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Hi All,I am trying to setup FDM on Grafana. Can somebody provide document for same?Thanks you.

I have loaded a 3D model but in the documentation, I only see a manual way to contextualize the 3D model (link an object in the model to an asset). I converted our model to NWD, imported and found the name of the asset appears in cognite.client.data_classes.three_d.ThreeDNode. So I pulled out the API and contextualized the 3D model with python. My question: is an automated method to contextualizing the model not provided? If it is not provided, does anyone care to share how they are contextualizing the model.

When Grafana Reports are scheduled and if aggregation is set to anything but None in Cognite Data Fusion query, the emailed report shows no data.When None is used in aggregation, the emailed report from Grafana shows data properly.

I am learning GrpahQL -----------list queryquery {  listMonitoring(    sort: { regenerationTime: DESC }    filter: { and: [ { cycleStartTime: { gte: "2024-01-01T00:00:00+00:00" } }                     { cycleEndTime: { lt: "2024-02-01T00:00:00+00:00" } }                     { unitName: { eq: "UNIT-II" } } ] }    first: 1  ) {    items {      unitName      cycleStartTime      regenerationTime      timeUOM { description }    }  }}-----Aggregated queryquery {  aggregateMonitoring (     groupBy: [ unitName ]     filter: { and: [ { cycleStartTime: { gte: "2024-01-01T00:00:00+00:00" } }                      { cycleEndTime: { lt: "2024-02-01T00:00:00+00:00" } }                     { unitName: { eq: "UNIT-II" } } ] } ) {     items {      count {         cycleNumber       }      sum {        idleTime        cycleTime      }             group  }}}Is there a way to get related type field timeUOM { description } in the output

Hello:Please share some code example in python sdk to link a file to an asset.thanks

What happens to the container or indexes or during query for a field that has @reversedirectrelation.Kindly point to the documentation that explains what actually happens internally when a field has @reversedirectrelation.

Cognite Hub Community, as a member of the Cognite Partnerships Team, I am thrilled to share that Cognite has been awarded the Microsoft Partner of the Year in Energy & Resources! This is the third year in a row Microsoft has recognized Cognite as a global leader for its ability to deliver meaningful, scalable, and user-friendly industrial data solutions for our customers. We are looking forward to furthering this partnership, and the great work being done with all of our partners and customers, as we continue to make groundbreaking innovations with Generative AI and low-code digitalization solutions together.Full Cognite Press ReleaseMicrosoft Partner Blog Post 

When saving and scheduling a calculation from Charts, the enter credentials dialog hangs in the “checking credentials” and keeps trying to verify in a loop. This happens only if you select the Use CDF client ID and Client Secret option at the top. If you do not choose the top radio button and simply enter the creds and click next, it works ok. 

I have solved the BufferGeometry issue was present earlier, apparently the react-app-wired was not the latest which is why the override was not working.import React, { useEffect } from "react"import { Cognite3DViewer } from "@cognite/reveal"import { CogniteClient, CogniteAuthentication } from "@cognite/sdk"import { getToken } from "../../utils/MsGraphApiCall"import "./Models.css"function Models() { const appId = "KPI-Dev" const project = "celanese-dev" const clientId = "my-client-d" const tenantId = "my-tenant-id" const cluster = "az-eastus-1" const modelId = 7575155737800092 const revisionId = 3624517118008353 async function start() { await client.authenticate(clientId, tenantId, cluster) const viewer = new Cognite3DViewer({ sdk: client, domElement: document.querySelector("#your-element-for-viewer"), }) viewer.addModel({ modelId: modelId, revisionId: revisionId }) } const legacyInstance = new CogniteAuthentication({ project, }) // getToken() const

We are proud to announce the publication of our latest research on the application of Topological Data Analysis (TDA) for Condition-Based Monitoring (CBM) of wind turbines. This new study will be presented next week at an international conference focused on equipment health and prognostics in Prague https://phm-europe.org/.Abstract: Our research investigates how TDA, a sophisticated branch of data analysis, can enhance the monitoring and maintenance of wind turbines. By analyzing complex datasets obtained from standard vibration sensors in turbine gearboxes, we identify patterns, anomalies, and trends that are often undetectable using traditional methods.Key Highlights:Data Source: gearbox vibration data, collected from a wind park in Norway, data contextualised in CDF. Methodology: Conversion of time series data into multi-dimensional point clouds through time-delay embedding Analysis Tools: Utilization of topological methods, including persistent homology Indicators: Key health indic

I am using the Python SDK endpoint to aggregate instances and passing an advanced Filter to the filter argument, i.e.:client.data_modeling.instances.aggregate(…, filter = Filter(...))But Nested filtering is not supported. It seems like the aggregate function only works on flat tables.So I am considering to create a solution data model with only flat tables. I am considering to split up the data over multiple containers in order to support the number of indexes I would like to have. My question is whether splitting up over multiple containers negatively affect performance at query time and how does this compare to trying to limit the number of indexes instead? 

hello:I want to associate files uploaded to CDF type pdf for example to my model, following this scheme:type Documentation { name: String # This is a CDF built in type file: File # this is the INVERSE relationship of the Pump.documentation # Pump.documentation <-> Documentation.relatedPumps relatedPumps: [Pump] @relation( direction: INWARDS, type: { externalId: "Pump.documentation", space: "Space_PabloPalacios" } )}but when I select the uploaded file it only shows me jpg, any ideas?

