for some unknown reasons the database has "NaN"-entries. This entries make problems with the "sum" and also with the "integral" calculations, they are simply wrong. The pattern why it happens is not clear for me, but I will check further.What I realized is, that a bunch of datapoints has the NaN-entry at the same timestamp.

Now my question. How can I clean this up?

The available backend-scripts are not showing such examples, but it looks like this will be the best way. API did offer some methods, but it is not documented that good that I can find a way.

Ideas for the script:

1. loop through all datapoints
2. query values of the first datapoint and search for NaN-values

This example query the values via sql-console, but "delete" command is not allowed here.

SELECT * FROM POINTVALUES WHERE DATAPOINTID = 28 AND nullif(POINTVALUE, 'NaN') is null

--output

ID	DATAPOINTID	DATATYPE	POINTVALUE	TS
14134	28	3	NaN	1700760554775
19447	28	3	NaN	1700762775326
20933	28	3	NaN	1700763381424
201309	28	3	NaN	1711605424899

3. use the informations "datapointid" and "ts" to delete the values
4. go on with next datapoint.

I want to use it also for cleaning values that are infinite or out of set value ranges (extreme), but this is an second requirement.

Are there some best practices and examples available?

Regards
Sasa