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Project description
slow-learner-convert
A library to convert slow-learner
output to different data model frameworks.
Usage
Following the example provided in the slow-learner
announcing post and assuming Release.py
is in the current directory, one would generate msgspec
structs equivalent to the typing.TypedDict
objects in Release.py
by executing:
slow-learner-convert --input-file Release.py --output-file Release_msgspec.py --framework msgspec
More specifically, if example.py
contains
from typing import TypedDict
class Foo(TypedDict):
bar: str
Baz = TypedDict("Baz", {"qux": int})
then the command line program
slow-learner-convert --input-file example.py --framework attrs
will generate example_attrs.py
, containing the following code.
import attrs
from typing import TypedDict
@attrs.define
class Foo:
bar: str
@attrs.define
class Baz:
qux: int
Currently four frameworks are supported:
Installation
python3 -m venv .venv
. .venv/bin/activate
python3 -m pip install -U pip
python3 -m pip install slow-learner-convert
Development
I used rye
(with uv
backend) while developing this:
git clone git@github.com:gorkaerana/slow-learner-convert.git
cd slow-learner-convert
rye sync
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