Related
I would like to replace an object instance by another instance inside a method like this:
class A:
def method1(self):
self = func(self)
The object is retrieved from a database.
It is unlikely that replacing the 'self' variable will accomplish whatever you're trying to do, that couldn't just be accomplished by storing the result of func(self) in a different variable. 'self' is effectively a local variable only defined for the duration of the method call, used to pass in the instance of the class which is being operated upon. Replacing self will not actually replace references to the original instance of the class held by other objects, nor will it create a lasting reference to the new instance which was assigned to it.
As far as I understand, If you are trying to replace the current object with another object of same type (assuming func won't change the object type) from an member function. I think this will achieve that:
class A:
def method1(self):
newObj = func(self)
self.__dict__.update(newObj.__dict__)
It is not a direct answer to the question, but in the posts below there's a solution for what amirouche tried to do:
Python object conversion
Can I dynamically convert an instance of one class to another?
And here's working code sample (Python 3.2.5).
class Men:
def __init__(self, name):
self.name = name
def who_are_you(self):
print("I'm a men! My name is " + self.name)
def cast_to(self, sex, name):
self.__class__ = sex
self.name = name
def method_unique_to_men(self):
print('I made The Matrix')
class Women:
def __init__(self, name):
self.name = name
def who_are_you(self):
print("I'm a women! My name is " + self.name)
def cast_to(self, sex, name):
self.__class__ = sex
self.name = name
def method_unique_to_women(self):
print('I made Cloud Atlas')
men = Men('Larry')
men.who_are_you()
#>>> I'm a men! My name is Larry
men.method_unique_to_men()
#>>> I made The Matrix
men.cast_to(Women, 'Lana')
men.who_are_you()
#>>> I'm a women! My name is Lana
men.method_unique_to_women()
#>>> I made Cloud Atlas
Note the self.__class__ and not self.__class__.__name__. I.e. this technique not only replaces class name, but actually converts an instance of a class (at least both of them have same id()). Also, 1) I don't know whether it is "safe to replace a self object by another object of the same type in [an object own] method"; 2) it works with different types of objects, not only with ones that are of the same type; 3) it works not exactly like amirouche wanted: you can't init class like Class(args), only Class() (I'm not a pro and can't answer why it's like this).
Yes, all that will happen is that you won't be able to reference the current instance of your class A (unless you set another variable to self before you change it.) I wouldn't recommend it though, it makes for less readable code.
Note that you're only changing a variable, just like any other. Doing self = 123 is the same as doing abc = 123. self is only a reference to the current instance within the method. You can't change your instance by setting self.
What func(self) should do is to change the variables of your instance:
def func(obj):
obj.var_a = 123
obj.var_b = 'abc'
Then do this:
class A:
def method1(self):
func(self) # No need to assign self here
In many cases, a good way to achieve what you want is to call __init__ again. For example:
class MyList(list):
def trim(self,n):
self.__init__(self[:-n])
x = MyList([1,2,3,4])
x.trim(2)
assert type(x) == MyList
assert x == [1,2]
Note that this comes with a few assumptions such as the all that you want to change about the object being set in __init__. Also beware that this could cause problems with inheriting classes that redefine __init__ in an incompatible manner.
Yes, there is nothing wrong with this. Haters gonna hate. (Looking at you Pycharm with your in most cases imaginable, there's no point in such reassignment and it indicates an error).
A situation where you could do this is:
some_method(self, ...):
...
if(some_condition):
self = self.some_other_method()
...
return ...
Sure, you could start the method body by reassigning self to some other variable, but if you wouldn't normally do that with other parametres, why do it with self?
One can use the self assignment in a method, to change the class of instance to a derived class.
Of course one could assign it to a new object, but then the use of the new object ripples through the rest of code in the method. Reassiging it to self, leaves the rest of the method untouched.
class aclass:
def methodA(self):
...
if condition:
self = replace_by_derived(self)
# self is now referencing to an instance of a derived class
# with probably the same values for its data attributes
# all code here remains untouched
...
self.methodB() # calls the methodB of derivedclass is condition is True
...
def methodB(self):
# methodB of class aclass
...
class derivedclass(aclass):
def methodB(self):
#methodB of class derivedclass
...
But apart from such a special use case, I don't see any advantages to replace self.
You can make the instance a singleton element of the class
and mark the methods with #classmethod.
from enum import IntEnum
from collections import namedtuple
class kind(IntEnum):
circle = 1
square = 2
def attr(y): return [getattr(y, x) for x in 'k l b u r'.split()]
class Shape(namedtuple('Shape', 'k,l,b,u,r')):
self = None
#classmethod
def __repr__(cls):
return "<Shape({},{},{},{},{}) object at {}>".format(
*(attr(cls.self)+[id(cls.self)]))
#classmethod
def transform(cls, func):
cls.self = cls.self._replace(**func(cls.self))
Shape.self = Shape(k=1, l=2, b=3, u=4, r=5)
s = Shape.self
def nextkind(self):
return {'k': self.k+1}
print(repr(s)) # <Shape(1,2,3,4,5) object at 139766656561792>
s.transform(nextkind)
print(repr(s)) # <Shape(2,2,3,4,5) object at 139766656561888>
Is there a way to conveniently define a C-like structure in Python? I'm tired of writing stuff like:
class MyStruct():
def __init__(self, field1, field2, field3):
self.field1 = field1
self.field2 = field2
self.field3 = field3
Update: Data Classes
With the introduction of Data Classes in Python 3.7 we get very close.
The following example is similar to the NamedTuple example below, but the resulting object is mutable and it allows for default values.
from dataclasses import dataclass
#dataclass
class Point:
x: float
y: float
z: float = 0.0
p = Point(1.5, 2.5)
print(p) # Point(x=1.5, y=2.5, z=0.0)
This plays nicely with the new typing module in case you want to use more specific type annotations.
I've been waiting desperately for this! If you ask me, Data Classes and the new NamedTuple declaration, combined with the typing module are a godsend!
Improved NamedTuple declaration
Since Python 3.6 it became quite simple and beautiful (IMHO), as long as you can live with immutability.
A new way of declaring NamedTuples was introduced, which allows for type annotations as well:
from typing import NamedTuple
class User(NamedTuple):
name: str
class MyStruct(NamedTuple):
foo: str
bar: int
baz: list
qux: User
my_item = MyStruct('foo', 0, ['baz'], User('peter'))
print(my_item) # MyStruct(foo='foo', bar=0, baz=['baz'], qux=User(name='peter'))
Use a named tuple, which was added to the collections module in the standard library in Python 2.6. It's also possible to use Raymond Hettinger's named tuple recipe if you need to support Python 2.4.
It's nice for your basic example, but also covers a bunch of edge cases you might run into later as well. Your fragment above would be written as:
from collections import namedtuple
MyStruct = namedtuple("MyStruct", "field1 field2 field3")
The newly created type can be used like this:
m = MyStruct("foo", "bar", "baz")
You can also use named arguments:
m = MyStruct(field1="foo", field2="bar", field3="baz")
You can use a tuple for a lot of things where you would use a struct in C (something like x,y coordinates or RGB colors for example).
