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There are instances where it's desirable to add or remove Access Control to a user created datasource. For instance, you may want to push data from Seeq Datalab to a Datasource that is only available to a sub-set of users. Below is a tip how to add additional entries to a Datasource Access Control List (ACL) using `spy.acl`. 1) Identify Datasource ID If not already known, retrieve the ID for the Datasource. This can be done via the Seeq REST API (accessible via the "hamburger" menu in the upper right corner of your browser window). This is different from the datasourceId identifier and will contain a series of characters as shown below. It can be retrieved via the `GET/datasources` endpoint under the `Datasources` section. The response body will contain the ID: 2) Identify User and/or Group ID We will also want to identify the User and/or Group ID. Below is an example of how to retrieve a Group ID via the `UserGroups` GET/usergroups endpoint: The response body will contain the ID: 3) Use `spy.acl()` to add group to the Datasource Access Control List (ACL) Launch a Seeq Datalab Project and create a new ipynb Notebook Assign the two IDs to a variable datasource_id='65BDFD4F-1FA3-4EB5-B21E-069FA5A772DF' group_id='19F1B7DC-69BE-4402-AD3B-DF89B1B9A1A4' (Optional) - Check the current Access Control List for the Datasource using `spy.acl.pull()` current_access_control_df=spy.acl.pull(datasource_id) current_access_control_df.iloc[0]['Access Control'] Add the group_id to the Datasource ACL using `spy.acl.push()` spy.acl.push(datasource_id, { 'ID': group_id, 'Read': True, 'Write': True, 'Manage': False }, replace=False) Note I've set `replace=False`, which means the group will be appended to the current access list. If the desire is to the replace the entire existing ACL list, this can be toggled to `replace=True`. Similarly, you can adjust the read/write/manage permissions to fit your need. For more information on `spy.acl.push`, reference the `spy.acl.ipynb` document located in the SPy Documentation folder or access the docstring by executing help(spy.acl.push) (Optional) - To confirm the correct ID has been added, re-pull the ACL via `spy.acl.pull()` current_access_control_df=spy.acl.pull(datasource_id) current_access_control_df.iloc[0]['Access Control']

I've got an excel monitoring tool that our operators will use daily. I'd like to be able to build a functionality within the excel tool for pulling data directly from SEEQ into the excel tool. Ideally, I could build some VBA query that works in the background to pull the data from SEEQ using the API, and our operators just click a button to run the VBA code that will query the SEEQ API. Is there a way to do this? To pull data directly from SEEQ without leaving the EXCEL environment?

When creating Seeq signals using Seeq Data Lab(SDL) it can be useful to know how to delete signals from Seeq after pushing the signal to a workbench using a Spy push. Fortunately, the process is as simple as adding a column to your metadata dataframe called 'Archived' and setting the value to 'True' for any signal(s) you would like to archive. Below is a snippet of code where an 'Archived' column is added to the spy.push example notebook metadata dataframe is the SDL Spy Documentation. The DEPTH(ft) signal will be archived after pushing the new metadata dataframe with the archived column set to 'True'. A couple other notes about deleting signals in Seeq: If you would like to keep the signal in Seeq, but want to update the data, you can do so with a subsequent push of the signal that includes the new data. The caveat is the new data must have the same keys (timestamps) as the old data. If the keys are different, the data will be appended to the existing signal. Otherwise, you will need to push the signal with a unique name/path/type. If you would like to fully delete the signal samples from Seeq, you can do so using Seeq's API call to delete the signal samples.

