• Perform the actual work of your program • Always execute in two scopes • globals - Module dictionary • locals - Enclosing function (if any) • exec(statements [, globals [, locals]])
names=None): if names is None: names = [] ... func(1) func(1, names=['x', 'y']) • Default values set at definition time • Only use immutable values (e.g., None)
block=True): ... def sum(*args, initial=0): ... • Named arguments appearing after '*' can only be passed by keyword recv(8192, block=False) # Ok recv(8192, False) # Error
add(): return x + y return add • You can make and return functions • Local variables are captured >>> a = make_adder(2, 3) >>> b = make_adder(10, 20) >>> a() 5 >>> b() 30 >>>
add(x, y): print('add') return x + y def sub(x, y): print('sub') return x - y def mul(x, y): print('mul') return x * y def div(x, y): print('div') return x / y
add(x, y): print('add') return x + y def sub(x, y): print('sub') return x - y def mul(x, y): print('mul') return x * y def div(x, y): print('div') return x / y Bleah!
function that creates a wrapper around another function • The wrapper is a new function that works exactly like the original function (same arguments, same return value) except that some kind of extra processing is carried out 34
import wraps def debug(func): msg = func.__qualname__ @wraps(func) def wrapper(*args, **kwargs): print(msg) return func(*args, **kwargs) return wrapper A decorator creates a "wrapper" function A decorator creates a "wrapper" function Around a function that you provide
don't use @wraps, weird things happen 39 def add(x,y): "Adds x and y" return x+y add = debug(add) >>> add.__qualname__ 'wrapper' >>> add.__doc__ >>> help(add) Help on function wrapper in module __main__: wrapper(*args, **kwargs) >>>
a function and wrapping almost always occur together 40 • @decorator syntax performs the same steps def add(x,y): return x+y add = debug(add) @debug def add(x,y): return x+y
+ y @debug def sub(x, y): return x - y @debug def mul(x, y): return x * y @debug def div(x, y): return x / y Example Use 42 Each function is decorated, but there are no other implementation details
isolated to single location • This makes it easy to change (or to disable) • User of a decorator doesn't worry about it • That's really the whole idea 43
import wraps import os def debug(func): if 'DEBUG' not in os.environ: return func msg = func.__qualname__ @wraps(func) def wrapper(*args, **kwargs): print(msg) return func(*args, **kwargs) return wrapper • Key idea: Can change decorator independently of code that uses it
knows you really need a prefix • A function with debugging def add(x, y): print('add') return x + y def add(x, y): print('***add') return x + y • You know, for grepping...
Spam: @debug def grok(self): pass @debug def bar(self): pass @debug def foo(self): pass • Debug all of the methods of a class • Can you decorate all methods at once?
name, val in vars(cls).items(): if callable(val): setattr(cls, name, debug(val)) return cls • Idea: • Walk through class dictionary • Identify callables (e.g., methods) • Wrap with a decorator
def grok(self): pass def bar(self): pass def foo(self): pass • One decorator application • Covers all definitions within the class • It even mostly works...
class Base: ... @debugmethods class Spam(Base): ... @debugmethods class Grok(Spam): ... @debugmethods class Mondo(Grok): ... • Many classes with debugging • Didn't we just solve this? • Bleah!!
