The following sample
import types
import pprint
class A:
def __call__(self, *args):
pprint.pprint('[A.__call__] self=%r, args=%r'
% (self, list(args)))
class B:
pass
if __name__ == '__main__':
a = A()
print(callable(a))
a(1, 2)
b = B()
b.meth = types.MethodType(a, b)
b.meth(3, 4)
prints
True
'[A.__call__] self=<__main__.A object at 0xb7233c2c>, args=[1, 2]'
('[A.__call__] self=<__main__.A object at 0xb7233c2c>, args=[<__main__.B '
'object at 0xb71687cc>, 3, 4]')
The number of the __call__ method arguments is changed in the
b.meth(3, 4) example. Please explain the first one (__main__.B
object...) and when Python does provide it?
Using Python 3.5.3 on Debian 9.9 Stretch
The important concept here is that a class function is a function that has 'self' bound to it as its first argument.
I'll demonstrate in a couple of examples. The following code will be identical for all examples:
import types
# Class with function
class A:
def func(*args):
print('A.func(%s)'%(', '.join([str(arg) for arg in args])))
# Callable function-style class
class A_callable:
def __call__(*args):
print('A_callable.__call__(%s)'%(', '.join([str(arg) for arg in args])))
# Empty class
class B():
pass
# Function without class
def func(*args):
print('func(%s)'%(', '.join([str(arg) for arg in args])))
Now let's consider a couple of examples:
>>> func(42)
func(42)
This one is obvious. It just calls the function func with argument 42.
The next ones are more interesting:
>>> A().func(42)
A.func(<__main__.A object at 0x7f1ed9ed2908>, 42)
>>> A_callable()(42)
A_callable.__call__(<__main__.A_callable object at 0x7f1ed9ed28d0>, 42)
You can see that the class object self is automatically given to the function as the first argument. It is important to note that the self argument is not added because the function is stored in an object, but because the function was constructed as part of the object, and therefore has the object bound to it.
To demonstrate:
>>> tmp = A().func
>>> tmp
<bound method A.func of <__main__.A object at 0x7f1ed9ed2978>>
>>> tmp(42)
A.func(<__main__.A object at 0x7f1ed9ed2978>, 42)
>>> tmp = A_callable().__call__
>>> tmp
<bound method A_callable.__call__ of <__main__.A_callable object at 0x7f1ed9ed2908>>
>>> tmp(42)
A_callable.__call__(<__main__.A_callable object at 0x7f1ed9ed2908>, 42)
The self argument does not get added because you write a. before it. It is part of the function object itself, storing it in a variable still keeps that binding.
You can also manually bind a class object to a function, like this:
>>> tmp = types.MethodType(func, B)
>>> tmp
<bound method func of <class '__main__.B'>>
>>> tmp(42)
func(<class '__main__.B'>, 42)
On the other hand, just assigning a function to a class does not bind self to the function. As previously mentioned, the argument does not get dynamically added when called, but statically when constructed:
>>> b = B()
>>> b.func = func
>>> b.func
<function func at 0x7f1edb58fe18>
>>> b.func(42)
func(42) # does NOT contain the `self` argument
That is why we need to explicitely bind self to the function if we want to add it to an object:
>>> b = B()
>>> b.func = types.MethodType(func, b)
>>> b.func
<bound method func of <__main__.B object at 0x7f1ed9ed2908>>
>>> b.func(42)
func(<__main__.B object at 0x7f1ed9ed2908>, 42)
The only thing left is to understand how binding works. If a method func has a parameter a bound to it, and gets called with *args, it will add a to the beginning of *args and then pass it to the function. The beginning is important here.
Now we know everything needed to understand your code:
>>> a = A_callable()
>>> b = B()
>>> b.func = types.MethodType(a, b)
>>> b.func
<bound method ? of <__main__.B object at 0x7f1ed97e9fd0>>
>>> b.func(42)
A_callable.__call__(<__main__.A_callable object at 0x7f1ed97fb2b0>, <__main__.B object at 0x7f1ed97e9fd0>, 42)
First of all, we can change the b.func to plain tmp because, as previously discussed, adding a function to an object does not change its type or functionality. Only binding self does.
Then, let's step through the code piece by piece:
>>> a = A_callable()
>>> b = B()
So far so good. We have an empty object b and a callable object a.
>>> tmp = types.MethodType(a,b)
This line is the crux. If you understand this, you will understand everything.
tmp is now the function a with b bound to it. That means, if we call tmp(42), it adds b to the beginning of its arguments. a will therefore receive b, 42. Then, because a is callable, it forwards its arguments to a.__call__.
