https://github.com/mmaelicke/dtype-decorate

Mini-library for data-type check and conversion decorators.
https://github.com/mmaelicke/dtype-decorate

Last synced: about 2 months ago
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Mini-library for data-type check and conversion decorators.

• Host: GitHub
• URL: https://github.com/mmaelicke/dtype-decorate
• Owner: mmaelicke
• License: mit
• Created: 2017-07-25T09:02:41.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-07-26T08:49:09.000Z (over 7 years ago)
• Last Synced: 2024-10-16T04:41:04.876Z (3 months ago)
• Language: Python
• Size: 8.79 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
DType-Decorate

==============

The DType-Decorate module defines two different decorators at the current state. These decorators can be used to

constrain the attributes of the decorated function to specific data types. This can help to keep functions clean

especially when they are written for a specific context. This is usually the case for scientific applications,

where functionality is often more important than clean code.

The basic structure of this module was heavily inspired / extended on the basis of:

https://stackoverflow.com/questions/15299878/how-to-use-python-decorators-to-check-function-arguments

Installation

~~~~~~~~~~~~

You can either use `pip` to install the version from PyPI or git to install the probably more recent version from

github.

.. code-block:: bash

  git clone http://github.com/mmaelicke/dtype-decorate.git

  cd dtype-decorate

  pip install -r requirements.txt

  python setup.py install

.. code-block:: bash

  pip install dtype-decorate

Usage

~~~~~

There are two decorators so far: `accept` and `enforce`. `accept` will restrict the attribute data types to the

the defined ones, while `enforce` will try to convert the given attribute to a desired data type.

Both can also be used together, where `accept` does only make sense to be used after `enforce`.

Define a function that does only accept an `int` and a `float`.

.. code-block:: python

  import ddec

  @ddec.accept(a=int, b=float)

  def f(a, b):

    pass

You can also specify more than one data type allowed. Any attribute not given in the decorator will just be

ignored.

.. code-block:: python

  @ddec.accept(a=(int, float))

  def f(a, be_any_type)

    pass

  f(5, 'mystr')   # will run fine

  f('mystr', 5)   # will raise a TypeError

The `accept` decorator can also handle None type and callables like functions or lambda. These have to be specified

as a string.

.. code-block:: python

  @ddec.accept(a='None', b=('None', 'callable'))

  def f(a, b):

    pass

  f(None, None)           # will run fine

  f(None, lambda x: x)    # will run fine

  f(5, None)              # will raise a TypeError