new types class Spam: pass >>> s = Spam() >>> type(s) <class '__main__.Spam'> >>> 64 • The class is the type of instances created • The class is a callable that creates instances
instances of types >>> type(int) <class 'type'> >>> type(list) <class 'type'> >>> type(Spam) <class 'type'> >>> isinstance(Spam, type) True >>> 65 • This requires some thought, but it should make some sense (classes are types)
67 class Spam(Base): def __init__(self, name): self.name = name def bar(self): print "I'm Spam.bar" • What are its components? • Name ("Spam") • Base classes (Base,) • Functions (__init__,bar)
during class definition? • Step1: Body of class is isolated body = ''' def __init__(self, name): self.name = name def bar(self): print "I'm Spam.bar" ''' 68 class Spam(Base): def __init__(self, name): self.name = name def bar(self): print "I'm Spam.bar"
class dictionary is created clsdict = type.__prepare__('Spam', (Base,)) • This dictionary serves as local namespace for statements in the class body • By default, it's a simple dictionary (more later) 69
is executed in returned dict exec(body, globals(), clsdict) • Afterwards, clsdict is populated >>> clsdict {'__init__': <function __init__ at 0x4da10>, 'bar': <function bar at 0x4dd70>} >>> 70
is constructed from its name, base classes, and the dictionary >>> Spam = type('Spam', (Base,), clsdict) >>> Spam <class '__main__.Spam'> >>> s = Spam('Guido') >>> s.bar() I'm Spam.bar >>> 71
argument • Sets the class used for creating the type class Spam(metaclass=type): def __init__(self,name): self.name = name def bar(self): print "I'm Spam.bar" 72 • By default, it's set to 'type', but you can change it to something else
typically inherit from type and redefine __new__ or __init__ class mytype(type): def __new__(cls, name, bases, clsdict): clsobj = super().__new__(cls, name, bases, clsdict) return clsobj 73 • To use class Spam(metaclass=mytype): ...
information about class definitions at the time of definition • Can inspect this data • Can modify this data • Essentially, similar to a class decorator • Question: Why would you use one? 74
class Base(metaclass=mytype): ... class Spam(Base): # metaclass=mytype ... class Grok(Spam): # metaclass=mytype ... 75 • Think of it as a genetic mutation
... class Spam(Base): ... class Grok(Spam): ... class Mondo(Grok): ... • Debugging gets applied across entire hierarchy • Implicitly applied in subclasses
import Parameter, Signature fields = ['name', 'shares', 'price'] parms = [ Parameter(name, Parameter.POSITIONAL_OR_KEYWORD) for name in fields] sig = Signature(parms) • Build a function signature object • Signatures are more than just metadata
bound_args = sig.bind(*args, **kwargs) for name, val in bound_args.arguments.items(): print(name, '=', val) • Argument binding • sig.bind() binds positional/keyword args to signature • .arguments is an OrderedDict of passed values
Parameter, Signature def make_signature(names): return Signature( Parameter(name, Parameter.POSITIONAL_OR_KEYWORD) for name in names) class Structure: __signature__ = make_signature([]) def __init__(self, *args, **kwargs): bound = self.__signature__.bind( *args, **kwargs) for name, val in bound.arguments.items(): setattr(self, name, val)
= make_signature( ['name','shares','price']) class Point(Structure): __signature__ = make_signature(['x', 'y']) class Host(Structure): __signature__ = make_signature( ['address', 'port']) • This is rather annoying • Can't it be simplified in some way?
__new__(cls, name, bases, clsdict): clsobj = super().__new__(cls, name, bases, clsdict) sig = make_signature(clsobj._fields) setattr(clsobj, '__signature__', sig) return clsobj class Structure(metaclass=StructMeta): _fields = [] def __init__(self, *args, **kwargs): bound = self.__signature__.bind( *args, **kwargs) for name, val in bound.arguments.items(): setattr(self, name, val) Read _fields attribute and make a proper signature out of it
['name', 'shares', 'price'] class Point(Structure): _fields = ['x', 'y'] class Host(Structure): _fields = ['address', 'port'] • It's back to original 'simple' solution • Signatures are created behind scenes
[] ... def __repr__(self): args = ', '.join(repr(getattr(self, name)) for name in self._fields) return type(self).__name__ + \ '(' + args + ')' • How much will the Structure class be expanded? • Example: supporting methods • Is type checking important? isinstance(s, Structure)
decorator if the goal is to tweak classes that might be unrelated • Use a metaclass if you're trying to perform actions in combination with inheritance • Don't be so quick to dismiss techniques (e.g., 'metaclasses suck so .... blah blah') • All of the tools are meant to work together
= Stock('ACME', 50, 91.1) >>> s.name = 42 >>> s.shares = 'a heck of a lot' >>> s.price = (23.45 + 2j) >>> • Types like a duck, rhymes with ... • Bah, real programmers use Haskell!
