ColumnPixelDefect

class cabaret.camera.ColumnPixelDefect(name: str = 'defect', rate: float = 0.0, seed: int = 0, apply_before_base: bool = False, _rng: Generator | None = None, _pixels: ndarray | None = None, value: int = 0, dim: int = 0, _lines: ndarray | None = None)

A pixel defect that simulates column noise by selecting entire rows or columns of pixels to be defective.

Examples

>>> from cabaret import Observatory, Sources
>>> pixel_defects = {
...     "column": {"type": "column", "value": 20, "rate": 0.01, "dim": 1}
... }
>>> observatory = Observatory(camera={"pixel_defects": pixel_defects})
>>> sources = Sources.get_test_sources()
>>> ra, dec = sources.center
>>> _, clean_image, image = observatory.generate_image_stack(
...     exp_time=3, ra=ra, dec=dec, sources=sources, convert_all_to_adu=True
... )

To plot the images, you can use the plot_image function from cabaret.plot:

>>> from cabaret.plot import plot_image
>>> import matplotlib.pyplot as plt
>>> fig, axes = plt.subplots(1, 2, figsize=(7, 5), sharex=True, sharey=True)
>>> _ = plot_image(clean_image, ax=axes[0], title="Image without defects")
>>> _ = plot_image(image, ax=axes[1], title="Image with column defects")
>>> plt.subplots_adjust(wspace=0.1)
>>> plt.show()

Methods

__init__([name, rate, seed, ...])
introduce_pixel_defect(image, camera, **kwargs)	Introduce the defect into the image.
number_of_defect_pixels(camera)	Calculate the number of pixels affected by the defect.
set_lines(lines, camera)	Set the lines for the defect.
set_pixels(pixels, camera)	Set the pixels for the defect.

Attributes

apply_before_base	If True, this defect is applied before the base image (bias, dark, read noise) is added.
dim	Dimension along which to apply the defect.
lines	Defective lines (rows or columns).
name	Name of the defect.
pixels	Pixel coordinates affected by the defect.
rate	Fraction of pixels affected by the defect.
rng	Random number generator instance.
seed	Random seed for reproducibility.
value	Constant value to assign to defect pixels.