Monitoring (monitoring)#

Monitoring data are time-series used to monitor the status or quality of hardware, software algorithms, the environment, or other data products. These contain values recorded periodically at different rates, and can be thought of as a set of tables with rows identified by a time-stamp. They are potentially acquired during the day or nighttime operation of the array and during subsequent data processing, but ataverage rates much slower than Event data and faster than the length of a typical observation block. Examples include telescope tracking positions, trigger rates, camera sensor conditions, weather conditions, and the status or quality-control data of a particular hardware or software component.

This module provides some code to help to generate monitoring data from processed event data, particularly for the purposes of calibration and data quality assessment.

Code related to Statistics Aggregator, Calibration Calculator, and Outlier Detector is implemented here.

Submodules#

Reference/API#

ctapipe.monitoring Package#

Module for handling monitoring data.

Classes#

PlainAggregator(**kwargs)

Compute aggregated statistic values from a chunk of images using numpy functions

SigmaClippingAggregator(**kwargs)

Compute aggregated statistic values from a chunk of images using astropy's sigma clipping functions

StatisticsAggregator(**kwargs)

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

OutlierDetector(**kwargs)

Base class for outlier detection algorithms.

RangeOutlierDetector(**kwargs)

Detect outliers based on a valid range.

MedianOutlierDetector(**kwargs)

Detect outliers based on the deviation from the camera median.

StdOutlierDetector(**kwargs)

Detect outliers based on the deviation from the camera standard deviation.

Interpolator(**kwargs)

Interpolator parent class.

PointingInterpolator(**kwargs)

Interpolator for pointing and pointing correction data