hypothesis
MonkeyType
hypothesis | MonkeyType | |
---|---|---|
20 | 9 | |
7,289 | 4,540 | |
0.9% | 0.6% | |
9.9 | 5.4 | |
2 days ago | 16 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
MonkeyType
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Enhance Your Project Quality with These Top Python Libraries
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.
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A Tale of Two Kitchens - Hypermodernizing Your Python Code Base
To alleviate the burden of manual annotation, MonkeyType offers a clever solution. It dynamically observes the types entering and leaving functions during code execution. Based on this observation, it generates a preliminary draft of type annotations. This significantly reduces the effort needed to add type hints to legacy code.
- Do you know any library that automatically detects unused files / functions inside a project folder?
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Programming Breakthroughs We Need
https://github.com/instagram/MonkeyType can perform the call logging, and can export a static typing file which is used by mypy, but also e.g. PyCharm. It doesn't expose such fine grained types, but you could build that based on the logged data.
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Gradually introduce type checking to an existing typed codebase.
Which introduces MonkeyType, a python library that generatics static type annotations by collecting runtime types.
- Call me naive, but would it not be possible to create a tool for python the auto adds type hints at run time?
- Is there any language that is as similar as possible to Python in syntax, readability, and features, but is statically typed?
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Typehole – Create TypeScript interfaces from JS runtime values automatically
Not sure if you're joking but there is something similar for python developed by a rather well known company https://github.com/Instagram/MonkeyType
- Cinder: Instagram's performance oriented fork of CPython
What are some alternatives?
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
PythonBuddy - 1st Online Python Editor With Live Syntax Checking and Execution
Robot Framework - Generic automation framework for acceptance testing and RPA
unimport - :rocket: The ultimate linter and formatter for removing unused import statements in your code.
Behave - BDD, Python style.
Cinder - Cinder is a community-developed, free and open source library for professional-quality creative coding in C++.
nose2 - The successor to nose, based on unittest2
typehole - TypeScript development tool for Visual Studio Code that helps you automate creating the initial static typing for runtime values
nose - nose is nicer testing for python
cinder - Cinder is Meta's internal performance-oriented production version of CPython.
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
plum - Multiple dispatch in Python