def display_telescope(calibrated_data,cam,tel_id,pdffilename):
logger.info("Tel_ID %d\n"%tel_id)
pp = PdfPages("%s"%pdffilename)
global fig
for event in calibrated_data:
tels = list(event.dl0.tels_with_data)
logger.debug(tels)
# Select telescope
if tel_id not in tels:
continue
fig.clear()
# geom = cam['CameraTable_VersionFeb2016_TelID%s'%tel_id]
geom = io.CameraGeometry.guess(*event.meta.pixel_pos[tel_id],
event.meta.optical_foclen[tel_id])
# Select number of pads to display. It depends on the numberr of gains:
nchan = event.dl0.tel[tel_id].num_channels
plt.suptitle("EVENT {} {:.1e} TeV @({:.1f},{:.1f})deg @{:.1f} m".format(
event.dl0.event_id, get_mc_shower_energy(),
get_mc_shower_altitude(), get_mc_shower_azimuth(),
np.sqrt(pow(get_mc_event_xcore(), 2) +
pow(get_mc_event_ycore(), 2))))
print("\t draw cam {} (gains={})...".format(tel_id,nchan))
print("RUN/EVT:",event.dl0.run_id,event.dl0.event_id)
ax=[]
disp=[]
signals=[]
npads=0
for i in range(nchan):
npads += 1
# Display the camera charge (HG/LG)
ax.append(plt.subplot(nchan, 2, npads))
disp.append(visualization.CameraDisplay(geom, ax=ax[-1],
title="CT{} [{} ADC cts]".format(tel_id,gain_label[i])))
disp[-1].pixels.set_antialiaseds(False)
signals.append(event.dl0.tel[tel_id].adc_sums[i])
disp[-1].image = signals[-1]
disp[-1].pixels.set_cmap(get_cmap(disp[-1].image))
disp[-1].add_colorbar(label=" [{} ADC cts]".format(gain_label[i]))
# Display the camera charge for significant pixels (HG/LG)
npads += 1
ax.append(plt.subplot(nchan, 2, npads))
disp.append(visualization.CameraDisplay(geom, ax=ax[-1],
title="CT{} [{} ADC cts]".format(tel_id,gain_label[i])))
disp[-1].pixels.set_antialiaseds(False)
signals.append(event.dl0.tel[tel_id].adc_sums[i])
m = (get_zero_sup_mode(tel_id) & 0x001) or (get_significant(tel_id) & 0x020)
disp[-1].image = signals[-1]*(m/m.max())
disp[-1].pixels.set_cmap(get_cmap(disp[-1].image))
disp[-1].add_colorbar(label=" [{} ADC cts]".format(gain_label[i]))
plt.pause(0.1)
pp.savefig(fig)
pp.close()
def display_var(hist,
geom,
title='Gain [ADC/p.e.]',
index_var=1,
limit_min=0.,
limit_max=10.,
bin_width=0.2):
f, ax = plt.subplots(1, 2, figsize=(20, 7))
plt.subplot(1, 2, 1)
vis_gain = visualization.CameraDisplay(geom,
title='',
norm='lin',
cmap='viridis')
vis_gain.add_colorbar()
vis_gain.colorbar.set_label(title)
h = np.copy(hist.fit_result[:, index_var, 0])
h_err = np.copy(hist.fit_result[:, index_var, 1])
h[np.isnan(h_err)] = limit_min
h[h < limit_min] = limit_min
h[h > limit_max] = limit_max
vis_gain.image = h
# plt.subplot(1,2,2)
hh, bin_tmp = np.histogram(h,
bins=np.arange(limit_min - bin_width / 2,
limit_max + 1.5 * bin_width,
bin_width))
hh_hist = histogram(data=hh.reshape(1, hh.shape[0]),
bin_centers=np.arange(limit_min, limit_max + bin_width,
bin_width),
xlabel=title,
ylabel='$\mathrm{N_{pixel}/%.2f}$' % bin_width,
label='All pixels')
hh_hist.show(which_hist=(0, ), axis=ax[1], show_fit=False)
plt.show()
def draw_several_cams(geom):
ncams = 4
cmaps = [plt.cm.jet, plt.cm.afmhot, plt.cm.terrain, plt.cm.autumn]
fig, ax = plt.subplots(1, ncams, figsize=(15, 4), sharey=True, sharex=True)
for ii in range(ncams):
disp = visualization.CameraDisplay(geom,
ax=ax[ii],
title="CT{}".format(ii + 1))
disp.cmap = cmaps[ii]
model = mock.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 = mock.make_mock_shower_image(geom,
model.pdf,
intensity=50,
nsb_level_pe=1000)
clean = image.copy()
clean[image <= 3.0 * image.mean()] = 0.0
hillas = hillas_parameters(geom.pix_x.value, geom.pix_y.value, clean)
disp.image = image
disp.set_limits_percent(95)
disp.overlay_moments(hillas, linewidth=3, color='blue')
def draw_several_cams(geom, ncams=4):
cmaps = ['jet', 'afmhot', 'terrain', 'autumn']
fig, axs = plt.subplots(1, ncams, figsize=(15, 4), sharey=True, sharex=True)
for ii in range(ncams):
disp = visualization.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,
)
clean = image.copy()
clean[image <= 3.0 * image.mean()] = 0.0
hillas = hillas_parameters(geom.pix_x, geom.pix_y, clean)
disp.image = image
disp.add_colorbar(ax=axs[ii])
disp.set_limits_percent(95)
disp.overlay_moments(hillas, linewidth=3, color='blue')
def _display_cam_animation():
plt.style.use("ggplot")
fig, ax = plt.subplots()
# load the camera
geom = io.get_camera_geometry("hess", 1)
disp = visualization.CameraDisplay(geom, ax=ax)
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 = mock.generate_2d_shower_model(centroid=centroid,
width=width,
length=length,
psi=angle * u.deg)
image, sig, bg = mock.make_mock_shower_image(geom,
model.pdf,
intensity=intens,
nsb_level_pe=5000)
image /= image.max()
disp.image = image
disp.set_limits_percent(100)
anim = FuncAnimation(fig, update, interval=50)
plt.show()
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 = visualization.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 get_camera_view(self, tel_id, image, axis):
"""
Create camera viewer for a given camera image
Parameters
----------
tel_id: int
Telescope ID number
image: ndarray
Array of calibrated pixel intensities
axis: matplotlib axis
Axis on which to draw plot
Returns
-------
Camera display
"""
#if tel_id not in self.cam_display:
# Argh this is annoying, for some reason we cannot cahe the displays
self.cam_display[tel_id] = visualization.CameraDisplay(
self.geom[tel_id], title="CT{0}".format(tel_id))
self.cam_display[tel_id].add_colorbar()
self.cam_display[tel_id].pixels.set_antialiaseds(False)
self.cam_display[tel_id].autoupdate = True
self.cam_display[tel_id].cmap = "viridis"
self.cam_display[tel_id].ax = axis
self.cam_display[tel_id].image = image
self.cam_display[tel_id].set_limits_percent(95)
return self.cam_display[tel_id]
def display_telescope(event, tel_id):
global fig
ntels = len(event.dl1.tels_with_data)
fig.clear()
plt.suptitle("EVENT {} {:.1e} TeV @({:.1f},{:.1f})deg @{:.1f} m".format(
event.dl1.event_id, get_mc_shower_energy(), get_mc_shower_altitude(),
get_mc_shower_azimuth(),
np.sqrt(pow(get_mc_event_xcore(), 2) + pow(get_mc_event_ycore(), 2))))
print("\t draw cam {}...".format(tel_id))
x, y = event.meta.pixel_pos[tel_id]
#geom = io.CameraGeometry.guess(x * u.m, y * u.m)
geom = io.CameraGeometry.guess(x, y)
npads = 1
# Only create two pads if there is timing information extracted
# from the calibration
if not event.dl1.tel[tel_id].tom is None:
npads = 2
ax = plt.subplot(1, npads, npads - 1)
disp = visualization.CameraDisplay(geom,
ax=ax,
title="CT{0}".format(tel_id))
disp.pixels.set_antialiaseds(False)
disp.autoupdate = False
disp.pixels.set_cmap('seismic')
chan = 0
signals = event.dl1.tel[tel_id].pe_charge
disp.image = signals
disp.add_colorbar()
if npads == 2:
ax = plt.subplot(1, npads, npads)
disp = visualization.CameraDisplay(geom,