On behalf of Celanese, I would like to report an issue we encountered with the SDK Data Modeling instances aggregate, where the filter format changed from yesterday, 06/11, to today, 06/12. Until yesterday, the "equals" filter worked, but today it broke all the calls that used it and had to be modified to the "in" format, which did not work correctly until yesterday.Another bug to report in the aggregate call is that when an array of group bys is sent, the return structure has also changed, no longer receiving a structure with externalId and space, but a string concatenating the two in the format: "space:externalId”, and its nomenclature changed from "GroupByName" to "GroupByName.__direct_relation". 

How to PAN the 3D Model left, right, up, down

Hi! I’m trying to create a Power BI Dataflow using CDF timeseries data. The same data is retrieved absolutely fine when using Power BI Desktop and Power Query inside the Power BI Desktop app, but when I try to use exactly the same query as a Dataflow instead, there is a warning about: “The query “Timeseries” contains columns with complex types that cannot be loaded. Some data is retrieved, but it’s incomplete with big parts of it missing. Looks like the CDF connector is not able to retrieve the data correctly when a Dataflow is used instead of a normal Power BI Desktop dataset. Is there a way to make Dataflow also compatible with the CDF connector?  

I want to urge the developers to write a documentation that tells us how to use the available functionality.Specifically I am looking at the filtering functionality. Let me explain my use case: I need to query CDF for a lot of time series. The unfiltered query returns a TimeSeriesList of 32k TS. I only need a subset of these, and want to perform the filtering on the CDF side. I want to fetch the subset of TSs that contains any of the following strings in the name attribute (yes, the dashes and underscores included)-PT- -PI- -PIC- -PIT-Can someone please explain how to do this? Search does not match the dashes and underscores, so I think it performs a fuzzy search. But it is not explained in the docs.ContainsAny appears to only work when the property of interest is an array. So I cannot use this filter to check if a string property contains any of a list of search strings. But this limitation is drastically undercommunicated in the docs. After reading the docs several times it looks lik

I am facing slow response issue while working with Jupyter Notebooks. Also, when I restart the kernel it takes almost 5-7 minutes to get it started.Is there any standard method to overcome this?   

Hello team,We are trying to use the data modeling query instances SDK method.client.data_modeling.instances.query(query) our query is:query { "with": { "Min_15_50ts": { "nodes": { "filter": { "and": [ { "hasData": [ { "type": "view", "space": "QA_Test_Space", "externalId": "Min_15_50ts", "version": "3a62f20104e445" } ] }, { "in": { "property": [ "QA_Test_Space", "Min_15_50ts/3a62f20104e445", "entity" ],

The camera control commands seem to change over time and I couldn't identify why that happens in our app, though I was able to see a similar behavior in CDF 3D Scenes.The default control  when dragging with mouse's left button is to describe a rotation of the camera, but keeping the focus point in place (Gif 1). Sometimes, however, the same mouse movement causes the camera position to change describing an arc-like movement (Gif 2). While I think that “static” mode is using the “Orbit” control, the “arc” mode does not look like the other option, “Fly” control, because on the Fly control the camera position (shown at the top-right) does not change, but in the “arc” mode it does.When I click the "Home" button, a red focal dot in the center of the screen vanishes and it goes to the “arc” mode. When I click the "Fit View" button, the red dot reappears and camera commands are back to the “static” mode. There is nothing particularly wrong with either “static” or “arc”, but I'd like to keep th

Can someone please suggest, how we can add additional python libraries in CDF jupyter notebook?

The information I found here is contradictory: Cognite HubSpecifically, I am interested in Aker BioMarine’s tag data on CDF (example shown below). I did not check the documentation, since I prefer to hear it here, “straight from the horse’s mouth”, if you will.Questions:Is the same time zone used for all of our assets, regardless of location of the geographic location of our manufacturing site? Is the time stamp preserved for time series downloaded using, e.g., Python / Jupyter notebook? Anything else I should be aware of?Thank you very much!

We need to understand if Cognite endpoints offer a way to do few shot learning (custom prompt)

I am unable to generate client code for the CDF openapi spec at https://api-docs.cognite.com/20230101/ using deepmap/oapi-codegen for Go, seemingly because the spec contains some errors. Do anyone know of a way to generate an api-client in go for the CDF-api spec? Both commands below rely on having openapi spec downloaded to the current directory from https://api-docs.cognite.com/20230101/ Testing the spec for errors with redocly/cli: docker run --rm -it -v $PWD/swagger.json:/swagger.json redocly/cli lint /swagger.jsonFor me this results in “Validation failed with 25 errors and 382 warnings” Generating the client library using deepmap/oapi-codegen: go run github.com/deepmap/oapi-codegen/v2/cmd/oapi-codegen@v2.0.0 --config oapi-codegen-config.yaml swagger.jsonwith the this config file:package: "cdf_api"output: "cdf_api.gen.go"generate: models: true client: truecompatibility: circular-reference-limit: 100For me this results in the message “error generating code: error creating operat

Hello Cognite Community,We are thrilled to invite you to join our Early Adopter Program focused on exploring what CDF usage metrics can help you evaluate the value of Cognite Data Fusion (CDF) for your operations.Why Join?Uncover Key Metrics: Help us identify the most impactful usage metrics that demonstrate CDF's value for your company. Drive Improvement: Your feedback will guide us in refining how we measure and communicate the benefits of CDF. Exclusive Engagement: Be among the first to provide insights and shape the future of CDF usage analytics.How to Participate:Comment Below: Share your experiences and thoughts on which metrics could best capture CDF's value for your operations. Direct Message Us: Prefer a more private discussion? Send us a direct message to discuss your usage metrics in detail. Like This Post: If you find this relevant, please like this post to show your interest, and we’ll send you an invite to join the program.By participating, you’ll play a key role in enhan