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I am fairly certain that Python bytecode is undocumented. It's a messy place and it's a scary place. I'll offer an alternative at the end, but first.... why is it scary? First of all Python is interpreted to bytecode and that bytecode gets ran on a virtual machine. That virtual machine is definitely undocumented. You can take a look here at the opcode commit history. Notice that it changes... a lot. Beyond that you also have things like f-strings getting implemented which means the underlying C code is going to change. It's a very messy place because so many people are changing it.

Now, here is where my suggestion comes in. The reason that stuff is complicated is because many people are changing it. You daughter is 11 weeks, she ain't gonna be programming for at least another 3 weeks ;). So instead, why not make your own language? I recommend reading https://craftinginterpreters.com/contents.html. It's completely free and walks you through making an interpreted language in Java using AST followed by how to make a virtual machine with byte code and various chunk operations (just like Python has). It's a very easy to read book with good, thought-provoking questions at the end of chapters. You could make a completely customizable language that you ultimately control. Want to change an op code? Go for it. Want all users to be on the same playing field and guarantee backwards compatibility? It's your programming language, do whatever you want.

At the end of the day this is something that is going to be fun for you. And if you have to worry about opcodes being added or changed or overloaded, you're probably not going to be having fun. And when something eventually goes wrong you're going to have to debug your interpreted language, your JIT compiler and Python's source. That's just a headache in the making.

As @juanpa.arrivillaga pointed out in a comment, you can replicate the behavior using the built-in super() function, however you don't need to pass it any arguments (in Python 3 at least). Example:

There are multiples ways to reuse the same model with different content, based on the situation.

A simple solution can be passing some parameters to the popUp_model function.

Since you seem to only want to match exact words regex is way more expensive then just looking the words up. Assuming you only need to match whole words and not a complicated regular expression (e.g. numbers etc.) you can split the description into words and perform a lookup. If the words are saved in sets lookup will be O(1)

Code would look something like this

All the threads run to completion. That's the point of the lesson. When multiple threads access the same variable simultaneously, you can get unexpected results.

I've modified the code slightly to print less distracting stuff and also a thread id. I think this should help clarify what is happening:

Couple of important things:

• Python interpreter GIL, runs on a single-thread; so technically you arent really running things in parallel
• But the catch is, most I/O operations 'hog' resources while your CPU during these periods is still idle. Thats where libraries like asyncio comes to your rescue.
• They try to ensure minimal CPU-idle-time, by running other tasks in your queue while major I/O operations are awaiting their results

In your case, update_posts() doesnt really seem like an async method in an ideal sense; because this method is technically only used to figure out which posts are to be downloaded and written

And since we are already discussing about download and writing, you can notice that you can actually make them run as independent tasks so ensure minimal downtime.

Here is how I might approach this:

The error message tells you exactly what is going on. Some of your geometries are not valid, so you have to make them valid before doing your apply. The simple trick, which works in most of the cases is using buffer(0).

Scipy optimization functions are fallable to local minima. Change optimization method to something diferent, e.g. anderson, and get what you expect to.

Proof