ax=ax,
title="CT{0}".format(tel_id))
disp.pixels.set_antialiaseds(False)
disp.autoupdate = False
disp.pixels.set_cmap('gnuplot')
chan = 0
disp.image = event.dl1.tel[tel_id].tom
disp.add_colorbar()
if __debug__:
print("All sum = %.3f\n" % sum(event.dl1.tel[tel_id].pe_charge))
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 = io.CameraGeometry.from_name("hess", 1)
disp = visualization.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 = cleaning.tailcuts_clean(geom, image, pedvars=80)
for ii in range(3):
cleaning.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 initialise_plots(self):
# first load in a very nasty way the camera geometry
pix_x, pix_y, pix_id = [], [], []
pixels = self.cts.camera.Pixels
pixelspresent = list(self.cts.pixel_to_led['DC'].keys())
for pix in pixels:
if pix.ID in pixelspresent:
pix_x.append(pix.center[0])
pix_y.append(pix.center[1])
pix_id.append(pix.ID)
else:
pix_x.append(-200.)
pix_y.append(-200.)
pix_id.append(pix.ID)
pix_x = list(pix_x)
pix_y = list(pix_y)
pix_id = list(pix_id)
neighbors_pix = find_neighbor_pixels(pix_x, pix_y, 30.)
geom = CameraGeometry(0,
pix_id,
pix_x * u.mm,
pix_y * u.mm,
np.ones((1296)) * 400.,
'hexagonal',
neighbors=neighbors_pix)
plt.figure(0, figsize=(20, 6))
self.plots = []
plt.subplot(1, 2, 1)
self.plots.append(
visualization.CameraDisplay(geom,
title='AC status',
norm='lin',
cmap='coolwarm'))
self.plots[-1].add_colorbar()
self.plots[-1].image = np.multiply(self.ac_status, self.ac_level)
plt.subplot(1, 2, 2)
self.plots.append(
visualization.CameraDisplay(geom,
title='DC status',
norm='lin',
cmap='coolwarm'))
self.plots[-1].add_colorbar()
self.plots[-1].image = np.multiply(self.dc_status, self.dc_level)
def display_fit_result(hist,
geom=None,
index_var=1,
limits=[0., 10.],
bin_width=0.2):
"""
A function to display a vaiable both as an histogram and as a camera view
:param hist: the histogram holding the fit result (utils.histogram.Histogram)
:param geom: the geometry. if None only display histogram (ctapipe.io.camera.CameraGeometry)
:param title: the z-label in camera view, x-label in histogram view (str)
:param index_var: the index of the variable in the fit_result array (int)
:param limits: the minimal and maximal values for the variable (list(int))
:param bin_width: the bin width for the variable (float)
:return:
"""
# Set the limits
h = np.copy(hist.fit_result[:, index_var, 0])
h_err = np.copy(hist.fit_result[:, index_var, 1])
h[np.isnan(h_err)] = limits[0]
h[h < limits[0]] = limits[0]
h[h > limits[1]] = limits[1]
f, ax = None, None
if geom:
f, ax = plt.subplots(1, 2, figsize=(20, 7))
plt.subplot(1, 2, 1)
vis_gain = visualization.CameraDisplay(geom,
title='',
norm='lin',
cmap='viridis')
vis_gain.add_colorbar()
vis_gain.colorbar.set_label(hist.fit_result_label[index_var])
vis_gain.image = h
plt.subplot(1, 2, 2)
else:
f, ax = plt.subplots(1, 1, figsize=(10, 7))
plt.subplot(1, 1, 1)
# Create the variable histogram
hh, bin_tmp = np.histogram(
h,
bins=np.arange(limits[0] - bin_width / 2, limits[2] + 1.5 * bin_width,
bin_width),
)
plt.step(hh,
axis=np.arange(limits[0], limits[1] + bin_width, bin_width),
label='All pixels',
color='k',
lw='1')
plt.errorbar(np.arange(limits[0], limits[1] + bin_width, hh, bin_width),
yerr=np.sqrt(hh))
plt.xlabel(hist.fit_result_label[index_var])
plt.ylabel('$\mathrm{N_{pixel}/%.2f}$' % bin_width)
