SymPy
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SymPy | Numba | |
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34 | 124 | |
12,365 | 9,404 | |
3.8% | 1.6% | |
10.0 | 9.9 | |
1 day ago | 8 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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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AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
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).
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SymPy: Symbolic Mathematics in Python
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.
- Matrix Cookbook examples using SymPy
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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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Solving a simple puzzle using SymPy
bug report opened https://github.com/sympy/sympy/issues/25507
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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
- Quantum Monism Could Save the Soul of Physics
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
SciPy - SciPy library main repository
NetworkX - Network Analysis in Python
NumPy - The fundamental package for scientific computing with Python.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
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
Dask - Parallel computing with task scheduling
cupy - NumPy & SciPy for GPU
ti84-forth - A Forth implementation for the TI-84+ calculator.
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
Ndless - The TI-Nspire calculator extension for native applications
statsmodels - Statsmodels: statistical modeling and econometrics in Python