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 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 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_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 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_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 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 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 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 test_camera_display_multiple():
""" create a figure with 2 subplots, each with a CameraDisplay """
from ..mpl_camera import CameraDisplay
geom = CameraGeometry.from_name("LSTCam")
fig, ax = plt.subplots(2, 1)
d1 = CameraDisplay(geom, ax=ax[0])
d2 = CameraDisplay(geom, ax=ax[1])
image = np.ones(len(geom.pix_x), dtype=float)
d1.image = image
d2.image = image
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 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_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 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 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 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_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 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 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 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 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 Simple_CamPlot(image, geom, pix_ids, title=""):
disp1 = CameraDisplay(geom, title=title)
disp1.add_colorbar()
disp1.set_limits_minmax(zmin=image.min() * 0.98, zmax=image.max() * 1.02)
blankam1 = np.zeros(1855)
blankam1[pix_ids] = image
disp1.image = blankam1
plt.show()
def 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 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 plot_event_with_shower_position_seperate_events(self, event1, event2):
x, y, alt, az = self.transform_to_telescope_xy_altaz_from_event(event1)
x = x.to_value(u.m)
y = y.to_value(u.m)
# Cameras
m1_cam = copy.deepcopy(event1.inst.subarray.tel[1].camera)
m2_cam = copy.deepcopy(event2.inst.subarray.tel[2].camera)
m1_cam.rotate(-19.1*u.deg)#(-90+70.9)
m2_cam.rotate((-90+70.9)*u.deg)
# Charge images
m1_event_image = event1.dl1.tel[1].image
m2_event_image = event2.dl1.tel[2].image
# Peak position maps
#m1_event_times = b5["M1"].dl1.tel[1].peakpos
#m2_event_times = b5["M2"].dl1.tel[2].peakpos
fig1, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15,10))
# M1 charge map
disp1 = CameraDisplay(m1_cam, m1_event_image, ax=ax1, title="MAGIC 1 image")
disp1.add_colorbar(ax=ax1)
# M2 charge map
disp2 = CameraDisplay(m2_cam, m2_event_image, ax=ax2, title="MAGIC 2 image")
disp2.add_colorbar(ax=ax2)
ax1.plot(x, y, marker='*', color='red')
ax1.annotate(
s="Peter", xy=(x, y), xytext=(5, 5),
textcoords='offset points', color='red',
)
#ax1.scatter(cam.pix_x, cam.pix_y, marker="o", s=10, c='red')
ax2.plot(x, y, marker='*', color='red')
ax2.annotate(
s="Peter", xy=(x, y), xytext=(5, 5),
textcoords='offset points', color='red',
)
def 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 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()