superset | Apache Superset is a Data Visualization and Data Exploration Platform | Analytics library

In TypeScript, methods are always checked bivariantly, meaning that you are allowed to widen the types of their parameters, which is safe, or to narrow the types of their parameters, which is unsafe, as shown here:

Without EIDs to check, it is tough to point to the precise cause. However, I'm 99% certain that your problem are missing abstracts in the .description property. It's sufficient when the first call is empty, because it will turn the column type into float, to which you wish to append a string. That's what the error says.

Thus your problem has nothing to do with pybliometrics or Scopus, but with the way you bild the code.

Try this instead:

I had some trouble with the Superset API myself, mostly because I did not handle the CSRF Token correctly:

It seems to be important that the retrieval of the JWT Token, the CSRF Token and the actual request happen in the same session.

If I don't do that, I can reproduce your error and are also sent to the login page (also you use a GET request in this example, but it should be POST).

Here an example from my local test-setup:

In relation to your question,

In the context of UML that is the official definition that you cited.

With your statement. Your statement 4 is correct but as mentioned by Jim the cardinality shouldn't be restricted.

You can have 1 or more leaders of a club along with 1 or more leaders for an individual club.

In addition, subsets help define inheritance. For example, in your diagram if Person is a subset of Club, and they are a leader of the club then automatically they are a member.

It can get a bit nit-picky with logic as in the diagram, the subset does not only inherit from the parent class (Club) but also has its own properties (Person) and at the same time points back to the parent.

Hopefully the links below, outline what I am trying to say.

Some useful links for context:

• What subset constraint means in the UML class diagram

• https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.5537&rep=rep1&type=pdf

• https://newbedev.com/how-to-use-subsetted-property-in-uml

The function you want in your head-scratching paragraph is possible directly -- though a bit noisily. With GADTs and DataKinds, values can be parameterized by numbers. You won't be able to use lists directly, because they don't mention their length in their type, but a straightforward variant that does works great. Here's how it looks.

Embedding dashboards & charts for Preset Cloud is still in active development! Getting the security and compliance of this correctly is no small feat.

The Preset team is hoping to have a v1 in a few months, stay tuned!

There are two components to get user impersonation working with Trino and Superset:

1. A version of Superset that supports user impersonation with Trino.

This was added officially in 1.3.0, and since you're on 1.3.2 that shouldn't be a problem.

1. A Trino client that supports user impersonation.

AFAIK the only Python client that currently works with Superset to connect to Trino is sqlalchemy-trino. I couldn't find any specific changes made for user impersonation until 0.4.0, but I have gotten this working with the older 0.3.0 version.

There may be some other possibilities that could prevent user impersonation from working, but less likely:

1. Make sure that all containers have a working version of sqlalchemy-trino installed. This depends on how you add Python requirements, but I believe I've seen cases where Superset containers don't have the same dependencies, i.e. the superset_app container has the correct module, but not the superset_worker container.

2. Make sure that the HTTP headers in the requests going to Trino are not being modified. User impersonation works by authenticating with basic authentication but impersonating the user added in a HTTP header called 'X-Trino-User'. If the HTTP header is removed or changed, then the user impersonation won't work as expected.

You shouldn't execute dockerd in your user data. This starts the docker daemon and freezes further executions. Instead it should be:

Read the 2 files into Dataframes and:

1. get the list of keys (columns) of the the first dataframe
2. do some transformations on the second dataframe which contains the data, by splitting the values first by , then second by : using combination of transform and map_from_entries functions to convert each row into a map column
3. finally using list comprehension on the list of keys select the columns and fillna to replace nulls by $: