Let's say I've made a simple class
class mercury:
class orbial_characteristics:
apehilion = 69816900
perihilion = 46001200
semi_major_axis = 57909050
eccentricity = 0.205630
orbital_period = 87.9691*86400
orbital_speed = 47.362*1e3
Now, the values given here are in SI units, the value of apehilion for example, is in km. I want to make another class that can convert the value to a given unit, let's say astronomical unit. One method is to pass the value of apehilion directly to that class
change_to_AU(value_of_apehilion)
Which is relatively easy to do. However, what I'm looking for is in the lines of python core operations. Something like this
merc_apehilion_km = mercury.orbital_characteristics.apehilion
merc_apehilion_au = merc_apehilion_km.change_to_AU()
I have recently started working on classes, by reading answers here and web tutorials but I do not get how such an operation can be performed. I even try reading the core files from numpy and pandas as these two libraries that I use most have a number of things that use this notation.
Edit:
A little research led me to this stack overflow page. Take a look at the libraries mentioned in it, make sure they are actively maintained, and consider using them over doing what I demonstrate below
End of Edit
Creating custom methods like this would involve you creating a custom Object for your SI unit values. Here is an example:
class SIUnit:
def __init__(self, value):
self.value = value
def __str__(self):
return self.value
def to_astronimical_units(self):
Calculations which convert apehilion to AU go here
return result
class mercury:
class orbial_characteristics:
apehilion = SIUnit(69816900)
perihilion = SIUnit(46001200)
semi_major_axis = SIUnit(57909050)
eccentricity = SIUnit(0.205630)
orbital_period = SIUnit(87.9691*86400)
orbital_speed = SIUnit(47.362*1e3)
Keep in mind that the to_astronomical_units method would exist for all SI units you use, not just distance so you may want to create a base SIUnit class then have a subclass for each SI Unit, e.g:
class SIUnit:
def __init__(self, value):
self.value = value
def __str__(self):
return self.value
class Kilometer(SIUnit):
def to_au(self):
Calculations which convert apehilion to AU go here
return result
class Ampere(SIUnit):
def to_volts(self, wattage):
return self.value / wattage
Related
I have more of a design question, but I am not sure how to handle that. I have a script preprocessing.py where I read a .csv file of text column that I would like to preprocess by removing punctuations, characters, ...etc.
What I have done now is that I have written a class with several functions as follows:
class Preprocessing(object):
def __init__(self, file):
self.my_data = pd.read_csv(file)
def remove_punctuation(self):
self.my_data['text'] = self.my_data['text'].str.replace('#','')
def remove_hyphen(self):
self.my_data['text'] = self.my_data['text'].str.replace('-','')
def remove_words(self):
self.my_data['text'] = self.my_data['text'].str.replace('reference','')
def save_data(self):
self.my_data.to_csv('my_data.csv')
def preprocessing(file_my):
f = Preprocessing(file_my)
f.remove_punctuation()
f.remove_hyphen()
f.remove_words()
f.save_data()
return f
if __name__ == '__main__':
preprocessing('/path/to/file.csv')
although it works fine, i would like to be able to expand the code easily and have smaller classes instead of having one large class. So i decided to use abstract class:
import pandas as pd
from abc import ABC, abstractmethod
my_data = pd.read_csv('/Users/kgz/Desktop/german_web_scraping/file.csv')
class Preprocessing(ABC):
#abstractmethod
def processor(self):
pass
class RemovePunctuation(Preprocessing):
def processor(self):
return my_data['text'].str.replace('#', '')
class RemoveHyphen(Preprocessing):
def processor(self):
return my_data['text'].str.replace('-', '')
class Removewords(Preprocessing):
def processor(self):
return my_data['text'].str.replace('reference', '')
final_result = [cls().processor() for cls in Preprocessing.__subclasses__()]
print(final_result)
So now each class is responsible for one task but there are a few issues I do not know how to handle since I am new to abstract classes. first, I am reading the file outside the classes, and I am not sure if that is good practice? if not, should i pass it as an argument to the processor function or have another class who is responsible to read the data.
Second, having one class with several functions allowed for a flow, so every transformation happened in order (i.e, first punctuation is removes, then hyphen is removed,...etc) but I do not know how to handle this order and dependency in abstract classes.
