lupl 0.1.2

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lupl 👾😺

A collection of potentially generally useful Python utilities.

Installation

lupl is a PEP-621-compliant package and available on PyPI.

Usage

ComposeRouter

The ComposeRouter class allows to route attributes access for registered methods through a functional pipeline constructed from components. The pipeline is only triggered if a registered method is accessed via the ComposeRouter namespace.

from lupl import ComposeRouter

class Foo:
	route = ComposeRouter(lambda x: x + 1, lambda y: y * 2)

	@route.register
	def method(self, x, y):
		return x * y

	foo = Foo()

print(foo.method(2, 3))           # 6
print(foo.route.method(2, 3))     # 13

By default, composition in ComposeRouter is right-associative.

Associativity can be controlled by setting the left_associative: bool kwarg either when creating the ComposeRouter instance or when calling it.

class Bar:
	route = ComposeRouter(lambda x: x + 1, lambda y: y * 2, left_associative=True)

	@route.register
	def method(self, x, y):
		return x * y

bar = Bar()

print(bar.method(2, 3))  # 6
print(bar.route.method(2, 3))  # 14
print(bar.route(left_associative=False).method(2, 3))  # 13

Chunk Iterator

The ichunk generator implements a simple chunk iterator that allows to lazily slice an Iterator into sub-iterators.

from collections.abc import Iterator
from lupl import ichunk

iterator: Iterator[int] = iter(range(10))
chunks: Iterator[Iterator[int]] = ichunk(iterator, size=3)

materialized = [tuple(chunk) for chunk in chunks]
print(materialized)  # [(0, 1, 2), (3, 4, 5), (6, 7, 8), (9,)]

Pydantic Tools

CurryModel

The CurryModel constructor allows to sequentially initialize (curry) a Pydantic model.

from lupl import CurryModel

class MyModel(BaseModel):
	x: str
	y: int
	z: tuple[str, int]


curried_model = CurryModel(MyModel)

curried_model(x="1")
curried_model(y=2)

model_instance = curried_model(z=("3", 4))
print(model_instance)

CurryModel instances are recursive so it is also possible to do this:

curried_model_2 = CurryModel(MyModel)
model_instance_2 = curried_model_2(x="1")(y=2)(z=("3", 4))
print(model_instance_2)

Currying turns a function of arity n into at most n functions of arity 1 and at least 1 function of arity n (and everything in between), so you can also do e.g. this:

curried_model_3 = CurryModel(MyModel)
model_instance_3 = curried_model_3(x="1", y=2)(z=("3", 4))
print(model_instance_3)

FlatInitModel

The FlatInitModel constructor allows to instantiate a potentially deeply nested Pydantic model from flat kwargs.

from lupl import FlatInitModel
from pydantic import BaseModel

class DeeplyNestedModel(BaseModel):
	z: int

class NestedModel(BaseModel):
	y: int
	deeply_nested: DeeplyNestedModel

class Model(BaseModel):
	x: int
	nested: NestedModel

constructor = FlatInitModel(model=Model)

instance: Model = constructor(x=1, y=2, z=3)
instance.model_dump()  # {'x': 1, 'nested': {'y': 2, 'deeply_nested': {'z': 3}}}

FlatInitModel also handles model union types by processing the first model type of the union.

A common use case for model union types is e.g. to assign a default value to a model union typed field in case a nested model instance does not meet certain criteria, i.e. fails a predicate.

The model_bool parameter in lupl.ConfigDict allows to specify the condition for model truthiness - if the existential condition of a model is met, the model instance gets assigned to the model field, else the constructor falls back to the default value.

The default condition for model truthiness is that any model field must be truthy for the model to be considered truthy.

The model_bool parameter takes either

• a callable object of arity 1 that receives the model instance at runtime,
• a str denoting a field of the model that must be truthy in order for the model to be truthy
• a set[str] denoting fields of the model, all of which must be truthy for the model to be truthy.

The following example defines the truth condition for DeeplyNestedModel to be gt3. NestedModel defines a model union type with a default value - if the model_bool predicate fails, the constructor falls back to the default:

from lupl import ConfigDict, FlatInitModel
from pydantic import BaseModel

class DeeplyNestedModel(BaseModel):
	model_config = ConfigDict(model_bool=lambda model: model.z > 3)

	z: int

class NestedModel(BaseModel):
	y: int
	deeply_nested: DeeplyNestedModel | str = "default"

class Model(BaseModel):
	x: int
	nested: NestedModel

constructor = FlatInitModel(model=Model)

instance: Model = constructor(x=1, y=2, z=3)
instance.model_dump()  # {'x': 1, 'nested': {'y': 2, 'deeply_nested': 'default'}}

If the existential condition of the model is met, the model instance gets assigned:

instance: Model = constructor(x=1, y=2, z=4)
instance.model_dump()  # {'x': 1, 'nested': {'y': 2, 'deeply_nested': {'z': 4}}}