['name', 'shares', 'price'] @property def shares(self): return self._shares @shares.setter def shares(self, value): if not isinstance(value, int): raise TypeError('expected int') if value < 0: raise ValueError('Must be >= 0') self._shares = value • You can upgrade attributes to have checks (getter) (setter)
return self._shares @shares.setter def shares(self, value): if not isinstance(value, int): raise TypeError('expected int') if value < 0: raise ValueError('Must be >= 0') self._shares = value • It works, but it quickly gets annoying • Imagine writing same code for many attributes
simplify, but how? • Two kinds of checking are intertwined • Type checking: int, float, str, etc. • Validation: >, >=, <, <=, !=, etc. • Question: How to structure it?
Spam: x = Descriptor() s = Spam() s.x # x.__get__(s, Spam) s.x = value # x.__set__(s, value) del s.x # x.__delete__(s) • Customized handling of a specific attribute
def __init__(self, name=None): self.name = name def __get__(self, instance, cls): if instance is None: return self else: return instance.__dict__[self.name] def __set__(self, instance, value): instance.__dict__[self.name] = value def __delete__(self, instance): del instance.__dict__[self.name] name of attribute being stored. A key in the instance dict.
def __init__(self, name=None): self.name = name def __get__(self, instance, cls): if instance is None: return self else: return instance.__dict__[self.name] def __set__(self, instance, value): instance.__dict__[self.name] = value def __delete__(self, instance): del instance.__dict__[self.name] Direct manipulation of the instance dict.
def __init__(self, name=None): self.name = name def __set__(self, instance, value): instance.__dict__[self.name] = value def __delete__(self, instance): raise AttributeError("Can't delete") • You don't need __get__() if it merely returns the normal dictionary value
= object def __set__(self, instance, value): if not isinstance(value, self.ty): raise TypeError('Expected %s' % self.ty) super().__set__(instance, value) • Specialization class Integer(Typed): ty = int class Float(Typed): ty = float class String(Typed): ty = str
__set__(self, instance, value): if value < 0: raise ValueError('Expected >= 0') super().__set__(instance, value) • Use as a mixin class class PosInteger(Integer, Positive): pass class PosFloat(Float, Positive): pass
Positive): pass >>> PosInteger.__mro__ (<class 'PosInteger'>, <class 'Integer'>, <class 'Typed'>, <class 'Positive'>, <class 'Descriptor'>, <class 'object'>) >>> This chain defines the order in which the value is checked by different __set__() methods • Base order matters (e.g., int before < 0)
classes with __init__() functions? class SizedRegexString(String, Sized, Regex): pass • Each with own unique signature a = String('name') b = Sized(maxlen=8) c = Regex(pat='[A-Z]+$') • This works, how?
__init__(self, name=None): ... class Sized(Descriptor): def __init__(self, *args, maxlen, **kwargs): ... super().__init__(*args, **kwargs) class Regex(Descriptor): def __init__(self, *args, pat, **kwargs): ... super().__init__(*args, **kwargs) SizedRegexString('name', maxlen=8, pat='[A-Z]+$') Keyword-only argument is isolated and removed from all other passed args
bit of repetition • Signatures and type checking not unified • Maybe we can push it further class Stock(Structure): _fields = ['name', 'shares', 'price'] name = SizedRegexString('name', maxlen=8, pat='[A-Z]+$') shares = PosInteger('shares') price = PosFloat('price')
import OrderedDict class StructMeta(type): @classmethod def __prepare__(cls, name, bases): return OrderedDict() def __new__(cls, name, bases, clsdict): fields = [ key for key, val in clsdict.items() if isinstance(val, Descriptor) ] for name in fields: clsdict[name].name = name clsobj = super().__new__(cls, name, bases, dict(clsdict)) sig = make_signature(fields) setattr(clsobj, '__signature__', sig) return clsobj
OrderedDict class StructMeta(type): @classmethod def __prepare__(cls, name, bases): return OrderedDict() def __new__(cls, name, bases, clsdict): fields = [ key for key, val in clsdict.items() if isinstance(val, Descriptor) ] for name in fields: clsdict[name].name = name clsobj = super().__new__(cls, name, bases, dict(clsdict)) sig = make_signature(fields) setattr(clsobj, '__signature__', sig) return clsobj __prepare__() creates and returns dictionary to use for execution of the class body. An OrderedDict will preserve the definition order.
