I've been researching a lot, but I haven't found a way.
I have Document clases with a _owner attribute which specifies the ObjectID of the owner, which is a per-request value, so it's globally available. I would like to be able to set part of the query by default.
For example, doing this query
MyClass.objects(id='12345')
should be the same as doing
MyClass.objects(id='12345', _owner=global.owner)
because _owner=global.owner is always added by default
I haven't found a way to override objects, and using a queryset_classis someway confusing because I still have to remember to call a ".owned()" manager to add the filter every time I want to query something.
It ends up like this...
MyClass.objects(id='12345').owned()
// same that ...
MyClass.objects(id='12345', _owner=global.owner)
Any Idea? Thanks!
The following should do the trick for querying (example is simplified by using a constant owned=True but it can easily be extended to use your global):
class OwnedHouseWrapper(object):
# Implements descriptor protocol
def __get__(self, instance, owner):
return House.objects.filter(owned=True)
def __set__(self, instance, value):
raise Exception("can't set .objects")
class House(Document):
address = StringField()
owned = BooleanField(default=False)
class OwnedHouse:
objects = OwnedHouseWrapper()
House(address='garbage 12', owned=True).save()
print(OwnedHouse.objects()) # [<House: House object>]
print(len(OwnedHouse.objects)) # 1
Related
yo, I have simple Question but it's answer sames to be not ,at all, maybe even not exist !
anyway, I just want to set a django-model field value based on existing ones, LIKE:
class Person(models.Model):
#here is the struggle
people = len(Person.objects.all())
name = medels.Charfield()
#pass it as argument
peopleBeforeYou = models.CharField(default= people-1)
the idea is to set the peopleBeforeYou field default value to that same model QuerySet len()
any idea ????!!!!
You could move the function that counts the number of existing rows to a separate function outside of the class
def count_people():
return Person.objects.count()
class Person(models.Model):
peopleBeforeYou = models.IntegerField(default=count_people)
List item
class Car:
def __init__(self, color, brand, number_of_seats):
self.color = color
self.brand = brand
self.number_of_seats = number_of_seats
self.number_of_wheels = 4
self.registration_number = GenerateRegistrationNumber()
Hi all,
1)Referring to the above example, could anyone tell me what is the difference between specific attributed and "the other" attributes? What will happen if registration_number is treated as a specific attribute?
2)
class MyInteger:
def __init__(self, newvalue):
# imagine self as an index card.
# under the heading of "value", we will write
# the contents of the variable newvalue.
self.value = newvalue
If we consider this example, shouldn't it be self.newvalue = newvalue?
I think I know what you're asking (let me know if I'm wrong), but I think you're asking what the difference is between the attributes that are assigned by the parameters of __init__ (Instance Attributes), ones that are assigned inside the __init__ method but not with parameters (also Instance Attributes), and ones that are not assigned in the initialiser at all (Class Attributes). The difference here is that all (well, pretty much all) cars have 4 wheels, and the number plate is generated, not supplied. You could also do this, for example:
class Car:
number_of_wheels = 4
def __init__(self, color, brand, number_of_seats):
self.color = color
self.brand = brand
self.number_of_seats = number_of_seats
self.registration_number = GenerateRegistrationNumber()
As the number of wheels here is always assigned to the same value, across all instances, it is said to be a "Class Attribute" in this case. All other attributes here are “Instance Attributes” as they are specifically assigned to each instance. For a slightly better explanation, I recommend reading this:
https://www.geeksforgeeks.org/class-instance-attributes-python/
It doesn't actually matter what the instance attribute (self.value here) is called, you could call it whatever you want and it'd still work, but in most cases, you would indeed want to name the attribute the same as the parameter.
init function also called as magic function is a constructor function for a class. if you remember in java whenever we make a class the constructor method should have the same name as the classname but this is not the scenario in python . In python we make use of the init method
the difference between the class attributes and instance attributes is that the class attributes are common to every object that is created but the instance attributes are only accessible by the object that is created.
consider a example where data of students in a class is stored. In such case the class division will be same for all the students of that particular class so it can be common but names of all students are different and also their marks and so on and hence they should be different for everyone
in previous scenario the class division can be class attribute and the data of student like name , marks has to be instance attributes
examples of class attribute is as shown
class A:
Division = 5A
here the division is a class attribute
class B:
def __init__(self,name,marks):
self.name = name
self.marks = marks
here the name and marks are instance variables
now here we can also write self.username = name because we are storing the value of name variable in self.username so you can write any name there is no constraint on that
Also whenever you write __ in front of method or variable it means that the attribute is private and accessible by only class.
