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Sanman Mehta

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Sanman Mehta last won the day on August 19

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  1. Hi SBC, Pls reach out with links and logs to support@seeq.com so we can look into specifics.
  2. Hi SBC, This is not friendly to pushing visuals. we can discuss more on our call later this week to discuss further.
  3. Oh ya these results don't have to be siloed in data-lab python environment. They can be used as time series signals in workbench as well as organizer topics for any type of visualizations or in further calculations by anyone within the organization.
  4. SBC, unless I am misinterpreting your message, the cache should behave in same performance. Please reach out to me directly if you are experiencing slowness in any case. Sanman
  5. Seeq stores all results in cache (most cases), so once you have done calculations once, they should not need recalculation for 10 years every time and resulting signal can be instantly used in further calculations. This should help alleviate calculation load from 2nd time onwards such that only new calculation would be for the last 1 day. You should not have to run scripts for 10 years everyday. Sanman
  6. Hi VpJ, You are on the right track. Schedule daily jobs by adding this code to your data lab project script spy.jobs.schedule("Every 1 Day") In a future release you will be able to create python visuals in qorkbench and just link it to organizer topic (same as you do with standard trends, tables, treemap views).
  7. Hi VpJ, If you build it out using any of the solutions mentioned above, it is relatively easy to make it available in all workbenches.
  8. yeah just shoot me an email with workbench link in which you had the worksheet.
  9. Hi SBC, This is by design. Although you can make the asset tree available in all workbenches by specifying “workbook”=None in your spy.push call near the bottom of the script. Add on tool is running script in background which you can find and update.
  10. The script is designed to only tag pi tag (not pi server). If searching pi tag in workbench results in one unique tag then the script will work. Otherwise please reach to me directly for support.
  11. Unfortunately not. It has been accepted in feature request. If you request via support@seeq.com we will make sure you get an email when it’s in product future release. Btw, there is a workaround using fragile method I would not want to advertise here as well so feel free to reach out to Seeq support.
  12. I have encountered this error but would need to see the specifics. Can you pls send details to support@seeq.com and Seeqers can help you.
  13. Check out the data lab script and video that walks through it to automate data pull->apply ml->push results to workbench in an efficient manner. Of course you can skin the cat many different ways however this gives a good way to do it in bulk. Use case details: Apply ML on Temperature signals across the whole Example Asset Tree on a weekly basis. For your case, you can build you own asset tree and filter the relevant attributes instead of Temperature and set spy.jobs.schedule frequency to whatever works for you. Let me know if there are any unanswered questions in my post or demo. Happy to update as needed. apply_ml_at_scale.mp4 Apply ML on Asset Tree Rev0.ipynb
  14. Yes, in Data Lab users can use spy.pull to get the data and then write it to S3. See attached notebook example written by @Chris Herrera that shows an example of how to write data to S3. Disclaimer: User will have to add their own credentials and unlimited performance is not a guarantee. Reach out to support@seeq.com if your use case is not performing up to your expectations. Publish to S3.ipynb
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