actix-telepathy
NumPy
actix-telepathy | NumPy | |
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2 | 272 | |
63 | 26,413 | |
- | 1.1% | |
7.7 | 10.0 | |
6 months ago | 5 days ago | |
Rust | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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actix-telepathy
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What are legitimate problems with Rust?
Well, one recent issue I came across recently is the lack of support for clusters. There are crates for parallelism on a local machine, but the crates wrapping MPI or coming up with a native solution are basically not maintained anymore. I've only found actix telepathy, which is not a complete solution tho, being an extension of Actix.
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What programming languages are most used for creating advanced math-related software/simulations?
Rust is also another possibility: it's basically C++ but more modern with added features and safety. It can be tricky to write mathematical stuff in it, because you may not care too much about all the safety concerns Rust forces you to handle, but it can be useful to catch bugs ahead of times. Sadly, Rust seems to have no library for running programs on clusters of PCs, except maybe this one, which takes the Actor model implemented by Actix and runs it on a cluster. I don't know how tricky it is to use the Actor model for a scientific simulation, tho.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
geogebra - GeoGebra apps (mirror)
SymPy - A computer algebra system written in pure Python
async-fundamentals-initiative
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
UEDGE - 2D fluid simulation of plasma and neutrals in magnetic fusion devices
blaze - NumPy and Pandas interface to Big Data
wg-cargo-std-aware - Repo for working on "std aware cargo"
SciPy - SciPy library main repository
actix - Actor framework for Rust.
Numba - NumPy aware dynamic Python compiler using LLVM
ponyc - Pony is an open-source, actor-model, capabilities-secure, high performance programming language
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).