click
hypothesis
click | hypothesis | |
---|---|---|
32 | 20 | |
15,049 | 7,289 | |
0.7% | 0.7% | |
8.0 | 9.9 | |
2 days ago | about 18 hours ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
click
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click-web: Serve click scripts over the web (Python)
Context: "click" - "Command Line Interface Creation Kit" - easily create CLIs from Python code, via adding decorators: https://github.com/pallets/click
"click-web" in turn turns the click CLI app into a web app with one line of code.
- Anyone want to start a project with me.
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How does "python3 *file* -*letter* work?
there is also click, it is more straight forward and also nice to keep the relevant code where the code is. https://github.com/pallets/click/
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Overhead of Python Asyncio Tasks
I don't have huge experience with Python, but I used async code with C#/Typescript and lately I had to use some asyncio magic.
I found this article: https://blog.dalibo.com/2022/09/12/monitoring-python-subproc... and while async/await syntax is the same, it's not entirely clear for me, why there's some event loop and what exactly happens, when I pass function to asyncio.run(), like here: https://github.com/pallets/click/issues/85#issuecomment-5034...
So, you can use it and it's not that hard, but there are some parts that are vague for me, no matter which language implements async support.
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I am sick of writing argparse boilerplate code, so I made "duckargs" to do it for me
Hmm… did you try such approaches, as [click](https://github.com/pallets/click) or[tap](https://github.com/swansonk14/typed-argument-parser)?
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lord-of-the-clips (lotc): CLI app to download, trim/clip, and merge videos. Supports lots of sites. Downloads/trims at multiple points. Merges multiple clips.
This app leverages these powerful libraries: - yt-dlp: video downloader - moviepy: video trimmer/merger - click: CLI app creator - rich / rich-click: CLI app styler
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Shells Are Two Things
I've used click [1] a lot to build Python tooling scripts the past few years. Click usage is "sort of" similar to the author's proposed solution. There's also a small section here [2] that describes some of the issues covered in the article (in context of argparse).
[1] - https://github.com/pallets/click
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Tomu – A family of devices which fit inside your USB port
I think the success of Arduino in the hardware world can be explained in a similar way, as the relative success of "command line app frameworks" like Click[1], or even much lighter-weight libraries like argparse[2]. You absolutely can get away with using just getopt[3] (and people experienced with it will likely strongly prefer it). However certain factors such as a more declarative API, a nice logo, the existence of an ecosystem (even if you're not actively drawing from it), an official "branded" forum, etc can all play into picking a more complex solution, with more baggage you don't need, certain oddities that may throw users off, etc.
[1]: https://click.palletsprojects.com/
[2]: https://docs.python.org/3/library/argparse.html
[3]: https://man.openbsd.org/getopt.3, https://linux.die.net/man/3/getopt
- something like python's click library?
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Advice for a final project in python without web?
Exactly! You can also use a library like click (https://github.com/pallets/click) to help take care of the command line side, while you focus on the 'business logic' of your application :)
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
What are some alternatives?
typer - Typer, build great CLIs. Easy to code. Based on Python type hints.
pytest - The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Python Fire - Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Robot Framework - Generic automation framework for acceptance testing and RPA
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
Behave - BDD, Python style.
cement - Application Framework for Python
nose2 - The successor to nose, based on unittest2
cliff - Command Line Interface Formulation Framework. Mirror of code maintained at opendev.org.
nose - nose is nicer testing for python
docopt - This project is no longer maintained. Please see https://github.com/jazzband/docopt-ng
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time