For everything else you can use dictionary, or a utility class like this one:
>>> class Bunch:
... def __init__(self, **kwds):
... self.__dict__.update(kwds)
...
>>> mystruct = Bunch(field1=value1, field2=value2)
I think the "definitive" discussion is here, in the published version of the Python Cookbook.
Perhaps you are looking for Structs without constructors:
class Sample:
name = ''
average = 0.0
values = None # list cannot be initialized here!
s1 = Sample()
s1.name = "sample 1"
s1.values = []
s1.values.append(1)
s1.values.append(2)
s1.values.append(3)
s2 = Sample()
s2.name = "sample 2"
s2.values = []
s2.values.append(4)
for v in s1.values: # prints 1,2,3 --> OK.
print v
print "***"
for v in s2.values: # prints 4 --> OK.
print v
How about a dictionary?
Something like this:
myStruct = {'field1': 'some val', 'field2': 'some val'}
Then you can use this to manipulate values:
print myStruct['field1']
myStruct['field2'] = 'some other values'
And the values don't have to be strings. They can be pretty much any other object.
dF: that's pretty cool... I didn't
know that I could access the fields in
a class using dict.
Mark: the situations that I wish I had
this are precisely when I want a tuple
but nothing as "heavy" as a
dictionary.
You can access the fields of a class using a dictionary because the fields of a class, its methods and all its properties are stored internally using dicts (at least in CPython).
...Which leads us to your second comment. Believing that Python dicts are "heavy" is an extremely non-pythonistic concept. And reading such comments kills my Python Zen. That's not good.
You see, when you declare a class you are actually creating a pretty complex wrapper around a dictionary - so, if anything, you are adding more overhead than by using a simple dictionary. An overhead which, by the way, is meaningless in any case. If you are working on performance critical applications, use C or something.
I would also like to add a solution that uses slots:
class Point:
__slots__ = ["x", "y"]
def __init__(self, x, y):
self.x = x
self.y = y
Definitely check the documentation for slots but a quick explanation of slots is that it is python's way of saying: "If you can lock these attributes and only these attributes into the class such that you commit that you will not add any new attributes once the class is instantiated (yes you can add new attributes to a class instance, see example below) then I will do away with the large memory allocation that allows for adding new attributes to a class instance and use just what I need for these slotted attributes".
Example of adding attributes to class instance (thus not using slots):
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
p1 = Point(3,5)
p1.z = 8
print(p1.z)
Output: 8
Example of trying to add attributes to class instance where slots was used:
class Point:
__slots__ = ["x", "y"]
def __init__(self, x, y):
self.x = x
self.y = y
p1 = Point(3,5)
p1.z = 8
Output: AttributeError: 'Point' object has no attribute 'z'
This can effectively works as a struct and uses less memory than a class (like a struct would, although I have not researched exactly how much). It is recommended to use slots if you will be creating a large amount of instances of the object and do not need to add attributes. A point object is a good example of this as it is likely that one may instantiate many points to describe a dataset.
You can subclass the C structure that is available in the standard library. The ctypes module provides a Structure class. The example from the docs:
>>> from ctypes import *
>>> class POINT(Structure):
... _fields_ = [("x", c_int),
... ("y", c_int)]
...
>>> point = POINT(10, 20)
>>> print point.x, point.y
10 20
>>> point = POINT(y=5)
>>> print point.x, point.y
0 5
>>> POINT(1, 2, 3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: too many initializers
>>>
>>> class RECT(Structure):
... _fields_ = [("upperleft", POINT),
... ("lowerright", POINT)]
...
>>> rc = RECT(point)
>>> print rc.upperleft.x, rc.upperleft.y
0 5
>>> print rc.lowerright.x, rc.lowerright.y
0 0
>>>
You can also pass the init parameters to the instance variables by position
# Abstract struct class
class Struct:
def __init__ (self, *argv, **argd):
if len(argd):
# Update by dictionary
self.__dict__.update (argd)
else:
# Update by position
attrs = filter (lambda x: x[0:2] != "__", dir(self))
for n in range(len(argv)):
setattr(self, attrs[n], argv[n])
# Specific class
class Point3dStruct (Struct):
x = 0
y = 0
z = 0
pt1 = Point3dStruct()
pt1.x = 10
print pt1.x
print "-"*10
pt2 = Point3dStruct(5, 6)
print pt2.x, pt2.y
print "-"*10
pt3 = Point3dStruct (x=1, y=2, z=3)
print pt3.x, pt3.y, pt3.z
print "-"*10
Whenever I need an "instant data object that also behaves like a dictionary" (I don't think of C structs!), I think of this cute hack:
class Map(dict):
def __init__(self, **kwargs):
super(Map, self).__init__(**kwargs)
self.__dict__ = self
Now you can just say:
struct = Map(field1='foo', field2='bar', field3=42)
self.assertEquals('bar', struct.field2)
self.assertEquals(42, struct['field3'])
Perfectly handy for those times when you need a "data bag that's NOT a class", and for when namedtuples are incomprehensible...
Some the answers here are massively elaborate. The simplest option I've found is (from: http://norvig.com/python-iaq.html):
class Struct:
"A structure that can have any fields defined."
def __init__(self, **entries): self.__dict__.update(entries)
Initialising:
>>> options = Struct(answer=42, linelen=80, font='courier')
>>> options.answer
42
adding more:
>>> options.cat = "dog"
>>> options.cat
dog
edit: Sorry didn't see this example already further down.
You access C-Style struct in python in following way.
class cstruct:
var_i = 0
var_f = 0.0
var_str = ""
if you just want use object of cstruct
obj = cstruct()
obj.var_i = 50
obj.var_f = 50.00
obj.var_str = "fifty"
print "cstruct: obj i=%d f=%f s=%s" %(obj.var_i, obj.var_f, obj.var_str)
if you want to create an array of objects of cstruct
obj_array = [cstruct() for i in range(10)]
obj_array[0].var_i = 10
obj_array[0].var_f = 10.00
obj_array[0].var_str = "ten"
#go ahead and fill rest of array instaces of struct
#print all the value
for i in range(10):
print "cstruct: obj_array i=%d f=%f s=%s" %(obj_array[i].var_i, obj_array[i].var_f, obj_array[i].var_str)
Note:
instead of 'cstruct' name, please use your struct name
instead of var_i, var_f, var_str, please define your structure's member variable.
This might be a bit late but I made a solution using Python Meta-Classes (decorator version below too).
When __init__ is called during run time, it grabs each of the arguments and their value and assigns them as instance variables to your class. This way you can make a struct-like class without having to assign every value manually.
My example has no error checking so it is easier to follow.
class MyStruct(type):
def __call__(cls, *args, **kwargs):
names = cls.__init__.func_code.co_varnames[1:]
self = type.__call__(cls, *args, **kwargs)
for name, value in zip(names, args):
setattr(self , name, value)
for name, value in kwargs.iteritems():
setattr(self , name, value)
return self
Here it is in action.
