WaterGenGodot VS NumPy

waterways

godot 3.4, the water shader came with a river builder addon.

You can try the godot 4.0 branch of the Waterways Add-on https://github.com/Arnklit/Waterways/tree/godot4_0 Should have all the features you need, or at least a good starting point to implement your own. I don't know if the godot 4.0 conversion is fully done or not, but it does run in 4.0

How about adding a water level with the waterways addon and rubber ducks for physics testing and meme purposes?

I wonder if this plugin could be ported to Godot 4

Really amazing! Regarding the river: Have you seen the Waterways Add-On by /u/Arnklit? I didn't try it myself (only got aware of it from his posts here), but looking at the demo it seems to be a perfect solution for your water problem.

You can look through the code here: https://github.com/Arnklit/WaterGenGodot

Well not all these changes aren't in my repo yet, but they will be soon and you can look through the code. https://github.com/Arnklit/WaterGenGodot.

What I'm doing in this example is then setting the "Clarity" to 0 so the water is opaque and then setting the new albedo gradient and gradient depth like seen in the bottom of this thread: https://github.com/Arnklit/WaterGenGodot/issues/5

Well people can join my Patreon if they want to support me a bit. The tools I'm making for Godot are just free. https://github.com/Arnklit/WaterGenGodot

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]