axes: list of `matplotlib.pyplot.axes` of shape (2,) or None
kwargs: kwargs for `ctapipe.visualization.CameraDisplay`
Returns
-------
axes: `matplotlib.pyplot.axes`
"""
if axes is None:
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
image = event.dl1.tel[tel_id].image
peak_time = event.dl1.tel[tel_id].peak_time
if image is None or peak_time is None:
raise Exception(
f"There is no calibrated image or pulse time map for telescope {tel_id}"
)
d1 = CameraDisplay(camera_geometry, image, ax=axes[0], **kwargs)
d1.add_colorbar(ax=axes[0])
d2 = CameraDisplay(camera_geometry, peak_time, ax=axes[1], **kwargs)
d2.add_colorbar(ax=axes[1])
return axes
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 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 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 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 plot_array_camera(data, label='', limits=None, **kwargs):
mask = np.isfinite(data)
if limits is not None:
mask *= (data >= limits[0]) * (data <= limits[1])
data[~mask] = 0
fig = plt.figure()
cam = DigiCam
geom = cam.geometry
cam_display = CameraDisplay(geom, **kwargs)
cam_display.cmap.set_bad(color='k')
cam_display.image = data
cam_display.axes.set_title('')
cam_display.axes.set_xticks([])
cam_display.axes.set_yticks([])
cam_display.axes.set_xlabel('')
cam_display.axes.set_ylabel('')
cam_display.axes.axis('off')
cam_display.add_colorbar(label=label)
cam_display.axes.get_figure().set_size_inches((10, 10))
plt.axis('equal')
if limits is not None:
if not isinstance(limits, tuple):
raise TypeError('Limits must be a tuple()')
cam_display.colorbar.set_clim(vmin=limits[0], vmax=limits[1])
cam_display.update()
return cam_display, fig
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 test_overlay_disp_vector():
from ctapipe.image import hillas_parameters
geom = CameraGeometry.from_name('LSTCam')
image = np.random.rand(geom.n_pixels)
display = CameraDisplay(geom, image)
hillas = hillas_parameters(geom, image)
disp = disp_parameters_event(hillas, 0.1 * u.m, 0.3 * u.m)
overlay_disp_vector(display, disp, hillas)
def main():
fig, axs = plt.subplots(1, 2, constrained_layout=True, figsize=(6, 3))
model = Gaussian(0 * u.m, 0.1 * u.m, 0.3 * u.m, 0.05 * u.m, 25 * u.deg)
cam = CameraGeometry.from_name('FlashCam')
image, *_ = model.generate_image(cam, 2500)
CameraDisplay(cam, ax=axs[0], image=image)
CameraDisplay(
cam.transform_to(EngineeringCameraFrame()),
ax=axs[1],
image=image,
)
axs[0].set_title('CameraFrame')
axs[1].set_title('EngineeringCameraFrame')
plt.show()
def plot_quick_camera(image, ax=None):
ax = ax if ax is not None else plt.gca()
cameraconfig = Config()
pos = cameraconfig.pixel_pos * u.m
foclen = cameraconfig.optical_foclen * u.m
geom = CameraGeometry.guess(*pos, foclen)
camera = CameraDisplay(geom, ax=ax, image=image, cmap='viridis')
return camera
def plot_nevent(events, nevent, filename, bad_pixels=None, norm="lin"):
displays = []
fig, axes = plt.subplots(
3,
4, # sharex='all', sharey='all',
figsize=[18, 12])
axes = axes.flatten()
for index, event in enumerate(events):
if index < nevent:
axe = index % 12
figure = int(np.floor(index / 12))
if index < 12:
displays.append(
CameraDisplay(DigiCam.geometry,
ax=axes[index],
norm=norm,
title=''))
displays[axe].cmap.set_bad('w')
displays[axe].cmap.set_over('w')
displays[axe].cmap.set_under('w')
displays[axe].add_colorbar(ax=axes[axe])
axes[axe].set_xlabel("")
axes[axe].set_ylabel("")
axes[axe].set_xlim([-400, 400])
axes[axe].set_ylim([-400, 400])
axes[axe].set_xticklabels([])
axes[axe].set_yticklabels([])
pe = event.data.reconstructed_number_of_pe
n_pix = len(pe)
mask = event.data.cleaning_mask
pe_masked = pe
pe_masked[~mask] = np.NaN
displays[axe].set_limits_minmax(0, np.nanmax(pe_masked))
displays[axe].image = pe_masked
# highlight only bad pixels which pass the tail-cut cleaning
highlighted_mask = np.zeros(n_pix, dtype=bool)
highlighted_mask[bad_pixels] = mask[bad_pixels]
highlighted = np.arange(n_pix)[highlighted_mask]
displays[axe].highlight_pixels(highlighted, color='k', linewidth=2)
displays[axe].overlay_moments(event.hillas,
with_label=False,
edgecolor='r',
linewidth=2)
if index % 12 == 11 or index == nevent - 1:
if filename.lower == "show":
plt.show()
else:
if figure == 0:
output = filename
else:
output = filename.replace('.png',
'_' + str(figure) + '.png')
plt.savefig(output)
print(output, 'created.')
