plugnparse 0.1.1

Python utilities package for plugin style architectures with parsable classes for parameters.

Plug-n-Parse

Python utilities package for plugin style architectures with parsable classes for parameters.

• Setting Up
• Testing
• Coverage
• Usage

Setting Up

Setting Up Local Environment

Install python >3.8 if it is not already installed.

Set Up the Virtual Environment

Set up the local environment and install pre-commit so that the hooks are automatically run locally:

python3 -m  venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Setting Up for the Build

To build the python wheels and distribution package, use the build python packages.

To install the build package use the following command:

pip install --upgrade build

Building the Packages

To execute the build, follow the commands below. More detailed instructions on using build can be found here.

python -m build

This should generate plugnparse-<version>.tar.gz and plugnparse-<version>-py3-none-any.whl files in either the current working directory, <cwd>/dist/, or to a desired output directory using the argument --outdir OUTDIR in the build command above.

Installing the Built Packages

Use pip to install the generated package in another virtual environment or computer. This can be done using the following command:

pip install OUTDIR/plugnparse-<verison>-py3-none-any.whl

The OUTDIR is the directory location of the generated python wheel.

Testing

To run the tests follow the below commands

cd plugnparse/src
pytest ./tests

Coverage

To generate coverage reports follow the below commands

pip install -U coverage
coverage run --rcfile=./.coveragerc -m pytest ./src/tests
coverage html
google-chrome ./artifacts/coverage_report/html/index.html

Usage

• Using the Parsable Class
• Using the Plugin and Parameters Classes

Using the Parsable Class

The Parsable class allows for automatic serialization and deserialization of entire classes in a JSON format. The following example demonstrates a basic usage of the Parsable class.

# --- external imports ---
from enum import Enum, auto
from typing import Optional
# --- internal imports ---
from plugnparse import Parsable, enum_setter, logger


class EnumClass(Enum):
    Foo = auto()
    Bar = auto()
    Baz = auto()


class ClassA(Parsable):

    def __init__(self, *args, **kwargs):
        # --- init the parent ---
        super().__init__(*args, **kwargs)
        # --- update the lists of serializable attributes ---
        self._serializable_attributes.extend(['foo'])
        self._enum_attributes.extend(['bar'])
        self._specialized_attributes.extend(['baz'])

        # --- set the properties ---
        self.foo = kwargs.get('foo')
        self.bar = kwargs.get('bar')
        self.baz = kwargs.get('baz')

    ##########################################################################
    # Foo Properties
    ##########################################################################
    @property
    def has_foo(self):
        return self._foo is not None

    @property
    def foo(self) -> int:
        if self._foo is None:
            logger.log_and_raise(AttributeError, "foo has not been set")
        return self._foo

    @foo.setter
    def foo(self, input_value: Optional[int]):
        if input_value is None or isinstance(input_value, int):
            self._foo = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")

    ##########################################################################
    # Bar Properties
    ##########################################################################
    @property
    def has_bar(self):
        return self._bar is not None

    @property
    def bar(self) -> EnumClass:
        if self._bar is None:
            logger.log_and_raise(AttributeError, "bar has not been set")
        return self._bar

    @bar.setter
    @enum_setter(EnumClass)
    def bar(self, input_value: Optional[EnumClass]):
        if input_value is None or isinstance(input_value, EnumClass):
            self._bar = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")

    ##########################################################################
    # Baz Properties
    ##########################################################################
    @property
    def has_baz(self):
        return self._baz is not None

    @property
    def baz(self) -> float:
        if self._baz is None:
            logger.log_and_raise(AttributeError, "baz has not been set.")
        return self._baz

    @baz.setter
    def baz(self, input_value: Optional[float]):
        if input_value is None or isinstance(input_value, float):
            self._baz = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")
    
    def baz_encode(self) -> dict:
        """Special encoder function for baz"""
        return {"baz_keyword": self.baz}
    
    def baz_decode(self, input_value: dict):
        """Special decoder function."""
        self.baz = input_value.get('baz_keyword')

You should then provide a json dictionary of serializable values with the from_dict() function.