That means, we are at the point where tmp(42) is equal to a.__call__(b, 42).
Because __call__ is a class function of A_callable, a is automatically bound to the __call__ function during the construction of a. Therefore before the arguments reach A_callable.__call__, a gets added to the beginning of the argument list, meaning the arguments are now a, b, 42.
Now we are at the point where tmp(42) equals A_callable.__call__(a, b, 42). This is exactly what you see:
>>> tmp = types.MethodType(a, b)
>>> tmp(42)
A_callable.__call__(<__main__.A_callable object at 0x7f1ed97fb2b0>, <__main__.B object at 0x7f1ed97e9fd0>, 42)
>>> A_callable.__call__(a, b, 42)
A_callable.__call__(<__main__.A_callable object at 0x7f1ed97fb2b0>, <__main__.B object at 0x7f1ed97e9fd0>, 42)
Now if you split your arguments into self, *args, you basically just take away the first argument and store it in self. Your first argument is a, so self will be a, and your other *args will be b, 42. Again, this is exactly what you see.
In a python project, my class has several properties that I need to be of specific type. Users of the class must have the ability to set the property.
What is the best way to do this? Two solutions come to my mind:
1. Have test routines in each setter function.
2. Use decorators for attributes
My current solution is 1 but I am not happy with it due to the code duplication. It looks like this:
class MyClass(object):
#property
def x(self):
return self._x
#x.setter
def x(self, val):
if not isinstance(self, int):
raise Exception("Value must be of type int")
self._x = val
#property
def y(self):
return self._y
#x.setter
def y(self, val):
if not isinstance(self, (tuple, set, list)):
raise Exception("Value must be of type tuple or set or list")
self._y = val
From what I know of decorators, it should be possible to have a decorator before def x(self) handle this job. Alas I fail miserably at this, as all examples I found (like this or this) are not targeted at what I want.
The first question is thus: Is it better to use a decorator to check property types? If yes, the next question is: What is wrong with below decorator (I want to be able write #accepts(int)?
def accepts(types):
"""Decorator to check types of property."""
def outer_wrapper(func):
def check_accepts(prop):
getter = prop.fget
if not isinstance(self[0], types):
msg = "Wrong type."
raise ValueError(msg)
return self
return check_accepts
return outer_wrapper
Appetizer
Callables
This is likely beyond your needs, since it sounds like you're dealing with end-user input, but I figured it may be helpful for others.
Callables include functions defined with def, built-in functions/methods such as open(), lambda expressions, callable classes, and many more. Obviously, if you only want to allow a certain type(s) of callables, you can still use isinstance() with types.FunctionType, types.BuiltinFunctionType, types.LambdaType, etc. But if this is not the case, the best solution to this that I am aware of is demonstrated by the MyDecoratedClass.z property using isinstance() with collections.abc.Callable. It's not perfect, and will return false positives in extraordinary cases (for example, if a class defines a __call__ function that doesn't actually make the class callable). The callable(obj) built-in is the only foolproof check function to my knowledge. The MyClass.z the use property demonstrates this function, but you'd have to write another/modify the existing decorator function in MyDecoratedClass in order to support the use of check functions other than isinstance().
Iterables (and Sequences and Sets)
The y property in the code you provided is supposed to be restricted to tuples, sets, and lists, so the following may be of some use to you.
Instead of checking if arguments are of individual types, you might want to consider using Iterable, Sequence, and Set from the collections.abc module. Please use caution though, as these types are far less restrictive than simply passing (tuple, set, list) as you have. abc.Iterable (as well as the others) work near-perfectly with isinstance(), although it does sometimes return false positives as well (e.g. a class defines an __iter__ function but doesn't actually return an iterator -- who hurt you?). The only foolproof method of determining whether or not an argument is iterable is by calling the iter(obj) built-in and letting it raise a TypeError if it's not iterable, which could work in your case. I don't know of any built-in alternatives to abc.Sequence and abc.Set, but almost every sequence/set object is also iterable as of Python 3, if that helps. The MyClass.y2 property implements iter() as a demonstration, however the decorator function in MyDecoratedClass does not (currently) support functions other than isinstance(); as such, MyDecoratedClass.y2 uses abc.Iterable instead.
For the completeness' sake, here is a quick comparison of their differences:
>>> from collections.abc import Iterable, Sequence, Set
>>> def test(x):
... print((isinstance(x, Iterable),
... isinstance(x, Sequence),
... isinstance(x, Set)))
...