yield event
plt.close(fig)
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 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 __init__(self, waveform_data, geometry,
camera_axis, waveform_axis_list, waveform_axis_ylabel):
self.__waveform_data = None
self.__waveform_axis_list = None
self.__integration_window = None
self.camera_axis = camera_axis
self.waveform_axis_list = []
self.waveform_title_list = []
self.waveform_window_list = []
self.waveforms = []
self.pixel_switch_num = 0
self.colors = itertools.cycle(['r', 'b', 'c', 'm', 'y', 'k', 'w', 'g'])
self.current_color = 'r'
self.active_pixels = []
self.active_pixel_patches = []
self.active_pixel_labels = []
self.window_start = None
self.window_end = None
CameraDisplay.__init__(self, geometry, ax=camera_axis)
self._active_pixel.set_linewidth(1.5)
self.geom = geometry
self.waveform_yaxis_label = waveform_axis_ylabel
self.waveform_data = waveform_data
self.waveform_axis_list = waveform_axis_list
geometry_text = "Geometry = {}".format(geometry.cam_id)
self.camera_axis.text(0.01, 0.99, geometry_text,
horizontalalignment='left',
verticalalignment='top',
transform=self.camera_axis.transAxes)
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 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
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()
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()
from ctapipe.image import toymodel
from ctapipe.io import CameraGeometry
from ctapipe.visualization import CameraDisplay
from matplotlib import pyplot as plt
if __name__ == '__main__':
plt.style.use('ggplot')
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
geom = CameraGeometry.from_name('hess', 1)
disp = CameraDisplay(geom, ax=ax)
disp.add_colorbar()
model = toymodel.generate_2d_shower_model(
centroid=(0.05, 0.0), width=0.005, length=0.025, psi='35d'
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=50, nsb_level_pe=20
)
disp.image = image
mask = disp.image > 15
disp.highlight_pixels(mask, linewidth=3)
plt.show()
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()
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=0.05, length=0.15, psi='35d'
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=1500, nsb_level_pe=5
)
disp.image = image
mask = disp.image > 10
disp.highlight_pixels(mask, linewidth=2, color='crimson')
plt.show()
def draw_neighbors(geom, pixel_index, color='r', **kwargs):
"""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,
from ctapipe.visualization import CameraDisplay
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,
plt.style.use("ggplot")
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,
)
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):
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
"""
Example of drawing a Camera using a toymodel shower image.
"""
import matplotlib.pylab as plt
from ctapipe.image import toymodel, hillas_parameters, tailcuts_clean
from ctapipe.instrument import CameraGeometry
from ctapipe.visualization import CameraDisplay
if __name__ == '__main__':
# Load the camera
geom = CameraGeometry.from_name("LSTCam")
disp = CameraDisplay(geom)
disp.add_colorbar()
# Create a fake camera image to display:
model = toymodel.generate_2d_shower_model(
centroid=(0.2, 0.0), width=0.05, length=0.15, psi='35d'
)
image, sig, bg = toymodel.make_toymodel_shower_image(
geom, model.pdf, intensity=1500, nsb_level_pe=3
)
# Apply image cleaning
cleanmask = tailcuts_clean(
geom, image, picture_thresh=10, boundary_thresh=5
)