>>> class MyClass(object):
__metaclass__ = MyStruct
def __init__(self, a, b, c):
pass
>>> my_instance = MyClass(1, 2, 3)
>>> my_instance.a
1
>>>
I posted it on reddit and /u/matchu posted a decorator version which is cleaner. I'd encourage you to use it unless you want to expand the metaclass version.
>>> def init_all_args(fn):
#wraps(fn)
def wrapped_init(self, *args, **kwargs):
names = fn.func_code.co_varnames[1:]
for name, value in zip(names, args):
setattr(self, name, value)
for name, value in kwargs.iteritems():
setattr(self, name, value)
return wrapped_init
>>> class Test(object):
#init_all_args
def __init__(self, a, b):
pass
>>> a = Test(1, 2)
>>> a.a
1
>>>
I wrote a decorator which you can use on any method to make it so that all of the arguments passed in, or any defaults, are assigned to the instance.
def argumentsToAttributes(method):
argumentNames = method.func_code.co_varnames[1:]
# Generate a dictionary of default values:
defaultsDict = {}
defaults = method.func_defaults if method.func_defaults else ()
for i, default in enumerate(defaults, start = len(argumentNames) - len(defaults)):
defaultsDict[argumentNames[i]] = default
def newMethod(self, *args, **kwargs):
# Use the positional arguments.
for name, value in zip(argumentNames, args):
setattr(self, name, value)
# Add the key word arguments. If anything is missing, use the default.
for name in argumentNames[len(args):]:
setattr(self, name, kwargs.get(name, defaultsDict[name]))
# Run whatever else the method needs to do.
method(self, *args, **kwargs)
return newMethod
A quick demonstration. Note that I use a positional argument a, use the default value for b, and a named argument c. I then print all 3 referencing self, to show that they've been properly assigned before the method is entered.
class A(object):
#argumentsToAttributes
def __init__(self, a, b = 'Invisible', c = 'Hello'):
print(self.a)
print(self.b)
print(self.c)
A('Why', c = 'Nothing')
Note that my decorator should work with any method, not just __init__.
I don't see this answer here, so I figure I'll add it since I'm leaning Python right now and just discovered it. The Python tutorial (Python 2 in this case) gives the following simple and effective example:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
That is, an empty class object is created, then instantiated, and the fields are added dynamically.
The up-side to this is its really simple. The downside is it isn't particularly self-documenting (the intended members aren't listed anywhere in the class "definition"), and unset fields can cause problems when accessed. Those two problems can be solved by:
class Employee:
def __init__ (self):
self.name = None # or whatever
self.dept = None
self.salary = None
Now at a glance you can at least see what fields the program will be expecting.
Both are prone to typos, john.slarly = 1000 will succeed. Still, it works.
Here is a solution which uses a class (never instantiated) to hold data. I like that this way involves very little typing and does not require any additional packages etc.
class myStruct:
field1 = "one"
field2 = "2"
You can add more fields later, as needed:
myStruct.field3 = 3
To get the values, the fields are accessed as usual:
>>> myStruct.field1
'one'
Personally, I like this variant too. It extends #dF's answer.
class struct:
def __init__(self, *sequential, **named):
fields = dict(zip(sequential, [None]*len(sequential)), **named)
self.__dict__.update(fields)
def __repr__(self):
return str(self.__dict__)
It supports two modes of initialization (that can be blended):
# Struct with field1, field2, field3 that are initialized to None.
mystruct1 = struct("field1", "field2", "field3")
# Struct with field1, field2, field3 that are initialized according to arguments.
mystruct2 = struct(field1=1, field2=2, field3=3)
Also, it prints nicer:
print(mystruct2)
# Prints: {'field3': 3, 'field1': 1, 'field2': 2}
There is a python package exactly for this purpose. see cstruct2py
cstruct2py is a pure python library for generate python classes from C code and use them to pack and unpack data. The library can parse C headres (structs, unions, enums, and arrays declarations) and emulate them in python. The generated pythonic classes can parse and pack the data.
For example:
typedef struct {
int x;
int y;
} Point;
after generating pythonic class...
p = Point(x=0x1234, y=0x5678)
p.packed == "\x34\x12\x00\x00\x78\x56\x00\x00"
How to use
First we need to generate the pythonic structs:
import cstruct2py
parser = cstruct2py.c2py.Parser()
parser.parse_file('examples/example.h')
Now we can import all names from the C code:
parser.update_globals(globals())
We can also do that directly:
A = parser.parse_string('struct A { int x; int y;};')
Using types and defines from the C code
a = A()
a.x = 45
print a
buf = a.packed
b = A(buf)
print b
c = A('aaaa11112222', 2)
print c
print repr(c)
The output will be:
{'x':0x2d, 'y':0x0}
{'x':0x2d, 'y':0x0}
{'x':0x31316161, 'y':0x32323131}
A('aa111122', x=0x31316161, y=0x32323131)
Clone
For clone cstruct2py run:
git clone https://github.com/st0ky/cstruct2py.git --recursive
Here is a quick and dirty trick:
>>> ms = Warning()
>>> ms.foo = 123
>>> ms.bar = 'akafrit'
How does it works? It just re-use the builtin class Warning (derived from Exception) and use it as it was you own defined class.
The good points are that you do not need to import or define anything first, that "Warning" is a short name, and that it also makes clear you are doing something dirty which should not be used elsewhere than a small script of yours.
By the way, I tried to find something even simpler like ms = object() but could not (this last exemple is not working). If you have one, I am interested.
NamedTuple is comfortable. but there no one shares the performance and storage.
from typing import NamedTuple
import guppy # pip install guppy
import timeit
class User:
def __init__(self, name: str, uid: int):
self.name = name
self.uid = uid
class UserSlot:
__slots__ = ('name', 'uid')
def __init__(self, name: str, uid: int):
self.name = name
self.uid = uid
class UserTuple(NamedTuple):
# __slots__ = () # AttributeError: Cannot overwrite NamedTuple attribute __slots__
name: str
uid: int
def get_fn(obj, attr_name: str):
def get():
getattr(obj, attr_name)
return get
if 'memory test':
obj = [User('Carson', 1) for _ in range(1000000)] # Cumulative: 189138883
obj_slot = [UserSlot('Carson', 1) for _ in range(1000000)] # 77718299 <-- winner
obj_namedtuple = [UserTuple('Carson', 1) for _ in range(1000000)] # 85718297
print(guppy.hpy().heap()) # Run this function individually.
"""
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 24 112000000 34 112000000 34 dict of __main__.User
1 1000000 24 64000000 19 176000000 53 __main__.UserTuple
2 1000000 24 56000000 17 232000000 70 __main__.User
3 1000000 24 56000000 17 288000000 87 __main__.UserSlot
...
