StatisticsAggregator#

class ctapipe.monitoring.StatisticsAggregator(**kwargs: Any)[source]#

Bases: TelescopeComponent

Base component to handle the computation of aggregated statistic values from a table containing e.g. charges, peak times and/or charge variances (images).

Attributes Summary

chunk_size

Size of the chunk used for the computation of aggregated statistic values

Methods Summary

__call__(table[, masked_pixels_of_sample, ...])

Divide table into chunks and compute aggregated statistic values.

compute_stats(images, masked_pixels_of_sample)

Attributes Documentation

chunk_size#

Size of the chunk used for the computation of aggregated statistic values

Methods Documentation

__call__(table, masked_pixels_of_sample=None, chunk_shift=None, col_name='image') Table[source]#

Divide table into chunks and compute aggregated statistic values.

This function divides the input table into overlapping or non-overlapping chunks of size chunk_size and call the relevant function of the particular aggregator to compute aggregated statistic values. The chunks are generated in a way that ensures they do not overflow the bounds of the table. - If chunk_shift is None, chunks will not overlap, but the last chunk is ensured to be of size chunk_size, even if it means the last two chunks will overlap. - If chunk_shift is provided, it will determine the number of samples to shift between the start of consecutive chunks resulting in an overlap of chunks. Chunks that overflows the bounds of the table are not considered.

Parameters:
tableastropy.table.Table

table with images of shape (n_images, n_channels, n_pixels), event IDs and timestamps of shape (n_images, )

masked_pixels_of_samplendarray, optional

boolean array of masked pixels of shape (n_channels, n_pixels) that are not available for processing

chunk_shiftint, optional

number of samples to shift between the start of consecutive chunks

col_namestring

column name in the table

Returns:
astropy.table.Table

table containing the start and end values as timestamps and event IDs as well as the aggregated statistic values (mean, median, std) for each chunk

abstract compute_stats(images, masked_pixels_of_sample) StatisticsContainer[source]#