eon | An open-source chart and map framework for realtime data | Chart library

In Result object with ID 385687 you have a property backdrop_path being null. Adjust your Result object and make the property nullable:

String? backdropPath;

First, the code you posted gave some errors so fixing them:

• replacing tk() with ~~Tk()~~ Toplevel() (see the comments)
• replacing '00c714' with '#00c714'
• removing parantheses here "login")

now it becomes "compile" time error-free. As for your question, 2 things need changing:

1. When we need to give a callback / command to a button, we give the function itself and not call it! IOW, command=main() will lead to calling main right away when program runs (without pressing to button). Instead, we should do command=main (note the lack of parantheses). Actually this is what is done here also: command=window1.destroy - we need to give the function itself and tkinter will call it with parantheses when button is pressed.

2. eone == "user" This compares the tkinter Entry widget directly with the string "user"! What you meant is through get method of entries: eone.get() == "user". Same goes for etwo too.

Overall, here is the code with these modifications (and some PEP-8 compliant formatting):

I solved this problem by downgrading the version of the Python extension to v2021.1.502429796. Hopefully this gets fixed at some point.

The async is just for asynchronously dispatching a task to a queue (running it as soon as the queue can). It is used to dispatch some block of code to another queue. For example, one might call it from the main thread it to dispatch computationally expensive code off to some background queue, to avoid blocking the main thread. Or, if you are already on a background queue, you use it to dispatch code that must run on the main thread back to the main queue (e.g., UI updates). You can also use asyncAfter if you want to specify when this dispatched task should run (e.g., after a specified time/delay).

The schedule is an API that largely serves the same purpose as async/asyncAfter, but was introduced with Combine in iOS 13. It just dispatches blocks of code to run on the specified queue, optionally with some delay (or other constraints). If you need to support older iOS versions before iOS 13, just use async/asyncAfter instead. But if you are supporting contemporary iOS versions (especially if you are using Combine), then you can use this API if you want.

The concurrentPerform serves a very different functional need, namely if you are for dispatching a block of code repeatedly and in parallel to as many worker threads as your device can support. It is often used when writing computationally intense and massively parallelized routines. It is uniquely well suited for solving those cases where you might otherwise have “thread explosion”. (The number of worker threads that can be used at any given moment at time is quite limited and if you exceed this, your app can deadlock if you accidentally “explode” how many threads you are trying to use at any moment in time.) So for example, if you want to run hundreds or thousands of iterations, in parallel, concurrentPerform automatically constrains the degree of concurrency to the capabilities of your device (e.g. if you have 8 cores in your device, it only runs a maximum of 8 concurrent tasks at any given time). Think of this as a for loop where the various iterations run in parallel with each other. But unless you are writing massively parallelized code, you might not need to ever use this. But when you are, it is extremely useful.

As @minatmerva put it in his comment:

Working on several nodes is what Massive Parallel Processing (MPP) is all about. Working on 2 nodes when the third is down is still MPP. Working on 1 node when the 2nd is down isn't MPP any more. So working MPP on 2 nodes is not foreseen at all.

With a small tweak to my answer to your previous question, still using GNU awk for FPAT and arrays of arrays:

Something like the below would work. To get a webelement of a[2] from an element and it's href.

I'd go for scraping, as the url itself gives you more control over what you're after. Also, you can easily get the tabular data with pandas.

For example: