data type check and conversion decorators
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
You can either use pip to install the version from PyPI or git to install the probably more recent version from github.
git clone http://github.com/mmaelicke/dtype-decorate.git cd dtype-decorate pip install -r requirements.txt python setup.py install
pip install dtype-decorate
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.
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.
@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.
@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
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