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Wants the option to print a checklist with selected attributes from InField.
Be able to copy the URL for a given checklist, template, etc. for sharing with a colleague.
Hello,With the current WITSML extractor configuration, we can only whitelist the mnemonics that we want to extract. Would it be possible to also support a blacklist configuration for mnemonics?In our case, the complete list of mnemonics to extract is not known beforehand, which makes maintaining a whitelist challenging.Thank you.
When listing instances of a view and using the to_pandas method, is there a simple way to use the data types defined on the container properties and use these to cast to similar python data types? Or could this be an option of the python SDK?In the classical CDF data types, this is implemented so that for example a date field and timestamp fields have the correct python data type. The data modelling instances will be either floats or string. It would be very convenient if the underlying container data type can be used so that you do not have to manually cast values in the pandas dataframe.
ContextWe currently run several custom Python extractors built on cognite-extractor-utils. For those, cognite-extractor-utils lets us configure a metrics.cognite block that periodically pushes the extractor's Prometheusmetrics (Counter/Gauge) directly to CDF Time Series — this gives us consistent,queryable observability (row counts, run duration, throughput, failure counts, etc.)across all our extractors, without any extra tooling.ProblemThe PI Extractor and the DB Extractor (official Cognite binaries) already exposePrometheus metrics internally (e.g. pi_extractor_timeseries_states,pi_extractor_stream_iterations, pi_extractor_pi_data_pipe_events,pi_extractor_streamer_data_points, pi_extractor_event_queue_size,pi_extractor_pi_data_loss, pi_extractor_pi_connection_time,pi_extractor_pi_connections, extractor_utils_cdf_datapoints, …), but today they canonly be exposed via a local Prometheus scrape endpoint (metrics.server) or a PushGateway (metrics.push-gateways). There is no equivalent of metrics.cognite to pushthese metrics natively to CDF Time Series, unlike our custom extractors.ImpactWithout native CDF Time Series push, we can't monitor these extractors the same way as our custom ones (no unified dashboarding/alerting in CDF/Grafana). We want to keep CDF as the single source of truth for observability, not add a separate tool just to collect metrics.RequestCould you add support for a metrics.cognite configuration block (or equivalent) in thePI Extractor and DB Extractor, consistent with the one already available viacognite-extractor-utils, so their internal Prometheus metrics can be pushedperiodically to CDF Time Series (external-id prefix, target data set, push interval)?
Today it is possible to specify the unit of a property of a View, enabling unit conversion on consumption (via the API). However, the CDF UI is currently not showing this unit. It means that we either have to create a dedicated “property_UOM”, or we have to add the unit into the @name in the View description. It would be preferable if the UI could show the unit, and also support the unit conversion.
The closest to that solution as for now would be adding a timeseries on the canvas, then you have an option to Open in charts and then choose a new one or existing one. This is too cumbersome.See picture below of suggested solution directly in canvas:
When you add new assets or files in Canvas, the data is added to an area of the Canvas where there are no objects, and the focus moves to that location.While this is great in that it ensures the added data doesn’t interfere with the existing Canvas layout, the following sequence of events occurs every time, which can be cumbersome.① Add new data② The data is added to an area without any objects, and the screen focus automatically shifts there (since the location where the data is added is usually quite far from the main screen, the focus also shifts significantly away from the main screen)③ Move the added data to where you actually want to place it (because the focus from step ② is significantly away from the main screen, you have to move the data a long way to position it, which is quite a hassle)Therefore, is there a way to add data to a location that makes step ③ as easy as possible for the user?
When creating a pdf export of a Canvas it appears that hidden layers in the original document become visible after the export process. I have an example of a “Preliminary” watermark, not visible in Canvas becoming visible in the export. Can the pdf export be limited to the visible layer only.
