I'm trying to write a Database Abstraction Layer in Python which lets you construct SQL statments using chained function calls such as:
results = db.search("book")
.author("J. K. Rowling")
.price("<40.00")
.title("Harry")
.execute()
but I am running into problems when I try to dynamically add the required methods to the db class.
Here is the important parts of my code:
import inspect
def myName():
return inspect.stack()[1][3]
class Search():
def __init__(self, family):
self.family = family
self.options = ['price', 'name', 'author', 'genre']
#self.options is generated based on family, but this is an example
for opt in self.options:
self.__dict__[opt] = self.__Set__
self.conditions = {}
def __Set__(self, value):
self.conditions[myName()] = value
return self
def execute(self):
return self.conditions
However, when I run the example such as:
print(db.search("book").price(">4.00").execute())
outputs:
{'__Set__': 'harry'}
Am I going about this the wrong way? Is there a better way to get the name of the function being called or to somehow make a 'hard copy' of the function?
You can simply add the search functions (methods) after the class is created:
class Search: # The class does not include the search methods, at first
def __init__(self):
self.conditions = {}
def make_set_condition(option): # Factory function that generates a "condition setter" for "option"
def set_cond(self, value):
self.conditions[option] = value
return self
return set_cond
for option in ('price', 'name'): # The class is extended with additional condition setters
setattr(Search, option, make_set_condition(option))
Search().name("Nice name").price('$3').conditions # Example
{'price': '$3', 'name': 'Nice name'}
PS: This class has an __init__() method that does not have the family parameter (the condition setters are dynamically added at runtime, but are added to the class, not to each instance separately). If Search objects with different condition setters need to be created, then the following variation on the above method works (the __init__() method has a family parameter):
import types
class Search: # The class does not include the search methods, at first
def __init__(self, family):
self.conditions = {}
for option in family: # The class is extended with additional condition setters
# The new 'option' attributes must be methods, not regular functions:
setattr(self, option, types.MethodType(make_set_condition(option), self))
def make_set_condition(option): # Factory function that generates a "condition setter" for "option"
def set_cond(self, value):
self.conditions[option] = value
return self
return set_cond
>>> o0 = Search(('price', 'name')) # Example
>>> o0.name("Nice name").price('$3').conditions
{'price': '$3', 'name': 'Nice name'}
>>> dir(o0) # Each Search object has its own condition setters (here: name and price)
['__doc__', '__init__', '__module__', 'conditions', 'name', 'price']
>>> o1 = Search(('director', 'style'))
>>> o1.director("Louis L").conditions # New method name
{'director': 'Louis L'}
>>> dir(o1) # Each Search object has its own condition setters (here: director and style)
['__doc__', '__init__', '__module__', 'conditions', 'director', 'style']
Reference: http://docs.python.org/howto/descriptor.html#functions-and-methods
If you really need search methods that know about the name of the attribute they are stored in, you can simply set it in make_set_condition() with
set_cond.__name__ = option # Sets the function name
(just before the return set_cond). Before doing this, method Search.name has the following name:
>>> Search.price
<function set_cond at 0x107f832f8>
after setting its __name__ attribute, you get a different name:
>>> Search.price
<function price at 0x107f83490>
Setting the method name this way makes possible error messages involving the method easier to understand.
Firstly, you are not adding anything to the class, you are adding it to the instance.
Secondly, you don't need to access dict. The self.__dict__[opt] = self.__Set__ is better done with setattr(self, opt, self.__Set__).
Thirdly, don't use __xxx__ as attribute names. Those are reserved for Python-internal use.
Fourthly, as you noticed, Python is not easily fooled. The internal name of the method you call is still __Set__, even though you access it under a different name. :-) The name is set when you define the method as a part of the def statement.
You probably want to create and set the options methods with a metaclass. You also might want to actually create those methods instead of trying to use one method for all of them. If you really want to use only one __getattr__ is the way, but it can be a bit fiddly, I generally recommend against it. Lambdas or other dynamically generated methods are probably better.
Here is some working code to get you started (not the whole program you were trying to write, but something that shows how the parts can fit together):
class Assign:
def __init__(self, searchobj, key):
self.searchobj = searchobj
self.key = key
def __call__(self, value):
self.searchobj.conditions[self.key] = value
return self.searchobj
class Book():
def __init__(self, family):
self.family = family
self.options = ['price', 'name', 'author', 'genre']
self.conditions = {}
def __getattr__(self, key):
if key in self.options:
return Assign(self, key)
raise RuntimeError('There is no option for: %s' % key)
def execute(self):
# XXX do something with the conditions.
return self.conditions
b = Book('book')
print(b.price(">4.00").author('J. K. Rowling').execute())
Related
I have a class holding some scientific data. Depending on an internal state, the values of this class can appear as normalized (i.e. unitless), or non-normalized. The values are always stored as normalized, but if the object is set in non-normalized status, the user-accessible properties (and methods) will give the non-normalized values. This way the class appears as non-normalized, while there's no need to duplicate the stored values.
