prometeo
mypy
prometeo | mypy | |
---|---|---|
11 | 112 | |
610 | 17,569 | |
- | 0.9% | |
0.0 | 9.7 | |
almost 2 years ago | 6 days ago | |
Python | Python | |
BSD 2-clause "Simplified" License | MIT License |
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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.
prometeo
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Borgo is a statically typed language that compiles to Go
Not impossible but I guess you might end up with an extra runtime layer and some more dynamic operations will not be very fast. Or you restrict it to a subset of Python like this project does: https://github.com/zanellia/prometeo
You could of course write a bytecode VM in Golang but I guess that defeats the purpose.
- Are there any libraries that can easily convert Python to C/C#/or C++? Ones where a person doesn't have to "calibrate" it, just, pip install library and then they can have their Python code in C,C#,or C++?
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I made a Python compiler, that can compile Python source down to fast, standalone executables.
Honest question: How does pycom compare to similar tools like Nuitka, prometeo, or mypyc?
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Profiling and Analyzing Performance of Python Programs
If you don't mind switching to a little different syntax of Python, then you also might want to take a look at prometeo - an embedded domain specific language based on Python, specifically aimed at scientific computing. Prometeo programs transpile to pure C code and its performance can be comparable with hand-written C code.
- GitHub - zanellia/prometeo: An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
- Show HN: Prometeo – a Python-to-C transpiler for high-performance computing
- An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
This is awesome! The direction of using a subset of python, while leveraging the user base and static typing to accomplish some other everyday task in a different language is very legit IMO.
I took a cursory look at:
https://github.com/zanellia/prometeo/blob/master/prometeo/cg...
It seems quite similar in spirit to
https://github.com/adsharma/py2many/blob/main/pyrs/transpile...
I'm not spending much time on py2many last few months (started a new job). Let me know if any of it sounds useful - especially the ability to transpile to 7-8 languages including Julia, C++ and Rust.
mypy
- The GIL can now be disabled in Python's main branch
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Polars – A bird's eye view of Polars
It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282
- Static Typing for Python
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Python 3.13 Gets a JIT
There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.
And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.
And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.
[0] https://nuitka.net/
[1] https://github.com/python/mypy/tree/master/mypyc
- Introducing Flask-Muck: How To Build a Comprehensive Flask REST API in 5 Minutes
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WeveAllBeenThere
In Python there is MyPy that can help with this. https://www.mypy-lang.org/
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It's Time for a Change: Datetime.utcnow() Is Now Deprecated
It's funny you should say this.
Reading this article prompted me to future-proof a program I maintain for fun that deals with time; it had one use of utcnow, which I fixed.
And then I tripped over a runtime type problem in an unrelated area of the code, despite the code being green under "mypy --strict". (and "100% coverage" from tests, except this particular exception only occured in a "# pragma: no-cover" codepath so it wasn't actually covered)
It turns out that because of some core decisions about how datetime objects work, `datetime.date.today() < datetime.datetime.now()` type-checks but gives a TypeError at runtime. Oops. (cause discussed at length in https://github.com/python/mypy/issues/9015 but without action for 3 years)
One solution is apparently to use `datetype` for type annotations (while continuing to use `datetime` objects at runtime): https://github.com/glyph/DateType
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What's New in Python 3.12
PEP 695 is great. I've been using mypy every day at work in last couple years or so with very strict parameters (no any type etc) and I have experience writing real life programs with Rust, Agda, and some Haskell before, so I'm familiar with strict type systems. I'm sure many will disagree with me but these are my very honest opinions as a professional who uses Python types every day:
* Some types are better than no types. I love Python types, and I consider them required. Even if they're not type-checked they're better than no types. If they're type-checked it's even better. If things are typed properly (no any etc) and type-checked that's even better. And so on...
* Having said this, Python's type system as checked by mypy feels like a toy type system. It's very easy to fool it, and you need to be careful so that type-checking actually fails badly formed programs.
* The biggest issue I face are exceptions. Community discussed this many times [1] [2] and the overall consensus is to not check exceptions. I personally disagree as if you have a Python program that's meticulously typed and type-checked exceptions still cause bad states and since Python code uses exceptions liberally, it's pretty easy to accidentally go to a bad state. E.g. in the linked github issue JukkaL (developer) claims checking things like "KeyError" will create too many false positives, I strongly disagree. If a function can realistically raise a "KeyError" the program should be properly written to accept this at some level otherwise something that returns type T but 0.01% of the time raises "KeyError" should actually be typed "Raises[T, KeyError]".
* PEP 695 will help because typing things particularly is very helpful. Often you want to pass bunch of Ts around but since this is impractical some devs resort to passing "dict[str, Any]"s around and thus things type-check but you still get "KeyError" left and right. It's better to have "SomeStructure[T]" types with "T" as your custom data type (whether dataclass, or pydantic, or traditional class) so that type system has more opportunities to reject bad programs.
* Overall, I'm personally very optimistic about the future of types in Python!
[1] https://github.com/python/mypy/issues/1773
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Mypy 1.6 Released
# is fixed: https://github.com/python/mypy/issues/12987.
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Ask HN: Why are all of the best back end web frameworks dynamically typed?
You probably already know but you can add type hints and then check for consistency with https://github.com/python/mypy in python.
Modern Python with things like https://learnpython.com/blog/python-match-case-statement/ + mypy + Ruff for linting https://github.com/astral-sh/ruff can get pretty good results.
I found typed dataclasses (https://docs.python.org/3/library/dataclasses.html) in python using mypy to give me really high confidence when building data representations.
What are some alternatives?
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
pyright - Static Type Checker for Python
llvm-cbe - resurrected LLVM "C Backend", with improvements
ruff - An extremely fast Python linter and code formatter, written in Rust.
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
pyre-check - Performant type-checking for python.
acados - Fast and embedded solvers for nonlinear optimal control
black - The uncompromising Python code formatter
textX - Domain-Specific Languages and parsers in Python made easy http://textx.github.io/textX/
pytype - A static type analyzer for Python code
MatrixEquations.jl - Solution of Lyapunov, Sylvester and Riccati matrix equations using Julia
pydantic - Data validation using Python type hints