>>> test(123) # int
False, False, False
>>> test("1, 2, 3") # str
True, True, False
>>> test([1, 2, 3]) # list
(True, True, False)
>>> test(range(3)) # range
(True, True, False)
>>> test((1, 2, 3)) # tuple
(True, True, False)
>>> test({1, 2, 3}) # set
(True, False, True)
>>> import numpy as np
>>> test(numpy.arange(3)) # numpy.ndarray
(True, False, False)
>>> test(zip([1, 2, 3],[4, 5, 6])) # zip
(True, False, False)
>>> test({1: 4, 2: 5, 3: 6}) # dict
(True, False, False)
>>> test({1: 4, 2: 5, 3: 6}.keys()) # dict_keys
(True, False, True)
>>> test({1: 4, 2: 5, 3: 6}.values()) # dict_values
(True, False, False)
>>> test({1: 4, 2: 5, 3: 6}.items()) # dict_items
(True, False, True)
Other Restrictions
Virtually all other argument type restrictions that I can think of must use hasattr(), which I'm not going to get into here.
Main Course
This is the part that actually answers your question. assert is definitely the simplest solution, but it has its limits.
class MyClass:
#property
def x(self):
return self._x
#x.setter
def x(self, val):
assert isinstance(val, int) # raises AssertionError if val is not of type 'int'
self._x = val
#property
def y(self):
return self._y
#y.setter
def y(self, val):
assert isinstance(val, (list, set, tuple)) # raises AssertionError if val is not of type 'list', 'set', or 'tuple'
self._y = val
#property
def y2(self):
return self._y2
#y2.setter
def y2(self, val):
iter(val) # raises TypeError if val is not iterable
self._y2 = val
#property
def z(self):
return self._z
#z.setter
def z(self, val):
assert callable(val) # raises AssertionError if val is not callable
self._z = val
def multi_arg_example_fn(self, a, b, c, d, e, f, g):
assert isinstance(a, int)
assert isinstance(b, int)
# let's say 'c' is unrestricted
assert isinstance(d, int)
assert isinstance(e, int)
assert isinstance(f, int)
assert isinstance(g, int)
this._a = a
this._b = b
this._c = c
this._d = d
this._e = e
this._f = f
this._g = g
return a + b * d - e // f + g
Pretty clean overall, besides the multi-argument function I threw in there at the end, demonstrating that asserts can get tedious. However, I'd argue that the biggest drawback here is the lack of Exception messages/variables. If the end-user sees an AssertionError, it has no message and is therefore mostly useless. If you write intermediate code that could except these errors, that code will have no variables/data to be able to explain to the user what went wrong. Enter the decorator function...
from collections.abc import Callable, Iterable
class MyDecoratedClass:
def isinstance_decorator(*classinfo_args, **classinfo_kwargs):
'''
Usage:
Always remember that each classinfo can be a type OR tuple of types.
If the decorated function takes, for example, two positional arguments...
* You only need to provide positional arguments up to the last positional argument that you want to restrict the type of. Take a look:
1. Restrict the type of only the first argument with '#isinstance_decorator(<classinfo_of_arg_1>)'
* Notice that a second positional argument is not required
* Although if you'd like to be explicit for clarity (in exchange for a small amount of efficiency), use '#isinstance_decorator(<classinfo_of_arg_1>, object)'
* Every object in Python must be of type 'object', so restricting the argument to type 'object' is equivalent to no restriction whatsoever
2. Restrict the types of both arguments with '#isinstance_decorator(<classinfo_of_arg_1>, <classinfo_of_arg_2>)'
3. Restrict the type of only the second argument with '#isinstance_decorator(object, <classinfo_of_arg_2>)'
* Every object in Python must be of type 'object', so restricting the argument to type 'object' is equivalent to no restriction whatsoever
Keyword arguments are simpler: #isinstance_decorator(<a_keyword> = <classinfo_of_the_kwarg>, <another_keyword> = <classinfo_of_the_other_kwarg>, ...etc)
* Remember that you only need to include the kwargs that you actually want to restrict the type of (no using 'object' as a keyword argument!)
