SymPy VS Ndless

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

That's interesting. You should consider yourself lucky to have met Wolfram employees, as they are obviously vastly outnumbered by users of Mathematica.

I have not met any developers for either of these products but I know that SymPy has a huge list of contributors for a project of its size. See: https://github.com/sympy/sympy/blob/master/AUTHORS

You may not be hearing about SymPy users because SymPy is not a monolithic product. It is a library. If you know mathematicians big into using Python, they are probably aware of SymPy as it is the main attraction when it comes to symbolic computation in Python.

https://github.com/sympy/sympy/issues/9479 suggests that multivariate inequalities are still unsolved in SymPy, though it looks like https://github.com/sympy/sympy/pull/21687 was merged in August. This probably isn't yet implemented in C++ in SymForce yet?

bug report opened https://github.com/sympy/sympy/issues/25507

https://news.ycombinator.com/item?id=36463580

From https://news.ycombinator.com/item?id=36159017 :

> sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df... :

>> """Convert a SymPy expression into a function that allows for fast numeric evaluation [with the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, SymPy, numexpr,]*

From https://westurner.github.io/hnlog/#comment-19084622 :

> "latex2sympy parses LaTeX math expressions and converts it into the equivalent SymPy form" and is now merged into SymPy master and callable with sympy.parsing.latex.parse_latex(). It requires antlr-python-runtime to be installed. https://github.com/augustt198/latex2sympy https://github.com/sympy/sympy/pull/13706

ENH: 'generate a Jupyter notebook' (nbformat .ipynb JSON) function from this stem formula

https://en.wikipedia.org/wiki/Vectorization :

> Array programming, a style of computer programming where operations are applied to whole arrays instead of individual elements

> Automatic vectorization, a compiler optimization that transforms loops to vector operations

> Image tracing, the creation of vector from raster graphics

> Word embedding, mapping words to vectors, in natural language processing

> Vectorization (mathematics), a linear transformation which converts a matrix into a column vector

Vector (disambiguation) https://en.wikipedia.org/wiki/Vector

> Vector (mathematics and physics):

> Row and column vectors, single row or column matrices

> Vector space

> Vector field, a vector for each point

And then there are a number of CS usages of the word vector for 1D arrays.

Compute kernel: https://en.m.wikipedia.org/wiki/Compute_kernel

GPGPU > Vectorization, Stream Processing > Compute kernels: https://en.wikipedia.org/wiki/General-purpose_computing_on_g...

sympy.utilities.lambdify.lambdify() https://github.com/sympy/sympy/blob/a76b02fcd3a8b7f79b3a88df... :

> """Convert a SymPy expression into a function that allows for fast numeric evaluation [with the CPython math module, mpmath, NumPy, SciPy, CuPy, JAX, TensorFlow, SymPt, numexpr,]

pyorch lambdify PR, sympytorch: https://github.com/sympy/sympy/pull/20516#issuecomment-78428...

Sympytorch:

> Turn SymPy expressions into PyTorch Modules.

> SymPy floats (optionally) become trainable parameters. SymPy symbols are inputs to the Module.

sympy2jax https://github.com/MilesCranmer/sympy2jax :

> Turn SymPy expressions into parametrized, differentiable, vectorizable, JAX functions.

> All SymPy floats become trainable input parameters. SymPy symbols become columns of a passed matrix.

Look at sympy.isprime for a carefully-optimized pure-Python solution (though if gmpy2 is installed, which it usually is, it will use that instead after trying the easiest cases)

I updated my calculator before I learned about Ndless and, from what I've read, there's no way to do a rollback. What are my options? Is there some program that can rewrite the OS or something? Is there a workaround?

The CEs are a good for numerical calculations and have a pretty active developer base which makes them great models to learn to program with, if you intend to use or want good programming functionality, these are the best models for it imho. The Nspires, especially models with CAS and that support Ndless, are good too (I've barely used mine ngl so idk what to put here).

That said, the hardware in modern calculators, especially CAS related ones, is quite powerful. Various hobbyists have actually developed tools to deploy relatively heavy Linux distros onto TI Nspire calculators. Practically speaking, they're pretty unwieldy and slow, but it is possible.

ndless

if you're talking about the TI-Nspire series, then yes through the toolchain/SDK of Ndless, the unofficial/fought-by-TI "jailbreak" (see here). It's a typical arm toolchain, so C/C++/ASM is supported (and more with enough work), and lots of things have been created and ported along the years.

As another person already said, there no builtin way to do that since you have the non-CAS version. However you can use third party CAS engines, like KhiCAS, if you're able to install Ndless (if you don't have a recent OS version it should be doable - check https://ndless.me)

I’m planning on installing Ndless (http://ndless.me) onto my CAS to play Minecraft (yes, I know… it’s dumb) and do fun little projects like programming a game on the CAS.

Emulator most likely require ndless to be installed (nspire jailbreak), sadly it’s version dependent, which is why I don’t advice anyone to upgrade to the newest os. Which that said you could explore the ndless apps (which has seemingly just shut down) which offer a mini Pokémon for example - this is not (at least to my knowledge) the „OG game“, but people definitely tried there best to make it good

I can recommend ndless to you, since it opens quite a lot of possibilities, like cas calculations via KHICAS, emulation support and neat other stuff that could be potentially useful (like a fake light), remember to NOT upgrade to the newest os, but to one on the list shown on the ndless website, which can also be found here.

-> get ndless from ndless.me -> go to ndless-apps.org -> look for PokeMini and install