MathInspector VS NumPy

> I expect that mathematics will be rewritten to suit computing, rather than vice versa

I agree with this. I believe pure mathematics is suffering greatly because many mathematicians refuse to fully embrace the computational power of modern technology.

My belief is the age of pretty formulas is coming to an end, and that the future of mathematics will be it focuses more and more on computational aspects of the subject, and problem sets in pure math courses will be done using programs that are much more advanced than anything which exists today, and everyone will think nothing more of those programs than they we do about calculators.

Apologies for the self plug, but this has been my vision with mathinspector[1]. I've been working very hard on that, and this is why I got so interested in your statement. Thank you for clarifying your thinking here. Makes sense to me, and you could be right

[1] https://github.com/MathInspector/MathInspector

Thank you so much! Well I tried to make it work so that literally any project you have sitting on your system can be imported into the app. In principle it should work, in practice the amount of block code elements can quickly become overwhelming.

At the end of wrapping up the beta version, I was using math inspector to create math inspector, this is how I wrote the TextParser[1] class, and there is a certain beauty to the way the files were synchronized, but since everything was still in development I ended up doing a lot in Sublime instead and have not had a chance to keep working on that aspect.

I think the more the app gets dog-fooded in this way, the more clear it will become what the optimal UI/UX is. Math Inspector is not an IDE, but it does create an additional visual layer that is kind of helpful during development, especially for quick iteration.

[1] https://github.com/MathInspector/MathInspector/blob/master/m...

There is! I didn't build and codesign the linux build yet (just ran out of steam and needed a few days of rest), but I set up everything in a VM and got it working on my mac through x11.

If you follow the instructions in the Vagrantfile, it should hopefully be simple to install from the source code on your system.

https://github.com/MathInspector/MathInspector/blob/master/V...

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]