I want to pass a string to a method/class function which resolves the correct attribute to modify. I'm pretty sure i've done this before, but I seem to have forgotten how to.
class A:
def __init__(self):
self.word = B.getWord()
self.phrase = "Some default string"
def set_dynamically(self, attribute, value):
self[attribute] = value
This would let me do something like A.set_dynamically('word', C.getWord())
I've tried searching for a question and answer for this but I'm having a hard time defining what this is called, so I didn't really find anything.
Python objects have a built-in method called __setattr__(self, name, value) that does this. You can invoke this method by calling setattr() with an object as the argument:
A = A()
setattr(A, 'word', C.getWord())
There's no reason to do this when you could just do something like A.word = C.getWord() (which, in fact, resolves down to calling __setattr__() the same way as the built-in setattr() function does), but if the property you're setting is named dynamically, then this is how you get around that limitation.
If you want to customize the behavior of how your class acts when you try to call setattr() on it (or when you try to set an attribute normally), you can override the __setattr__(self, name, value) method in much the same way as you're overriding __init__(). Be careful if you do this, because it's really easy to accidentally produce an infinite recursion error - to avoid this you can use object.__setattr__(self, name_value) inside your overridden __setattr__(self, name, value).
Just wanted to add my own solution as well. I created a mapping object;
def _mapper(self, attr, object):
m = { "funcA" : object.funcA,
"funcB" : object.funcB,
... : ...,
}
return m.get(attr)
def call_specific_func_(self, attr):
--do stuff--
for a in some-list:
attr = a.get_attr()
retvals = self._mapper(attr, a)
-- etc --
I am trying to build a marshmallow scheme to both load and dump data. And I get everything OK except one field.
Problem description
(If you understand the problem, you don't have to read this).
For load data its type is Decimal. And I used it like this before. Now I want to use this schema for dumping and for that my flask API responses with: TypeError: Object of type Decimal is not JSON serializable. OK, I understand. I changed the type to Float. Then my legacy code started to get an exception while trying to save that field to database (it takes Decimal only). I don't want to change the legacy code so I looked for any solution at the marshmallow docs and found load_only and dump_only params. It seems like those are what I wanted, but here is my problem - I want to set them to the same field. So I just wondered if I can define both fields and tried this:
class PaymentSchema(Schema):
money = fields.Decimal(load_only=True)
money = fields.Float(dump_only=True)
I have been expected for a miracle, of course. Actually I was thinking that it will skip first definition (correctly, re-define it). What I got is an absence of the field at all.
Workaround solution
So I tried another solution. I created another schema for dump and inherit it from the former schema:
class PaymentSchema(Schema):
money = fields.Decimal(load_only=True)
class PaymentDumpSchema(PaymentSchema):
money = fields.Float(dump_only=True)
It works. But I wonder if there's some another, native, "marshmallow-way" solution for this. I have been looking through the docs but I can't find anything.
You can use the marshmallow decorator #pre_load in this decorator you can do whatever you want and return with your type
from marshmallow import pre_load
import like this and in this you will get your payload and change the type as per your requirement.
UPD: I found a good solution finally.
NEW SOLUTION
The trick is to define your field in load_fields and dump_fields inside __init__ method.
from marshmallow.fields import Integer, String, Raw
from marshmallow import Schema
class ItemDumpLoadSchema(Schema):
item = Raw()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if not (self.only and 'item' not in self.only) and \
not (self.exclude and 'item' in self.exclude):
self.load_fields['item'] = Integer(missing=0)
self.dump_fields['item'] = String()
Usage:
>>> ItemDumpLoadSchema().load({})
{'item': 0}
>>> ItemDumpLoadSchema().dump({'item': 0})
{'item': '0'}
Don't forget to define field in a schema with some field (Raw in my example) - otherwise it may raise an exception in some cases (e.g. using of only and exclude keywords).