Right now I implemented this using getters. While it works, it gives a lot of repeating structure, and I wonder if there's a more Pythonic way of managing this without overcomplicating things.
Am I doing this right? Is there a more elegant way to switch between two sets of data in a similar fashion?
class CoolPhysicsData(object):
def __init__(self, lambda0, *args, normed=False):
self.lambda0 = lambda0 # some normalization factor (wavelength of some wave)
self.normalized = normed # user can change this state as he pleases
self._normed_tmin, self._normed_tmax, self._normed_r = self.calculate_stuffs(*args)
...
#property
def tmin(self):
if self.normalized:
return self._normed_tmin
else:
return denormalize(self.lambda0, self._normed_tmin, unit_type="time")
#property
def tmax(self):
if self.normalized:
return self._normed_tmax
else:
return denormalize(self.lambda0, self._normed_tmax, unit_type="time")
#property
def r(self):
if self.normalized:
return self._normed_r
else:
return denormalize(self.lambda0, self._normed_r, unit_type="len")
... # about 15 getters alike these
One way is to avoid using properties, and implement __getattr__, __setattr__ and __delattr__. Since you need to know which quantity you're denormalizing, there's really no way to escape definitions: these must be handcoded somewhere. I'd do this way:
class CoolPhysicsData:
def _get_normalization_params(self, value):
# set up how individual properties should be denormalized..
params = {
# 'property_name' : (norm_factor, norm_value, 'unit_type')
'tmin': (self.lambda0, self._normed_tmin, 'time'),
'tmax': (self.lambda0, self._normed_tmax, 'time'),
'r': (self.lambda0, self._normed_r, 'len'),
}
return params[value]
and I would implement __getattr__ something like this:
...
def __getattr__(self, value):
# extract the parameters needed
norm_factor, normed_value, unit_type = self._get_normalization_params(f'{value}')
if self.normed:
return normed_value
else:
return self.denormalize(norm_factor, normed_value, unit_type)
...
Note that you might want to write __setattr__ and __delattr__ too.
One little addition: dataclasses might be useful to you. I'm not sure if *args in your __init__ function is the exact signature, or you just simplified for the sake of the example. If you have known arguments (no varargs), this can be easily turned into a dataclass.
from dataclasses import dataclass, field
#dataclass
class CoolPhysicsData:
lambda0: float
normed: bool = field(default=False)
def __post_init__(self):
# set up some test values for simplicity
# of course you can run custom calculations here..
self._normed_tmin = 1
self._normed_tmax = 2
self._normed_r = 3
def __getattr__(self, value):
norm_factor, normed_value, unit_type = self._get_normalization_params(f'{value}')
if self.normed:
return normed_value
else:
return self.denormalize(norm_factor, normed_value, unit_type)
# you may want to implement the following methods too:
# def __setattr__(self, name, value):
# # your custom logic here
# ...
# def __delattr__(self, name):
# # your custom logic here
# ...
def denormalize(self, v1, v2, v3):
# just for simplicity
return 5
def _get_normalization_params(self, value):
# setup how individual properties should be denormalized..
params = {
# 'property_name' : (norm_factor, norm_value, 'unit_type')
'tmin': (self.lambda0, self._normed_tmin, 'time'),
'tmax': (self.lambda0, self._normed_tmax, 'time'),
'r': (self.lambda0, self._normed_r, 'len'),
}
return params[value]
Is it more pythonic? It's up to you to decide. It surely takes away some repetition, but you introduce a little more complexity, and - in my opinion - it's more prone to bugs.
I'm new in Python and I'm trying to get my head around how are managed attributes between methods of a class.
In the following example, I'm trying to modify a list in the method "regex" and use it afterwards in another method "printsc".
The "regex" part works without issues, but the attribute "self.mylist" is not updated so when I call "printsc" the result is "None".
class MyClass():
def __init__(self):
self.mylist = None
def regex(self, items):
self.mylist = []
for item in items:
if re.match(r"^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$", item):
self.mylist.append("IP:" + item)
else:
self.mylist.append("DNS:" + item)
return self.mylist
def printsc(self):
print(self.mylist)
items = ['192.168.0.1', 'hostname1', '10.0.1.15', 'server.local.fr']
MyClass().regex(items)
MyClass().printsc()
What am I missing ? What is the best way to achieve this goal ?
Thank you for your answers!