"""
if 'performance test':
obj = User('Carson', 1)
obj_slot = UserSlot('Carson', 1)
obj_tuple = UserTuple('Carson', 1)
time_normal = min(timeit.repeat(get_fn(obj, 'name'), repeat=20))
print(time_normal) # 0.12550550000000005
time_slot = min(timeit.repeat(get_fn(obj_slot, 'name'), repeat=20))
print(time_slot) # 0.1368690000000008
time_tuple = min(timeit.repeat(get_fn(obj_tuple, 'name'), repeat=20))
print(time_tuple) # 0.16006120000000124
print(time_tuple/time_slot) # 1.1694481584580898 # The slot is almost 17% faster than NamedTuple on Windows. (Python 3.7.7)
If your __dict__ is not using, please choose between __slots__ (higher performance and storage) and NamedTuple (clear for reading and use)
You can review this link(Usage of slots
) to get more __slots__ information.
https://stackoverflow.com/a/32448434/159695 does not work in Python3.
https://stackoverflow.com/a/35993/159695 works in Python3.
And I extends it to add default values.
class myStruct:
def __init__(self, **kwds):
self.x=0
self.__dict__.update(kwds) # Must be last to accept assigned member variable.
def __repr__(self):
args = ['%s=%s' % (k, repr(v)) for (k,v) in vars(self).items()]
return '%s(%s)' % ( self.__class__.__qualname__, ', '.join(args) )
a=myStruct()
b=myStruct(x=3,y='test')
c=myStruct(x='str')
>>> a
myStruct(x=0)
>>> b
myStruct(x=3, y='test')
>>> c
myStruct(x='str')
The following solution to a struct is inspired by the namedtuple implementation and some of the previous answers. However, unlike the namedtuple it is mutable, in it's values, but like the c-style struct immutable in the names/attributes, which a normal class or dict isn't.
_class_template = """\
class {typename}:
def __init__(self, *args, **kwargs):
fields = {field_names!r}
for x in fields:
setattr(self, x, None)
for name, value in zip(fields, args):
setattr(self, name, value)
for name, value in kwargs.items():
setattr(self, name, value)
def __repr__(self):
return str(vars(self))
def __setattr__(self, name, value):
if name not in {field_names!r}:
raise KeyError("invalid name: %s" % name)
object.__setattr__(self, name, value)
"""
def struct(typename, field_names):
class_definition = _class_template.format(
typename = typename,
field_names = field_names)
namespace = dict(__name__='struct_%s' % typename)
exec(class_definition, namespace)
result = namespace[typename]
result._source = class_definition
return result
Usage:
Person = struct('Person', ['firstname','lastname'])
generic = Person()
michael = Person('Michael')
jones = Person(lastname = 'Jones')
In [168]: michael.middlename = 'ben'
Traceback (most recent call last):
File "<ipython-input-168-b31c393c0d67>", line 1, in <module>
michael.middlename = 'ben'
File "<string>", line 19, in __setattr__
KeyError: 'invalid name: middlename'
If you don't have a 3.7 for #dataclass and need mutability, the following code might work for you. It's quite self-documenting and IDE-friendly (auto-complete), prevents writing things twice, is easily extendable and it is very simple to test that all instance variables are completely initialized:
class Params():
def __init__(self):
self.var1 : int = None
self.var2 : str = None
def are_all_defined(self):
for key, value in self.__dict__.items():
assert (value is not None), "instance variable {} is still None".format(key)
return True
params = Params()
params.var1 = 2
params.var2 = 'hello'
assert(params.are_all_defined)
The best way I found to do this was to use a custom dictionary class as explained in this post: https://stackoverflow.com/a/14620633/8484485
If iPython autocompletion support is needed, simply define the dir() function like this:
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def __dir__(self):
return self.keys()
You then define your pseudo struct like so: (this one is nested)
my_struct=AttrDict ({
'com1':AttrDict ({
'inst':[0x05],
'numbytes':2,
'canpayload':False,
'payload':None
})
})
You can then access the values inside my_struct like this:
print(my_struct.com1.inst)
=>[5]
The cleanest way I can think of is to use a class decorator that lets you declare a static class and rewrite it to act as a struct with normal, named properties:
from as_struct import struct
#struct
class Product():
name = 'unknown product'
quantity = -1
sku = '-'
# create instance
p = Product('plush toy', sku='12-345-6789')
# check content:
p.name # plush toy
p.quantity # -1
p.sku # 12-345-6789
Using the following decorator code:
def struct(struct_class):
# create a new init
def struct_init(self, *args, **kwargs):
i = 0 # we really don't need enumerate() here...
for value in args:
name = member_names[i]
default_value = member_values[i]
setattr(self, name, value if value is not None else default_value)
i += 1 # ...we just need to inc an int
for key,value in kwargs.items():
i = member_names.index(key)
default_value = member_values[i]
setattr(self, key, value if value is not None else default_value)
# extract the struct members
member_names = []
member_values = []
for attr_name in dir(struct_class):
if not attr_name.startswith('_'):
value = getattr(struct_class, attr_name)
if not callable(value):
member_names.append(attr_name)
member_values.append(value)
# rebind and return
struct_class.init = struct_init
return struct_class
Which works by taking the class, extracting the field names and their default values, then rewriting the class's __init__ function to set self attributes based on knowing which argument index maps to which property name.
I think Python structure dictionary is suitable for this requirement.
d = dict{}
d[field1] = field1
d[field2] = field2
d[field2] = field3
Extending #gz.'s (generally superior to this one) answer, for a quick and dirty namedtuple structure we can do:
import collections
x = collections.namedtuple('foobar', 'foo bar')(foo=1,bar=2)
y = collections.namedtuple('foobar', 'foo bar')(foo=3,bar=4)
print(x,y)
>foobar(foo=1, bar=2) foobar(foo=3, bar=4)
I want to replace string literals in my code, as I want to minimize risk of typos, especially in dict key sets:
a['typoh'] = 'this is bad'
I don't want to type things in twice (risk of a missed typo on the value)
I want it to be "trackable" by various IDEs (i.e. click thru to see where it is defined and escape completion).
Enums are out: 'E.a.name' to get 'a' is dumb.
