Cleaning/denoising of images (tailcuts cleaning, dilation,
filtering).
ctapipe.image.cleaning Module
Image Cleaning Algorithms (identification of noisy pixels)
All algorithms return a boolean mask that is True for pixels surviving
the cleaning.
To get a zero-suppressed image and pixel
list, use image[mask], geom.pix_id[mask], or to keep the same
image size and just set unclean pixels to 0 or similar, use
image[~mask] = 0
Functions
tailcuts_clean(geom, image[, …])
|
Clean an image by selection pixels that pass a two-threshold tail-cuts procedure. |
dilate(geom, mask)
|
Add one row of neighbors to the True values of a pixel mask and return the new mask. |
mars_cleaning_1st_pass(geom, image[, …])
|
Clean an image by selection pixels that pass a three-threshold tail-cuts procedure. |
fact_image_cleaning(geom, image, arrival_times)
|
Clean an image by selection pixels that pass the fact cleaning procedure. |
apply_time_delta_cleaning(geom, mask, …)
|
Identify all pixels from selection that have less than N neighbors that arrived within a given timeframe. |
apply_time_average_cleaning(geom, mask, …)
|
Extract all pixels that arrived within a given timeframe with respect to the time average of the pixels on the main island. |
time_constrained_clean(geom, image, …[, …])
|
time constrained cleaning by MAGIC |
Classes
ImageCleaner(**kwargs)
|
Abstract class for all configurable Image Cleaning algorithms. |
TailcutsImageCleaner(**kwargs)
|
Clean images using the standard picture/boundary technique. |
Class Inheritance Diagram
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"TelescopeComponent" -> "ImageCleaner" [arrowsize=0.5,style="setlinewidth(0.5)"];
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