godot_heightmap_plugin VS NumPy

Zylann's repo has a v4-compatible branch that was last updated 4 days ago, and there seems to be another one working on this, and another one published a few months ago, plus there are lots of videos out there about doing it in other tools and then just importing the heightmaps / splatmaps in Godot (e.g. from devmar's YT channel).

godot_heightmap_plugin

Back in 3.0 I was using this plugin for heightmap terrain: https://github.com/Zylann/godot_heightmap_plugin

source: https://github.com/Zylann/godot_heightmap_plugin/issues/279

You can create terrain and paint it with the zylann.hterrain plugin. Currently only supports Godot 3.x.

Sweet, how's the tooling to actually make a playable textured level from a heightmap though? In 3.0 I had to rely on Zylann's excellent plugin which was all well and good but I needed to compile it for each platform I wanted to ship on, and editor integration was good but not perfect. Also procedural generation of terrain in-game wasn't practical.

As we know, there's a lack of a node similar to Unreal's Landscape and Unity's Terrain. I believe this hurts Godot more than generally assumed. There is of course Zylann's plugin for 3.x, but I believe an officially supported node for this would be incredibly beneficial. Zylann's plugin will most likely get updated for 4.x eventually, but I personally know people who were shocked to find out that this functionality was not in Godot 4 when it had so many improvements for 3D - and it ended up being the deal breaker that pushed them away.

The one I know is Zylann.hterrain which is gdscript but also not updated for 4.x

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]