I have been told this can be done with slots, but I can't figure out how without a little trickery. I can think of a few ways below:
This is an unacceptable answer:
class TwiceIsNotNice(object):
this_is_a_string = 'this_is_a_string'
... (five thousand string constants in)
this_has_a_hard_to_spot_typographical_error =
'this_has_a_had_to_spot_typographical_error'
... (five thousand more string constants)
A clear but annoying way is with a "Stringspace" class/object where the attributes are set via a string list passed in. This solves the minimized typo risk, is VERY easy to read, but has neither IDE trackability nor autocompletion. It's okay, but makes people complain (please don't complain here, I am simply showing how it could be done):
string_consts = Stringspace('a', 'b',...,'asdfasdfasdf')
print(string_consts.a)
... where:
class Stringspace(object):
def __init__(self, *strlist):
for s in strlist:
setattr(self, s, s)
Another way is to define a class using a sentinel object, setting the value in a post phase. This is okay, is trackable, presents itself as an actual class, allows for aliases, etc. But it requires an annoying extra call at the end of the class:
same = object()
class StrList(object):
this_is_a_strval = same
this_is_another_strval = same
this_gets_aliased = "to something else"
# This would of course could become a function
for attr in dir(StrList):
if getattr(StrList, attr) is same:
setattr(StrList, attr, attr)
print(StrList.a)
If this is what the slot magic is supposedly about, then I am disappointed, as one would have to actually instantiate an object:
class SlotEnum(object):
__slots__ = []
def __init__(self):
for k in self.__slots__:
setattr(self, k, k)
class Foo(SlotEnum):
__slots__ = ['a', 'b']
foo_enum_OBJECT = Foo()
print(foo_enum_OBJECT.a)
Enums are out: E.a.name to get a is dumb.
from enum import Enum, auto
class StrEnum(str, Enum):
"base class for Enum members to be strings matching the member's name"
def __repr__(self):
return '<%s.%s>' % (self.__class__.__name__, self.name)
def __str__(self):
return self.name
class E(StrEnum):
a = auto()
this_is_a_string = auto()
no_typo_here = auto()
>>> print(repr(E.a))
<E.a>
>>> print(E.a)
a
>>> print('the answer is: %s!' % E.a)
the answer is: a!
I found one solution at this external link using a custom meta class, for your class containing the string member variables:
Step 1 of 2: The custom meta class can be defined like this:
class MetaForMyStrConstants(type):
def __new__(metacls, cls, bases, classdict):
object_attrs = set(dir(type(cls, (object,), {})))
simple_enum_cls = super().__new__(metacls, cls, bases, classdict)
simple_enum_cls._member_names_ = set(classdict.keys()) - object_attrs
non_members = set()
for attr in simple_enum_cls._member_names_:
if attr.startswith('_') and attr.endswith('_'):
non_members.add(attr)
else:
setattr(simple_enum_cls, attr, attr)
simple_enum_cls._member_names_.difference_update(non_members)
return simple_enum_cls
Step 2 of 2: The class defining your strings can be defined like this (with dummy values, eg, empty tuples):
class MyStrConstants(metaclass=MetaForMyStrConstants):
ONE_LONG_STR = ()
ANOTHER_LONG_STR = ()
THE_REAL_LONGEST_STR = ()
Testing it out:
print (MyStrConstants.ONE_LONG_STR)
print (MyStrConstants.ANOTHER_LONG_STR)
print (MyStrConstants.THE_REAL_LONGEST_STR)
Output:
ONE_LONG_STR
ANOTHER_LONG_STR
THE_REAL_LONGEST_STR
I have an object that holds lots of ids that are accessed statically. I want to split that up into another object which holds only those ids without the need of making modifications to the already existen code base. Take for example:
class _CarType(object):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
class Car(object):
types = _CarType
I want to be able to access _CarType.DIESEL_CAR_ENGINE either by calling Car.types.DIESEL_CAR_ENGINE, either by Car.DIESEL_CAR_ENGINE for backwards compatibility with the existent code. It's clear that I cannot use __getattr__ so I am trying to find a way of making this work (maybe metaclasses ? )
Although this is not exactly what subclassing is made for, it accomplishes what you describe:
class _CarType(object):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
class Car(_CarType):
types = _CarType
Something like:
class Car(object):
for attr, value in _CarType.__dict__.items():
it not attr.startswith('_'):
locals()[attr] = value
del attr, value
Or you can do it out of the class declaration:
class Car(object):
# snip
for attr, value in _CarType.__dict__.items():
it not attr.startswith('_'):
setattr(Car, attr, value)
del attr, value
This is how you could do this with a metaclass:
class _CarType(type):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
def __init__(self,name,bases,dct):
for key in dir(_CarType):
if key.isupper():
setattr(self,key,getattr(_CarType,key))
class Car(object):
__metaclass__=_CarType
print(Car.DIESEL_CAR_ENGINE)
print(Car.GAS_CAR_ENGINE)
Your options fall into two substantial categories: you either copy the attributes from _CarType into Car, or set Car's metaclass to a custom one with a __getattr__ method that delegates to _CarType (so it isn't exactly true that you can't use __getattr__: you can, you just need to put in in Car's metaclass rather than in Car itself;-).
The second choice has implications that you might find peculiar (unless they are specifically desired): the attributes don't show up on dir(Car), and they can't be accessed on an instance of Car, only on Car itself. I.e.:
>>> class MetaGetattr(type):
... def __getattr__(cls, nm):
... return getattr(cls.types, nm)
...
>>> class Car:
... __metaclass__ = MetaGetattr
... types = _CarType
...
>>> Car.GAS_CAR_ENGINE
1
>>> Car().GAS_CAR_ENGINE
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Car' object has no attribute 'GAS_CAR_ENGINE'
You could fix the "not from an instance" issue by also adding a __getattr__ to Car:
>>> class Car:
... __metaclass__ = MetaGetattr
... types = _CarType
... def __getattr__(self, nm):
... return getattr(self.types, nm)
...
to make both kinds of lookup work, as is probably expected:
>>> Car.GAS_CAR_ENGINE
1
>>> Car().GAS_CAR_ENGINE
1
However, defining two, essentially-equal __getattr__s, doesn't seem elegant.
So I suspect that the simpler approach, "copy all attributes", is preferable. In Python 2.6 or better, this is an obvious candidate for a class decorator:
def typesfrom(typesclass):
def decorate(cls):
cls.types = typesclass
for n in dir(typesclass):
if n[0] == '_': continue
v = getattr(typesclass, n)
setattr(cls, n, v)
return cls
return decorate
#typesfrom(_CarType)
class Car(object):
pass
In general, it's worth defining a decorator if you're using it more than once; if you only need to perform this task for one class ever, then expanding the code inline instead (after the class statement) may be better.
If you're stuck with Python 2.5 (or even 2.4), you can still define typesfrom the same way, you just apply it in a slightly less elegant matter, i.e., the Car definition becomes:
class Car(object):
pass
Car = typesfrom(_CarType)(Car)
Do remember decorator syntax (introduced in 2.2 for functions, in 2.6 for classes) is just a handy way to wrap these important and frequently recurring semantics.
class _CarType(object):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
class Car:
types = _CarType
def __getattr__(self, name):
return getattr(self.types, name)
If an attribute of an object is not found, and it defines that magic method __getattr__, that gets called to try to find it.
Only works on a Car instance, not on the class.
Is there a way to conveniently define a C-like structure in Python? I'm tired of writing stuff like:
class MyStruct():
def __init__(self, field1, field2, field3):
self.field1 = field1
self.field2 = field2
self.field3 = field3
Update: Data Classes
With the introduction of Data Classes in Python 3.7 we get very close.
The following example is similar to the NamedTuple example below, but the resulting object is mutable and it allows for default values.
from dataclasses import dataclass
#dataclass
class Point:
x: float
y: float
z: float = 0.0
p = Point(1.5, 2.5)
print(p) # Point(x=1.5, y=2.5, z=0.0)
This plays nicely with the new typing module in case you want to use more specific type annotations.
