SymPy

The SymPy introductory tutorial. docs.sympy.org/latest/tutoria…

It used @SymPy, not NumPy. When I started SymPy almost 20 years ago, my university classmates were telling me that it could never match Mathematica, so what's the point? And now the most popular (?) LLM uses SymPy by default. That's amazing!

Cool to see @openai demo solving a @sympy issue (from SWE-bench) in their o3 demo today. At a glance the AI's fix looks correct (though not quite as straightforward as the true fix that was made).

@DynamicWebPaige @numpy_team Thank you for actually shipping the latest version of SymPy.

👩‍💻 Code execution with Gemini is so helpful! It gives Gemini the ability to write, execute, and recursively fix code in a sandboxed Python environment - even output plots and use files (up to 10MB in size) as inputs. 👇altair, chess, cv2, matplotlib, mpmath, @numpy_team, pandas, pdfminer, reportlab, seaborn, @scikit_learn, statsmodels, striprtf, @sympy, and tabulate are all supported:

@mmaaz_98 The best place to start would be to send an email to the mailing list groups.google.com/g/sympy

@SymPy I'd be very interested! should I email/dm?

A decision procedure for the existential theory of the reals written purely in @SymPy !! Takes a system of polynomials and provides a point that satisfies the system or an empty list if inconsistent.

I made a @SymPy GPT to play around with this. Unlike normal ChatGPT it has direct access to the latest version of SymPy (1.12) and its docs. It also tries to actually execute code whenever it can. chat.openai.com/g/g-AePkBhsFf-…

"Towards a new SymPy: part 2 - Polynomials" Part 2 of this series of blog posts discusses the SymPy polynomials module and how it relates to SymPy's performance oscarbenjamin.github.io/blog/czi/post2…

"Towards a new SymPy: part 1" In this first of a series of blog posts, Oscar Benjamin writes about the ongoing work to improve the performance of SymPy. oscarbenjamin.github.io/blog/czi/post1…

Tutorials @SciPyConf are in full swing! Ever want to do algebra, calculus, or advanced differential equations directly in Python? Check out the “SymPy Introductory Tutorial” co-led by our @asmeurer to learn the basics of @SymPy. cfp.scipy.org/2023/talk/LJQP…

Large numbers have fewer prime factors than you might expect. johndcook.com/blog/2023/06/2…

@muziejus If you're looking for an exact answer use rational numbers instead of floats, like Rational("0.95")

Can @sympy / algebra twitter tell me what I am doing wrong here?

Hand drawn, and live demo. using @SymPy easier than typing TeX ! And we can now derivate, or integrate with @numpy_team

@rmiranda00 SymPy has the builtin function groebner() but this blog post shows how to implement it from scratch. #sympy.polys.polytools.groebner" target="_blank" rel="nofollow noopener">docs.sympy.org/latest/modules…

"Use Gröbner Bases To Solve Polynomial Equations" by @jingnanshi Mice blog post showing how Gröbner Bases work using SymPy. jingnanshi.com/blog/groebner_…

@OndrejCertik @TheRealAdamG @OpenAI @SymPy @mathgpt_app does this in @streamlit. Give it a try mathgpt.streamlit.app

@Michael_Zingale @ExecutableBooks By default it generates both mathjax and png output. The notebook interface prefers mathjax but nbconvert sometimes prefers png instead. This could be considered an upstream bug, but either way it's better to omit those pngs if you don't need them anyway.

@Michael_Zingale @ExecutableBooks Try setting use_latex="mathjax" in init_printing()/init_session()

I noticed @OpenAI uses @SymPy in their "Code interpreter" demo at their ChatGPT plugins webpage (openai.com/blog/chatgpt-p…). I am very happy to see SymPy being used by default like this.

I integrated ChatGPT into cocalc.com as a chatbot (though much more is to come). You can just do @ and select chatgpt when chatting anywhere in cocalc (doc.cocalc.com/chat.html) and it responds. Here's an example of it knowing #sympy.

@gruntyGrunter @Hansen73337824 The maximum occurs where the derivative is zero. #SymPy code: >>> from sympy import symbols, diff, log, solveset >>> c = symbols('c') >>> diff(c ** (1 / c), c) c**(1/c)*(-log(c)/c**2 + c**(-2)) >>> solveset(-log(c)/c**2 + c**(-2), c) {E}