We often get asked how to use the various API endpoints via the python SDK so I thought it would be helpful to write a guide on how to use the API/SDK in Seeq Data Lab. As some background, Seeq is built on a REST API that enables all the interactions in the software. Whenever you are trending data, using a Tool, creating an Organizer Topic, or any of the other various things you can do in the Seeq software, the software is making API calls to perform the tasks you are asking for. From Seeq Data Lab, you can use the python SDK to interact with the API endpoints in the same way as users do in the interface, but through a coding environment. Whenever users want to use the python SDK to interact with API endpoints, I recommend opening the API Reference via the hamburger menu in the upper right hand corner of Seeq: This will open a page that will show you all the different sections of the API with various operations beneath them. For some orientation, there are blue GET operations, green POST operations, and red DELETE operations. Although these may be obvious, the GET operations are used to retrieve information from Seeq, but are not making any changes - for instance, you may want to know what the dependencies of a Formula are so you might GET the item's dependencies with GET/items/{id}/dependencies. The POST operations are used to create or change something in Seeq - as an example, you may create a new workbook with the POST/workbooks endpoint. And finally, the DELETE operations are used to archive something in Seeq - for instance, deleting a user would use the DELETE/users/{id} endpoint. Each operation endpoint has model example values for the inputs or outputs in yellow boxes, along with any required or optional parameters that need to be filled in and then a "Try it out!" button to execute the operation. For example, if I wanted to get the item information for the item with the ID "95644F20-BD68-4DFC-9C15-E4E1D262369C" (if you don't know where to get the ID, you can either use spy.search in python or use Item Properties: https://seeq.atlassian.net/wiki/spaces/KB/pages/141623511/Item+Properties) , I could do the following: Using the API Reference provides a nice easy way to see what the inputs are and what format they have to be in. As an example, if I wanted to post a new property to an item, you can see that there is a very specific syntax format required as specified in the Model on the right hand side below: I typically recommend testing your syntax and operation in the API Reference to ensure that it has the effect that you are hoping to achieve with your script before moving into python to program that function. How do I code the API Reference operations into Python? Once you know what API endpoint you want to use and the format for the inputs, you can move into python to code that using the python SDK. The python SDK comes with the seeq package that is loaded by default in Seeq Data Lab or can be installed for your Seeq version from pypi if not using Seeq Data Lab (see https://pypi.org/project/seeq/). Therefore, to import the sdk, you can simply do the following command: from seeq import sdk Once you've done that, you will see that if you start typing sdk. and hit "tab" after the period, it will show you all the possible commands underneath the SDK. Generally the first thing you are looking for is the ones that end in "Api" and there should be one for each section observed in the API Reference that we will need to login to using "spy.client". If I want to use the Items API, then I would first want to login using the following command: items_api = sdk.ItemsApi(spy.client) Using the same trick as mentioned above with "tab" after "items_api." will provide a list of the possible functions that can be performed on the ItemsApi: While the python functions don't have the exact same names as the operations in the API Reference, it should hopefully be clear which python function corresponds to the API endpoint. For example, if I want to get the item information, I would use "get_item_and_all_properties". Similar to the "tab" trick mentioned above, you can use "shift+tab" with any function to get the Documentation for that function: Opening the documentation fully with the "^" icon shows that this function has two possible parameters, id and callback where the callback is optional, but the id is required, similar to what we saw in the API Reference above. Therefore, in order to execute this command in python, I can simply add the ID parameter (as a string as denoted by "str" in the documentation) by using the following command: items_api.get_item_and_all_properties(id='95644F20-BD68-4DFC-9C15-E4E1D262369C') In this case, because I executed a "GET" function, I return all the information about the item that I requested: This same approach can be used for any of the API endpoints that you desire to work with. How do I use the information output from the API endpoint? Oftentimes, GET endpoints are used to retrieve a piece of information to use it in another function later on. From the previous example, maybe you want to retrieve the value for the "name" of the item. In this case, all you have to do is save the output as a variable, change it to a dictionary, and then request the item you desire. For example, first save the output as a variable, in this case, we'll call that "item": item = items_api.get_item_and_all_properties(id='95644F20-BD68-4DFC-9C15-E4E1D262369C') Then convert the output "item" into a dictionary and request whatever key you would like: item.to_dict()['name']

Hello, this topic came up during a customer training. The presentation of the individual assets in the Treemap view looks strange at first glance. In the screenshot, the assets "Area J" and "Area K" are displayed as long rectangles, although all assets have the same number of sub-elements: When the size of the display pane is changed it looks better: According to your KB entry the size of the rectangles is determined by its number of assets and may be changed by the API. Is there an example for this? Regards, Thorsten