I've been waiting desperately for this! If you ask me, Data Classes and the new NamedTuple declaration, combined with the typing module are a godsend!
Improved NamedTuple declaration
Since Python 3.6 it became quite simple and beautiful (IMHO), as long as you can live with immutability.
A new way of declaring NamedTuples was introduced, which allows for type annotations as well:
from typing import NamedTuple
class User(NamedTuple):
name: str
class MyStruct(NamedTuple):
foo: str
bar: int
baz: list
qux: User
my_item = MyStruct('foo', 0, ['baz'], User('peter'))
print(my_item) # MyStruct(foo='foo', bar=0, baz=['baz'], qux=User(name='peter'))
Use a named tuple, which was added to the collections module in the standard library in Python 2.6. It's also possible to use Raymond Hettinger's named tuple recipe if you need to support Python 2.4.
It's nice for your basic example, but also covers a bunch of edge cases you might run into later as well. Your fragment above would be written as:
from collections import namedtuple
MyStruct = namedtuple("MyStruct", "field1 field2 field3")
The newly created type can be used like this:
m = MyStruct("foo", "bar", "baz")
You can also use named arguments:
m = MyStruct(field1="foo", field2="bar", field3="baz")
You can use a tuple for a lot of things where you would use a struct in C (something like x,y coordinates or RGB colors for example).
For everything else you can use dictionary, or a utility class like this one:
>>> class Bunch:
... def __init__(self, **kwds):
... self.__dict__.update(kwds)
...
>>> mystruct = Bunch(field1=value1, field2=value2)
I think the "definitive" discussion is here, in the published version of the Python Cookbook.
Perhaps you are looking for Structs without constructors:
class Sample:
name = ''
average = 0.0
values = None # list cannot be initialized here!
s1 = Sample()
s1.name = "sample 1"
s1.values = []
s1.values.append(1)
s1.values.append(2)
s1.values.append(3)
s2 = Sample()
s2.name = "sample 2"
s2.values = []
s2.values.append(4)
for v in s1.values: # prints 1,2,3 --> OK.
print v
print "***"
for v in s2.values: # prints 4 --> OK.
print v
How about a dictionary?
Something like this:
myStruct = {'field1': 'some val', 'field2': 'some val'}
Then you can use this to manipulate values:
print myStruct['field1']
myStruct['field2'] = 'some other values'
And the values don't have to be strings. They can be pretty much any other object.
dF: that's pretty cool... I didn't
know that I could access the fields in
a class using dict.
Mark: the situations that I wish I had
this are precisely when I want a tuple
but nothing as "heavy" as a
dictionary.
You can access the fields of a class using a dictionary because the fields of a class, its methods and all its properties are stored internally using dicts (at least in CPython).
...Which leads us to your second comment. Believing that Python dicts are "heavy" is an extremely non-pythonistic concept. And reading such comments kills my Python Zen. That's not good.
You see, when you declare a class you are actually creating a pretty complex wrapper around a dictionary - so, if anything, you are adding more overhead than by using a simple dictionary. An overhead which, by the way, is meaningless in any case. If you are working on performance critical applications, use C or something.
I would also like to add a solution that uses slots:
class Point:
__slots__ = ["x", "y"]
def __init__(self, x, y):
self.x = x
self.y = y
Definitely check the documentation for slots but a quick explanation of slots is that it is python's way of saying: "If you can lock these attributes and only these attributes into the class such that you commit that you will not add any new attributes once the class is instantiated (yes you can add new attributes to a class instance, see example below) then I will do away with the large memory allocation that allows for adding new attributes to a class instance and use just what I need for these slotted attributes".
Example of adding attributes to class instance (thus not using slots):
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
p1 = Point(3,5)
p1.z = 8
print(p1.z)
Output: 8
Example of trying to add attributes to class instance where slots was used:
class Point:
__slots__ = ["x", "y"]
def __init__(self, x, y):
self.x = x
self.y = y
p1 = Point(3,5)
p1.z = 8
Output: AttributeError: 'Point' object has no attribute 'z'
This can effectively works as a struct and uses less memory than a class (like a struct would, although I have not researched exactly how much). It is recommended to use slots if you will be creating a large amount of instances of the object and do not need to add attributes. A point object is a good example of this as it is likely that one may instantiate many points to describe a dataset.
You can subclass the C structure that is available in the standard library. The ctypes module provides a Structure class. The example from the docs:
>>> from ctypes import *
>>> class POINT(Structure):
... _fields_ = [("x", c_int),
... ("y", c_int)]
...
>>> point = POINT(10, 20)
>>> print point.x, point.y
10 20
>>> point = POINT(y=5)
>>> print point.x, point.y
0 5
>>> POINT(1, 2, 3)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: too many initializers
>>>
>>> class RECT(Structure):
... _fields_ = [("upperleft", POINT),
... ("lowerright", POINT)]
...
>>> rc = RECT(point)
>>> print rc.upperleft.x, rc.upperleft.y
0 5
>>> print rc.lowerright.x, rc.lowerright.y
0 0
>>>
You can also pass the init parameters to the instance variables by position
# Abstract struct class
class Struct:
def __init__ (self, *argv, **argd):
if len(argd):
# Update by dictionary
self.__dict__.update (argd)
else:
# Update by position
attrs = filter (lambda x: x[0:2] != "__", dir(self))
for n in range(len(argv)):
setattr(self, attrs[n], argv[n])
# Specific class
class Point3dStruct (Struct):
x = 0
y = 0
z = 0
pt1 = Point3dStruct()
pt1.x = 10
print pt1.x
print "-"*10
pt2 = Point3dStruct(5, 6)
print pt2.x, pt2.y
print "-"*10
pt3 = Point3dStruct (x=1, y=2, z=3)
print pt3.x, pt3.y, pt3.z
print "-"*10
Whenever I need an "instant data object that also behaves like a dictionary" (I don't think of C structs!), I think of this cute hack:
class Map(dict):
def __init__(self, **kwargs):
super(Map, self).__init__(**kwargs)
self.__dict__ = self
Now you can just say:
struct = Map(field1='foo', field2='bar', field3=42)
self.assertEquals('bar', struct.field2)
self.assertEquals(42, struct['field3'])
Perfectly handy for those times when you need a "data bag that's NOT a class", and for when namedtuples are incomprehensible...
Some the answers here are massively elaborate. The simplest option I've found is (from: http://norvig.com/python-iaq.html):
class Struct:
"A structure that can have any fields defined."
def __init__(self, **entries): self.__dict__.update(entries)
Initialising:
>>> options = Struct(answer=42, linelen=80, font='courier')
>>> options.answer
42
adding more:
>>> options.cat = "dog"
>>> options.cat
dog
edit: Sorry didn't see this example already further down.
