 All right, so hi guys, this is Vinod Manamala and I'll be doing this demo with my colleagues Jose Levier-Hure and Shweta Kota. So we are really excited about this tip-corn OSTU collaboration and we are actually working towards this effort and we released a lot of offerings for the tip-corn spotfire. So in this case, we have a complete set of tools for the tip-corn spotfire, which handles everything from authentication and to data handling, to data visualization against this OSTU platform. And for the detailed offerings and how to download most of the offerings and make them work in a spotfire client, you can just follow this link and this link to get most of the details regarding this. The first offering that we are excited about is the custom OSTU connector for spotfire. So the custom connector here allows the user to authenticate interactively with the OSTU platform using the open ID and the OR 2.0. So what this connector does is actually it authenticates against the OSTU platform that you are working on and it brings back the service endpoint as a spotfire data table that as you can see here. So currently we support four cloud providers, GCP, Azure, AWS and IBM. The current release supports only the GCP, Azure and AWS and IBM would be part of the next release. And so if you need to understand more about this custom OSTU connector, you can click on this link here and this PPD will be sharing after this presentation. I'll be showing a demo, so which would be explaining how most of our offerings would be working within the spotfire client. The second offering would be a custom OSTU dashboard for the spotfire. So what this dashboard does is it makes use of all the service endpoint that you get from the OSTU connector and it allows you to create, you know, Python data functions. Python data function is a method for calling, you know, calling Python from within the spotfire client. So it allows the spotfire users to interactively call Python to the spotfire client to greatly enhance your visual experience and analytics within the spotfire. Now going back to the demo. So the first step is to download the custom visualization dashboard from the link that I just showed you. And once you are downloaded to your local machine, you can just open your spotfire client and choose to open the dashboard. And the first step would be to authenticate as you can see that this is the OSTU custom OSTU spotfire connector. And like I said, we currently support GCP Azure and AWS and the next version would be also supporting IBM. For this demo purpose, we would be just authenticating against Amazon like AWS. And as you can see that here within the setup, we have the, you know, luxury to add all the service endpoints and authentication details that you need to be. Can you hear me? Yeah. We see the slide presentation. We don't see the actual. Oh, okay. Yeah. Can you see this now? No, yes. Okay, perfect. Sorry guys. So yeah, so once you open the custom dashboard, the first step would be like the dashboard would prompt you to authenticate against the cloud vendor that you want to work with. So as you can see here that we currently support GCP Azure and AWS and going forward with the next release we'd be supporting IBM. So when you click on the setup, you know, you can great to see the service endpoints that you need to add as part of the authentication process. And these are the endpoints that we'd be working with today. So once you click on sign in with AWS, it would actually take you to the browser for authentication purposes. And once you click sign in, it'll do all the authentication. And once the authentication is done, it will bring you back to the dashboard. So this custom dashboard is already prebuilt with tables and images and graphs. So this is just an example. But this can be made into a much more bigger dashboard based on user requirements. This is just for demoing purposes. So this is just an example. So the first step here is more like a step-by-step process to access the data. And if you just follow these steps, you'd be able to understand what is happening within this dashboard. Before we get into the actual demo within the dashboard, let's talk about the Python data function. So the Python data function, like I said, it's more of a method for calling languages such as Python, R, and we even support MATLAB. And like I said, this allows the Spotify users to interactively call Python code from within the Spotify client. And you can do much more. In this case, like we are bringing information regarding the VEL, VELBOR, and we are getting much more deeper into the work product components like VEL logs and market data and LAS files and so on. So a simple Python data function would look something like this. So you would have your entire script written here. And you would provide your inputs. In this case, the inputs are from the table that we generated from the OSDU custom Portable Connector. And we are bringing those authentication information and service endpoints into the Python data function. And then we are outputting the results from the script as in DataFrame. And this DataFrame would show up in your Spotify dashboard as in Spotify table. So just so you know, the results from the OSDU Spotify Connector would actually show up as in table with all the service endpoints. So in this case, we are working with AWS, so the provider would be AWS. Then you have your service that you would be using as part of the API calls in the Python data function. And these are your service endpoints and your data partition. And then you would have your token information, things like access token and refresh token. So now let's get into the demo itself. So they're like the first thing is to talk about the refresh token, right? So we know that we're bringing back a refresh token as part of the authentication process. And you could click this to refresh your token in case your session gets expired. And so next is to follow step by step procedure to extract the information. And in this case, we are working with the TNO dataset. So all this information that we bring back as part of the TNO dataset. So the first step is to zoom in and zoom out. So this is a custom map visualization that we built for this OSDU testing. So here we can just zoom in and zoom out to set up boundaries. So this would be a boundary. And in this case, we are trying to bring back all the wells within this boundary in the map region. So once you zoom in and zoom out, you can click here. So what happens is it will bring back all the wells within this boundary. And as you can see that I already preloaded this map with the well information from the TNO dataset. And the second step would be to choose the number of wells or set of wells that you want to work with. Once you highlight that, what happens is another set of data function is invoked. And as you can see that the highlighted maps are focused in this map selection. And you can see the selected wells and wells in a matter of time. So