#195: Runtime type checking for Python type hints
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Michael #1: watchdog
- via Prayson Daniel
- Python API and shell utilities to monitor file system events.
observer = Observer() observer.schedule(event_handler, path, recursive=True) observer.start()
- Watchdog comes with an optional utility script called
$ watchmedo logand see what happens in that folder.
- Why Watchdog? Compared to other similar libs
Brian #2: Status code 418
- Thanks Andy Howe for the suggestion
- Python 3.9 rc1 is out.
- One nice enhancement that has made it into 3.9, a fix for http library missing HTTP status code 418 “I’m a teapot”.
- Title: http library missing HTTP status code 418 "I'm a teapot"
- See also status code 418 is also supported by HTCPCP, Hyper Text Coffee Pot Control Protocol, https://tools.ietf.org/html/rfc2324
- 418 I'm a teapot
Any attempt to brew coffee with a teapot should result in the error code "418 I'm a teapot". The resulting entity body MAY be short and stout.
- 418 I'm a teapot
- The only other unique HTCPCP code is 406
- 406 Not Acceptable
… In HTCPCP, this response code MAY be returned if the operator of the coffee pot cannot comply with the Accept-Addition request. Unless the request was a HEAD request, the response SHOULD include an entity containing a list of available coffee additions.
- 406 Not Acceptable
- This has been going on since 1998 and I'm just now hearing about it.
- A nice reference site: httpstatuses.com
Michael #3: pydantic’s new Validation decorator
- via Andy Shapiro
- Built-in type checking for any function via a decorator
- easy to add for any public methods in a package
- pydantic uses lots of cython under the hood so it should be fast....
- The validate_arguments decorator allows the arguments passed to a function to be parsed and validated using the function's annotations before the function is called.
- Under the hood this uses the same approach of model creation and initialization; it provides an extremely easy way to apply validation to your code with minimal boilerplate.
from pydantic import validate_arguments, ValidationError @validate_arguments def repeat(s: str, count: int, *, separator: bytes = b'') -> bytes: b = s.encode() return separator.join(b for _ in range(count)) a = repeat('hello', 3) print(a) #> b'hellohellohello' b = repeat('x', '4', separator=' ') print(b) #> b'x x x x' try: c = repeat('hello', 'wrong') except ValidationError as exc: print(exc) """ 1 validation error for Repeat count value is not a valid integer (type=type_error.integer) """
- Anthony Shaw
- From twitter announcement:
- “After a series of highly questionable life decisions, my Python extension written in pure assembly is now on PyPI. https://pypi.org/project/pymult/ it required writing an Assembly extension for distutils, I also added GitHub Actions support so its running CI/CD and testing with pytest”.
- A proof-of-concept to demonstrate how you can create a Python Extension in 100% assembly.
- How to write a Python module in pure assembly
- How to write a function in pure assembly and call it from Python with Python objects
- How to call the C API to create a PyObject and parse PyTuple (arguments) into raw pointers
- How to pass data back into Python
- How to register a module from assembly
- How to create a method definition in assembly
- How to write back to the Python stack using the dynamic module loader
- How to package a NASM/Assembly Python extension with distutils
- The simple proof-of-concept function takes 2 parameters,
>>> import pymult >>> pymult.multiply(2, 4) 8
- May need a few more test cases:
>>> pymult.multiply(2, 3) 6 >>> pymult.multiply(-2, -3) 6 >>> pymult.multiply(-2, 3) 4294967290
- Also, clearly Anthony has too much time on his hands. Just saying.
Michael #5: easy property
- via Ruud van der Ham
- The easy_property module, developed by me, offers a more intuitive way to define a Python property with getter, setter, deleter, getter_setter and documenter decorators.
- Normally when you want to define a property that has a getter and a setter, you have to do something like
Class Demo: def __init__(self, val): self.a = val @property def a(self): return self._a @a.setter def a(self, val): self._a = val
- IMHO, the @a.setter is a rather ugly decorator, and hard to remember. And there's no way to not define the getter.
- With the easy_property module, one can use the decorators
- as in:
Class Demo: def __init__(self, val): self.a = val @getter def a(self): return self._a @setter def a(self, val): self._a = val @deleter def a(self): print('delete') del self._a
- In contrast with an ordinary property, the order of definition of getter, setter and deleter is not important. And it is even possible to define a setter only (without a getter), just in case.
- With easy_property, you can even create a combined getter/setter decorator:
Class Demo: def __init__(self, val): self.a = val @getter_setter def a(self, val=None): if val is None: return self._a self._a = val
- Finally, it is possible to add a docstring to the property, with the @documenter decorator:
Class Demo: def __init__(self, val): self.a = val @getter def a(self): return self._a @documenter: def a(self): return "this is the docstring of Demo.a"
Although this might not be always a good solution, I think in many cases this will make it easier and more intuitive to define properties.
- Ryan Howard wrote an article about a project of mine on the TestProject blog.
- I think it’s a first that someone else wrote an article about something I made. So that’s cool.
- Most tests do the “check” part with assert statements.
- The problem is assert stops after the first failure and you often want to check lots of stuff, and you want to see all the failures.
- Ryan has a good example with checking web pages using selenium and a simple example of wanting to check both the content of an element on the page, and the url.
- Cool use of pytest-check
- See also:
- PSA: There are no capital letters in pytest, even if it begins a sentence.
- PyMC core devs, we are currently planning the first ever PyMCon (pronounce "PyMC ON", because "it is oooon" ;))! This is an asynchronous-first conference for the Bayesian community, with three goals:
- Create a space and time for community members to meet each other and interact
- Record and organize the expertise and experience around PyMC
- Help folks find ways to contribute to PyMC, authentic to themselves
- urlify! https://twitter.com/mkennedy/status/1292955438552506370 get it on github at https://github.com/mikeckennedy/urlify
- Thumbnails in the video player at talk python training
- XKCD git - xkcd.com/1597
- “I used to do low-level programming. Then a product I bought told me, "No assembly required." Since then, I've been coding in Python.” - From Rueven Lerner, Inspired by Anthony Shaw