How to use Python decorators to check function arguments?
Question:
I would like to define some generic decorators to check arguments before calling some functions.
Something like:
@checkArguments(types = ['int', 'float'])
def myFunction(thisVarIsAnInt, thisVarIsAFloat)
''' Here my code '''
pass
Side notes:
- Type checking is just here to show an example
- I’m using Python 2.7 but Python 3.0 whould be interesting too
EDIT 2021: funny that type checking did not go antipythonic in the long run with type hinting and mypy.
Answers:
On Python 3.3, you can use function annotations and inspect:
import inspect
def validate(f):
def wrapper(*args):
fname = f.__name__
fsig = inspect.signature(f)
vars = ', '.join('{}={}'.format(*pair) for pair in zip(fsig.parameters, args))
params={k:v for k,v in zip(fsig.parameters, args)}
print('wrapped call to {}({})'.format(fname, params))
for k, v in fsig.parameters.items():
p=params[k]
msg='call to {}({}): {} failed {})'.format(fname, vars, k, v.annotation.__name__)
assert v.annotation(params[k]), msg
ret = f(*args)
print(' returning {} with annotation: "{}"'.format(ret, fsig.return_annotation))
return ret
return wrapper
@validate
def xXy(x: lambda _x: 10<_x<100, y: lambda _y: isinstance(_y,float)) -> ('x times y','in X and Y units'):
return x*y
xy = xXy(10,3)
print(xy)
If there is a validation error, prints:
AssertionError: call to xXy(x=12, y=3): y failed <lambda>)
If there is not a validation error, prints:
wrapped call to xXy({'y': 3.0, 'x': 12})
returning 36.0 with annotation: "('x times y', 'in X and Y units')"
You can use a function rather than a lambda to get a name in the assertion failure.
From the Decorators for Functions and Methods:
Python 2
def accepts(*types):
def check_accepts(f):
assert len(types) == f.func_code.co_argcount
def new_f(*args, **kwds):
for (a, t) in zip(args, types):
assert isinstance(a, t),
"arg %r does not match %s" % (a,t)
return f(*args, **kwds)
new_f.func_name = f.func_name
return new_f
return check_accepts
Python 3
In Python 3 func_code
has changed to __code__
and func_name
has changed to __name__
.
def accepts(*types):
def check_accepts(f):
assert len(types) == f.__code__.co_argcount
def new_f(*args, **kwds):
for (a, t) in zip(args, types):
assert isinstance(a, t),
"arg %r does not match %s" % (a,t)
return f(*args, **kwds)
new_f.__name__ = f.__name__
return new_f
return check_accepts
Usage:
@accepts(int, (int,float))
def func(arg1, arg2):
return arg1 * arg2
func(3, 2) # -> 6
func('3', 2) # -> AssertionError: arg '3' does not match <type 'int'>
arg2
can be either int
or float
To enforce string arguments to a parser that would throw cryptic errors when provided with non-string input, I wrote the following, which tries to avoid allocation and function calls:
from functools import wraps
def argtype(**decls):
"""Decorator to check argument types.
Usage:
@argtype(name=str, text=str)
def parse_rule(name, text): ...
"""
def decorator(func):
code = func.func_code
fname = func.func_name
names = code.co_varnames[:code.co_argcount]
@wraps(func)
def decorated(*args,**kwargs):
for argname, argtype in decls.iteritems():
try:
argval = args[names.index(argname)]
except ValueError:
argval = kwargs.get(argname)
if argval is None:
raise TypeError("%s(...): arg '%s' is null"
% (fname, argname))
if not isinstance(argval, argtype):
raise TypeError("%s(...): arg '%s': type is %s, must be %s"
% (fname, argname, type(argval), argtype))
return func(*args,**kwargs)
return decorated
return decorator
I have a slightly improved version of @jbouwmans sollution, using python decorator module, which makes the decorator fully transparent and keeps not only signature but also docstrings in place and might be the most elegant way of using decorators
from decorator import decorator
def check_args(**decls):
"""Decorator to check argument types.
Usage:
@check_args(name=str, text=str)
def parse_rule(name, text): ...