When you do MyClass(), it returns you an object.. And you are calling your methods on the object. Since you are doing it twice, each time a new object is created and regex and printsc are called on different objects.
what you should do is
myObj = MyClass()
myObj.regex(items)
myObj.printsc()
The problem is that when you do:
MyClass().regex(items)
MyClass().printsc()
You are creating 2 separate instances of MyClass, each of which will have a different .mylist attribute.
Either mylist is an instance attribute, and then this will work:
instance = MyClass()
instance.regex(items)
instance.printsc()
Or, if you want to share .mylist across instances, it should be
a class attribute:
class MyClass():
class_list = None
def __init__(self):
pass
def regex(self, items):
cls = self.__class__
if cls.class_list is None:
cls.class_list = []
for item in items:
if re.match(r"^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$", item):
cls.class_list.append("IP:" + item)
else:
cls.class_list.append("DNS:" + item)
return cls.class_list
def printsc(self):
# Going throuhgh `.__class__.` is actually optional for
# reading an attribute - if it is not in the instance
# Python will fetch it from the class instead.
# i.e. , the line bellow would work with `self.class_list`
print(self.__class__.class_list)
This way, the list persists across different instances of the class, as you try to do in your example.
You should create an object of the class:
a = MyClass()
a.regex(items)
a.printsc()
>>> ['IP:192.168.0.1', 'DNS:hostname1', 'IP:10.0.1.15', 'DNS:server.local.fr']
I would like to define a decorator that will register classes by a name given as an argument of my decorator. I could read from stackoverflow and other sources many examples that show how to derive such (tricky) code but when adapted to my needs my code fails to produce the expected result. Here is the code:
import functools
READERS = {}
def register(typ):
def decorator_register(kls):
#functools.wraps(kls)
def wrapper_register(*args, **kwargs):
READERS[typ] = kls
return wrapper_register
return decorator_register
#register(".pdb")
class PDBReader:
pass
#register(".gro")
class GromacsReader:
pass
print(READERS)
This code produces an empty dictionary while I would expect a dictionary with two entries. Would you have any idea about what is wrong with my code ?
Taking arguments (via (...)) and decoration (via #) both result in calls of functions. Each "stage" of taking arguments or decoration maps to one call and thus one nested functions in the decorator definition. register is a three-stage decorator and takes as many calls to trigger its innermost code. Of these,
the first is the argument ((".pdb")),
the second is the class definition (#... class), and
the third is the class call/instantiation (PDBReader(...))
This stage is broken as it does not instantiate the class.
In order to store the class itself in the dictionary, store it at the second stage. As the instances are not to be stored, remove the third stage.
def register(typ): # first stage: file extension
"""Create a decorator to register its target for the given `typ`"""
def decorator_register(kls): # second stage: Reader class
"""Decorator to register its target `kls` for the previously given `typ`"""
READERS[typ] = kls
return kls # <<< return class to preserve it
return decorator_register
Take note that the result of a decorator replaces its target. Thus, you should generally return the target itself or an equivalent object. Since in this case the class is returned immediately, there is no need to use functools.wraps.
READERS = {}
def register(typ): # first stage: file extension
"""Create a decorator to register its target for the given `typ`"""
def decorator_register(kls): # second stage: Reader class
"""Decorator to register its target `kls` for the previously given `typ`"""
READERS[typ] = kls
return kls # <<< return class to preserve it
return decorator_register
#register(".pdb")
class PDBReader:
pass
#register(".gro")
class GromacsReader:
pass
print(READERS) # {'.pdb': <class '__main__.PDBReader'>, '.gro': <class '__main__.GromacsReader'>}
If you don't actually call the code that the decorator is "wrapping" then the "inner" function will not fire, and you will not create an entry inside of READER. However, even if you create instances of PDBReader or GromacsReader, the value inside of READER will be of the classes themselves, not an instance of them.
If you want to do the latter, you have to change wrapper_register to something like this:
def register(typ):
def decorator_register(kls):
#functools.wraps(kls)
def wrapper_register(*args, **kwargs):
READERS[typ] = kls(*args, **kwargs)
return READERS[typ]
return wrapper_register
return decorator_register
I added simple init/repr inside of the classes to visualize it better:
#register(".pdb")
class PDBReader:
def __init__(self, var):
self.var = var
def __repr__(self):
return f"PDBReader({self.var})"
#register(".gro")
class GromacsReader:
def __init__(self, var):
self.var = var
def __repr__(self):
return f"GromacsReader({self.var})"
And then we initialize some objects:
x = PDBReader("Inside of PDB")
z = GromacsReader("Inside of Gromacs")
print(x) # Output: PDBReader(Inside of PDB)
print(z) # Output: GromacsReader(Inside of Gromacs)
print(READERS) # Output: {'.pdb': PDBReader(Inside of PDB), '.gro': GromacsReader(Inside of Gromacs)}
If you don't want to store the initialized object in READER however, you will still need to return an initialized object, otherwise when you try to initialize the object, it will return None.
