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 = cleaning.tailcuts_clean(geom, image, pedvars=80)
for ii in range(3):
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
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
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
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 = io.CameraGeometry.from_name("hess", 1)
disp = visualization.CameraDisplay(geom)
disp.set_limits_minmax(0, 300)
disp.add_colorbar()
# Create a fake camera image to display:
model = mock.generate_2d_shower_model(centroid=(0.2, 0.0),
width=0.01,
length=0.1,
psi='35d')
image, sig, bg = mock.make_mock_shower_image(geom, model.pdf,
intensity=50,
nsb_level_pe=1000)
# Apply really stupid image cleaning (single threshold):
clean = image.copy()
clean[image <= 3.0 * image.mean()] = 0.0
# Calculate image parameters
hillas = hillas_parameters(geom.pix_x, geom.pix_y, clean)
print(hillas)
# Show the camera image and overlay Hillas ellipse
from ctapipe.visualization import CameraDisplay
from matplotlib.colors import PowerNorm
from matplotlib.style import use
if __name__ == '__main__':
use('ggplot')
# load the camera
fig, axs = plt.subplots(1, 3, figsize=(15, 5))
geom = CameraGeometry.from_name("hess", 1)
titles = 'Linear Scale', 'Log-Scale', 'PowerNorm(gamma=2)'
model = mock.generate_2d_shower_model(
centroid=(0.2, 0.0),
width=0.01,
length=0.1,
psi='35d',
)
image, sig, bg = mock.make_mock_shower_image(
geom,
model.pdf,
intensity=50,
nsb_level_pe=1000,
)
disps = []
for ax, title in zip(axs, titles):
disps.append(CameraDisplay(geom, ax=ax, image=image, title=title))
disps[0].norm = 'lin'
"""Example how to make a mock shower image and plot it.
"""
import matplotlib.pyplot as plt
from ctapipe.io.camera import make_rectangular_camera_geometry
from ctapipe.image.mock import generate_2d_shower_model, make_mock_shower_image
NX = 40
NY = 40
geom = make_rectangular_camera_geometry(NX, NY)
showermodel = generate_2d_shower_model(centroid=[0.25, 0.0], length=0.1,
width=0.02, psi='40d')
image, signal, noise = make_mock_shower_image(geom, showermodel.pdf,
intensity=20, nsb_level_pe=30)
# make them into 2D arrays so we can plot them with imshow
image.shape = (NX, NY)
signal.shape = (NX, NY)
noise.shape = (NX, NY)
# here we just plot the images using imshow(). For a more general
# case, one should use a ctapipe.visualization.CameraDisplay
plt.figure(figsize=(10, 3))
plt.subplot(1, 3, 1)
plt.imshow(signal, interpolation='nearest', origin='lower')
plt.title("Signal")
plt.colorbar()
plt.subplot(1, 3, 2)
plt.imshow(noise, interpolation='nearest', origin='lower')
from ctapipe.image import mock
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 = mock.generate_2d_shower_model(
centroid=(0.05, 0.0), width=0.005, length=0.025, psi='35d'
)
image, sig, bg = mock.make_mock_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()
"""Example how to make a mock shower image and plot it.
"""
import matplotlib.pyplot as plt
from ctapipe.io.camera import make_rectangular_camera_geometry
from ctapipe.image.mock import generate_2d_shower_model, make_mock_shower_image
NX = 40
NY = 40
geom = make_rectangular_camera_geometry(NX, NY)
showermodel = generate_2d_shower_model(centroid=[0.25, 0.0],
length=0.1,
width=0.02,
psi='40d')
image, signal, noise = make_mock_shower_image(geom,
showermodel.pdf,
intensity=20,
nsb_level_pe=30)
# make them into 2D arrays so we can plot them with imshow
image.shape = (NX, NY)
signal.shape = (NX, NY)
noise.shape = (NX, NY)
# here we just plot the images using imshow(). For a more general
# case, one should use a ctapipe.visualization.CameraDisplay
plt.figure(figsize=(10, 3))
plt.subplot(1, 3, 1)
plt.imshow(signal, interpolation='nearest', origin='lower')
def mock_event_source(geoms, events=100, single_tel=False, n_channels=1, n_samples=25, p_trigger=0.3):
"""
An event source that produces array
Parameters
----------
geoms : list of CameraGeometry instances
Geometries for the telescopes to simulate
events : int, default: 100
maximum number of events to create
n_channels : int
how many channels per telescope
n_samples : int
how many adc samples per pixel
p_trigger : float
mean trigger probability for the telescopes
"""
n_telescopes = len(geoms)
container = EventContainer()
container.meta.add_item('mock__max_events', events)
container.meta.source = "mock"
tel_ids = np.arange(n_telescopes)
for event_id in range(events):
n_triggered = np.random.poisson(n_telescopes * 0.3)
if n_triggered > n_telescopes:
n_triggered = n_telescopes
triggered_tels = np.random.choice(tel_ids, n_triggered, replace=False)
container.dl0.event_id = event_id
container.dl0.tels_with_data = triggered_tels
container.count = event_id
# handle single-telescope case (ignore others:
if single_tel:
if single_tel not in container.dl0.tels_with_data:
continue
container.dl0.tels_with_data = [single_tel, ]
container.dl0.tel = dict() # clear the previous telescopes
t = np.arange(n_samples)
for tel_id in container.dl0.tels_with_data:
geom = geoms[tel_id]
# fill pixel position dictionary, if not already done:
if tel_id not in container.meta.pixel_pos:
container.meta.pixel_pos[tel_id] = (
geom.pix_x.value,
geom.pix_y.value,
)
centroid = np.random.uniform(geom.pix_x.min(), geom.pix_y.max(), 2)
length = np.random.uniform(0.02, 0.2)
width = np.random.uniform(0.01, length)
psi = np.random.randint(0, 360)
intensity = np.random.poisson(int(10000 * width * length))
model = mock.generate_2d_shower_model(
centroid,
width,
length,
'{}d'.format(psi)
)
image, _, _ = mock.make_mock_shower_image(
geom,
model.pdf,
intensity,
)
container.dl0.tel[tel_id] = RawCameraData(tel_id)
container.dl0.tel[tel_id].num_channels = n_channels
n_pix = len(geom.pix_id)
samples = np.empty((n_pix, n_samples))
means = np.random.normal(15, 1, (n_pix, 1))
stds = np.random.uniform(3, 6, (n_pix, 1))
samples = image[:, np.newaxis] * norm.pdf(t, means, stds)
for chan in range(n_channels):
container.dl0.tel[tel_id].adc_samples[chan] = samples
container.dl0.tel[tel_id].adc_sums[chan] = image
yield container