"""
@decorator
def wrapper(func, *args, **kwargs):
code = func.func_code
fname = func.func_name
names = code.co_varnames[:code.co_argcount]
for argname, argtype in decls.iteritems():
try:
argval = args[names.index(argname)]
except IndexError:
argval = kwargs.get(argname)
if argval is None:
raise TypeError("%s(...): arg '%s' is null"
% (fname, argname))
if not isinstance(argval, argtype):
raise TypeError("%s(...): arg '%s': type is %s, must be %s"
% (fname, argname, type(argval), argtype))
return func(*args, **kwargs)
return wrapper
As you certainly know, it’s not pythonic to reject an argument only based on its type.
Pythonic approach is rather “try to deal with it first”
That’s why I would rather do a decorator to convert the arguments
def enforce(*types):
def decorator(f):
def new_f(*args, **kwds):
#we need to convert args into something mutable
newargs = []
for (a, t) in zip(args, types):
newargs.append( t(a)) #feel free to have more elaborated convertion
return f(*newargs, **kwds)
return new_f
return decorator
This way, your function is fed with the type you expect
But if the parameter can quack like a float, it is accepted
@enforce(int, float)
def func(arg1, arg2):
return arg1 * arg2
print (func(3, 2)) # -> 6.0
print (func('3', 2)) # -> 6.0
print (func('three', 2)) # -> ValueError: invalid literal for int() with base 10: 'three'
I use this trick (with proper conversion method) to deal with vectors.
Many methods I write expect MyVector class as it has plenty of functionalities; but sometime you just want to write
transpose ((2,4))
I think the Python 3.5 answer to this question is beartype. As explained in this post it comes with handy features. Your code would then look like this
from beartype import beartype
@beartype
def sprint(s: str) -> None:
print(s)
and results in
>>> sprint("s")
s
>>> sprint(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 13, in func_beartyped
TypeError: sprint() parameter s=3 not of <class 'str'>
All of these posts seem out of date – pint now provides this functionality built in. See here. Copied here for posterity:
Checking dimensionality When you want pint quantities to be used as
inputs to your functions, pint provides a wrapper to ensure units are
of correct type – or more precisely, they match the expected
dimensionality of the physical quantity.
Similar to wraps(), you can pass None to skip checking of some
parameters, but the return parameter type is not checked.
>>> mypp = ureg.check('[length]')(pendulum_period)
In the decorator format:
>>> @ureg.check('[length]')
... def pendulum_period(length):
... return 2*math.pi*math.sqrt(length/G)
The package typeguard
provides a decorator for this, it reads the type information from type annotations, it requires Python >=3.5.2 though. I think the resulting code is quite nice.
@typeguard.typechecked
def my_function(this_var_is_an_int: int, this_var_is_a_float: float)
''' Here my code '''
pass
you could try with the pydantic validation_decorator. from the documentation pydantic:
Data validation and settings management using python type annotations.
pydantic enforces type hints at runtime, and provides user friendly
errors when data is invalid.
In benchmarks pydantic is faster than all other tested libraries.
from pydantic import validate_arguments, ValidationError
@validate_arguments
def repeat(s: str, count: int, *, separator: bytes = b'') -> bytes:
b = s.encode()
return separator.join(b for _ in range(count))
a = repeat('hello', 3)
print(a)
#> b'hellohellohello'
b = repeat('x', '4', separator=' ')
print(b)
#> b'x x x x'
try:
c = repeat('hello', 'wrong')
except ValidationError as exc:
print(exc)
"""
1 validation error for Repeat
count
value is not a valid integer (type=type_error.integer)
"""
For me, the codes shared above looks complicated. What I did for defining ‘generic decorator’ for type-check:
I used *args, **kwargs feature, little extra work when using function/method but easy to manage.
Appropriate example definition for test
argument_types = {
'name':str,
'count':int,
'value':float
}
Decoration Defination
//from functools import wraps
def azure_type(func):
@wraps(func)
def type_decorator(*args, **kwargs):
for key, value in kwargs.items():
if key in argument_types:
if type(value) != argument_types[key]:
#enter code here
return 'Error Message or what ever you like to do'
return func(*args, **kwargs)
return type_decorator
Simple sample in code
// all other definitions
@azure_type
def stt(name:str, value:float)->(int):
#some calculation and creation of int output
count_output = #something int
return count_output
// call the function:
stt(name='ati', value=32.90) #can test from that
I would like to define some generic decorators to check arguments before calling some functions.