I have a BaseClass and two classes (Volume and testing) which inherits from the BaseClass. The class "Volume" use a method "driving_style" from another python module. I am trying to write another method "test_Score" which wants to access variables computed in the method "driving_style" which I want to use to compute further. These results will be accessed to the class "testing" as shown.
from training import Accuracy
import ComputeData
import model
class BaseClass(object):
def __init__(self, connections):
self.Type = 'Stock'
self.A = connections.A
self.log = self.B.log
def getIDs(self, assets):
ids = pandas.Series(assets.ids, index=assets.B)
return ids
class Volume(BaseClass):
def __init__(self, connections):
BaseClass.__init__(self, connections)
self.daystrade = 30
self.high_low = True
def learning(self, data, rootClass):
params.daystrade = self.daystrade
params.high_low = self.high_low
style = Accuracy.driving_style()
return self.Object(data.universe, style)
class testing(BaseClass):
def __init__(self, connections):
BaseClass.__init__(self, connections)
def learning(self, data, rootClass):
test_score = Accuracy.test_score()
return self.Object(data.universe, test_score)
def driving_style(date, modelDays, params):
daystrade = params.daystrade
high_low = params.high_low
DriveDays = model.DateRange(date, params.daystrade)
StopBy = ComputeData.instability(DriveDays)
if high_low:
style = ma.average(StopBy)
else:
style = ma.mean(StopBy)
return style
def test_score(date, modelDays, params):
"want to access the following from the method driving_style:"
DriveDays =
StopBy =
return test_score ("which i compute using values DriveDays and StopBy and use test_score in the method learning inside
the 'class - testing' which inherits some params from the BaseClass")
You can't use locals from a call to a function that was made elsewhere and has already returned.
A bad solution is to store them as globals that you can read from later (but that get replaced on every new call). A better solution might to return the relevant info to the caller along with the existing return values (return style, DriveDays, StopBy) and somehow get it to where it needs to go. If necessary, you could wrap the function into a class and store the computed values as attributes on an instance of the class, while keeping the return type the same.
But the best solution is probably to refactor, so the stuff you want is computed by dedicated methods that you can call directly from test_score and driving_style independently, without duplicating code or creating complicated state dependencies.
In short, basically any time you think you need to access locals from another function, you're almost certainly experiencing an XY problem.
I'm a structured programming guy. So my attempts with object oriented programming are always "work in progress..."
My intent is to have a class which will adapt itself according to an external input. I saw in another post (which I was unable to find again) that I can change the class of an object, so I made this MWE, which works:
class Base:
def __init__(self, name):
self.name = name
def set_text(self, text):
self.text = text
class Terminator(Base):
terminator = '!'
def __init__(self):
super().__init__('terminator')
def get(self):
return self.text + terminator
class Prefix(Base):
def __init__(self):
super().__init__('prefix')
def get(self):
return str(len(self.text)) + self.text
class_list = {
'terminator': Terminator,
'prefix': Prefix
}
class Selector():
def __init__(self, option):
self.__class__ = class_list[option]
def main():
selection = input("Choose 'terminator' or 'prefix': ")
obj = Selector(selection)
obj.set_text('something')
print(obj.get())
if __name__ == '__main__':
main()
Terminator is a class to produce a text terminated with a special character (!); Prefix produces the same text prefixed with its length.
With Selector, I can use o = Selector('prefix') to get o as a Prefix instance.
The question
My question is if I can add extra arguments to Selector and pass them to the respective class. For example:
o = Selector('prefix', number_of_digits = 2) # '05hello' intead of '5hello'
or
o = Selector('terminator', terminator = '$') # use '$' instead of '!'
For now, I couldn't figure out how to accomplish this task. I tried to use *args and **kwargs, but unsuccessfully.
Additional information
The code I'm working on is intended to undergraduate students and I want to make it simple for teaching purposes, so Selector should be used to hide other classes and their details from the students (to hide Terminator and Prefix, for example).
I expect to have about 15 distinct classes to hide behind Selector.
Also, I'm ready to hear I'm completely wrong about this approach if there are alternatives.
Try calling the appropriate class's __init__() manually, and set the variables like you otherwise would:
class Terminator(Base):
# make terminator an instance variable instead of a class variable,
# and set it as an overridable default arg for the constructor
def __init__(self, terminator='!'):
super().__init__('terminator')
self.terminator = terminator
def get(self):
return self.text + self.terminator
class Selector():
def __init__(self, option, *args, **kwargs):
self.__class__ = class_list[option]
self.__class__.__init__(self, *args, **kwargs)
...
o = Selector('terminator', terminator='$')
o.set_text("Hello World")
print(o.get())
# Hello World$
I should leave a disclaimer: what you're trying to do is essentially a version of the Factory method pattern, which is usually easier to maintain if you bundle it into a method instead of messing with class types and reflection:
def Selector(option: str, *args, **kwargs) -> Base:
return class_list[option](*args, **kwargs)
# this will do .__new()__ and .__init__() normally,
# and is indistinguishable from normal class creation
Using a method to do this instead of overriding the class metadata also has the advantage of being easy to fit into a type system (see the type hinting in the above snippet), which is difficult to do with .__init__(). This is a common design pattern in Java, for example, which is very strongly and statically typed, requires a factory method to have a signature with the superclass of anything it could possibly return, and makes it impossible for an object to change its own type at runtime.
The disadvantage of your current approach, dynamically changing .__class__, is that the .__new__() and .__init__() methods which were called on the resulting object will not match with each other (it would be using Selector.__new__() but Terminator.__init__(), for example), which may cause weird and hard-to-diagnose problems in the future. It's a fun experiment, but be knowledgeable of the risks before using this in something you'll have to maintain for a long time.