plt.show()
def _display_cam_animation(self):
plt.style.use("ggplot")
fig = plt.figure(num="ctapipe Camera Demo", figsize=(7, 7))
ax = plt.subplot(111)
# load the camera
geom = io.CameraGeometry.from_name("hess", 1)
disp = visualization.CameraDisplay(geom, ax=ax)
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 = mock.generate_2d_shower_model(centroid=centroid,
width=width,
length=length,
psi=angle * u.deg)
image, sig, bg = mock.make_mock_shower_image(geom,
model.pdf,
intensity=intens,
nsb_level_pe=5000)
# alternate between cleaned and raw images
if self._counter > 20:
plt.suptitle("Image Cleaning ON")
cleanmask = reco.cleaning.tailcuts_clean(geom,
image,
pedvars=80)
for ii in range(3):
reco.cleaning.dilate(geom, cleanmask)
image[cleanmask == 0] = 0 # zero noise pixels
if self._counter >= 40:
plt.suptitle("Image Cleaning OFF")
self._counter = 0
disp.image = image
disp.set_limits_percent(100)
self._counter += 1
anim = FuncAnimation(fig, update, interval=100)
plt.show()
import matplotlib.pylab as plt
import numpy as np
from astropy import units as u
from ctapipe import io, visualization
from ctapipe.image import toymodel
from matplotlib.animation import FuncAnimation
if __name__ == '__main__':
plt.style.use("ggplot")
fig, ax = plt.subplots()
# load the camera
geom = io.CameraGeometry.from_name("hess", 1)
disp = visualization.CameraDisplay(geom, ax=ax)
disp.cmap = plt.cm.terrain
disp.add_colorbar(ax=ax)
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,
)
disp = None
print('SELECTING EVENTS FROM TELESCOPE {}'.format(args.tel))
print('=' * 70)
for event in source:
print('Scanning input file... count = {}'.format(event.count))
print(event.trig)
print(event.mc)
print(event.dl0)
if disp is None:
x, y = event.meta.pixel_pos[args.tel]
geom = io.CameraGeometry.guess(x, y)
disp = visualization.CameraDisplay(geom, title='CT%d' % args.tel)
disp.enable_pixel_picker()
disp.add_colorbar()
plt.show(block=False)
# display the event
disp.axes.set_title('CT{:03d}, event {:010d}'.format(
args.tel, event.dl0.event_id))
if args.show_samples:
# display time-varying event
data = event.dl0.tel[args.tel].adc_samples[args.channel]
for ii in range(data.shape[1]):
disp.image = apply_mc_calibration(data[:, ii], args.tel)
disp.set_limits_percent(70)
plt.pause(0.01)
if args.write:
def display_event(event, calibrate = 0, max_tel = 4, cleaning=None):
"""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 = min(max_tel, len(event.dl0.tels_with_data))
fig.clear()
plt.suptitle("EVENT {}".format(event.dl0.event_id))
disps = []
mc_sum = None
all_moments = []
for ii, tel_id in enumerate(event.dl0.tels_with_data):
if ii >= max_tel: break
print("\t draw cam {}...".format(tel_id))
nn = int(ceil((ntels)**.5))
ax = plt.subplot(nn, 2*nn, 2*(ii+1)-1)
x, y = event.inst.pixel_pos[tel_id]
geom = io.CameraGeometry.guess(x, y, event.inst.optical_foclen[tel_id])
disp = visualization.CameraDisplay(geom, ax=ax,
title="CT{0} DetectorResponse".format(tel_id))
disp.pixels.set_antialiaseds(False)
disp.autoupdate = False
disp.cmap = plt.cm.hot
chan = 0
signals = event.dl0.tel[tel_id].adc_sums[chan].astype(float)[:]
if calibrate:
signals = apply_mc_calibration_ASTRI(event.dl0.tel[tel_id].adc_sums, tel_id)
if cleaning == 'tailcut':
mask = tailcuts_clean(geom, signals, 1,picture_thresh=10.,boundary_thresh=8.)