Something like:
@checkArguments(types = ['int', 'float'])
def myFunction(thisVarIsAnInt, thisVarIsAFloat)
''' Here my code '''
pass
Side notes:
- Type checking is just here to show an example
- I’m using Python 2.7 but Python 3.0 whould be interesting too
EDIT 2021: funny that type checking did not go antipythonic in the long run with type hinting and mypy.
On Python 3.3, you can use function annotations and inspect:
import inspect
def validate(f):
def wrapper(*args):
fname = f.__name__
fsig = inspect.signature(f)
vars = ', '.join('{}={}'.format(*pair) for pair in zip(fsig.parameters, args))
params={k:v for k,v in zip(fsig.parameters, args)}
print('wrapped call to {}({})'.format(fname, params))
for k, v in fsig.parameters.items():
p=params[k]
msg='call to {}({}): {} failed {})'.format(fname, vars, k, v.annotation.__name__)
assert v.annotation(params[k]), msg
ret = f(*args)
print(' returning {} with annotation: "{}"'.format(ret, fsig.return_annotation))
return ret
return wrapper
@validate
def xXy(x: lambda _x: 10<_x<100, y: lambda _y: isinstance(_y,float)) -> ('x times y','in X and Y units'):
return x*y
xy = xXy(10,3)
print(xy)
If there is a validation error, prints:
AssertionError: call to xXy(x=12, y=3): y failed <lambda>)
If there is not a validation error, prints:
wrapped call to xXy({'y': 3.0, 'x': 12})
returning 36.0 with annotation: "('x times y', 'in X and Y units')"
You can use a function rather than a lambda to get a name in the assertion failure.
From the Decorators for Functions and Methods:
Python 2
def accepts(*types):
def check_accepts(f):
assert len(types) == f.func_code.co_argcount
def new_f(*args, **kwds):
for (a, t) in zip(args, types):
assert isinstance(a, t),
"arg %r does not match %s" % (a,t)
return f(*args, **kwds)
new_f.func_name = f.func_name
return new_f
return check_accepts
Python 3
In Python 3 func_code
has changed to __code__
and func_name
has changed to __name__
.
def accepts(*types):
def check_accepts(f):
assert len(types) == f.__code__.co_argcount
def new_f(*args, **kwds):
for (a, t) in zip(args, types):
assert isinstance(a, t),
"arg %r does not match %s" % (a,t)
return f(*args, **kwds)
new_f.__name__ = f.__name__
return new_f
return check_accepts
Usage:
@accepts(int, (int,float))
def func(arg1, arg2):
return arg1 * arg2
func(3, 2) # -> 6
func('3', 2) # -> AssertionError: arg '3' does not match <type 'int'>
arg2
can be either int
or float
To enforce string arguments to a parser that would throw cryptic errors when provided with non-string input, I wrote the following, which tries to avoid allocation and function calls:
from functools import wraps
def argtype(**decls):
"""Decorator to check argument types.
Usage:
@argtype(name=str, text=str)
def parse_rule(name, text): ...
"""
def decorator(func):
code = func.func_code
fname = func.func_name
names = code.co_varnames[:code.co_argcount]
@wraps(func)
def decorated(*args,**kwargs):
for argname, argtype in decls.iteritems():
try:
argval = args[names.index(argname)]
except ValueError:
argval = kwargs.get(argname)
if argval is None:
raise TypeError("%s(...): arg '%s' is null"
% (fname, argname))
if not isinstance(argval, argtype):
raise TypeError("%s(...): arg '%s': type is %s, must be %s"
% (fname, argname, type(argval), argtype))
return func(*args,**kwargs)
return decorated
return decorator
I have a slightly improved version of @jbouwmans sollution, using python decorator module, which makes the decorator fully transparent and keeps not only signature but also docstrings in place and might be the most elegant way of using decorators
from decorator import decorator
def check_args(**decls):
"""Decorator to check argument types.
Usage:
@check_args(name=str, text=str)
def parse_rule(name, text): ...
