Skip to main content

Package for aiding writing classes with lots of similar simple properties without the boilerplate

Project description

Package for aiding writing classes with lots of similar simple properties without the boilerplate.

Status

Latest Release

PyPI

Travis CI

https://travis-ci.com/brocksam/pyproprop.svg?branch=master

Docs

Documentation Status

Appveyor

https://ci.appveyor.com/api/projects/status/github/brocksam/pyproprop?svg=true

PyPI

PyPI - Downloads

Coverage

Codecov

Anaconda

Conda

License

https://img.shields.io/badge/license-MIT-brightgreen.svg

What is pyproprop?

Do you often find yourself writing classes with properties such as:

from some_other_module import DefaultObject, some_type

class ExampleClass:

    def __init__(self,
                 type_checked_value,
                 bounded_numeric_value,
                 specific_length_sequence_value,
                 obj_with_method_applied_value,
                 ):
        self.type_check_attr = type_checked_value
        self.bounded_numeric_attr = bounded_numeric_value
        self.specific_length_sequence_attr = specific_length_sequence_value
        self.obj_with_method_applied_attr = obj_with_method_applied_value
        self.instantiate_default_if_none_attr = None

    @property
    def type_checked_attr(self):
        return self._type_checked_attr

    @type_checked_attr.setter
    def type_checked_attr(self, val):
        if not isinstance(val, some_type):
            msg = "`type_checked_attr` must be of `some_type`"
            raise TypeError(msg)
        self._type_checked_attr = val

    @property
    def bounded_numeric_attr(self):
        return self._bounded_numeric_attr

    @bounded_numeric_attr.setter
    def bounded_numeric_attr(self, val):
        val = float(val)
        lower_bound = -1.0
        upper_bound = 2.5
        if val < lower_bound:
            msg = f"`bounded_numeric_attr` must be greater than {lower_bound}"
            raise ValueError(msg)
        if val >= upper_bound:
            msg = (f"`bounded_numeric_attr` must be less than or equal to "
                   f"{upper_bound}.")
            raise ValueError(msg)
        self._type_checked_attr = val

    @property
    def specific_length_sequence_attr(self):
        return self._specific_length_sequence_attr

    @specific_length_sequence_attr.setter
    def specific_length_sequence_attr(self, val):
        if len(val) != 2:
            msg = "`specific_length_sequence` must be an iterable of length 2."
            raise ValueError(msg)
        self._specific_length_sequence_attr = val

    @property
    def obj_with_method_applied_value(self):
        return self._obj_with_method_applied_value

    @obj_with_method_applied_value.setter
    def obj_with_method_applied_value(self, val):
        val = str(val)
        self._obj_with_method_applied_value = val.title()

    @property
    def instantiate_default_if_none_attr(self):
        return self._instantiate_default_if_none_attr

    @instantiate_default_if_none_attr.setter
    def instantiate_default_if_none_attr(self, val):
        if val is None:
            val = DefaultObject()
        self._instantiate_default_if_none_attr = val

With pyproprop all of this boilerplate can be removed and instead the exact same class can be rewritten as:

from pyproprop import processed_property
from some_other_module import DefaultObject, some_type

class ExampleClass:

    type_checked_attr = processed_property(
        "type_checked_attr",
        description="property with enforced type of `some_type`",
        type=some_type,
    )
    bounded_numeric_attr = processed_property(
        "bounded_numeric_attr",
        description="numerical attribute with upper and lower bounds"
        type=float,
        cast=True,
        min=-1.0,
        max=2.5,
    )
    specific_length_sequence_attr = processed_property(
        "specific_length_sequence_attr",
        description="sequence of length exactly 2",
        len=2,
    )
    obj_with_method_applied_attr = processed_property(
        "obj_with_method_applied_attr",
        description="sting formatted to use title case"
        type=str,
        cast=True,
        method="title",
    )
    instantiate_default_if_none_attr = processed_property(
        "instantiate_default_if_none_attr",
        default=DefaultObject,
    )

    def __init__(self,
                 type_checked_value,
                 bounded_numeric_value,
                 specific_length_sequence_value,
                 obj_with_method_applied_value,
                 ):
        self.type_check_attr = type_checked_value
        self.bounded_numeric_attr = bounded_numeric_value
        self.specific_length_sequence_attr = specific_length_sequence_value
        self.obj_with_method_applied_attr = obj_with_method_applied_value
        self.instantiate_default_if_none_attr = None

Installation

The easiest way to install pyproprop is using the Anaconda Python distribution and its included Conda package management system. To install pyproprop and its required dependencies, enter the following command at a command prompt:

conda install pyproprop

To install using pip, enter the following command at a command prompt:

pip install pyproprop

For more information, refer to the installation documentation.

Contribute

License

This project is licensed under the terms of the MIT license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyproprop-0.3.0.tar.gz (31.0 kB view details)

Uploaded Source

Built Distribution

pyproprop-0.3.0-py3-none-any.whl (28.5 kB view details)

Uploaded Python 3

File details

Details for the file pyproprop-0.3.0.tar.gz.

File metadata

  • Download URL: pyproprop-0.3.0.tar.gz
  • Upload date:
  • Size: 31.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for pyproprop-0.3.0.tar.gz
Algorithm Hash digest
SHA256 e7c7692d6177ea8d6624820166096b5d503bef3886864116fdeef2d7f6f762d3
MD5 8edbccdf16e497050c00756184e31dd9
BLAKE2b-256 7707526fd0610bea8c3e28cefee63401c281f0d3e274281f2448c91b1093ee59

See more details on using hashes here.

File details

Details for the file pyproprop-0.3.0-py3-none-any.whl.

File metadata

  • Download URL: pyproprop-0.3.0-py3-none-any.whl
  • Upload date:
  • Size: 28.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for pyproprop-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 484476c7619849ed60db25e1f5dcae676a5dd7c5c25fb7a4ca50f0b8d57d8693
MD5 a6f47d05589d33a76d30e723fcc5e07b
BLAKE2b-256 8be8443b400d0d0a7db1739a10e3bb305a58cbe8f20d7f9e4219be1bc9434620

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page