I write a simple app, While drag or scale the MainView, The PartView rubberband will show scene area in PartView.But sometime the rubber-band become a line, and sometime the rubberband disappear.So How to aviod this phenomenon appear?And sometime I want the rubberband only show it's border-line, not contain it's light-blue rectangle,So how can I write code ?
My Code
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
import random
import math
r = lambda : random.randint(0, 255)
r255 = lambda : (r(), r(), r())
class Scene(QGraphicsScene):
def __init__(self):
super().__init__()
for i in range(1000):
item = QGraphicsEllipseItem()
item.setRect(0, 0, r(), r())
item.setBrush(QColor(*r255()))
item.setPos(r()*100, r()*100)
self.addItem(item)
class MainView(QGraphicsView):
sigExposeRect = pyqtSignal(QRectF)
def drawBackground(self, painter: QPainter, rect: QRectF) -> None:
super().drawBackground(painter, rect)
self.sigExposeRect.emit(rect)
def wheelEvent(self, event: QWheelEvent) -> None:
factor = math.pow(2.7, event.angleDelta().y()/360)
self.scale(factor, factor)
class PartView(QGraphicsView):
def __init__(self):
super().__init__()
self.r = QRubberBand(QRubberBand.Rectangle, self)
self.r.setWindowOpacity(1)
self.r.show()
class View(QSplitter):
def __init__(self):
super().__init__()
self.m = MainView()
self.m.setMouseTracking(True)
self.m.setDragMode(QGraphicsView.ScrollHandDrag)
self.m.sigExposeRect.connect(self.onExposeRect)
self.p = PartView()
self.m.setScene(Scene())
self.p.setScene(self.m.scene())
self.p.fitInView(self.m.scene().itemsBoundingRect())
self.addWidget(self.m)
self.addWidget(self.p)
def onExposeRect(self, rect: QRectF):
prect = self.p.mapFromScene(rect).boundingRect()
self.p.r.setGeometry(prect)
app = QApplication([])
v = View()
v.show()
app.exec()
My Result
I think the problem is that the qrect passed to the drawBackground method is only includes the portion of the background that wasn't previously in the viewport. Not positive about that though.
Either way I was able to achieve your goal of avoiding only a section of the rubber band being drawn, by sending the area for the entire viewport to the onExposeRect slot.
class MainView(QGraphicsView):
sigExposeRect = pyqtSignal(QRectF)
def drawBackground(self, painter: QPainter, rect: QRectF) -> None:
# Adding this next line was the only change I made
orect = self.mapToScene(self.viewport().geometry()).boundingRect()
super().drawBackground(painter, rect)
self.sigExposeRect.emit(orect) # and passing it to the slot.
def wheelEvent(self, event: QWheelEvent) -> None:
factor = math.pow(2.7, event.angleDelta().y()/360)
self.scale(factor, factor)
A fundamental aspect about Graphics View is its high performance in drawing even thousands of elements.
To achieve this, one of the most important optimization is updating only the portions of the scene that really need redrawing, similar to what item views do, as they normally only redraw the items that actually require updates, instead of always painting the whole visible area, which can be a huge bottleneck.
This is the reason for which overriding drawBackground is ineffective: sometimes, only a small portion of the scene is updated (and, in certain situations, even no update is done at all), and the rect argument of drawBackground only includes that portion, not the whole visible area. The result is that in these situations, the signal will emit a rectangle that will not be consistent with the visible area.
Since the visible area is relative to the viewport of the scroll area, the only safe way to receive updates about that area is to connect to the horizontal and vertical scroll bars (which always work even if they are hidden).
A further precaution is to ensure that the visible rectangle is also updated whenever the scene rect is changed (since that change might not be reflected by the scroll bars), by connecting to the sceneRectChanged signal and also overriding the setSceneRect() of the source view. Considering that the changes in vertical and scroll bars might coincide, it's usually a good idea to delay the signal with a 0-delay QTimer, so that it's only sent once when more changes to the visible area happen at the same time.