* Using kwargs is probably more efficient than using example 3 above; I would avoid having to use 'object' as a positional argument as much as possible
Programming-Related Errors:
Raises IndexError if given more positional arguments than decorated function
Raises KeyError if given keyword argument that decorated function isn't expecting
Raises TypeError if given argument that is not of type 'type'
* Raised by 'isinstance()' when fed improper 2nd argument, like 'isinstance(foo, 123)'
* Virtually all UN-instantiated objects are of type 'type'
Examples:
example_instance = ExampleClass(*args)
# Neither 'example_instance' nor 'ExampleClass(*args)' is of type 'type', but 'ExampleClass' itself is
example_int = 100
# Neither 'example_int' nor '100' are of type 'type', but 'int' itself is
def example_fn: pass
# 'example_fn' is not of type 'type'.
print(type(example_fn).__name__) # function
print(type(isinstance).__name__) # builtin_function_or_method
# As you can see, there are also several types of callable objects
# If needed, you can retrieve most function/method/etc. types from the built-in 'types' module
Functional/Intended Errors:
Raises TypeError if a decorated function argument is not an instance of the type(s) specified by the corresponding decorator argument
'''
def isinstance_decorator_wrapper(old_fn):
def new_fn(self, *args, **kwargs):
for i in range(len(classinfo_args)):
classinfo = classinfo_args[i]
arg = args[i]
if not isinstance(arg, classinfo):
raise TypeError("%s() argument %s takes argument of type%s' but argument of type '%s' was given" %
(old_fn.__name__, i,
"s '" + "', '".join([x.__name__ for x in classinfo]) if isinstance(classinfo, tuple) else " '" + classinfo.__name__,
type(arg).__name__))
for k, classinfo in classinfo_kwargs.items():
kwarg = kwargs[k]
if not isinstance(kwarg, classinfo):
raise TypeError("%s() keyword argument '%s' takes argument of type%s' but argument of type '%s' was given" %
(old_fn.__name__, k,
"s '" + "', '".join([x.__name__ for x in classinfo]) if isinstance(classinfo, tuple) else " '" + classinfo.__name__,
type(kwarg).__name__))
return old_fn(self, *args, **kwargs)
return new_fn
return isinstance_decorator_wrapper
#property
def x(self):
return self._x
#x.setter
#isinstance_decorator(int)
def x(self, val):
self._x = val
#property
def y(self):
return self._y
#y.setter
#isinstance_decorator((list, set, tuple))
def y(self, val):
self._y = val
#property
def y2(self):
return self._y2
#y2.setter
#isinstance_decorator(Iterable)
def y2(self, val):
self._y2 = val
#property
def z(self):
return self._z
#z.setter
#isinstance_decorator(Callable)
def z(self, val):
self._z = val
#isinstance_decorator(int, int, e = int, f = int, g = int, d = (int, float, str))
def multi_arg_example_fn(self, a, b, c, d, e, f, g):
# Identical to assertions in MyClass.multi_arg_example_fn
self._a = a
self._b = b
self._c = c
self._d = d
return a + b * e - f // g
Clearly, multi_example_fn is one place where this decorator really shines. The clutter made by assertions has been reduced to a single line. Let's take a look at some example error messages:
>>> test = MyClass()
>>> dtest = MyDecoratedClass()
>>> test.x = 10
>>> dtest.x = 10
>>> print(test.x == dtest.x)
True
>>> test.x = 'Hello'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 7, in x
AssertionError
>>> dtest.x = 'Hello'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 100, in new_fn
TypeError: x() argument 0 takes argument of type 'int' but argument of type 'str' was given
>>> test.y = 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 15, in y
AssertionError
>>> test.y2 = 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 23, in y2
TypeError: 'int' object is not iterable
>>> dtest.y = 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 100, in new_fn
TypeError: y() argument 0 takes argument of types 'list', 'set', 'tuple' but argument of type 'int' was given
>>> dtest.y2 = 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 100, in new_fn
TypeError: y2() argument 0 takes argument of type 'Iterable' but argument of type 'int' was given
>>> test.z = open
>>> dtest.z = open
>>> test.z = None
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 31, in z
AssertionError
>>> dtest.z = None
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 100, in new_fn
TypeError: z() argument 0 takes argument of type 'Callable' but argument of type 'NoneType' was given
Far superior in my opinion. Everything looks good except...
>>> test.multi_arg_example_fn(9,4,[1,2],'hi', g=2,e=1,f=4)
11
>>> dtest.multi_arg_example_fn(9,4,[1,2],'hi', g=2,e=1,f=4)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 102, in new_fn
KeyError: 'd'
>>> print('I forgot that you have to merge args and kwargs in order for the decorator to work properly with both but I dont have time to fix it right now. Absolutely safe for properties for the time being though!')
I forgot that you have to merge args and kwargs in order for the decorator to work properly with both but I dont have time to fix it right now. Absolutely safe for properties for the time being though!
Edit Notice: My previous answer was completely incorrect. I was suggesting the use of type hints, forgetting that they aren't actually ensured in any way. They are strictly a development/IDE tool. They still are insanely helpful though; I recommend looking into using them.
