geogebra VS NumPy

https://github.com/geogebra/geogebra How can I obtain the source code for the Android platform and embed it into our own app with only the core layer code, DeskTop, and web platform source code? Thank you for your reply.

I don't think their codebases are particularly connected: AFAIK Desmos is a client-side browser app, so the software is distributed to the user for using in that way, which seems incompatible with the GPL3+ licensing of the GeoGebra codebase.

https://github.com/geogebra/geogebra

Edit: while learning derivatives you should definitely play around with a graphing calculator like https://www.geogebra.org/ . It will make understanding what's going on a lot more intuitive.

There are a lot of different cases, it's quite tricky. Here's the numerical algorithm in full https://github.com/geogebra/geogebra/blob/master/common/src/main/java/org/geogebra/common/kernel/algos/AlgoIntersectConics.java

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It's very simple and should stay that way - changing it would probably add bugs https://github.com/geogebra/geogebra/blob/master/common/src/main/java/org/geogebra/common/kernel/advanced/AlgoDynamicCoordinates.java

Can you redraw this on something like Geogebra and post a screenshot with the relevant points? Can you also explain where the circles come from and/or why you can just use trig?

There are some decent physics examples built out in Geogebra that you might be able to use directly or modify. https://www.geogebra.org/

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]