sympy2jax
devito
sympy2jax | devito | |
---|---|---|
1 | 2 | |
307 | 527 | |
4.2% | 2.8% | |
4.4 | 9.8 | |
about 1 month ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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sympy2jax
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Scientific computing in JAX
sympy2jax: sympy->JAX conversion;
devito
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Galerkin Approximation
Another project that works like this is devito https://www.devitoproject.org/ - the python code generates C code, calls gcc to compile it, dynamically links the object code with dlopen(), then calls the compiled code. That way, the hot code loop doesn't run in Python
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Where should I look to learn about how to apply my math skills to options?
This may help https://github.com/devitocodes/devito/blob/master/examples/finance/bs_ivbp.ipynb
What are some alternatives?
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
pyccel - Python extension language using accelerators
sympytorch - Turning SymPy expressions into PyTorch modules.
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
AMaDiA - Astus' Mathematical Display Application : A GUI for Mathematics (Calculator, LaTeX Converter, Plotter, ... )
chocopy-python-compiler - Ahead-of-time compiler for Chocopy, a statically typed subset of Python 3, built in Python 3.
vibora - Fast, asynchronous and elegant Python web framework.
Nuitka - Nuitka 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. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
qiskit-symb - Python package for symbolic quantum computation in Qiskit