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Top 15 Code Analysis and Linter Open-Source Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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scalene
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
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MonkeyType
A Python library that generates static type annotations by collecting runtime types (by Instagram)
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coala
coala provides a unified command-line interface for linting and fixing all your code, regardless of the programming languages you use.
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Flake8
flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
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prospector
Inspects Python source files and provides information about type and location of classes, methods etc
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unimport
:rocket: The ultimate linter and formatter for removing unused import statements in your code. (by hakancelikdev)
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Project mention: Introducing Tapyr: Create and Deploy Enterprise-Ready PyShiny Dashboards with Ease | dev.to | 2024-05-05Leverage Python Tools: Tapyr takes advantage of Python’s ecosystem tools, including ruff, pytest, and others.
Project mention: The GIL can now be disabled in Python's main branch | news.ycombinator.com | 2024-03-11
I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md
Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).
I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.
I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.
There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).
Somehow this experience so far was very disappointing.
(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)
A little introduction about pylint. Pylint is a static code analyzer, it analyses your code without actually running it. Pylint looks for potential errors, gives suggestions on coding standards that your code is not adhering to, potential places where refactoring might help, and also warnings about smelly code.
MonkeyType collects runtime types of function arguments and return values, and can automatically generate stub files or add type annotations directly to your Python code based on the types collected at runtime.
Project mention: To Review or Not to Review: The Debate on Mandatory Code Reviews | dev.to | 2024-04-24Automating code checks with static code analysis allows us to enforce code styling effectively. By integrating tools into our workflow, we can identify errors at an early stage, while coding instead of blocking us at the end. For instance, flake8 checks Python code for style and errors, eslint performs similar checks for JavaScript, and prettier automatically formats code to maintain consistency.
Pylama is a code audit tool for Python that wraps tools like Pylint, pycodestyle, PyFlakes, McCabe, and others to provide a unified interface.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Code Analysis and Linter related posts
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Introducing Tapyr: Create and Deploy Enterprise-Ready PyShiny Dashboards with Ease
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To Review or Not to Review: The Debate on Mandatory Code Reviews
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Enhance Your Project Quality with These Top Python Libraries
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The GIL can now be disabled in Python's main branch
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Polars – A bird's eye view of Polars
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Ask HN: What interesting project ideas you've got but have no time to work on?
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Rye: A Vision Continued
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A note from our sponsor - SaaSHub
www.saashub.com | 5 May 2024
Index
What are some of the best open-source Code Analysis and Linter projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | ruff | 26,725 |
| 2 | mypy | 17,569 |
| 3 | scalene | 11,174 |
| 4 | Pylint | 5,127 |
| 5 | MonkeyType | 4,540 |
| 6 | vprof | 3,948 |
| 7 | code2flow | 3,709 |
| 8 | coala | 3,515 |
| 9 | Flake8 | 3,263 |
| 10 | prospector | 1,907 |
| 11 | pydeps | 1,617 |
| 12 | pysonar2 | 1,367 |
| 13 | pylama | 1,039 |
| 14 | PythonBuddy | 273 |
| 15 | unimport | 238 |
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