| window | int | `25` |The number of datapoints to be included in each comparison. |
| window | int | `25` |The number of datapoints to be included in each comparison window. |
| min_distance | float | `0.5` |The minimum distance of two graphs to be classified as "different". |
| method_dtw | string | `"fast"` |Implementation of DTW algorithm - "exact" for the normal implementation of DTW, "fast" for the fast implementation. |
| ref_datafield | string | |Name of the reference datafield ("correct" values) with which the actual datafield is compared. |
This function compares the data with a reference datafield (given in `ref_datafield`) of values we assume to be correct, and flags those values that exceed a minimum distance value (given in `min_distance`).
This function compares the data with a reference datafield (given in `ref_datafield`) of values we assume to be correct. The comparison is undertaken window-based, i.e. the two data fields are compared window by window, with overlapping windows. The function flags those values that lie in the middle of a window that exceeds a minimum distance value (given in `min_distance`).
As comparison algorithm, we use the [Dynamic Time Warping (DTW) Algorithm](https://en.wikipedia.org/wiki/Dynamic_time_warping) that accounts for temporal and spacial offsets when calculating the distance. For a demonstration of the DTW, see the Wiki entry "Results for rain data set" in [Pattern Recognition with Wavelets](https://git.ufz.de/rdm-software/saqc/-/wikis/Pattern-Recognition-with-Wavelets#Results).