__setitem__(self, key, value): if key in self: raise NameError('%s already defined' % key) super().__setitem__(key, value) • If inclined, you could do even better • Make a new kind of dict • Use in place of OrderedDict
= String() shares = PosInteger() price = PosFloat() shares = PosInteger() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 5, in Stock File "./typestruct.py", line 107, in __setitem__ raise NameError('%s already defined' % key) NameError: shares already defined • Won't pursue further, but you get the idea
OrderedDict class StructMeta(type): @classmethod def __prepare__(cls, name, bases): return OrderedDict() def __new__(cls, name, bases, clsdict): fields = [ key for key, val in clsdict.items() if isinstance(val, Descriptor) ] for name in fields: clsdict[name].name = name clsobj = super().__new__(cls, name, bases, dict(clsdict)) sig = make_signature(fields) setattr(clsobj, '__signature__', sig) return clsobj Collect Descriptors and set their names
= ['name', 'shares', 'price'] name = SizedRegexString('name', ...) shares = PosInteger('shares') price = PosFloat('price') • Old code • New Code class Stock(Structure): name = SizedRegexString(...) shares = PosInteger() price = PosFloat() Names are set from dict keys
OrderedDict class StructMeta(type): @classmethod def __prepare__(cls, name, bases): return OrderedDict() def __new__(cls, name, bases, clsdict): fields = [ key for key, val in clsdict.items() if isinstance(val, Descriptor) ] for name in fields: clsdict[name].name = name clsobj = super().__new__(cls, name, bases, dict(clsdict)) sig = make_signature(fields) setattr(clsobj, '__signature__', sig) return clsobj Make the class and signature exactly as before.
OrderedDict class StructMeta(type): @classmethod def __prepare__(cls, name, bases): return OrderedDict() def __new__(cls, name, bases, clsdict): fields = [ key for key, val in clsdict.items() if isinstance(val, Descriptor) ] for name in fields: clsdict[name].name = name clsobj = super().__new__(cls, name, bases, dict(clsdict)) sig = make_signature(fields) setattr(clsobj, '__signature__', sig) return clsobj A technicality: Must create a proper dict for class contents
def __new__(cls, name, bases, clsdict): fields = [ key for key, val in clsdict.items() if isinstance(val, Descriptor) ] for name in fields: clsdict[name].name = name if fields: exec(_make_init(fields),globals(),clsdict) clsobj = super().__new__(cls, name, bases, dict(clsdict)) setattr(clsobj, '_fields', fields) return clsobj
def __new__(cls, name, bases, clsdict): fields = [ key for key, val in clsdict.items() if isinstance(val, Descriptor) ] for name in fields: clsdict[name].name = name if fields: exec(_make_init(fields),globals(),clsdict) clsobj = super().__new__(cls, name, bases, dict(clsdict)) setattr(clsobj, '_fields', fields) return clsobj No signature, but set _fields for code that wants it
class Stock(Structure): name = SizedRegexString(...) shares = PosInteger() price = PosFloat() Instance creation: Simple 1.1s Old Meta (w/signatures) 91.8s New Meta (w/exec) 17. 6s
def __set__(self, instance, value): instance.__dict__[self.name] = value class Typed(Descriptor): def __set__(self, instance, value): if not isinstance(value, self.ty): raise TypeError('Expected %s' % self.ty) super().__set__(instance, value) class Positive(Descriptor): def __set__(self, instance, value): if value < 0: raise ValueError('Expected >= 0') super().__set__(instance, value) Could you merge this code together?