OLD SOLUTION
A little perverted one. It based on #prashant-suthar answer. I named load field with suffix _load and implemented #pre_load, #post_load and error handling.
class ArticleSchema(Schema):
id = fields.String()
title = fields.String()
text = fields.String()
class FlowSchema(Schema):
article = fields.Nested(ArticleSchema, dump_only=True)
article_load = fields.Int(load_only=True)
#pre_load
def pre_load(self, data, *args, **kwargs):
if data.get('article'):
data['article_load'] = data.pop('article')
return data
#post_load
def post_load(self, data, *args, **kwargs):
if data.get('article_load'):
data['article'] = data.pop('article_load')
return data
def handle_error(self, exc, data, **kwargs):
if 'article_load' in exc.messages:
exc.messages['article'] = exc.messages.pop('article_load')
raise exc
Why the old solution is not a good solution?
It doesn't allow to inheritate schemas with different handle_error methods defined. And you have to name pre_load and post_load methods with different names.
pass data_key argument to the field definition
Documentation mentions, data_key parameter can be used along with dump_only or load_only to be able to have same field with different functionality.
So you can write your schema as...
class PaymentSchema(Schema):
decimal_money = fields.Decimal(data_key="money", load_only=True)
money = fields.Float(dump_only=True)
This should solve your problem. I am using data_key for similar problem in marshmallow with SQLAlchemyAutoSchema and this fixed my issue.
Edit
Note: The key in ValidationError.messages (error messages) will be decimal_money by default. You may tweak the handle_error method of Schema class to replace decimal_money with money but it is not recommended as you yourself may not be able to differentiate between the error messages fields.
Thanks.
If I have the following :
class A:
attrs = [...]
A_attr = [...]
class B(A):
B_attr = [...]
Is there a way to prevent my B subclass from inheriting the A_attr from the A class?
Or would this be considered a bad design and I should better subclass both A and B from a third C class containing all the attrs attributes and add the particular attribute to each subclass like this?
class C:
attrs = [...]
class A(C):
A_attr = [...]
class B(C):
B_attr = [...]
Better idea is to dump the common functionality in a class.
class Commmon:
attrs = [...]
Extend this class who want this extra functonality.
class A(Common):
# only one attribute added in this class
A_attr = [...]
classB(Common):
attrs_B = [...]
Extend class A when that extra attribute is needed in the class, this will bring all those other attributes.
class C(A):
attrs_C = [...]
What this will allow is wherever you want an object of type Common you can provide instance of B as well as C. And wherever you want instance of class A you can provide instance of C. If you add specific instance in each of your subclasses you will not be able to do so.
From Comment
So according to you I should use the second solution I exposed in my question.
No.
Instead of adding the attribute in each subclass, my advice is to add the attribute in a separate class and let your new classes inherit this intermediate class. So you do not have to add the specific attribute in each one of those subclass.
Example is already provided above. Lets see what is the benefit of doing this, as opposed to your suggestion. Take the following function
def foo(obj):
# check to make sure object has the specific attribute
if (isinstance(obj, A)):
pass; #do something
else:
raise TypeError("Object is not an instance of A")
But if we add the specific attribute in each class, the method will need to be changed to something like this:
def foo(obj):
# check to make sure object has the those type which has that specific attribute
if( isinstance(obj, class1) or (isinstance(obj, class2) or ...):
pass; #do something
else:
raise TypeError("Object does not have specific attribute")
Of course, you can perform a check using something like this:
def foo(obj):
# check to make sure obj has attribute
if hasattr(obj, 'property')
pass; # do something
else:
raise TypeError("Object does not have necessary attribute")
Using correct inheritance relationship (as shown in 1st example) will also help your IDE (if you are using one) in inferring types, because IDE can determine which type of object it expects. You can even augment the function with type information like this:
def foo(obj : A):
pass; #do something
That A after colon is a hint to the IDE that function expects an object of type or subtype of A.