You can then simply change wrapper_register to:
def wrapper_register(*args, **kwargs):
READERS[typ] = kls
return kls(*args, **kwargs)
I'm writing decorators, and part of what I need to do is discern whether a function is a function or a method. Is there a way I can find what class a given method is a part of?
e.g. If I was to run this code, what could I write in getOwner to make exampleFunc print something like <class '__main__'.Example>?
class Example:
def method(self):
print("I'm a method")
def exampleFunc(func):
owner = getOwner(func)
print(owner)
test = Example()
exampleFunc(test.method)
If all you need to do is figure out of the thing behaving like a function is a method or a function, that is one purpose of the types module.
import types
def is_method(f):
return type(f) == types.MethodType
In the event that the function-like object is a method, you can find its parent class as follows.
Update Patched for Python3 compatibility.
def method_parent(f):
return f.__self__
If you have a reference to the classes defined in your scope, you'd need to check for each one:
def exampleFunc(f):
class_list = [...]
return any(f in vars(c).values() for c in class_List)
This will return True if function f is an instance method. However, if you wish to return the actual class name:
def exampleFunc(f):
class_list = [...]
for c in class_list:
if f in vars(c).values():
return c.__name__
return 'global function' if 'lambda' not in f.__name__ else 'lambda'
Note that this does not work for __dunder__ methods, and methods that your class inherits. For example,
class A:
def f1(self): pass
class B(A):
def f2(self): pass
print(vars(B))
mappingproxy({'__doc__': None,
'__module__': '__main__',
'f2': <function __main__.B.f2>})
Note that f1 is not a part of B's mappingproxy.
I have a special statemachine implemented in Python, which uses class methods as state representation.
class EntityBlock(Block):
def __init__(self, name):
self._name = name
#classmethod
def stateKeyword1(cls, parserState : ParserState):
pass
#classmethod
def stateWhitespace1(cls, parserState : ParserState):
token = parserState.Token
if isinstance(token, StringToken):
if (token <= "generate"):
parserState.NewToken = GenerateKeyword(token)
parserState.NewBlock = cls(....)
else:
raise TokenParserException("....", token)
raise TokenParserException("....", token)
#classmethod
def stateDelimiter(cls, parserState : ParserState):
pass
Visit GitHub for full source code off pyVHDLParser.
When I debug my parser FSM, I get the statenames printed as:
State: <bound method Package.stateParse of <class 'pyVHDLParser.DocumentModel.Sequential.Package.Package'>>
I would like to get better reports, so I would like to overwrite the default behavior of __repr__ of each bound method object.
Yes, I could write a metaclass or apply a second decorator, but I was questioning myself:
Is it possible to derive from classmethod and have only one decorator called e.g. state?
According to PyCharm's builtins.py (a collection of dummy code for Python's builtins), classmethod is a class-based decorator.
Yes, you can write your own class that derives from classmethod if you want. It's a bit complicated though. You'll need to implement the descriptor protocol (overriding classmethod's implementation of __get__) so that it returns an instance of another custom class that behaves like a bound method object. Unfortunately, you can't inherit from Python's builtin bound method type (I'm not sure why not).
Probably the best approach then is to wrap one of the normal method objects in an instance of a custom class. I'm not sure how much of the method API you need to replicate though, so that might get a bit complicated. (Do you need your states to be comparable to one another? Do they need to be hashable? Picklable?)
Anyway, here's a bare bones implementation that does the minimum amount necessary to get a working method (plus the new repr):
class MethodWrapper:
def __init__(self, name, method):
self.name = name if name is not None else repr(method)
self.method = method
def __call__(self, *args, **kwargs):
return self.method(*args, **kwargs)
def __repr__(self):
return self.name
class State(classmethod):
def __init__(self, func):
self.name = None
super().__init__(func)
def __set_name__(self, owner, name):
self.name = "{}.{}".format(owner.__name__, name)
def __get__(self, owner, instance):
method = super().__get__(owner, instance)
return MethodWrapper(self.name, method)
And a quick demo of it in action:
>>> class Foo:
#State
def foo(cls):
print(cls)
>>> Foo.foo
Foo.foo
>>> Foo.foo()
<class '__main__.Foo'>
>>> f = Foo()
>>> f.foo()
<class '__main__.Foo'>
Note that the __set_name__ method used by the State descriptor is only called by Python 3.6. Without that new feature, it would be much more difficult for the descriptor to learn its own name (you might need to make a decorator factory that takes the name as an argument).