You access C-Style struct in python in following way.
class cstruct:
var_i = 0
var_f = 0.0
var_str = ""
if you just want use object of cstruct
obj = cstruct()
obj.var_i = 50
obj.var_f = 50.00
obj.var_str = "fifty"
print "cstruct: obj i=%d f=%f s=%s" %(obj.var_i, obj.var_f, obj.var_str)
if you want to create an array of objects of cstruct
obj_array = [cstruct() for i in range(10)]
obj_array[0].var_i = 10
obj_array[0].var_f = 10.00
obj_array[0].var_str = "ten"
#go ahead and fill rest of array instaces of struct
#print all the value
for i in range(10):
print "cstruct: obj_array i=%d f=%f s=%s" %(obj_array[i].var_i, obj_array[i].var_f, obj_array[i].var_str)
Note:
instead of 'cstruct' name, please use your struct name
instead of var_i, var_f, var_str, please define your structure's member variable.
This might be a bit late but I made a solution using Python Meta-Classes (decorator version below too).
When __init__ is called during run time, it grabs each of the arguments and their value and assigns them as instance variables to your class. This way you can make a struct-like class without having to assign every value manually.
My example has no error checking so it is easier to follow.
class MyStruct(type):
def __call__(cls, *args, **kwargs):
names = cls.__init__.func_code.co_varnames[1:]
self = type.__call__(cls, *args, **kwargs)
for name, value in zip(names, args):
setattr(self , name, value)
for name, value in kwargs.iteritems():
setattr(self , name, value)
return self
Here it is in action.
>>> class MyClass(object):
__metaclass__ = MyStruct
def __init__(self, a, b, c):
pass
>>> my_instance = MyClass(1, 2, 3)
>>> my_instance.a
1
>>>
I posted it on reddit and /u/matchu posted a decorator version which is cleaner. I'd encourage you to use it unless you want to expand the metaclass version.
>>> def init_all_args(fn):
#wraps(fn)
def wrapped_init(self, *args, **kwargs):
names = fn.func_code.co_varnames[1:]
for name, value in zip(names, args):
setattr(self, name, value)
for name, value in kwargs.iteritems():
setattr(self, name, value)
return wrapped_init
>>> class Test(object):
#init_all_args
def __init__(self, a, b):
pass
>>> a = Test(1, 2)
>>> a.a
1
>>>
I wrote a decorator which you can use on any method to make it so that all of the arguments passed in, or any defaults, are assigned to the instance.
def argumentsToAttributes(method):
argumentNames = method.func_code.co_varnames[1:]
# Generate a dictionary of default values:
defaultsDict = {}
defaults = method.func_defaults if method.func_defaults else ()
for i, default in enumerate(defaults, start = len(argumentNames) - len(defaults)):
defaultsDict[argumentNames[i]] = default
def newMethod(self, *args, **kwargs):
# Use the positional arguments.
for name, value in zip(argumentNames, args):
setattr(self, name, value)
# Add the key word arguments. If anything is missing, use the default.
for name in argumentNames[len(args):]:
setattr(self, name, kwargs.get(name, defaultsDict[name]))
# Run whatever else the method needs to do.
method(self, *args, **kwargs)
return newMethod
A quick demonstration. Note that I use a positional argument a, use the default value for b, and a named argument c. I then print all 3 referencing self, to show that they've been properly assigned before the method is entered.
class A(object):
#argumentsToAttributes
def __init__(self, a, b = 'Invisible', c = 'Hello'):
print(self.a)
print(self.b)
print(self.c)
A('Why', c = 'Nothing')
Note that my decorator should work with any method, not just __init__.
I don't see this answer here, so I figure I'll add it since I'm leaning Python right now and just discovered it. The Python tutorial (Python 2 in this case) gives the following simple and effective example:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
That is, an empty class object is created, then instantiated, and the fields are added dynamically.
The up-side to this is its really simple. The downside is it isn't particularly self-documenting (the intended members aren't listed anywhere in the class "definition"), and unset fields can cause problems when accessed. Those two problems can be solved by:
class Employee:
def __init__ (self):
self.name = None # or whatever
self.dept = None
self.salary = None
Now at a glance you can at least see what fields the program will be expecting.
Both are prone to typos, john.slarly = 1000 will succeed. Still, it works.
Here is a solution which uses a class (never instantiated) to hold data. I like that this way involves very little typing and does not require any additional packages etc.
class myStruct:
field1 = "one"
field2 = "2"
You can add more fields later, as needed:
myStruct.field3 = 3
To get the values, the fields are accessed as usual:
>>> myStruct.field1
'one'
Personally, I like this variant too. It extends #dF's answer.
class struct:
def __init__(self, *sequential, **named):
fields = dict(zip(sequential, [None]*len(sequential)), **named)
self.__dict__.update(fields)
def __repr__(self):
return str(self.__dict__)
It supports two modes of initialization (that can be blended):
# Struct with field1, field2, field3 that are initialized to None.
mystruct1 = struct("field1", "field2", "field3")
# Struct with field1, field2, field3 that are initialized according to arguments.
mystruct2 = struct(field1=1, field2=2, field3=3)
Also, it prints nicer:
print(mystruct2)
# Prints: {'field3': 3, 'field1': 1, 'field2': 2}
There is a python package exactly for this purpose. see cstruct2py
cstruct2py is a pure python library for generate python classes from C code and use them to pack and unpack data. The library can parse C headres (structs, unions, enums, and arrays declarations) and emulate them in python. The generated pythonic classes can parse and pack the data.
For example:
typedef struct {
int x;
int y;
} Point;
after generating pythonic class...
p = Point(x=0x1234, y=0x5678)
p.packed == "\x34\x12\x00\x00\x78\x56\x00\x00"
How to use
First we need to generate the pythonic structs:
import cstruct2py
parser = cstruct2py.c2py.Parser()
parser.parse_file('examples/example.h')
Now we can import all names from the C code:
parser.update_globals(globals())
We can also do that directly:
A = parser.parse_string('struct A { int x; int y;};')
Using types and defines from the C code
a = A()
a.x = 45
print a
buf = a.packed
b = A(buf)
print b
c = A('aaaa11112222', 2)
print c
print repr(c)
The output will be:
{'x':0x2d, 'y':0x0}
{'x':0x2d, 'y':0x0}
{'x':0x31316161, 'y':0x32323131}
A('aa111122', x=0x31316161, y=0x32323131)
Clone
For clone cstruct2py run:
git clone https://github.com/st0ky/cstruct2py.git --recursive
Here is a quick and dirty trick:
>>> ms = Warning()
>>> ms.foo = 123
>>> ms.bar = 'akafrit'
How does it works? It just re-use the builtin class Warning (derived from Exception) and use it as it was you own defined class.
The good points are that you do not need to import or define anything first, that "Warning" is a short name, and that it also makes clear you are doing something dirty which should not be used elsewhere than a small script of yours.
By the way, I tried to find something even simpler like ms = object() but could not (this last exemple is not working). If you have one, I am interested.