json_dictionary = {'foo': 1, 'bar': "Foo", 'baz': {'baz_keyword': 10.0}}
class_a = ClassA()
class_a.from_dict(json_dictionary)

To then get the original json dictionary from the updated class you use the to_dict() function

print(class_a.to_dict())
>>> {'parsable_type': 'ClassA', 'parsable_module': '__main__', 'foo': 1, 'bar': 'Foo', 'baz': {'baz_keyword': 10.0}}

Notice that the additional key-value pairs, parsable_type and parsable_module. These two key-values allow for generic creation and parsing directly from the json object without needing to know the class type that is being parsed. To leverage this type of functionality you can utilize the plugnparse properties module and specifically its parse() function.

from plugnparse import properties

full_json_dictionary = class_a.to_dict()
new_class_a = properties.parse(full_json_dictionary)
print("parsed type: ", type(new_class_a))
print("parsed information: ", new_class_a.to_dict())

>>> parsed type:  <class '__main__.ClassA'>
>>> parsed information:  {'parsable_type': 'ClassA', 'parsable_module': '__main__', 'foo': 1, 'bar': 'Foo', 'baz': {'baz_keyword': 10.0}}

Once there is a single Parsable implementation then other Parsable classes can have attributes which can also be Parsable types. The class below demonstrates the additional type of attributes that can be automatically parsed which are:

• Parsable attributes
• A dictionary of parsable key-value pairs, specifically where the values are subclasses of Parsable.
• A list of parsable values.

from typing import Dict


class ClassB(Parsable):

    def __init__(self, *args, **kwargs):
        # --- init the parent ---
        super().__init__(*args, **kwargs)
        # --- update the attributes ---
        self._parsable_attributes.extend(['bingo'])
        self._dict_of_parsables.extend(['bingo_dictionary'])
        self._list_of_parsables.extend(['bango_list'])

        # --- set the components ---
        self.bingo = kwargs.get('bingo')
        self.bingo_dictionary = kwargs.get('bingo_dictionary')
        self.bango_list = kwargs.get('bango_list')

    ##########################################################################
    # Bingo Properties
    ##########################################################################
    @property
    def has_bingo(self):
        return self._bingo is not None

    @property
    def bingo(self) -> ClassA:
        if self._bingo is None:
            logger.log_and_raise(AttributeError, "bingo has not been set")
        return self._bingo

    @bingo.setter
    @ClassA.static_class_setter()
    def bingo(self, input_value: Optional[ClassA]):
        if input_value is None or isinstance(input_value, ClassA):
            self._bingo = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")

    ##########################################################################
    # Bingo Dictionary Properties
    ##########################################################################
    @property
    def has_bingo_dictionary(self):
        return self._bingo_dictionary is not None

    @property
    def bingo_dictionary(self) -> Dict[str, ClassA]:
        if self._bingo_dictionary is None:
            logger.log_and_raise(AttributeError, "bingo_dictionary has not been set")
        return self._bingo_dictionary

    @bingo_dictionary.setter
    def bingo_dictionary(self, input_value: Optional[Dict[str, ClassA]]):
        if input_value is None:
            self._bingo_dictionary = None
        elif isinstance(input_value, dict):
            for key, value in input_value.items():
                if not (isinstance(key, str) and isinstance(value, ClassA)):
                    logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")
            self._bingo_dictionary = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")

    ##########################################################################
    # Bango List Properties
    ##########################################################################
    @property
    def has_bango_list(self):
        return self._bango_list is not None

    @property
    def bango_list(self) -> List[ClassA]:
        if self._bango_list is None:
            logger.log_and_raise(AttributeError, "bango_list has not been set")
        return self._bango_list

    @bango_list.setter
    def bango_list(self, input_value: Optional[List[ClassA]]):
        if input_value is None:
            self._bango_list = None
        elif isinstance(input_value, list):
            for value in input_value:
                if not isinstance(value, ClassA):
                    logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")
            self._bango_list = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")

Again to populate the ClassB, you would provide it a serialized form of the json dictionary.