Currently data modelling in CDF only support “soft deletes”, which in day to day operations works well and support the necessary sync features. However we have scenarios where we need to delete vast amounts of instances, ideally in a very short timeframe.During testing we sometimes need to delete and recreate a data model We are currently migrating data models and have two living in parallel for a time, eventually we will delete the old one, which has more than 50 million instances, with the current limit of 10 million soft deletes this would 15 days due to soft delete retention period. Cleanup of old test data Decommissioning of data models (or oil rigs for that matter)This is an ongoing issue in one of test environments where we have a need to do a full wipe of all instances within the CDF project.The feature I am looking for is a way to skip the soft delete default, and simply do a hard delete right away. In the scenarios I’ve listed the features of soft deletes are not needed as we do planned and coordinated complete deletes rather than a regular update from a source system. This is the first time we are facing this issue, but we see that this will be a recurring problem in the time to come for us. Markus PettersenAker BP - Data Platform Architect
Views that extends CogniteActivity can be included as an object in Charts, shown as a colored overlay in the timeseries. However, it seems it is a forced filter on assets that are included in the Chart. I can remove the default startTime and endTime filter, but not the asset filter. It is a very likely scenario that I want to add a notification or workorder for an asset that doesn’t contain the timeseries I’m interested in(sensor is upstream). It also might be the case that the asset with the notification/workorder do not have a timeseries at all (it resides further down in the hierarchy).It can be helpful to add a preconfigured asset filter based on the assets indirectly included via the timeseries, but it is an absolute requirement that I can remove that filter. Right now, if the asset with the notification do not have any timeseries I have not found a workaround allowing me to add the notifications I want.
The “Scheduled” feature, currently in beta, has proven to be user-friendly and well-received.I believe this feature is currently available only to users with both “Checklist admin” and “Template admin” roles, but would it be possible to make it available exclusively to “Checklist admins”?In our company, we assign permissions based on the following roles, and it is often the people who actually perform the inspections (i.e., “Checklist admins”) who issue checklists from the “Scheduled” tab.Therefore, making this feature available to “Checklist admins” as well would make it even easier to use.Template admin → People who edit templates. Only a few people.Checklist admin → People who actually perform inspections. All InField users.This may differ from the original design intent of this feature, but I would appreciate it if you could consider this as one suggestion.
Currently, when attempting to schedule certain functions—such as moving averages—the results may differ from the normal calculation results verified before the scheduled execution.Additionally, the results of scheduled executions vary depending on the execution cycle.A sample is shown in the screenshot.Although I haven’t been able to verify everything, I suspect that similar issues may occur with other functions that set the window size.Users typically assume that scheduled calculations are performing correctly without being aware of these discrepancies, and as things stand now, there is a risk that they may rely on incorrect results. Therefore, I expect that the correct calculation results will be recorded regardless of the schedule settings.
In Charts, you currently have to set up monitoring jobs on an individual basis. I’d like the ability to set up a single monitoring job and have it available to apply to multiple charts.
Today there is limited to no API access to Charts, creating several issues.One obvious issue is that we cannot automate the setup of Charts. Eg, when an anomaly detection method detects an issue, we would like to be able to auto-create the Charts with the necessary timeseries, timerange, calculations and overlay anomaly insight (CogniteActivity extension). This is not possible today (only pre-build url with instances and range).But another issue is around the maintainability of Charts. Due to the limit on the number or Views/revisions, we need to deprecate and delete old revisions on a regular basis (in particular in periods with heavy data model development). As far as I have seen, Atlas, Canvas and Chart lock the View version to the latest version (or maybe based on the location filter) during config. For Atlas agents and Canvas’ we can find the View versions via the API, allowing us to identify users depending on models that are deprecated, and also auto-duplicate with the updated versions/views. However, with Charts this is not possible. If Charts is supposed to only be a do-my-analysis-and-then-throw-it-away tool, that is ok, but if it is intended to be used for more long term analysis (it does offer functionality for this like calculations and threshold monitoring) we need a way to monitor status and update the views/instances in the Charts on an enterprise level (via API)
This might already be reported, but I was not able to find it when I searched.Currently Chart only uses an internal, and very limited, unit conversion. It would be a big step forward if we could also use the native unit conversion in timeseries, which is much richer than what is offered via Charts.Since you might have timeseries with no CogniteUnit, Charts still needs to support unit conversion defined in the UI, as a backup solution
When calculating daily totals or averages using the Resample function, the calculation seems to be performed based on the 00:00 – 24:00 UTC timeframe. I would like the system to account for time zone differences so that the totals or averages are calculated based on the 00:00 – 24:00 period in Japan Standard Time (UTC+9).