Note that since you're not actually using the features of QRubberBand, there's little use in its usage, especially if you also need custom painting. Also, since the rubber band is a child of the view, it will always keep its position even if the preview view is scrolled.
In the following example I'll show two ways of drawing the "fake" rubber band (but choose only one of them, either comment one or the other to test them) that will always be consistent with both the source and target views.
class MainView(QGraphicsView):
sigExposeRect = pyqtSignal(QRectF)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.signalDelay = QTimer(self, singleShot=True, interval=0,
timeout=self.emitExposeRect)
# signals might have arguments that collide with the start(interval)
# override of QTimer, let's use a basic lambda that ignores them
self.delayEmit = lambda *args: self.signalDelay.start()
self.verticalScrollBar().valueChanged.connect(self.delayEmit)
self.horizontalScrollBar().valueChanged.connect(self.delayEmit)
def emitExposeRect(self):
topLeft = self.mapToScene(self.viewport().geometry().topLeft())
bottomRight = self.mapToScene(self.viewport().geometry().bottomRight())
self.sigExposeRect.emit(QRectF(topLeft, bottomRight))
def setScene(self, scene):
if self.scene() == scene:
return
if self.scene():
try:
self.scene().sceneRectChanged.disconnect(self.delayEmit)
except TypeError:
pass
super().setScene(scene)
if scene:
scene.sceneRectChanged.connect(self.delayEmit)
def setSceneRect(self, rect):
super().setSceneRect(rect)
self.delayEmit()
def wheelEvent(self, event: QWheelEvent) -> None:
factor = math.pow(2.7, event.angleDelta().y()/360)
self.scale(factor, factor)
class PartView(QGraphicsView):
exposeRect = None
def updateExposeRect(self, rect):
if self.exposeRect != rect:
self.exposeRect = rect
self.viewport().update()
def paintEvent(self, event):
super().paintEvent(event)
if not self.exposeRect:
return
rect = self.mapFromScene(self.exposeRect).boundingRect()
# use either *one* of the following:
# 1. QStyle implementation, imitates QRubberBand
qp = QStylePainter(self.viewport())
opt = QStyleOptionRubberBand()
opt.initFrom(self)
opt.rect = rect
qp.drawControl(QStyle.CE_RubberBand, opt)
# 2. basic QPainter
qp = QPainter(self.viewport())
color = self.palette().highlight().color()
qp.setPen(self.palette().highlight().color())
# for background
bgd = QColor(color)
bgd.setAlpha(40)
qp.setBrush(bgd)
qp.drawRect(rect)
class View(QSplitter):
def __init__(self):
super().__init__()
self.m = MainView()
self.m.setMouseTracking(True)
self.m.setDragMode(QGraphicsView.ScrollHandDrag)
self.p = PartView()
self.m.setScene(Scene())
self.p.setScene(self.m.scene())
self.p.fitInView(self.m.scene().itemsBoundingRect())
self.addWidget(self.m)
self.addWidget(self.p)
self.m.sigExposeRect.connect(self.p.updateExposeRect)
PS: please use single letter variables when they actually make sense (common variables, coordinates, loop placeholders, etc.), not for complex objects, and especially for attributes: there's no benefit in using self.m or self.p, and the only result you get is to make code less readable to you and others.
I know that Python does not support method overloading, but I've run into a problem that I can't seem to solve in a nice Pythonic way.
I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:
def add_bullet(sprite, start, headto, speed):
# Code ...
But I want to write other functions for creating bullets like:
def add_bullet(sprite, start, direction, speed):
def add_bullet(sprite, start, headto, spead, acceleration):
def add_bullet(sprite, script): # For bullets that are controlled by a script
def add_bullet(sprite, curve, speed): # for bullets with curved paths
# And so on ...
And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.
To address some answers:
No I can't create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.
I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0
What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.
However, before looking at that, we'll first tackle why overloading is not really what you want in Python.
Why Not Overloading?
First, one needs to understand the concept of overloading and why it's not applicable to Python.
