Kin How

You can try the solution below. Set a low threshold to detect the minima, for example 5. Create a formula $signal.remove(($signal < 5).removeshorterthan(1h)). This will remove the short dips (shorter than 1 hour) from your data. Calculate the yearly minimum value of the signal in step 2 using Signal from Condition tool with a Yearly periodic condition as the bounding condition.

You can create a condition for when the performance rate is equal to or around 60%. Then, create the prediction model -> expand the "Advanced" tab on the prediction tool -> set the 60% performance rate condition under the "Limit to condition within training window" box. The prediction model will be trained using 60% performance rate data only by doing so. I hope this answers your question.

If I understand your request correctly, you would like to group your Dent Count into 3 categories: MST, PS, or Gluten Thickener. One way to accomplish this is by creating new scorecard metrics to count the number of KPI 1: MST, KPI 2: PS, and KPI 3: Gluten Thickener capsules, respectively, over your $s shift condition. This process will result in 3 additional columns in your table, with each column representing the count of capsules for each KPI condition.

Asset Groups are locally scoped and limited to the workbook in which they were created. This means that you can only access the Asset Groups within that specific workbook. If you need to access the Asset Tree in multiple workbooks, you should consider pushing an Asset Tree instead.

It seems like you could create an Asset Group to populate all conditions across all signals. Please refer to the resources below for more information about creating an Asset Group. KB Article: https://support.seeq.com/kb/latest/cloud/asset-groups

Starting from version R63, we have introduced a new method for inserting data lab content in the organizer topic using IOTA content. It's important to note that this feature is currently in Preview mode, and the process for inserting and configuring this type of content is subject to change in the future.

Please submit an IT support ticket here. Our team will assist you.

You can use the trash button on the Item Properties to remove the test signals.

You can try archiving the conditions using API. The code below should archive the conditions in your worksheet. from seeq import sdk import pandas as pd # Setting up the SDK end point items_api = sdk.ItemsApi(spy.client) worksheet_url = 'worksheet_url' condition_df = spy.search(worksheet_url) for id in condition_df['ID']: items_api.archive_item(id=id)

The dotted border and lighter color indicate uncertainty. We are still working on a new version to ensure consistency between uncertain and certain data trends.

The "Samples table grid" has "Ungridded original timestamps" added as an option in R63. You will see this option after upgrading to R63. https://support.seeq.com/kb/latest/cloud/odata-export

Hi Nitish, You need to deselect "Auto" before setting a custom "Axis Max".

Let's continue the conversation on the SUP ticket you raised.

Unsure why, since the Formula shouldn't care if the signal variable name is reused. I was told the variables don't work globally, e.g. either of the following would work as long as they are tied appropriately to the formula parameter. Is this not true? ($OFCoC2 + $OFCoC3 + $OFCoC5 + $OFCoC6 + $OFCoC7) / 100 * $FaPOF ($a + $b + $c + $d +$e) / 100 * $g Yes, either of the formula parameter combinations will work for your case. The error message you received, SPy Error, indicates that there were multiple entries of $OFPOF in the DataFrame. This suggests that there might be more than one match found in your df. To confirm this, could you please print df and check?

Your code should return the actual ID of the item after adding ['ID].iloc[0] to the end of each line. Please try the "Formula Parameter" below: { '$OFCoC2':df[(df['Asset'] =='advisor_elements_sOiltags_leastCleaned_v2_csv(import)') & (df['Name'] == 'Olefin Feed Composition_online C3=')]['ID'].iloc[0], '$OFCoC3':df[(df['Asset'] =='advisor_elements_sOiltags_leastCleaned_v2_csv(import)') & (df['Name'] == 'Olefin Feed Composition_online C4=1')]['ID'].iloc[0], '$OFCoC5':df[(df['Asset'] =='advisor_elements_sOiltags_leastCleaned_v2_csv(import)') & (df['Name'] == 'Olefin Feed Composition_online C4=2c')]['ID'].iloc[0], '$OFCoC6':df[(df['Asset'] =='advisor_elements_sOiltags_leastCleaned_v2_csv(import)') & (df['Name'] == 'Olefin Feed Composition_online C4=2t')]['ID'].iloc[0], '$OFCoC7':df[(df['Asset'] =='advisor_elements_sOiltags_leastCleaned_v2_csv(import)') & (df['Name'] == 'Olefin Feed Composition_online C4=i')]['ID'].iloc[0], '$FaPOF':df[(df['Asset'] =='advisor_elements_sOiltags_leastCleaned_v2_csv(import)') & (df['Name'] == 'Feed and Products Olefin Feed+HYC')]['ID'].iloc[0] } After that, the spy.push() should run without error. Let me know the outcome after trying it.