Do we have a feature in the roadmap to allow for showing annotation to annotation lines (manually added in blue) vs. today just having annotations to file? (I realize there are some DM limitations) See below. The doc to doc annotations (data) and visualization (screenshots) are currently going from a bounding box around the text in the arrow (source doc) to the document (target doc). The line target destination is the center of the document.We have the reverse connection from the bounding box to the center of the doc.What we need is a connection between the bounding box around the text in one document to the corresponding bounding box in the other document.Example: on the left attachment,The left document L12... 74A-1296 annotation goes to the document 1296 on the right. But it attaches to the center of the doc.And R11 ... -74A-1295 on the right connect to the document 1295, connects to the center of the doc.These 2 connections are in orange.The requirement is to have only one connection L12... 74A-1296 to R11 ... -74A-1295 (represented with the big blue line, done manually to show you)
When using the "scheduled calculation" function in Charts, there is a situation where you can select either "CDF sign-in credentials" or "CDF Client ID and Client secret" as the authority to perform the calculation. Of these, "CDF Client ID and Client secret" is supposed to be selected in order to perform "scheduled calculation" stably, but general users do not know "CDF Client ID and Client secret", so they cannot use it casually.Therefore, I would like to be able to perform "scheduled calculation" stably even if "CDF sign-in credentials" is used. In that case, there is no situation where you want to use "CDF Client ID and Client secret", so you don't need to select this permission in the first place, and I would like you to be able to perform all "scheduled calculation" with "CDF sign-in credentials".
Hello,We wanted to check if we can have transaction capabilities when inserting or deleting data in data model in Cognite using SDK.Right now we have requirement to insert or delete into multiple views corresponding to single operation on UI Application, so either operation should be marked as done completely and data should be affected in all views or operation should be failed and data should not be affected partially. So either whole transaction is committed or whole is rollbacked.If it is already available can you share the documentation link.If not available can we take this as requirement. Please share ETA around the same.
For upcoming work we need the ability to define a subscope of a data model (example: A data model covers all aspects of a house, the subscope is the bathroom that is up for a makeover). I suggest this to be implemented as a new base class like “Cognite_versionable”. This needs to encompass both the values stored in the model, but also the structure of the model.Again - the bathroom example:Version 1: The same structure as the original bathroom just different specs (values) like for replacing stuff with better products.Version 2: Replace the shower with a bathtub and add an extra sinkIn V2 above this also means a different structure of elements linked to the bathroom.
Dears, How can we have the 3D Model pushed into a power BI dashboard with the annotation as well, since the one we had is like a static 3D and it says that the annotation cannot be extracted along with it.is there a way to have it annotated in a dashboard? Thank you?Fatima
I would like to suggest upgrading the Large Language Model (LLM) used by Canvas AI.Currently, the model appears to struggle with understanding technical documents and often provides incomplete or inaccurate answers to relatively straightforward questions. It also has limited ability to interpret visual content such as graphs, charts, and engineering plots, which are common in technical documentation.An improved LLM with stronger reasoning and multimodal capabilities would significantly enhance the user experience by enabling it to:Provide more accurate and context-aware answers. Better understand engineering and technical terminology. Interpret graphs, charts, tables, and other visual elements within documents. Generate more comprehensive and reliable document summaries. Handle complex documents while maintaining context across multiple pages.These improvements would make Canvas AI much more valuable for users working with engineering reports, operational documents, and technical studies, where understanding both text and visual information is essential.
As a CDF Platform Administrator,I want the ability to configure the PI Extractor to include ptsecurity and datasecurity attributes in the extracted metadata,So that I can use these source-level access control lists (ACLs) to automate and enforce data access security for groups of users in Cognite Data Fusion. Configuration OptionsGIVEN a standard PI Extractor installation,WHEN looking at the extractor configuration file (e.g., config.yml),THEN there must be a new, optional configuration parameter (e.g., include-internal-security-metadata: true/false or an override setting).GIVEN the configuration parameter is omitted or set to false,WHEN the extractor runs,THEN it must continue to exclude ptsecurity and datasecurity metadata as it currently does (default secure behavior). Extraction BehaviorGIVEN the configuration parameter is set to true,WHEN the extractor performs discovery and extracts Time Series metadata from PI,THEN the ptsecurity and datasecurity attributes must be successfully captured and populated as key-value pairs in the CDF Time Series metadata block.GIVEN the configuration parameter is set to true,WHEN a source PI point has empty or null values for these security fields,THEN the extractor should gracefully skip writing those specific keys to CDF metadata without throwing errors or halting the extraction process.
The comment feature in Canvas is really great and we are planning to use it for our shift handovers. We plan to add multiple comments but having to click the comment icon every time to read the content is quite tedious. Since we want the comments to be immediately visible as soon as we open Canvas, we would love to have a feature, like ”pin” comment content.
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