logic-processes-layer 1.2025.1.14

Abstractions for create business logic

logic_processes_layer

The logic_processes_layer package provides a framework for structuring the logic of your Python programs in a flexible and maintainable way. It allows you to divide your program logic into separate processes, each of which can be easily modified or replaced without affecting the others.

Features

• Separation of concerns: Each process in your program can be developed and tested independently.
• Flexibility: Processes can be easily added, removed, or modified without affecting the rest of your program.
• Ease of testing: By isolating each process, you can write more focused and effective unit tests.

Installation

You can install the logic_processes_layer package via pip:

pip install logic-processes-layer

New Features

• ProcessAsSubprocess: Use any process as a subprocess.
• InitMapper: Simplifies process initialization by mapping attributes from the context to processor arguments.
• ProcessAttr: Retrieve attributes from the process context or directly from the process.
• Conditions Support: Add logical conditions to control the execution of processes.
  • AttrCondition: Define conditions based on attributes of the process or context.
  • FunctionCondition: Wrap custom functions as conditions.
  • Logical Operators: Combine conditions with & (AND), | (OR), ~ (NOT), and ^ (XOR) for advanced logic.
• Examples of how to use the logic_processes_layer package.

Using ProcessAttr and InitMapper

In many cases, you may want to initialize your process with specific values drawn from the current context or from the process itself. To simplify this, the package provides two utilities: ProcessAttr and InitMapper.

ProcessAttr

ProcessAttr makes it easy to fetch required attributes from the context or from the process. This class can be declared as generic (i.e., Generic[AttrResultT]) to enable strict typing if needed:

import dataclasses
from operator import attrgetter
import typing


AnyTupleT = typing.Tuple[typing.Any, ...]
DictStrAnyT = typing.Dict[str, typing.Any]
AttrResultT = typing.TypeVar("AttrResultT")


@dataclasses.dataclass
class ProcessAttr(typing.Generic[AttrResultT]):
    attr_name: str
    from_context: bool = dataclasses.field(default=False)
    cast: typing.Callable[[typing.Any], AttrResultT] = dataclasses.field(default=lambda arg: arg)

    def get_value(self, context: typing.Any) -> AttrResultT:  # noqa: ANN401
        source = (context.process, context)[self.from_context]
        source_value = attrgetter(self.attr_name)(source)
        return self.cast(source_value)

• attr_name: The attribute name to retrieve.
• from_context: A flag indicating whether to take the attribute from context.process (default) or directly from context.
• cast: A callable to cast (transform) the retrieved value into a desired type (default simply returns the original value).

InitMapper

InitMapper helps you gather the needed args and kwargs for initializing a process or any other object. If certain values should come from the context, you can use ProcessAttr objects for those parameters.

Example:

from typing import Any
import dataclasses

AnyTupleT = tuple[Any, ...]
DictStrAnyT = dict[str, Any]

@dataclasses.dataclass
class InitMapper:
    _init_attrs: tuple[Any, ...] = ()
    _init_kwargs: dict[str, Any] = dataclasses.field(default_factory=dict)

    def __call__(self, context: Any) -> tuple[AnyTupleT, DictStrAnyT]:
        args = self._load_args(context)
        kwargs = self._load_kwargs(context)
        return args, kwargs

    def _load_args(self, context: Any) -> AnyTupleT:
        args = []
        for init_attr in self._init_attrs:
            value = init_attr
            if isinstance(init_attr, ProcessAttr):
                value = init_attr.get_value(context)
            args.append(value)
        return tuple(args)

    def _load_kwargs(self, context: Any) -> DictStrAnyT:
        kwargs = {}
        for key, value in self._init_kwargs.items():
            init_value = value
            if isinstance(value, ProcessAttr):
                init_value = value.get_value(context)
            kwargs[key] = init_value
        return kwargs

• _init_attrs: A tuple of positional arguments to be passed to the constructor.
• _init_kwargs: A dictionary of keyword arguments (key is the argument name, value is either a fixed value or a ProcessAttr to load from the context).

Usage Example

# Suppose we have a class MyProcessor that needs two arguments: name (str) and age (int).

class MyProcessor:
    def __init__(self, name: str, age: int) -> None:
        self.name = name
        self.age = age

    def run(self):
        print(f"Name: {self.name}, Age: {self.age}")

# Assume the required values are stored in context.process, for example:
# context.process.name = "Alice", context.process.age = 30

mapper = InitMapper(
    _init_attrs=(),
    _init_kwargs={
        "name": ProcessAttr[str]("name"),
        "age": ProcessAttr[int]("age")
    }
)

# When mapper(context) is called, it will build (args, kwargs),
# where args is an empty tuple and kwargs is {"name": "Alice", "age": 30}.

args, kwargs = mapper(context)
processor = MyProcessor(*args, **kwargs)
processor.run()  # Prints: Name: Alice, Age: 30

In this way, InitMapper provides a flexible mechanism for assembling parameters for object initialization, while ProcessAttr enables you to reference attributes from either the context or the process.