When working with languages that can discriminate data types at
compile-time, selecting among the alternatives can occur at
compile-time. The act of creating such alternative functions for
compile-time selection is usually referred to as overloading a
function. (Wikipedia)
Python is a dynamically typed language, so the concept of overloading simply does not apply to it. However, all is not lost, since we can create such alternative functions at run-time:
In programming languages that defer data type identification until
run-time the selection among alternative
functions must occur at run-time, based on the dynamically determined
types of function arguments. Functions whose alternative
implementations are selected in this manner are referred to most
generally as multimethods. (Wikipedia)
So we should be able to do multimethods in Python—or, as it is alternatively called: multiple dispatch.
Multiple dispatch
The multimethods are also called multiple dispatch:
Multiple dispatch or multimethods is the feature of some
object-oriented programming languages in which a function or method
can be dynamically dispatched based on the run time (dynamic) type of
more than one of its arguments. (Wikipedia)
Python does not support this out of the box1, but, as it happens, there is an excellent Python package called multipledispatch that does exactly that.
Solution
Here is how we might use multipledispatch2 package to implement your methods:
>>> from multipledispatch import dispatch
>>> from collections import namedtuple
>>> from types import * # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True
>>> Sprite = namedtuple('Sprite', ['name'])
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Curve = namedtuple('Curve', ['x', 'y', 'z'])
>>> Vector = namedtuple('Vector', ['x','y','z'])
>>> #dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
... print("Called Version 1")
...
>>> #dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
... print("Called version 2")
...
>>> #dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
... print("Called version 3")
...
>>> #dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
... print("Called version 4")
...
>>> sprite = Sprite('Turtle')
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s**2
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away
>>> add_bullet(sprite, start, direction, speed)
Called Version 1
>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2
>>> add_bullet(sprite, script)
Called version 3
>>> add_bullet(sprite, curve, speed)
Called version 4
1. Python 3 currently supports single dispatch
2. Take care not to use multipledispatch in a multi-threaded environment or you will get weird behavior.
Python does support "method overloading" as you present it. In fact, what you just describe is trivial to implement in Python, in so many different ways, but I would go with:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, sprite=default, start=default,
direction=default, speed=default, accel=default,
curve=default):
# do stuff with your arguments
In the above code, default is a plausible default value for those arguments, or None. You can then call the method with only the arguments you are interested in, and Python will use the default values.
You could also do something like this:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, **kwargs):
# here you can unpack kwargs as (key, values) and
# do stuff with them, and use some global dictionary
# to provide default values and ensure that ``key``
# is a valid argument...
# do stuff with your arguments
Another alternative is to directly hook the desired function directly to the class or instance:
def some_implementation(self, arg1, arg2, arg3):
# implementation
my_class.add_bullet = some_implementation_of_add_bullet
Yet another way is to use an abstract factory pattern:
class Character(object):
def __init__(self, bfactory, *args, **kwargs):
self.bfactory = bfactory
def add_bullet(self):
sprite = self.bfactory.sprite()
speed = self.bfactory.speed()
# do stuff with your sprite and speed
class pretty_and_fast_factory(object):
def sprite(self):
return pretty_sprite
def speed(self):
return 10000000000.0
my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory
# now, if you have another factory called "ugly_and_slow_factory"
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()
# In the last example you can see abstract factory and "method
# overloading" (as you call it) in action
You can use "roll-your-own" solution for function overloading. This one is copied from Guido van Rossum's article about multimethods (because there is little difference between multimethods and overloading in Python):
registry = {}
class MultiMethod(object):
def __init__(self, name):
self.name = name
self.typemap = {}
def __call__(self, *args):
types = tuple(arg.__class__ for arg in args) # a generator expression!