I have got one question: why do I need to call super().--init--() in metaclasses? Because metaclass is factory of classes, I think we don`t need to call initialization for making objects of class Shop. Or with using super().--init-- we initializing the class? (Because my IDE says, that I should call it. But without super().--init-- nothing happens, my class working without mistakes).
Can you explane me, why?
Thanks in advance!
class Descriptor:
_counter = 0
def __init__(self):
self.attr_name = f'Descriptor attr#{Descriptor._counter}'
Descriptor._counter += 1
def __get__(self, instance, owner):
return self if instance is None else instance.__dict__[self.attr_name]
def __set__(self, instance, value):
if value > 0:
instance.__dict__[self.attr_name] = value
else:
msg = 'Value must be > 0!'
raise AttributeError(msg)
class Shop():
weight = Descriptor()
price = Descriptor()
def __init__(self, name, price, weight):
self.name = name
self.price = price
self.weight = weight
def __repr__(self):
return f'{self.name}: price - {self.price} weight - {self.weight}'
def buy(self):
return self.price * self.weight
class Meta(type):
def __init__(cls, name, bases, attr_dict):
super().__init__(name, bases, attr_dict) # <- this is that func. call
for key, value in attr_dict.items():
if isinstance(value, Descriptor): # Here I rename attributes name of descriptor`s object.
value.attr_name = key
#classmethod
def __prepare__(metacls, name, bases):
return OrderedDict()
You don't "need" to - and if your code use no other custom metaclasses, not calling the metaclass'__init__.super() will work just the same.
But if one needs to combine your metaclass with another, through inheritance, without the super() call, it won't work "out of the box": the super() call is the way to ensure all methods in the inheritance chain are called.
And if at first it looks like that a metaclass is extremely rare, and combining metaclasses would likely never take place: a few libraries or frameworks have their own metaclasses, including Python's "abc"s (abstract base classes), PyQT, ORM frameworks, and so on. If any metaclass under your control is well behaved with proper super() calls on the __new__, __init__ and __call__ methods, (if you override those), what you need to do to combine both superclasses and have a working metaclass can be done in a single line:
CompatibleMeta = type("CompatibleMeta", (meta, type(OtherClassBase)), {})
This way, for example, if you want to use the mechanisms in your metaclass in a class using the ABCMeta functionalities in Python, you just do it. The __init__ method in your Meta will call the other metaclass __init__. Otherwise it would not run, and some subtle unexpectd thing would not be initialized in your classes, and this could be a very hard to find bug.
On a side note: there is no need to declare __prepare__ in a metaclass if all it does is creating an OrderedDict on a Python newer than 3.6: Since that version, dicitionaries used as the "locals()" while executing class bodies are ordered by default. Also, if another metaclass you are combining with also have a __prepare__, there is no way to make that work automatically by using "super()" - you have to check the code and verify which of the two __prepare__s should be used, or create a new mapping type with features to attend both metaclasses.
I am wanting to make a Python program to help in choosing a Nerf war load out by allowing me to enter data that I get, such as how far the gun shoots, it's size, weight, type, ect. then I want to create different functions that will allow me to pull up optimal builds such as a sniper build for example.
I made a class so I can enter all the information for each gun into it's own class.
I am not sure how to approach making a function for example "sniper" that will pull only sniper types then put them in order by how far their shooting distance is.
I am thinking about a for loop but I am not sure how to approach it.
class Nerf_Gun:
def __init__(self, name, size, weight, DPS, distance, slots, type):
self.name = name
self.size = size
self.weight = weight
self.DPS = DPS
self.distance = distance
self.slots = slots
self.type = type
def sniper(self):
if Nerf_Gun.type() == "Sniper":
You want to create different types of guns which may belong to broad categories, hence you are looking for the concept of inheritance. Look up https://www.w3schools.com/python/python_inheritance.asp. In general, if you are looking to make those attributes configurable for each "instance" of a gun, I would make it through attrs.
#attr.s
class Gun:
name: str = attr.ib()
size: int = attr.ib()
weight: int = attr.ib()
DPS: str = attr.ib()
distance: str = attr.ib()
slots: str = attr.ib()
type: str = attr.ib()
Sniper = attr.make_class('Sniper', {}, bases=(Gun,))
#initialize instance
nerf_gun = Sniper(name='sniper', size=2, weight=3, DPS='123', distance='123', slots='456', type='789')
You can play around with making these attributes optional or override them in Sniper class etc.
However, if you want to keep them constant across the class (all snipers have the same values for each gun), I would initialize the class with variables, exactly like you were, but make subclasses for Sniper etc like so:
class Sniper(Nerf_Gun):
def __init__(self):
self.name = 'Sniper'
self.weight = 123
# fix the rest of attrs