Performance metrics¶

Pecos can be used to track performance of time series data over time. The quality control index (QCI) is a general metric which indicates the percent of the data points that passed quality control tests. Duplicate and non-monotonic indexes are not counted as failed tests (duplicates are removed and non-monotonic indexes are reordered). QCI is defined as:

\[QCI = \frac{\sum_{d \in D}\sum_{t \in T} X_{dt}}{|DT|}\]

where \(D\) is the set of data columns and \(T\) is the set of timestamps in the analysis. \(X_{dt}\) is a data point for column \(d\) time t` that passed all quality control test. \(|DT|\) is the number of data points in the analysis.

A value of 1 indicates that all data passed all tests. For example, if the data consists of 10 columns and 720 times that are used in the analysis, then \(|DT|\) = 7200. If 7000 data points pass all quality control tests, then the QCI is 0.972.

To compute QCI:

QCI = pecos.metrics.qci(pm)

Additional metrics can be added to the QCI DataFrame and saved to a file:

pecos.io.write_metrics(metrics_filename, QCI)

If ‘metrics_filename’ already exists, the metrics will be appended to the file.