code = 'def __set__(self, instance, value):\n' for d in dcls.__mro__: if 'set_code' in d.__dict__: for line in d.set_code(): code += ' ' + line + '\n' return code • Takes a descriptor class as input • Walks its MRO and collects output of set_code() • Concatenate to make a __set__() method
__init__(self, clsname, bases, clsdict): if '__set__' not in clsdict: code = _make_setter(self) exec(code, globals(), clsdict) setattr(self, '__set__', clsdict['__set__']) else: raise TypeError('Define set_code()') class Descriptor(metaclass=DescriptorMeta): ... • For each Descriptor class, create setter code • exec() and drop result onto created class
Stock(Structure): name = SizedRegexString(...) shares = PosInteger() price = PosFloat() • User has no idea about this code generation • They're just using the same code as before • It's an implementation detail of descriptors
= SizedRegexString(maxlen=8, pat='[A-Z]+$') shares = PosInteger() price = PosFloat() class Point(Structure): x = Integer() y = Integer() class Address(Structure): hostname = String() port = Integer() • Convincing a manager about all of this
>>> print(code) import typestruct as _ts class Stock(_ts.Structure): name = _ts.SizedRegexString(maxlen=8, pat='[A-Z]+ shares = _ts.PosInteger() price = _ts.PosFloat() class Point(_ts.Structure): x = _ts.Integer() y = _ts.Integer() class Address(_ts.Structure): hostname = _ts.String() port = _ts.Integer() >>>
__init__(self, path): self._path = path def find_module(self, fullname, path=None): name = fullname.rpartition('.')[-1] if path is None: path = self._path for dn in path: filename = os.path.join(dn, name+'.xml') if os.path.exists(filename): return StructXmlLoader(filename) return None Walk path, check for existence of .xml file and return a loader
class StructXMLLoader: def __init__(self, filename): self._filename = filename def load_module(self, fullname): mod = sys.modules.setdefault(fullname, imp.new_module(fullname)) mod.__file__ = self._filename mod.__loader__ = self code = _xml_to_code(self._filename) exec(code, mod.__dict__, mod.__dict__) return mod Create a new module and put in sys.modules
to sys.meta_path def install_importer(path=sys.path): sys.meta_path.append(StructImporter(path)) install_importer() • From this point, structure .xml files will import >>> import datadefs >>> s = datadefs.Stock('ACME', 50, 91.1) >>> s.name 'ACME' >>> datadefs <module 'datadefs' from './datadefs.xml'> >>>
= SizedRegexString(maxlen=8, pat='[A-Z]+$') shares = PosInteger() price = PosFloat() • Think about all of the neat things we did • Descriptors as building blocks • Hiding of annoying details (signatures, etc.) • Dynamic code generation • Even customizing import
Python 3 is designed to do this sort of stuff • More advanced metaclasses (e.g., __prepare__) • Signatures • Import hooks • Keyword-only args • Observe: I didn't do any mind-twisting "hacks" to work around a language limitation.
3 makes a lot of little things easier • Example : Python 2 keyword-only args def __init__(self, *args, **kwargs): self.maxlen = kwargs.pop('maxlen') ... • In Python 3 def __init__(self, *args, maxlen, **kwargs): self.maxlen = maxlen ... • There are a lot of little things like this
only scratched the surface • Function annotations def add(x:int, y:int) -> int: return x + y • Non-local variables def outer(): x = 0 def inner(): nonlocal x x = newvalue ...
• Metaprogramming is not for "normal" coding • Frameworks/libraries are a different story • If using a framework, you may be using this features without knowing it • You can do a lot of cool stuff • OTOH: Keeping it simple is not a bad strategy
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