function = self.typemap.get(types)
if function is None:
raise TypeError("no match")
return function(*args)
def register(self, types, function):
if types in self.typemap:
raise TypeError("duplicate registration")
self.typemap[types] = function
def multimethod(*types):
def register(function):
name = function.__name__
mm = registry.get(name)
if mm is None:
mm = registry[name] = MultiMethod(name)
mm.register(types, function)
return mm
return register
The usage would be
from multimethods import multimethod
import unittest
# 'overload' makes more sense in this case
overload = multimethod
class Sprite(object):
pass
class Point(object):
pass
class Curve(object):
pass
#overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
# ...
#overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
# ...
#overload(Sprite, str)
def add_bullet(sprite, script):
# ...
#overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
# ...
Most restrictive limitations at the moment are:
methods are not supported, only functions that are not class members;
inheritance is not handled;
kwargs are not supported;
registering new functions should be done at import time thing is not thread-safe
A possible option is to use the multipledispatch module as detailed here:
http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch
Instead of doing this:
def add(self, other):
if isinstance(other, Foo):
...
elif isinstance(other, Bar):
...
else:
raise NotImplementedError()
You can do this:
from multipledispatch import dispatch
#dispatch(int, int)
def add(x, y):
return x + y
#dispatch(object, object)
def add(x, y):
return "%s + %s" % (x, y)
With the resulting usage:
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'
In Python 3.4 PEP-0443. Single-dispatch generic functions was added.
Here is a short API description from PEP.
To define a generic function, decorate it with the #singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:
from functools import singledispatch
#singledispatch
def fun(arg, verbose=False):
if verbose:
print("Let me just say,", end=" ")
print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:
#fun.register(int)
def _(arg, verbose=False):
if verbose:
print("Strength in numbers, eh?", end=" ")
print(arg)
#fun.register(list)
def _(arg, verbose=False):
if verbose:
print("Enumerate this:")
for i, elem in enumerate(arg):
print(i, elem)
The #overload decorator was added with type hints (PEP 484).
While this doesn't change the behaviour of Python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484
This type of behaviour is typically solved (in OOP languages) using polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:
class Bullet(object):
def __init__(self):
self.curve = None
self.speed = None
self.acceleration = None
self.sprite_image = None
class RegularBullet(Bullet):
def __init__(self):
super(RegularBullet, self).__init__()
self.speed = 10
class Grenade(Bullet):
def __init__(self):
super(Grenade, self).__init__()
self.speed = 4
self.curve = 3.5
add_bullet(Grendade())
def add_bullet(bullet):
c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y)
void c_function(double speed, double curve, double accel, char[] sprite, ...) {
if (speed != null && ...) regular_bullet(...)
else if (...) curved_bullet(...)
//..etc..
}
Pass as many arguments to the c_function that exist, and then do the job of determining which c function to call based on the values in the initial c function. So, Python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.
You're essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.
When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. It doesn't sound too bad I guess.
It is impossible by definition to overload a function in python (read on for details), but you can achieve something similar with a simple decorator
class overload:
def __init__(self, f):
self.cases = {}
def args(self, *args):
def store_function(f):
self.cases[tuple(args)] = f
return self
return store_function
def __call__(self, *args):
function = self.cases[tuple(type(arg) for arg in args)]
return function(*args)
You can use it like this
#overload
def f():
pass
#f.args(int, int)
def f(x, y):
print('two integers')
#f.args(float)
def f(x):
print('one float')
f(5.5)
f(1, 2)
Modify it to adapt it to your use case.
A clarification of concepts
function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
static/compile-time dispatch (aka. "overloading"). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.
To clear up, assume that we define, in a hypothetical static language, the functions
void f(Integer x):
print('integer called')
void f(Float x):
print('float called')
void f(Number x):
print('number called')
Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)
With static dispatch (overloading) you will see "number called" twice, because x has been declared as Number, and that's all overloading cares about. With dynamic dispatch you will see "integer called, float called", because those are the actual types of x at the time the function is called.
By passing keyword args.
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things
Python 3.8 added functools.singledispatchmethod
Transform a method into a single-dispatch generic function.