class_b_json_dictionary = {'bingo': {'foo': 1, 'bar': "Foo"},
                           'bingo_dictionary': {'a': {'parsable_type': 'ClassA', 'parsable_module': '__main__', 'foo': 5}},
                           'bango_list': [{'parsable_type': 'ClassA', 'parsable_module': '__main__', 'foo': 10}]}
class_b = ClassB()
class_b.from_dict(class_b_json_dictionary)
print(class_b.to_dict())
>>> {'parsable_type': 'ClassB', 'parsable_module': '__main__', 'bingo': {'parsable_type': 'ClassA', 'parsable_module': '__main__', 'foo': 1, 'bar': 'Foo'}, 'bingo_dictionary': {'a': {'parsable_type': 'ClassA', 'parsable_module': '__main__', 'foo': 5}}, 'bango_list': [{'parsable_type': 'ClassA', 'parsable_module': '__main__', 'foo': 10}]}

Using the Plugin and Parameters Classes

Together the Parameters and the Plugin classes can be utilized to generate a plugin architecture very simply. The example below demonstrates the bare minimum implementations needed to create a plugin architecture.

# --- external imports ---
from typing import Optional
from abc import ABC, abstractmethod
# --- internal imports ---
from plugnparse import Plugin, Parameters, logger


class ExampleParameters(Parameters):
    plugin_property_name = 'plugin_type'
    plugin_module_property_name = 'plugin_module'
    
    def __init__(self, *args, **kwargs):
        # --- init the parent ---
        super().__init__(*args, **kwargs)
        # --- update the attributes ---
        self._serializable_attributes.extend(['plugin_type', 'plugin_module'])
        
        # --- set components ---
        self.plugin_type = kwargs.get('plugin_type')
        self.plugin_module = kwargs.get('plugin_module')
        
    ##########################################################################
    # Plugin Type Properties
    ##########################################################################
    @property
    def has_plugin_type(self):
        return self._plugin_type is not None

    @property
    def plugin_type(self) -> str:
        if self._plugin_type is None:
            logger.log_and_raise(AttributeError, "plugin_type has not been set")
        return self._plugin_type

    @plugin_type.setter
    def plugin_type(self, input_value: Optional[str]):
        if input_value is None or isinstance(input_value, str):
            self._plugin_type = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")

    ##########################################################################
    # Plugin Module Properties
    ##########################################################################
    @property
    def has_plugin_module(self):
        return self._plugin_module is not None

    @property
    def plugin_module(self) -> str:
        if self._plugin_module is None:
            logger.log_and_raise(AttributeError, "plugin_module has not been set")
        return self._plugin_module

    @plugin_module.setter
    def plugin_module(self, input_value: Optional[str]):
        if input_value is None or isinstance(input_value, str):
            self._plugin_module = input_value
        else:
            logger.log_and_raise(TypeError, "Invalid input type [", type(input_value), "].")


class BasePlugin(Plugin, ABC):
    parameters_cls = ExampleParameters

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    ##########################################################################
    # Dynamic Function
    ##########################################################################
    @abstractmethod
    def execute(self):
        pass  # pragma: no cover


class PluginExampleA(BasePlugin):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    ##########################################################################
    # Dynamic Function
    ##########################################################################
    def execute(self):
        print("PluginExampleA: Foo!")


class PluginExampleB(BasePlugin):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    ##########################################################################
    # Dynamic Function
    ##########################################################################
    def execute(self):
        print("PluginExampleB: Bar!")

To then utilize the architecture you would populate your parameters class and construct the desired plugins from it.

parameters = ExampleParameters(plugin_type="PluginExampleA", plugin_module="__main__")
generated_plugin = BasePlugin.construct_from_parameters(parameters)
generated_plugin.execute()
>>> PluginExampleA: Foo!

To create a different plugin all you need to do is update the parameters used to generate the plugin (generally the module is also updated but since this code is all in one file the module is the same).

parameters.plugin_type = "PluginExampleB"
second_generated_plugin = BasePlugin.construct_from_parameters(parameters)
second_generated_plugin.execute()
>>> PluginExampleB: Bar!