from rocc import Threshold, rocc

result = rocc(
   timeseries=a_htimeseries_object,
   thresholds=(
      Threshold("10min", 10),
      Threshold("20min", 15),
      Threshold("H", 40),
   ),
   symmetric=True,
   flag="MYFLAG",
)

timeseries is a HTimeseries object. thresholds is, obviously, a list of thresholds. Threshold is a named tuple whose items are delta_t (a pandas interval specification) and allowed_diff (a floating point number).

The function checks whether there exist intervals during which the value of the time series changes by more than the specified threshold. The offending records are flagged with the specified flag.

It returns a list of strings describing where the thresholds have been exceeded.

If flag is None or the empty string, then the offending records are not flagged, and the only result is the returned value.

Here is an example time series:

2020-10-06 14:30    24.0
2020-10-06 14:40    25.0  
2020-10-06 14:50    36.0 *
2020-10-06 15:01    51.0
2020-10-06 15:21    55.0  
2020-10-06 15:31    65.0  
2020-10-06 15:41    75.0 *
2020-10-06 15:51    70.0

After running rocc() with the thresholds specified in the example above, the records marked with a star will be flagged. The record 14:50 will be flagged because in the preceding 10-minute interval the value increases by 11, which is more than 10. The record 15:41 will be flagged because in the preceding 20-minute interval the value increases by 20, which is more than 15. The record 15:01 will be unflagged; although there's a large difference since 14:40, this is 21 minutes, not 20, so the 20-minute threshold of 15 does not apply; likewise, there's a difference of 15 from 14:50, which does not exceed the 20-minute threshold of 15, and while it does exceed the 10-minute threshold of 10, it's 11 minutes, not 10. There's also not any difference larger than 40 within an hour anywhere.

The return value in this example will be a list of two strings:

"2020-10-06T14:50  +11.0 in 10min (> 10.0)"
"2020-10-06T15:41  +20.0 in 20min (> 15.0)"

The return value should only be used for consumption by humans; it is subject to change.

If symmetric is True, it is the absolute value of the change that matters, not its direction. In this case, allowed_diff must be positive. If symmetric is False (the default), only rates larger than positive allow_diff or rates smaller than negative allow_diff are flagged.