To define a generic method, decorate it with the #singledispatchmethod
decorator. Note that the dispatch happens on the type of the first
non-self or non-cls argument, create your function accordingly:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(self, arg: int):
return -arg
#neg.register
def _(self, arg: bool):
return not arg
negator = Negator()
for v in [42, True, "Overloading"]:
neg = negator.neg(v)
print(f"{v=}, {neg=}")
Output
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
#singledispatchmethod supports nesting with other decorators such as
#classmethod. Note that to allow for dispatcher.register,
singledispatchmethod must be the outer most decorator. Here is the
Negator class with the neg methods being class bound:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
#staticmethod
def neg(arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(arg: int) -> int:
return -arg
#neg.register
def _(arg: bool) -> bool:
return not arg
for v in [42, True, "Overloading"]:
neg = Negator.neg(v)
print(f"{v=}, {neg=}")
Output:
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
The same pattern can be used for other similar decorators:
staticmethod, abstractmethod, and others.
I think your basic requirement is to have a C/C++-like syntax in Python with the least headache possible. Although I liked Alexander Poluektov's answer it doesn't work for classes.
The following should work for classes. It works by distinguishing by the number of non-keyword arguments (but it doesn't support distinguishing by type):
class TestOverloading(object):
def overloaded_function(self, *args, **kwargs):
# Call the function that has the same number of non-keyword arguments.
getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
print "This is overload 3"
print "Sprite: %s" % str(sprite)
print "Start: %s" % str(start)
print "Direction: %s" % str(direction)
def _overloaded_function_impl_2(self, sprite, script):
print "This is overload 2"
print "Sprite: %s" % str(sprite)
print "Script: "
print script
And it can be used simply like this:
test = TestOverloading()
test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")
Output:
This is overload 3
Sprite: I'm a Sprite
Start: 0
Direction: Right
This is overload 2
Sprite: I'm another Sprite
Script:
while x == True: print 'hi'
You can achieve this with the following Python code:
#overload
def test(message: str):
return message
#overload
def test(number: int):
return number + 1
Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.
I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.
Updated
The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python's explicit metaclass syntax, which varies between the two versions.
To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).
class BulletMeta(type):
def __new__(cls, classname, bases, classdict):
""" Create Bullet class or a subclass of it. """
classobj = type.__new__(cls, classname, bases, classdict)
if classname != 'BulletMetaBase':
if classname == 'Bullet': # Base class definition?
classobj.registry = {} # Initialize subclass registry.
else:
try:
alias = classdict['alias']
except KeyError:
raise TypeError("Bullet subclass %s has no 'alias'" %
classname)
if alias in Bullet.registry: # unique?
raise TypeError("Bullet subclass %s's alias attribute "
"%r already in use" % (classname, alias))
# Register subclass under the specified alias.
classobj.registry[alias] = classobj
return classobj
def __call__(cls, alias, *args, **kwargs):
""" Bullet subclasses instance factory.
Subclasses should only be instantiated by calls to the base
class with their subclass' alias as the first arg.
"""
if cls != Bullet:
raise TypeError("Bullet subclass %r objects should not to "
"be explicitly constructed." % cls.__name__)
elif alias not in cls.registry: # Bullet subclass?
raise NotImplementedError("Unknown Bullet subclass %r" %
str(alias))
# Create designated subclass object (call its __init__ method).
subclass = cls.registry[alias]
return type.__call__(subclass, *args, **kwargs)
class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
# Presumably you'd define some abstract methods that all here
# that would be supported by all subclasses.
# These definitions could just raise NotImplementedError() or
# implement the functionality is some sub-optimal generic way.
# For example:
def fire(self, *args, **kwargs):
