Skip to main content

No project description provided

Project description

PIE

PyPI version Build Status codecov MIT License

Installation & Documentation

pip install painless-import-extension.

Basically there's only one thing exported from package pie:

from pie import LoaderForBetterLife

It's an abstract generic type. When you want to load a file to type A, write such a loader:

class ALoader(LoaderForBetterLife[A]):
    pass

IDEs and static type checkers will help you to finish the following steps of implementing your expecting loader, and this is what I'd call a documentation here.

Motivation

Fuck it, I must say something at first.

Once you use the Python import hooks to support files with extensions other than '.py', you will feel extremely disgusting, and if you're expert enough to use Python internal stuffs to track the implementation of PathFinder.find_spec(usually sys.meta_path[2].find_spec or sys.meta_path[1].find_spec), the awfulness of the module finding mechanism will lead you to a strong suspicion of the reliability of Python import statements, and finally, likely to bring about the crash of one's faith in writing reliable codes.

I for one encountered this, and fortunately I didn't jump down from my dormitory after working with that for months(and knew it for years).

Hence, I realized that it should be my duty to keep people away from being killed when working with Python's module finding mechanism, which might save several people's lives.

What is PIE?

PIE, aka painless-import-extension, is not aimed at providing a higher level interface of Python's import system, but is actually useful for solving most of the related problems.

Technically, PIE "did nothing", all the codes involved in this project are so far pretty easy, that even a newbie to Python could understand it thoroughly with any question. However, PIE is useful, because it shows a healthy mental model for using Python import system, in a simplest way, and also a most practical way if you want to use the same module file search strategy of Python's.

For instance, if you want to import data.json via PIE, other than the JSON file, you should also prepare a file with just suffix changed to .py(hence you got data.py), and fill the following contents:

from pie.json_loader import JsonLoader

data = JsonLoader(__file__, __name__).load()

where, JsonLoader(__file__, __name__).load() will load the JSON data.

Looks trivial? Yes, that's expected, and I'm telling you that this way is powerful, and subtly, much more powerful than json.load(pathlib.Path(__file__).with_suffix('.json').open()).

A PIE loader, and what happened when invoking LoaderForBetterLife.load()?

Given such a file directory:

- proj
    - data.json
    - data.py

And we fill data.py with

from pie.json_loader import JsonLoader

data = JsonLoader(__file__, __name__).load()

Next, when you're import proj.data,

  • for the first time, things similar to json.load happened, JSON is loaded from data.json. However, a default caching system is introduced, and when you exit current Python interpreter, and reopen the interpreter to import proj.data

  • for the second time, json.load might not be invoked. There're some cases:

    • when proj/data.json doesn't exist, we'll get the JSON from a binary file cached on disk.
    • when proj/data.json exists, we'll check if the content of proj/data.json has changed. If true, we import proj/data.json just as what we did at the first time; otherwise, we use the cached binary contents.

Things become quite useful when you're loading DSLs(domain specific languages), or other programming languages that compile to the Python.

For example, we give an implementation of loading the script of muridesu language(Python 3.7 only).

Check test/zenzen_muridesu.py, note that if you're using an IDE, you will have a good experience of auto completion and static checking due to our thorough support of Python type hints.

from pathlib import Path
from typing import Union, Tuple
from muridesu.parse_stmts import parse_stmts as parse
from pie import LoaderForBetterLife
from types import CodeType
import marshal


class MuridesuLoader(LoaderForBetterLife[CodeType]):
    def source_to_prog(self, src: bytes, path: Path):
        mod = parse(src.decode('utf8'), str(path.absolute()))
        code = compile(mod, self.file.with_suffix(self.suffix()), 'exec')
        return code

    def load_program(self, b: bytes):
        return marshal.loads(b)

    def dump_program(self, prog: CodeType):
        return marshal.dumps(prog)

    def suffix(self) -> Union[str, Tuple[str, ...]]:
        return '.muridesu'


exec(MuridesuLoader(__file__, __name__).load(), globals())

In test/zenzen_muridesu.muridesu, write down

class Animal {
    func bark(self){
        print("hoho")
    }
}

class Dog <: Animal {
    bark = fn (self) -> {
        print("dogy")
    }
}

list(
    map(
        fn (it) -> {
            print(it.__class__)
            it.bark()
        },
        [Dog(), Animal()]
    )
)

Importing test/zenzen_muridesu.py will give following STD output:

<class '__main__.Dog'>
dogy
<class '__main__.Animal'>
hoho

Conclusion

Make sure if you really want to introduce the complexity of importlib?

When you just need searching extension files just as searching normal python files, use PIE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for painless-import-extension, version 0.2.2
Filename, size File type Python version Upload date Hashes
Filename, size painless_import_extension-0.2.2-py3-none-any.whl (7.6 kB) File type Wheel Python version py3 Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page