Metadata-Version: 2.1
Name: pipelayer
Version: 0.3.0
Summary: A lightweight pipeline framework
Home-page: https://github.com/greater-than/PipeLayer
Author: greaterThan, LLC
Author-email: info@greaterthan.solutions
License: UNKNOWN
Description: # PipeLayer
        PipeLayer is a lightweight Python pipeline framework. Define a series of steps, and chain them together to create modular applications.
        <br>
        
        ### Table of Contents
        
        * [Installation](#install)
        * [Getting Started](#get-started)
        * [The Framework](#framework)
        <br><br>
        
        
        <div id="install"></div>
        
        ## Installation
        
        From the command line:
        ```sh
        pip install pipelayer
        ```
        
        
        <div id="get-started"></div>
        
        ## Getting Started
        
        ### Step 1: Create Pipeline Filters
        
        `hello_world_filters.py`
        ```python
        from pipelayer import Filter
        
        
        class HelloFilter(Filter):
            def run(self, data, context):
                return "Hello"
        
        
        class WorldFilter(Filter):
            def run(self, data, context):
                return f"{data},  World!"
        ```
        
        `functions.py`
        ```python
        def create_message_dict(data, context):
            return {"message": data}
        ```
        
        ### Step 2: Create a Pipeline
        Create a module to run the pipeline:
        
        `app.py`
        ```python
        from pipelayer import Pipeline
        
        from functions import create_message
        from hello_world_filters import HelloFilter, WorldFilter
        
        
        if __name__ = "__main__":
            hello_world_pipeline = Pipeline([
                HelloFilter,                           # pipeline.Filter type
                WorldFilter,                           # pipeline.Filter instance
                create_message_dict                    # function type
                lambda data, context: json.dumps(data) # anonymous function
            ])
        
            output = hello_world_pipeline.run()
        
            # output = '{"message": "Hello, World!"}'
        
            print(f"Pipeline Output: {output}")
            print(hello_world_pipeline.manifest.__dict__)
        
        ```
        
        ### Step 3: Run the Pipeline
        from the command line:
        ```sh
        run app.py
        ```
        
        ---
        
        <div id="framework"></div>
        
        ## The Framework
        * [Pipeline](#pipeline)
        * [Filter](#filter)
        * [Context](#context)
        * [Manifest](#manifest)
        * [Utilities](#utilities)
        <br><br>
        
        
        <div id="pipeline"></div>
        
        ### __`pipelayer.Pipeline(`[`Step`](#step)`)`__
        
        ***Constructor***
        
        __`__init__(self: Pipeline, steps: List[Union[Step, Callable[Any, [Context]]]] = None, name: str = "")`__<br>
        The type hints for the `steps` arg may look confusing. Here's what's allowed:
        
        - Classes and Instances that derive from `pipelayer.Filter` and implement the `run` method
        - Functions (instance/class/static/module) that have the following signature
        
          ```python
          def func(data: Any, context: Any)
          ```
        
        - Anonymous functions (lambda) with two arguments that follow the same pattern for regular functions:
        
          ```python
          my_func = lambda data, context: data
          ```
        
        - **Instances of `pipelayer.Pipeline` (new in v0.3.0)**
        
        ***Properties***
        
        __`manifest`__ (Manifest)<br>
        An instance of [`pipelayer.Manifest`](#manifest) that is created when the run method is called.
        
        ***Methods***
        
        __`run(data: Any, context: Optional[`[`Context`](#context)`]) -> Any`__<br>
        The pipeline runner that iterates through the `steps` and pipes filter output to the next step.
        <br><br>
        
        
        <div id="filter"></div>
        
        ### __`pipelayer.Filter(`[`Step`](#step)`)`__
        A base class with an abstract `run` method.
        
        ***Properties***
        
        __`pre_process`__ (callable)<br>
        Optional.
        
        __`post_process`__ (callable)<br>
        Optional.
        
        ***Methods***
        
        __`@abstractmethod`__<br>
        __`run(data: Any, context: Optional[`[`Context`](#context)`]) -> Any`__<br>
        The abstract filter runner.
        <br><br>
        
        
        <div id="step"></div>
        
        ### __`pipelayer.Step`__
        The base class that is sub-classed by [`pipelayer.Pipeline`](#pipeline) and [`pipelayer.Filter`](#filter).
        
        ***Abstract Methods***
        
        __`@abstractmethod`__<br>
        __`run(data: Any, context: Optional[`[`Context`](#context)`]) -> Any`__<br>
        The abstract
        
        ***Properties***
        
        __`name`__ (str)<br>
        Optional. Used by the Manifest
        <br><br>
        
        
        <div id="context"></div>
        
        ### __`pipelayer.Context`__
        A extensible base class for runtime app config.
        <br><br>
        
        
        <div id="manifest"></div>
        
        ### __`pipelayer.Manifest`__
        The Manifest keeps a record of [`Pipeline`](#pipeline) and [`Filter`](#filter) activity.
        
        
        ### Utilities
        
        __`pipelayer.util.render_manifest(manifest: Manifest, indent: int = 2) -> str`__
        Static function that renders formatted JSON data
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