the performance of this dashboard depends on the volume of data that you're going to work with. So the more number of data, it's going to take a little more time to load the information regarding that wells and wells. So as you can see that I brought back a number of wells. And you can see the coordinates and you can see the well ID and well bore ID. So the next step would be to filter the wells. So in this case, I'm taking this table. I'm just creating a filter. So let's say that I want to bring back all the work product component regarding these wells. So I can just click here. And once I click here, you can see that I'm already populating this table with all the well bores and the datasets that are associated with those well ID and well bore IDs. So the third step or the final step would be to just choose one of those well bore ID and their corresponding files. So once you click on them, you can see that my visualizations here would automatically update with those information from the data from the files like the trajectory and the marker files and the well log files. Just so you know that there's another third component called well log mods. So the well log mods are the third offering and my colleague right now, the colleague Jose will be talking more about the mods. This is an inbuilt visualization within the spot where these visualizations will be replaced by the mods. And Jose will be talking more about the mods after this demo. So the the second page is about the volume dashboard. So we we worked with devices from the strategy from Catalyst. We took their requirements and we built this volume dashboard to just to start the, you know, every version of. It's gone quiet again. Oh, sorry. So you're bringing back. Starting the master data and the work product components. And as you can see that you're bringing different kinds of master data within the data set. And we're also bringing back all the different kinds of work product component within the work product component data. So once you bring back all this data, you can then slice and dice and also you can drill down into, you know, my new levels of details to see what's happening with your, you know, osg environment. In this case, let's say that I want to drill in to see like what what this user has been doing and what kind of files he has uploaded and what he is working on. You can just click on or, you know, slice it by this username. And you can also like say you want to see what data is associated with Norway country. You can see that too. And you can also like slice and dice with the organization. And this is a test data set. We don't have much information, but you could make it much more intuitive once we have like, you know, proper data set. And we can also look into the digital format that's been uploaded. For example, in this case, we're looking at, you know, the PDF that have been uploaded and you can also see the size of the PDF that have been uploaded and when they have been uploaded. And let's get back to the presentation. There we go. Yeah. Okay. So I'm going to take it from there. My name is Jose le via here from tipco. You don't know how to pronounce my name. You just call me Jose, or Joe, if it's that easier. So what what's spotfire mods spotfire mod is a framework to extend spotfire capabilities, in particular for creating custom visualizations that look and feel like native spotfire charts. Not concepts comes from the gaming community short form is short for modification. And it's the process of altering one or more more aspect of a video game to feed specific needs. In the spotfire context, developers can create sophisticated visualization that can be used like native visualizations with no code required by the user. Simply to use by consumers. It's easy for programmers to code or develop. And they are secure because they, they have a trust mechanisms and digital signatures and they also run in a secure environment. So with that, let's go to the next slide because I want to go into how you can download spot farm out if you have an equation just feel free to put them. So spot for much like any other visualization here I'm use using these well lock mod by just dragging and dropping the visualization right there. And are available since version 11. So let's go to the next slide so I can show you how how to get mods or how to use more once you download the mod you just drag and drop into your file like you will do with a file or any other visualization and then you start configuring your, your, your mod or your visualization this in particular has specific configuration options so you can add tracks, unlimited number of tracks or unlimited numbers of curves in one track and then you can do the shading. And once you're finished with your configuration, it's going to look something like this. And we're going to jump into the demo you can see the tooltip for each line. It interacts just like any other visualization you just drag select the marking everything comes for free when when when it comes to coding. And finally, you can save the mod into your library by clicking the three little dots and save it to library. That's how you, how you do it. So I'm going to share my screen now. I'm going to show you how to search for mods, use them, and then do a little demo. Okay. Are you able to see my screen. Okay, I hope. Yeah, perfect. Perfect. So I'm going to search for spotfire mods. And then I'm going to skip the ads and go to spotfire mods spotfire mods are available in the Tico exchange. And here you can see what you have. You have two minutes, I think. You have the interesting part. Then you click, click on one of them. And then you will see a link. Oops. My internet. There we go. Then you have here the you click on releases. Then you click on download the seed file. And then you'll look at the mods like this. I already show you how to how to use the mods. I'm going to drag and drop one just for your chart here. I need to have some data. And I go to my fly out and save to my library. And it's always to save your mods in into the mods folder. So everyone can use the mods, the mods in your organization. So for the well log mod, I'm going to give you a little bit of history. They will log it's open source. And it was supported in JSVs, which was the earlier version, JavaScript based version of that. And some customers created this. But this is the data is not pivoted. So you're locked into only a couple of tracks, only two curves in one track. And that's it. So what we did is change the data that looks like this into a pivoted data on pivoted data where you have one category, one value. And then on the well log, it's going to look like this where you can just add more, more, more logs. For example, I want to copy this track. I just duplicate and then I make my, my modifications as I need it. And yeah, it just interacts with, as you saw in the demo, interacts the songs that you're interested in and so forth. That's it. That's all I have. Oh, fantastic.