NamedTuple is comfortable. but there no one shares the performance and storage.
from typing import NamedTuple
import guppy # pip install guppy
import timeit
class User:
def __init__(self, name: str, uid: int):
self.name = name
self.uid = uid
class UserSlot:
__slots__ = ('name', 'uid')
def __init__(self, name: str, uid: int):
self.name = name
self.uid = uid
class UserTuple(NamedTuple):
# __slots__ = () # AttributeError: Cannot overwrite NamedTuple attribute __slots__
name: str
uid: int
def get_fn(obj, attr_name: str):
def get():
getattr(obj, attr_name)
return get
if 'memory test':
obj = [User('Carson', 1) for _ in range(1000000)] # Cumulative: 189138883
obj_slot = [UserSlot('Carson', 1) for _ in range(1000000)] # 77718299 <-- winner
obj_namedtuple = [UserTuple('Carson', 1) for _ in range(1000000)] # 85718297
print(guppy.hpy().heap()) # Run this function individually.
"""
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 24 112000000 34 112000000 34 dict of __main__.User
1 1000000 24 64000000 19 176000000 53 __main__.UserTuple
2 1000000 24 56000000 17 232000000 70 __main__.User
3 1000000 24 56000000 17 288000000 87 __main__.UserSlot
...
"""
if 'performance test':
obj = User('Carson', 1)
obj_slot = UserSlot('Carson', 1)
obj_tuple = UserTuple('Carson', 1)
time_normal = min(timeit.repeat(get_fn(obj, 'name'), repeat=20))
print(time_normal) # 0.12550550000000005
time_slot = min(timeit.repeat(get_fn(obj_slot, 'name'), repeat=20))
print(time_slot) # 0.1368690000000008
time_tuple = min(timeit.repeat(get_fn(obj_tuple, 'name'), repeat=20))
print(time_tuple) # 0.16006120000000124
print(time_tuple/time_slot) # 1.1694481584580898 # The slot is almost 17% faster than NamedTuple on Windows. (Python 3.7.7)
If your __dict__ is not using, please choose between __slots__ (higher performance and storage) and NamedTuple (clear for reading and use)
You can review this link(Usage of slots
) to get more __slots__ information.
https://stackoverflow.com/a/32448434/159695 does not work in Python3.
https://stackoverflow.com/a/35993/159695 works in Python3.
And I extends it to add default values.
class myStruct:
def __init__(self, **kwds):
self.x=0
self.__dict__.update(kwds) # Must be last to accept assigned member variable.
def __repr__(self):
args = ['%s=%s' % (k, repr(v)) for (k,v) in vars(self).items()]
return '%s(%s)' % ( self.__class__.__qualname__, ', '.join(args) )
a=myStruct()
b=myStruct(x=3,y='test')
c=myStruct(x='str')
>>> a
myStruct(x=0)
>>> b
myStruct(x=3, y='test')
>>> c
myStruct(x='str')
The following solution to a struct is inspired by the namedtuple implementation and some of the previous answers. However, unlike the namedtuple it is mutable, in it's values, but like the c-style struct immutable in the names/attributes, which a normal class or dict isn't.
_class_template = """\
class {typename}:
def __init__(self, *args, **kwargs):
fields = {field_names!r}
for x in fields:
setattr(self, x, None)
for name, value in zip(fields, args):
setattr(self, name, value)
for name, value in kwargs.items():
setattr(self, name, value)
def __repr__(self):
return str(vars(self))
def __setattr__(self, name, value):
if name not in {field_names!r}:
raise KeyError("invalid name: %s" % name)
object.__setattr__(self, name, value)
"""
def struct(typename, field_names):
class_definition = _class_template.format(
typename = typename,
field_names = field_names)
namespace = dict(__name__='struct_%s' % typename)
exec(class_definition, namespace)
result = namespace[typename]
result._source = class_definition
return result
Usage:
Person = struct('Person', ['firstname','lastname'])
generic = Person()
michael = Person('Michael')
jones = Person(lastname = 'Jones')
In [168]: michael.middlename = 'ben'
Traceback (most recent call last):
File "<ipython-input-168-b31c393c0d67>", line 1, in <module>
michael.middlename = 'ben'
File "<string>", line 19, in __setattr__
KeyError: 'invalid name: middlename'
If you don't have a 3.7 for #dataclass and need mutability, the following code might work for you. It's quite self-documenting and IDE-friendly (auto-complete), prevents writing things twice, is easily extendable and it is very simple to test that all instance variables are completely initialized:
class Params():
def __init__(self):
self.var1 : int = None
self.var2 : str = None
def are_all_defined(self):
for key, value in self.__dict__.items():
assert (value is not None), "instance variable {} is still None".format(key)
return True
params = Params()
params.var1 = 2
params.var2 = 'hello'
assert(params.are_all_defined)
The best way I found to do this was to use a custom dictionary class as explained in this post: https://stackoverflow.com/a/14620633/8484485
If iPython autocompletion support is needed, simply define the dir() function like this:
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def __dir__(self):
return self.keys()
You then define your pseudo struct like so: (this one is nested)
my_struct=AttrDict ({
'com1':AttrDict ({
'inst':[0x05],
'numbytes':2,
'canpayload':False,
'payload':None
})
})
You can then access the values inside my_struct like this:
print(my_struct.com1.inst)
=>[5]
The cleanest way I can think of is to use a class decorator that lets you declare a static class and rewrite it to act as a struct with normal, named properties:
from as_struct import struct
#struct
class Product():
name = 'unknown product'
quantity = -1
sku = '-'
# create instance
p = Product('plush toy', sku='12-345-6789')
# check content:
p.name # plush toy
p.quantity # -1
p.sku # 12-345-6789
Using the following decorator code:
def struct(struct_class):
# create a new init
def struct_init(self, *args, **kwargs):
i = 0 # we really don't need enumerate() here...
for value in args:
name = member_names[i]
default_value = member_values[i]
setattr(self, name, value if value is not None else default_value)
i += 1 # ...we just need to inc an int
for key,value in kwargs.items():
i = member_names.index(key)
default_value = member_values[i]
setattr(self, key, value if value is not None else default_value)
# extract the struct members
member_names = []
member_values = []
for attr_name in dir(struct_class):
if not attr_name.startswith('_'):
value = getattr(struct_class, attr_name)
if not callable(value):
member_names.append(attr_name)
member_values.append(value)
# rebind and return
struct_class.init = struct_init
return struct_class
Which works by taking the class, extracting the field names and their default values, then rewriting the class's __init__ function to set self attributes based on knowing which argument index maps to which property name.
I think Python structure dictionary is suitable for this requirement.
d = dict{}
d[field1] = field1
d[field2] = field2
d[field2] = field3
Extending #gz.'s (generally superior to this one) answer, for a quick and dirty namedtuple structure we can do:
import collections
x = collections.namedtuple('foobar', 'foo bar')(foo=1,bar=2)
y = collections.namedtuple('foobar', 'foo bar')(foo=3,bar=4)
print(x,y)
>foobar(foo=1, bar=2) foobar(foo=3, bar=4)