Why I am getting this error, actually I have already defined ' a' before using in both codes.
def add(a,b):
return a+b
kwargs={'a':3,'b':5,'c':add(a,b)}
print("a is {a},b is {b}".format(**kwargs))
Traceback (most recent call last):
File "E:\GitRepo\Advanced_Python\logNew.py", line 13, in <module>
kwargs={'a':3,'b':5,'c':add(a,b)}
NameError: name 'a' is not defined
import logging
logging.basicConfig(level=logging.INFO)
def add(a,b):
return a+b
logging.info("Summation of {a},{b} is {c}".format(a=3,b=4,c=add(a,b)))
Traceback (most recent call last):
File "E:\GitRepo\Advanced_Python\logNew.py", line 5, in <module>
logging.info("Summation of {a},{b} is {c}".format(a=3,b=4,c=add(a,b)))
NameError: name 'a' is not defined
def add(a,b):
return a+b
kwargs={'a':3,'b':5}
kwargs['c']=add(kwargs['a'],kwargs['b'])
print("a is {a},b is {b}".format(**kwargs))
you have to write like this , a is not variable here, it is constant 'a'.
import logging
logging.basicConfig(level=logging.INFO)
def add(a,b):
return a+b
a=3
b=4
logging.info("Summation of {a},{b} is {c}".format(a=a,b=b,c=add(a,b)))
you have to declare variables first then use it.
When the python interpreter encounters the line: kwargs={'a':3,'b':5,'c':add(a,b)}. This is what it does:
Evaluate all the values of the keys being assigned to
This includes 3, 5 and add(a, b)
3 & 5 get defined as usual - since those are constants
add(a, b) - here add is defined (the function you defined above)
But a or b are not defined!
Hence the error. a & b are not variables (you can check this by checking the locals() & globals() - variables should show up there). They are keys in the dictionary.
This will work:
def add(a,b):
return a+b
a = 3
b = 5
kwargs={'a':a,'b':b,'c':add(a,b)}
print("a is {a},b is {b}".format(**kwargs))
Because the variables a & b are defined before add(a, b) is evaluated.
Your "add" function is expecting two integers, but you are not giving it when in dictionary expression. Look:
def add(a,b):
return a+b
kwargs={'a':3,'b':5,'c':add(5,2)}
print("a is {a},b is {b}".format(**kwargs))
I am passing 5 and 2 in dictionary expression.
Hope this helps.
I am getting a syntax error when trying to do the following MCVE in Python 3.
HEIGHT = 26
WIDTH = 26
OTHERVAR = 5
class Foo():
def __init__(self, OTHERVAR, HEIGHT*WIDTH):
print (str(OTHERVAR + HEIGHT*WIDTH))
foo_inst = Foo()
Below is the error
File "a.py", line 6
def __init__(self, OTHERVAR, HEIGHT*WIDTH):
^
SyntaxError: invalid syntax
I'm wondering why the multiplication * operator is invalid syntax in this scenario.
If someone could explain why this is bad syntax and offer a potential workaround, that would be great. Thank you.
A function parameter supposes to be a variable, your HEIGHT*WIDTH produces a value, not a variable.
Are you probably looking for this (default value)?
>>> a = 1
>>> b = 2
>>> def test(c=a*b):
... print(c)
...
>>> test()
2
>>> def test(c=a*b, d):
... print(c, d)
...
File "<stdin>", line 1
SyntaxError: non-default argument follows default argument
>>> def test(d, c=a*b):
... print(d, c)
...
>>> test(10)
(10, 2)
And called by named parameters
>>> def test(d, c=a*b, e=20):
... print(d, c, e)
...
>>> test(10, e=30)
(10, 2, 30)
I'm trying to make a function the returns all possible permutations for a list of numbers for example:
List= [1,2,3]
[[1,2,3], [1,3,2], [2,1,3], [2,3,1], [3,1,2],[3,2,1]]
however I keep running into the same error
TypeError: 'int' object is not callable
in this line
return permutations(result,variable,List,permutations)
the rest of the code is
def permutationsaux(List):
if List==[]:
return []
else:
return permutations([List],0,List,countpermutations(List))
def permutations(result,variable,List,permutations):
if len(result)==permutations:
return result
elif len(result[variable])==len(List):
result.append([])
variable=variable+1
return permutations(result,variable,List,permutations)
return permutations(result[variable]+reorderlist(List),variable+1,reorderlist(lista),permutations)
def countpermutations(List):
if List==[]:
return 1
return len(List)*countpermutations(List[1:])
def reorderlist(List):
temp=List[len(List)-2]
List[len(List)-2]=LIst[len(List)-1]
List[len(List)-1]=temp
return List
Your function "permutations" has an argument named "permutations". Guess which one is in scope within the function.