SymPy
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SymPy | py4e | |
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33 | 159 | |
12,008 | 2,747 | |
1.7% | - | |
10.0 | 8.9 | |
about 15 hours ago | about 1 month ago | |
Python | JavaScript | |
GNU General Public License v3.0 or later | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SymPy
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SymPy: Symbolic Mathematics in Python
A decade ago when I was interested in General Relativity I wanted to write a simple program to handle symbolic calculations for Einstein field equations (Starting with metric and calculated affine connections, ricci tensor …etc.). Sympy was an option (better because python was the only language I know well) but I found it hard and actually couldn't make it work. I used mathematica which was new for me but did it in a couple of hours. I expanded it later and used it to calculate a lot of things in a black hole paper I published later.
I checked now, and it seems that on this front a lot of development in sympy made it possible that we know how very good libraries built on top of it [1] [2]. There is even now a Jupyter notebook example on schwarzschild metric [3].
[1] https://docs.einsteinpy.org
[2]https://github.com/spacetimeengineer/spacetimeengine
[3] https://github.com/sympy/sympy/blob/master/examples/intermed...
Oldest commit was from today: https://github.com/sympy/sympy
Where are you getting 14 years from?
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.
That's the newest commit. The oldest commit on GitHub is from 2007: https://github.com/sympy/sympy/commit/99b21ff58ad2e2ba831725...
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Fast Symbolic Computation for Robotics
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?
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Stem Formulas
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
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Vectorization: Introduction
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.
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Has anyone solved the prime number problem on SPOJ yet using pure python?
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)
- What can I contribute to SciPy (or other) with my pure math skill? I’m pen and paper mathematician
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Argue in comments 💅
Funny we had this conversation today. I stumbled across this resource today, and it seems like a great path as it starts at python, to Django, to PostgreSQL, to web apps, to C, with more courses inbound. Dr. Chuck is legit.
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Best online course to actually learn to use Python
Py4e
What are some alternatives?
SciPy - SciPy library main repository
NumPy - The fundamental package for scientific computing with Python.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Numba - NumPy aware dynamic Python compiler using LLVM
NetworkX - Network Analysis in Python
CyberChef - The Cyber Swiss Army Knife - a web app for encryption, encoding, compression and data analysis
ti84-forth - A Forth implementation for the TI-84+ calculator.
Ndless - The TI-Nspire calculator extension for native applications
PyMC - Bayesian Modeling and Probabilistic Programming in Python
symengine.py - Python wrappers for SymEngine
PyDy - Multibody dynamics tool kit.
astropy - Astronomy and astrophysics core library