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
# Abstract base class's __init__ should never be called.
# If subclasses need to call super class's __init__() for some
# reason then it would need to be implemented.
def __init__(self, *args, **kwargs):
raise NotImplementedError("Bullet is an abstract base class")
# Subclass definitions.
class Bullet1(Bullet):
alias = 'B1'
def __init__(self, sprite, start, direction, speed):
print('creating %s object' % self.__class__.__name__)
def fire(self, trajectory):
print('Bullet1 object fired with %s trajectory' % trajectory)
class Bullet2(Bullet):
alias = 'B2'
def __init__(self, sprite, start, headto, spead, acceleration):
print('creating %s object' % self.__class__.__name__)
class Bullet3(Bullet):
alias = 'B3'
def __init__(self, sprite, script): # script controlled bullets
print('creating %s object' % self.__class__.__name__)
class Bullet4(Bullet):
alias = 'B4'
def __init__(self, sprite, curve, speed): # for bullets with curved paths
print('creating %s object' % self.__class__.__name__)
class Sprite: pass
class Curve: pass
b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')
Output:
creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
File "python-function-overloading.py", line 93, in <module>
b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
File "python-function-overloading.py", line 49, in fire
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method
You can easily implement function overloading in Python. Here is an example using floats and integers:
class OverloadedFunction:
def __init__(self):
self.router = {int : self.f_int ,
float: self.f_float}
def __call__(self, x):
return self.router[type(x)](x)
def f_int(self, x):
print('Integer Function')
return x**2
def f_float(self, x):
print('Float Function (Overloaded)')
return x**3
# f is our overloaded function
f = OverloadedFunction()
print(f(3 ))
print(f(3.))
# Output:
# Integer Function
# 9
# Float Function (Overloaded)
# 27.0
The main idea behind the code is that a class holds the different (overloaded) functions that you would like to implement, and a Dictionary works as a router, directing your code towards the right function depending on the input type(x).
PS1. In case of custom classes, like Bullet1, you can initialize the internal dictionary following a similar pattern, such as self.D = {Bullet1: self.f_Bullet1, ...}. The rest of the code is the same.
PS2. The time/space complexity of the proposed solution is fairly good as well, with an average cost of O(1) per operation.
Use keyword arguments with defaults. E.g.
def add_bullet(sprite, start=default, direction=default, script=default, speed=default):
In the case of a straight bullet versus a curved bullet, I'd add two functions: add_bullet_straight and add_bullet_curved.
Overloading methods is tricky in Python. However, there could be usage of passing the dict, list or primitive variables.
I have tried something for my use cases, and this could help here to understand people to overload the methods.
Let's take your example:
A class overload method with call the methods from different class.
def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):
Pass the arguments from the remote class:
add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}
Or
add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}
So, handling is being achieved for list, Dictionary or primitive variables from method overloading.
Try it out for your code.
Plum supports it in a straightforward pythonic way. Copying an example from the README below.
from plum import dispatch
#dispatch
def f(x: str):
return "This is a string!"
#dispatch
def f(x: int):
return "This is an integer!"
>>> f("1")
'This is a string!'
>>> f(1)
'This is an integer!'
You can also try this code. We can try any number of arguments
# Finding the average of given number of arguments
def avg(*args): # args is the argument name we give
sum = 0
for i in args:
sum += i
average = sum/len(args) # Will find length of arguments we given
print("Avg: ", average)
# call function with different number of arguments
avg(1,2)
avg(5,6,4,7)
avg(11,23,54,111,76)
I have a constructor in my parent class that takes four parameters. In my subclass, I need to have one that takes three parameters. In the parent class I the first parameter is length. The subclass will have a set length.
I have tried many things, but none of them have worked. The one in the code that I added is one of them.
class X:
def __init__(self, len, speed, locate, direction):
self._len = len
self._speed = speed
self._locate = locate
self._direction = direction
from X import X
class Y(X):
def __int__(self, speed, locate, direction):
super().__init__(speed, locate, direction)
# one thing I've tried
self._len = 3.0
When I create an object and try to pass three parameters in it says that I am missing a direction.
Simply pass *args and **kwargs to both of your class inits:
class X:
def __init__(self, len, speed, locate, direction, *args, **kwargs):
self._len = len
self._speed = speed
self._locate = locate
self._direction = direction
from X import X
class Y(X):
def __int__(self, speed, locate, direction, *args, **kwargs):
super().__init__(speed, locate, direction, *args, **kwargs)
If you are unsure about what the asterisk does, this question is a good resource.
