Convert camera images to pixels on a s square grid¶

[1]:

from ctapipe.utils import get_dataset_path
from ctapipe.visualization import CameraDisplay
from ctapipe.instrument import SubarrayDescription
from ctapipe.io import EventSource
from ctapipe.image.toymodel import Gaussian
import matplotlib.pyplot as plt
import astropy.units as u

[2]:

# get the subarray from an example file
subarray = SubarrayDescription.read("dataset://gamma_prod5.simtel.zst")

Geometries with square pixels¶

Define a camera geometry and generate a dummy image:

[3]:

geom = subarray.tel[40].camera.geometry
model = Gaussian(
    x=0.05 * u.m,
    y=0.05 * u.m,
    width=0.01 * u.m,
    length=0.05 * u.m,
    psi="30d",
)
_, image, _ = model.generate_image(geom, intensity=500, nsb_level_pe=3)

[4]:

CameraDisplay(geom, image)

[4]:

<ctapipe.visualization.mpl_camera.CameraDisplay at 0x7f962e5a8670>

../_images/examples_convert_images_to_2d_5_1.png

The CameraGeometry has functions to convert the 1d image arrays to 2d arrays and back to the 1d array:

[5]:

image_square = geom.image_to_cartesian_representation(image)

[6]:

plt.imshow(image_square)

[6]:

<matplotlib.image.AxesImage at 0x7f962df0c3d0>

../_images/examples_convert_images_to_2d_8_1.png

[7]:

image_1d = geom.image_from_cartesian_representation(image_square)

[8]:

CameraDisplay(geom, image_1d)

[8]:

<ctapipe.visualization.mpl_camera.CameraDisplay at 0x7f962c84b0a0>

../_images/examples_convert_images_to_2d_10_1.png

Geometries with hexagonal pixels¶

Define a camera geometry and generate a dummy image:

[9]:

geom = subarray.tel[1].camera.geometry
model = Gaussian(
    x=0.5 * u.m,
    y=0.5 * u.m,
    width=0.1 * u.m,
    length=0.2 * u.m,
    psi="30d",
)
_, image, _ = model.generate_image(geom, intensity=5000)

[10]:

CameraDisplay(geom, image)

[10]:

<ctapipe.visualization.mpl_camera.CameraDisplay at 0x7f962e5b7e80>

../_images/examples_convert_images_to_2d_13_1.png

[11]:

image_square = geom.image_to_cartesian_representation(image)

Conversion into square geometry¶

Since the resulting array has square pixels, the pixel grid has to be rotated and distorted. This is reversible (The image_from_cartesian_representation method takes care of this):

[12]:

plt.imshow(image_square)

[12]:

<matplotlib.image.AxesImage at 0x7f962ebf96d0>

../_images/examples_convert_images_to_2d_16_1.png

[13]:

image_1d = geom.image_from_cartesian_representation(image_square)

[14]:

disp = CameraDisplay(geom, image_1d)

../_images/examples_convert_images_to_2d_18_0.png