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Thanks for the FAQ. 2 questions:When selecting a time resolution for download (say 1h), are the values in the exported time series 1h averages? When exporting charts, no meta data for the different tags is exported. What I miss specifically are: Tag group (where does it belong to) Tag description (what is it) Unit of measurement Any smart way how to export that data, either in a separate table or along with the data itself?
Thank you, this is very much appreciated. Final question for today:I just downloaded 58 time series (365 days each, 1h granularity). This worked very well, but quite frequently, there are time steps missing in the tables downloaded. In the screenshot below, for example, column “Delta [sec]” shows the difference between timesteps in seconds. Note the high value. Is this normal behavior and would you recommend rather downloading each timeseries individually (which is extremely time consuming...).
You may reach out to me anytime starting Monday. Below I’ve copied in another screenshot from a different table I downloaded (62 data series, 365 days, 1h granularity). Only the data highlighted in green corresponds with expectation of 1h time resolution. Have a good weekend!
Adding yet more input here. Today, I exported a timeseries for a single tag (365 days, 1h granularity). In the exported file, there were a couple of rows with timesteps deviating from 1h. However, what was even more disconcerting was the existence of a duplicate timestamp with different tag readings (see 1st screenshot). I could almost understand why the these glitches would happen for very large data tables, but 365 days at 1h granularity are only 8760 rows. That’s actually a quite small table, and so far every single one of them I downloaded has these issues. Edit 1: I also tried downloading the raw data, but this does not work :( (see screenshot below).
Apologies, but I am interested in perfectly understanding what is happening here, not what “could” be happening. Alas, I have exported another table twice and as you correctly suspected, October 30th 02:00 last year had two rows also there (see screenshot below). What is disoncerting though is that the row with value 90,1618519 for tag TIC3120.CVEU swapped places between the different tables. How am I as a user to decide which row is the one to use? You can also see that I have changed the timestamp format to show raw numbers. These are not in agreement with the values mentioned in the previous comment. I also don’t think that missing raw data is a good explanation for missing rows in exported tables or a deviation from the user specified 1h timestep. I am not even sure whether that is just a hypothesis or whether that is the actual cause, seeing how I’ve downloaded a couple of other tables where tag data was missing yet the 1h granularity was maintained for a time series despite dat
@roman.chesnokov, that was surprisingly easy to follow for someone who only last week familiarized with Jupyter notebooks. Thank you.However, with the code you provided I can only download the list of “Directly linked timeseries (11)”. Is there a way to download the list of 127 linked timeseries highlighted below? For completeness, here’s the code I used:from cognite.client import CogniteClientclient = CogniteClient()asset_xid = "houston.11. QHP" ## which is the external id of the asset in the screenshot## there are 11 directly linked and 127 linked time series for this asset## I want the list of the 127 linked time series.tss = client.time_series.list(asset_external_ids=[asset_xid])tss.to_pandas().to_csv(f"Timeseries for {asset_xid}.csv")
@roman.chesnokov, perfect!Final question: is it OK to request/discuss solutions like yours for other issues as well in this forum? Suppose, when I am stuck with not knowing how to write a loop in Python or something. Totally fine if this is not the right place.Thank you for your help with this issue!
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