This works. You had a typo: def __int__( should be def __init__(
class X:
def __init__(self, length, speed, locate, direction):
self._len = length
self._speed = speed
self._locate = locate
self._direction = direction
class Y(X):
def __init__(self, speed, locate, direction):
super().__init__(3.0, speed, locate, direction)
y = Y(10, 20, 30)
print(y._len, y._speed, y._locate, y._direction)
# prints 3.0 10 20 30
I would suggest below code
class Y(X):
def __int__(self, speed, locate, direction):
X.__init__(3.0, speed, locate, direction)
Here are some points to note
I prefer using parent class name (X) because it will be unambiguous incase of multiple inheritance
Now your y class use _len member of parent class
I would suggest to avoid *args, **kwargs because explicit is better than implicit, you don't know what behaviour you want of new parameters added latter in superclass so it is better if it generates error in that case so you can explicitly decide what to do with them
Say we have a product which is identifiable by an ID number and a Quality field which is in turn comprised of several values, one of which is 2d dimensions values. Is this a case of inheritance or just using an object as another classes' field?
In trying to implement the above in Python using OOP and classes I thought of the following:
class Dimensions:
def __init__(self, x, y):
self.x = x
self.y = y
class Quality:
def __init__(self, val1, val2, dimensions):
self.val1 = val1
self.val2 = val2
self.dimensions = Dimensions
class Product:
def __init__(self, id, quality):
self.id = id
self.quality = Quality
Is what I am trying to do above achieved via inheritance?
e.g. class Product (Quality):
If so, how it would look like in defining the classes above in Python and how would I be passing the fields of a specific instance of a parent class (assuming Dimensions is the parent of Quality etc.)?
Perhaps my question is naive but I am trying to understand Python's approach of Class inheritance and structure; searched online for examples in Python but could not find one to help me understand. If this has an answer elsewhere, please share the link or, instead, point me to the right direction.
Completing the example above in code from defining the classes and instantiating each of them would be much helpful.
Is what I am trying to do above achieved via inheritance?
Inheritance describes a "is a" relationship so the question are: 1/ is a Product a Quality ? and 2/ is a Product a Dimension ?
Given your very own words:
we have a product which is identifiable by an ID number and a Quality field which is in turn comprised of several values, one of which is 2d dimensions values
the answer to both questions is a clear "NO" : your product "is" not a quality, it "has" a quality. And it's not a dimension, it's the quality which has a dimension. IOW, you want composition (what you already have in your snippet), not inheritance.
Perhaps my question is naive but I am trying to understand Python's approach of Class inheritance and structure
This is nothing python-specific, just basic OO design. The first part of the famous "Design patterns" book (the original one) is possibly one of the best texts I know about OO design.
If you could also complete my code snippet to illustrate how class composition (definition could be illustrated in this example? e.g. when instantiating a Product instance, will I provide the values for the other classes it is composed from?
There's no one-size-fits-all rule here, it depends on the context, and specially on the various objects lifecycle. In it's simplest form, you explicitely instanciate each objects and pass them to each other, ie:
class Dimensions:
def __init__(self, x, y):
self.x = x
self.y = y
class Quality:
def __init__(self, val1, val2, dimensions):
self.val1 = val1
self.val2 = val2
self.dimensions = dimensions
class Product:
def __init__(self, id, quality):
self.id = id
self.quality = quality
dimensions = Dimensions(1, 2)
quality = Quality("A", "B", dimensions)
product = Product(42, quality)
At the other end of the spectrum, you pass all "raw" values to the product, which creates it's quality, which defines it's dimensions:
class Dimensions:
def __init__(self, x, y):
self.x = x
self.y = y
class Quality:
def __init__(self, val1, val2, x, y):
self.val1 = val1
self.val2 = val2
self.dimensions = Dimensions(x, y)
class Product:
def __init__(self, id, val1, val2, x, y):
self.id = id
self.quality = Quality(val1, val2, x, y)
product = Product(42, "A", "B", 1, 2)
and you can of course use any variant, and even use them all providing alternate constructors. The main forces here is are whether dimensions and/or quality should have a life outside products, and whether they should or not be shared amongst products.