"""
@decorator
def wrapper(func, *args, **kwargs):
code = func.func_code
fname = func.func_name
names = code.co_varnames[:code.co_argcount]
for argname, argtype in decls.iteritems():
try:
argval = args[names.index(argname)]
except IndexError:
argval = kwargs.get(argname)
if argval is None:
raise TypeError("%s(...): arg '%s' is null"
% (fname, argname))
if not isinstance(argval, argtype):
raise TypeError("%s(...): arg '%s': type is %s, must be %s"
% (fname, argname, type(argval), argtype))
return func(*args, **kwargs)
return wrapper
As you certainly know, it’s not pythonic to reject an argument only based on its type.
Pythonic approach is rather “try to deal with it first”
That’s why I would rather do a decorator to convert the arguments
def enforce(*types):
def decorator(f):
def new_f(*args, **kwds):
#we need to convert args into something mutable
newargs = []
for (a, t) in zip(args, types):
newargs.append( t(a)) #feel free to have more elaborated convertion
return f(*newargs, **kwds)
return new_f
return decorator
This way, your function is fed with the type you expect
But if the parameter can quack like a float, it is accepted
@enforce(int, float)
def func(arg1, arg2):
return arg1 * arg2
print (func(3, 2)) # -> 6.0
print (func('3', 2)) # -> 6.0
print (func('three', 2)) # -> ValueError: invalid literal for int() with base 10: 'three'
I use this trick (with proper conversion method) to deal with vectors.
Many methods I write expect MyVector class as it has plenty of functionalities; but sometime you just want to write
transpose ((2,4))
I think the Python 3.5 answer to this question is beartype. As explained in this post it comes with handy features. Your code would then look like this
from beartype import beartype
@beartype
def sprint(s: str) -> None:
print(s)
and results in
>>> sprint("s")
s
>>> sprint(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 13, in func_beartyped
TypeError: sprint() parameter s=3 not of <class 'str'>
All of these posts seem out of date – pint now provides this functionality built in. See here. Copied here for posterity:
Checking dimensionality When you want pint quantities to be used as
inputs to your functions, pint provides a wrapper to ensure units are
of correct type – or more precisely, they match the expected
dimensionality of the physical quantity.Similar to wraps(), you can pass None to skip checking of some
parameters, but the return parameter type is not checked.>>> mypp = ureg.check('[length]')(pendulum_period)
In the decorator format:
>>> @ureg.check('[length]') ... def pendulum_period(length): ... return 2*math.pi*math.sqrt(length/G)
The package typeguard
provides a decorator for this, it reads the type information from type annotations, it requires Python >=3.5.2 though. I think the resulting code is quite nice.
@typeguard.typechecked
def my_function(this_var_is_an_int: int, this_var_is_a_float: float)
''' Here my code '''
pass
you could try with the pydantic validation_decorator. from the documentation pydantic:
Data validation and settings management using python type annotations.
pydantic enforces type hints at runtime, and provides user friendly
errors when data is invalid.
In benchmarks pydantic is faster than all other tested libraries.
from pydantic import validate_arguments, ValidationError
@validate_arguments
def repeat(s: str, count: int, *, separator: bytes = b'') -> bytes:
b = s.encode()
return separator.join(b for _ in range(count))
a = repeat('hello', 3)
print(a)
#> b'hellohellohello'
b = repeat('x', '4', separator=' ')
print(b)
#> b'x x x x'
try:
c = repeat('hello', 'wrong')
except ValidationError as exc:
print(exc)
"""
1 validation error for Repeat
count
value is not a valid integer (type=type_error.integer)
"""
For me, the codes shared above looks complicated. What I did for defining ‘generic decorator’ for type-check:
I used *args, **kwargs feature, little extra work when using function/method but easy to manage.
Appropriate example definition for test
argument_types = {
'name':str,
'count':int,
'value':float
}
Decoration Defination
//from functools import wraps
def azure_type(func):
@wraps(func)
def type_decorator(*args, **kwargs):
for key, value in kwargs.items():
if key in argument_types:
if type(value) != argument_types[key]:
#enter code here
return 'Error Message or what ever you like to do'
return func(*args, **kwargs)
return type_decorator
Simple sample in code
// all other definitions
@azure_type
def stt(name:str, value:float)->(int):
#some calculation and creation of int output
count_output = #something int
return count_output
// call the function:
stt(name='ati', value=32.90) #can test from that