---

Basic Usage

Below is a basic example of how to use the logic_processes_layer package, creating a process with pre- and post-run steps:

import dataclasses
from logic_processes_layer import BaseProcessor, BaseSubprocessor

class MyPreProcess(BaseSubprocessor):

    def __call__(self):
        print("This is the pre-run step.")
        self.context.data['message'] = "Hello, World!"

class MyPostProcess(BaseSubprocessor):
    allow_context = True

    def __call__(self):
        print("This is the post-run step.")
        print(self.context.data['message'])

@dataclasses.dataclass
class MyClass(BaseProcessor):
    pre_run = (MyPreProcess(), )
    post_run = (MyPostProcess(), )

    def run(self):
        print("This is the run step.")

process = MyClass()
process()

Advanced Example: Processing Data from Multiple APIs

This example demonstrates how to use the logic_processes_layer package to process data from multiple APIs. The process is divided into three steps: pre-run, run, and post-run.

Pre-run

class PreProcess1(BaseSubprocessor):
    def __call__(self):
        # Assuming a GET request to the first API
        response1 = requests.get('http://api1.com')
        return response1.json()

class PreProcess2(BaseSubprocessor):
    def __call__(self):
        # Assuming a GET request to the second API
        response2 = requests.get('http://api2.com')
        return response2.json()

Run

def run(self):
    # Retrieve and process the data from the pre_run step
    api1_response = self.results.pre_run[self.pre_run[0]]
    api2_response = self.results.pre_run[self.pre_run[1]]
    result = process_data(api1_response, api2_response)  # process_data is hypothetical
    return result

Post-run

class PostProcess(BaseSubprocessor):
    def __call__(self):
        result = context.process.results.run
        # Send the result to another API
        response3 = requests.post('http://api3.com', data=result)
        return response3.json()

Advanced Example: ChainPipeline with Custom Mapper and Steps

In this example, we dive deeper into how ChainPipeline, AbstractMapper, and AbstractPipelineStep can be used to create more complex processes:

1. Processors: Three processors (ProcessorOne, ProcessorTwo, ProcessorThree), each performing a certain task and returning a result.
2. Mappers: Three mappers (MapperOne, MapperTwo, MapperThree) to build attribute dictionaries to be passed into the processors.
3. Steps: Three steps (StepOne, StepTwo, StepThree), each using one of the processors and one of the mappers.
4. ChainPipeline: A ChainPipeline that combines all three steps into a sequence.

import dataclasses

from logic_processes_layer import BaseProcessor
from logic_processes_layer.abc.abc_chain import AbstractChainPipeline
from logic_processes_layer.abc.abc_mapper import AbstractMapper
from logic_processes_layer.abc.abc_pipeline import AbstractPipelineStep
from logic_processes_layer.structures import AttrsData


@dataclasses.dataclass
class ProcessorOne(BaseProcessor):
    data_for_init: str

    def run(self):
        return {"one": 1}


@dataclasses.dataclass
class ProcessorTwo(BaseProcessor):
    data_for_init: str
    data_for_init_two: int

    def run(self):
        return None


class ProcessorThree(BaseProcessor):
    def run(self):
        return "RESULT THREE"


class MapperOne(AbstractMapper):
    def build_attrs_strategy(self, prev_results):
        return self.start_attrs


class MapperTwo(AbstractMapper):
    def build_attrs_strategy(self, prev_results):
        return AttrsData(
            args=self.start_attrs.args,
            kwargs={"data_for_init_two": prev_results["one"]}
        )


class MapperThree(AbstractMapper):
    def build_attrs_strategy(self, prev_results):
        return AttrsData(
            args=tuple(),
            kwargs={}
        )


class StepOne(AbstractPipelineStep):
    processor = ProcessorOne
    attr_mapper_cls = MapperOne


class StepTwo(AbstractPipelineStep):
    processor = ProcessorTwo
    attr_mapper_cls = MapperTwo


class StepThree(AbstractPipelineStep):
    processor = ProcessorThree
    attr_mapper_cls = MapperThree


class ChainPipeline(AbstractChainPipeline):
    step_classes = (StepOne, StepTwo, StepThree)
    start_attrs = AttrsData(args=("DATA FOR INIT",), kwargs={})


pipeline = ChainPipeline()
result = pipeline()
print(result)