hypothesis
Flake8
hypothesis | Flake8 | |
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20 | 33 | |
7,289 | 3,263 | |
0.9% | 1.0% | |
9.9 | 7.3 | |
2 days ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
hypothesis
- Hypothesis
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
Hypothesis for Property-Based Testing: Hypothesis is a Python library facilitating property-based testing. It offers a distinct advantage by generating a wide array of input data based on specified properties or invariants within the code. The perks of Hypothesis include:
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Pix2tex: Using a ViT to convert images of equations into LaTeX code
But then add tests! Tests for LaTeX equations that had never been executable as code.
https://github.com/HypothesisWorks/hypothesis :
> Hypothesis is a family of testing libraries which let you write tests parametrized by a source of examples. A Hypothesis implementation then generates simple and comprehensible examples that make your tests fail. This simplifies writing your tests and makes them more powerful at the same time, by letting software automate the boring bits and do them to a higher standard than a human would, freeing you to focus on the higher level test logic.
> This sort of testing is often called "property-based testing", and the most widely known implementation of the concept is the Haskell library QuickCheck, but Hypothesis differs significantly from QuickCheck and is designed to fit idiomatically and easily into existing styles of testing that you are used to, with absolutely no familiarity with Haskell or functional programming needed.
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pgregory.net/rapid v1.0.0, modern Go property-based testing library
pgregory.net/rapid is a modern Go property-based testing library initially inspired by the power and convenience of Python's Hypothesis.
- Was muss man als nicht-technischer Quereinsteiger in Data Science *wirklich* können?
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Python toolkits
Hypothesis to generate dummy data for test.
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Best way to test GraphQL API using Python?
To create your own test cases, I recommend you use hypothesis-graphql in combination with hypothesis. hypothesis is a property-based testing library. Property-based testing is an approach to testing in which you make assertions about the result of a test given certain conditions and parameters. For example, if you have a mutation that requires a boolean parameter, you can assert that the client will receive an error if it sends a different type. hypothesis-graphql is a GraphQL testing library that knows how to use hypothesis strategies to generate query documents.
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Fuzzcheck (a structure-aware Rust fuzzer)
The Hypothesis stateful testing code is somewhat self-contained, since it mostly builds on top of internal APIs that already existed.
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Running C unit tests with pytest
We've had a lot of success combining that approach with property-based testing (https://github.com/HypothesisWorks/hypothesis) for the query engine at backtrace: https://engineering.backtrace.io/2020-03-11-how-hard-is-it-t... .
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Machine Readable Specifications at Scale
Systems I've used for this include https://agda.readthedocs.io/en/v2.6.0.1/getting-started/what... https://coq.inria.fr https://www.idris-lang.org and https://isabelle.in.tum.de
An easier alternative is to try disproving the statement, by executing it on thousands of examples and seeing if any fail. That gives us less confidence than a full proof, but can still be better than traditional "there exists" tests. This is called property checking or property-based testing. Systems I've used for this include https://hypothesis.works https://hackage.haskell.org/package/QuickCheck https://scalacheck.org and https://jsverify.github.io
Flake8
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To Review or Not to Review: The Debate on Mandatory Code Reviews
Automating 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.
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Enhance Your Project Quality with These Top Python Libraries
Flake8. This library is a wrapper around pycodestyle (PEP8), pyflakes, and Ned Batchelder’s McCabe script. It is a great toolkit for checking your code base against coding style (PEP8), programming errors (like SyntaxError, NameError, etc) and to check cyclomatic complexity.
- Django Code Formatting and Linting Made Easy: A Step-by-Step Pre-commit Hook Tutorial
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Enhancing Python Code Quality: A Comprehensive Guide to Linting with Ruff
Flake8 combines the functionalities of the PyFlakes, pycodestyle, and McCabe libraries. It provides a streamlined approach to code linting by detecting coding errors, enforcing style conventions, and measuring code complexity.
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Which is your favourite or go-to YouTube channel for being up-to-date on Python?
He made yesqa and pyupgrade (among others), and also works on flake8. His main job is for https://sentry.io/.
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The Power of Pre-Commit for Python Developers: Tips and Best Practices
repos: - repo: https://github.com/psf/black rev: 21.7b0 hooks: - id: black language_version: python3.8 - repo: https://github.com/PyCQA/flake8 rev: 3.9.2 hooks: - id: flake8
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Is it considered rude to completely change the formatting of someone else's code when making a PR?
https://github.com/psf/black it’s a PEP8 compliant formatter for Python codebases. If you don’t like auto formatting files you can use https://github.com/PyCQA/flake8 it just lists out all of the style issues so you can fix them manually.
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Ruff: one Python linter to rule them all
I have no stake in that, but my observation is that the actual discussion appears to have both supporters and detractors rather than overwhelming support. Either way, it has nothing to do with whether or not it is realistic to say that Ruff is the "one Python linter to rule them all".
- Improve your Django Code with pre-commit
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Pylint - It's not just a linter that annoys you!
Robot Framework - Generic automation framework for acceptance testing and RPA
black - The uncompromising Python code formatter [Moved to: https://github.com/psf/black]
Behave - BDD, Python style.
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
nose2 - The successor to nose, based on unittest2
pylama - Code audit tool for python.
nose - nose is nicer testing for python
autoflake - Removes unused imports and unused variables as reported by pyflakes
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
prospector - Inspects Python source files and provides information about type and location of classes, methods etc