dilate(geom, mask)
signals[mask==False] = 0
moments = hillas_parameters_2(geom.pix_x,
geom.pix_y,
signals)
disp.image = signals
disp.overlay_moments(moments, color='seagreen', linewidth=3)
disp.set_limits_percent(95)
disp.add_colorbar()
disps.append(disp)
ax = plt.subplot(nn, 2*nn, 2*(ii+1))
geom = io.CameraGeometry.guess(x, y, event.inst.optical_foclen[tel_id])
disp = visualization.CameraDisplay(geom, ax=ax,
title="CT{0} PhotoElectrons".format(tel_id))
disp.pixels.set_antialiaseds(False)
disp.autoupdate = False
disp.cmap = plt.cm.hot
chan = 0
#print (event.mc.tel[tel_id].photo_electrons)
for jj in range(len(event.mc.tel[tel_id].photo_electron_image)):
event.dl0.tel[tel_id].adc_sums[chan][jj] = event.mc.tel[tel_id].photo_electron_image[jj]
signals2 = event.dl0.tel[tel_id].adc_sums[chan].astype(float)
moments2 = hillas_parameters_2(geom.pix_x,
geom.pix_y,
signals2)
all_moments.append(moments2)
if mc_sum is None:
mc_sum = signals2
else:
scale = 3 if tel_id == 37 else 1
for i, val in enumerate(signals2):
mc_sum[i] += val/scale
disp.image = mc_sum
for moments2 in all_moments:
disp.overlay_moments(moments2, color='seagreen', linewidth=2)
disp.set_limits_percent(95)
disp.add_colorbar()
disps.append(disp)
return disps
if datatype == 'MC':
inputfile_reader = hessio_event_source(filename)
else:
inputfile_reader = zfits.zfits_event_source(
url=
"/data/datasets/CTA/[email protected]_0_000.36.fits.fz",
data_type='r1',
max_events=500)
formatter = LogFormatter(10, labelOnlyBase=False)
plt.figure(0)
displayType = []
plt.subplot(2, 3, 1)
displayType.append(
visualization.CameraDisplay(geom,
title='Pedestal Variation log',
norm='lin',
cmap='coolwarm'))
displayType[-1].add_colorbar()
plt.subplot(2, 3, 2)
displayType.append(
visualization.CameraDisplay(geom,
title='Pedestals',
norm='lin',
cmap='coolwarm'))
displayType[-1].add_colorbar()
plt.subplot(2, 3, 3)
displayType.append(
visualization.CameraDisplay(geom,
title='Pedestal With Low var',
norm='lin',
cmap='coolwarm'))
axis=0)
inputfile_reader = zfits.zfits_event_source(
url=
"/data/datasets/CTA/[email protected]_0_000.36.fits.fz",
data_type='r1',
max_events=2130)
if plotting:
plt.figure(0)
displayType = []
plt.subplot(1, 1, 1)
displayType.append(
visualization.CameraDisplay(
geom,
title='Integrated ADC over 200ns, pedestal subtracted',
norm='lin',
cmap='coolwarm'))
neigh_mask = generate_neighbourg_masks()
hollow_neigh_mask = generate_hollow_neighbourg_masks()
min_evtcounter = 3383 - 3018
best_pix = -1
h, h1 = [], []
evt_counter = 0
cnt = 0
for event in inputfile_reader:
evt_counter += 1
if evt_counter < min_evtcounter: continue
for telid in event.r1.tels_with_data:
