Develop Reference

How to maintain a table of all proxy models in Django?

I have a model A and want to make subclasses of it.
class A(models.Model):
type = models.ForeignKey(Type)
data = models.JSONField()
def compute():
pass
class B(A):
def compute():
df = self.go_get_data()
self.data = self.process(df)
class C(A):
def compute():
df = self.go_get_other_data()
self.data = self.process_another_way(df)
# ... other subclasses of A
B and C should not have their own tables, so I decided to use the proxy attirbute of Meta. However, I want there to be a table of all the implemented proxies.
In particular, I want to keep a record of the name and description of each subclass.
For example, for B, the name would be "B" and the description would be the docstring for B.
So I made another model:
class Type(models.Model):
# The name of the class
name = models.String()
# The docstring of the class
desc = models.String()
# A unique identifier, different from the Django ID,
# that allows for smoothly changing the name of the class
identifier = models.Int()
Now, I want it so when I create an A, I can only choose between the different subclasses of A.
Hence the Type table should always be up-to-date.
For example, if I want to unit-test the behavior of B, I'll need to use the corresponding Type instance to create an instance of B, so that Type instance already needs to be in the database.
Looking over on the Django website, I see two ways to achieve this: fixtures and data migrations.
Fixtures aren't dynamic enough for my usecase, since the attributes literally come from the code. That leaves me with data migrations.
I tried writing one, that goes something like this:
def update_results(apps, schema_editor):
A = apps.get_model("app", "A")
Type = apps.get_model("app", "Type")
subclasses = get_all_subclasses(A)
for cls in subclasses:
id = cls.get_identifier()
Type.objects.update_or_create(
identifier=id,
defaults=dict(name=cls.__name__, desc=cls.__desc__)
)
class Migration(migrations.Migration):
operations = [
RunPython(update_results)
]
# ... other stuff
The problem is, I don't see how to store the identifier within the class, so that the Django Model instance can recover it.
So far, here is what I have tried:
I have tried using the fairly new __init_subclass__ construct of Python. So my code now looks like:
class A:
def __init_subclass__(cls, identifier=None, **kwargs):
super().__init_subclass__(**kwargs)
if identifier is None:
raise ValueError()
cls.identifier = identifier
Type.objects.update_or_create(
identifier=identifier,
defaults=dict(name=cls.__name__, desc=cls.__doc__)
)
# ... the rest of A
# The identifier should never change, so that even if the
# name of the class changes, we still know which subclass is referred to
class B(A, identifier=3):
# ... the rest of B
But this update_or_create fails when the database is new (e.g. during unit tests), because the Type table does not exist.
When I have this problem in development (we're still in early stages so deleting the DB is still sensible), I have to go
comment out the update_or_create in __init_subclass__. I can then migrate and put it back in.
Of course, this solution is also not great because __init_subclass__ is run way more than necessary. Ideally this machinery would only happen at migration.
So there you have it! I hope the problem statement makes sense.
Thanks for reading this far and I look forward to hearing from you; even if you have other things to do, I wish you a good rest of your day :)

With a little help from Django-expert friends, I solved this with the post_migrate signal.
I removed the update_or_create in __init_subclass, and in project/app/apps.py I added:
from django.apps import AppConfig
from django.db.models.signals import post_migrate
def get_all_subclasses(cls):
"""Get all subclasses of a class, recursively.
Used to get a list of all the implemented As.
"""
all_subclasses = []
for subclass in cls.__subclasses__():
all_subclasses.append(subclass)
all_subclasses.extend(get_all_subclasses(subclass))
return all_subclasses
def update_As(sender=None, **kwargs):
"""Get a list of all implemented As and write them in the database.
More precisely, each model is used to instantiate a Type, which will be used to identify As.
"""
from app.models import A, Type
subclasses = get_all_subclasses(A)
for cls in subclasses:
id = cls.identifier
Type.objects.update_or_create(identifier=id, defaults=dict(name=cls.__name__, desc=cls.__doc__))
class MyAppConfig(AppConfig):
default_auto_field = "django.db.models.BigAutoField"
name = "app"
def ready(self):
post_migrate.connect(update_As, sender=self)
Hope this is helpful for future Django coders in need!

What is the most Pythonic way to organise constants?

I use marshmallow and have a series of object serialization. Because of technical reasons, the names of the field and class can be the same.
For example,
class Sample(Schema):
SampleField = fields.Nested(SampleField)
class SampleField(Schema):
# Some other fields
When I need to reference SampleField as a string in the code. I create constant and name it SAMPLE_FIELD = SampleField. Often I need to have the same constant in both places in the definition class and where it is used as a field.
How to organize it the way, it will not become messy?

The marshmallow field does not have to be named after the field name in serialiazed data.
Would this solve your problem?
class SampleSchema(Schema):
sample_field = fields.Nested(SampleFieldSchema, data_key='SampleField')
class SampleFieldSchema(Schema):
# Some other fields

creating nested classes in Python reflectively

I am trying to create nested Python classes using the 3 argument type function. I want to construct an analogue of this:
In [15]: class CC:
...: class DD:
...: pass
...:
The naive attempt is
In [17]: AA = type('AA', (), {'BB': type('BB', (), {})})
but that is not quite right, since BB is actually created outside
and before AA and is only put inside `AA later.
The difference isdemonstrated by:
In [18]: AA.BB
Out[18]: __main__.BB
In [16]: CC.DD
Out[16]: __main__.CC.DD
How can I create nested classes reflectively/dynamically that are completely equivalent to nested definitions?
I want to use this to reflectively generate a graphene-sqlalchemy api. The idiom there is to create an outer Graphene class with an inner Meta class pointing to the correponding SQLAchemy model class (http://docs.graphene-python.org/projects/sqlalchemy/en/latest/tutorial/#schema) eg:
from sqlalchemy import Column, Integer, String
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class UserModel(Base):
__tablename__ = 'department'
id = Column(Integer, primary_key=True)
name = Column(String)
last_name = Column(String)
from graphene_sqlalchemy import SQLAlchemyObjectType
class User(SQLAlchemyObjectType):
class Meta:
model = UserModel
# only return specified fields
only_fields = ("name",)
# exclude specified fields
exclude_fields = ("last_name",)
The User class above is pretty cookie cutter and should be constructable programmatically from the UserModel class. This should then be doable for an entire schema.

In the Python 3.7 type documentation, it says:
The [1st argument] is the class name and becomes the __name__ attribute.
So, I think the only difference between your 2 examples is that AA.BB.__name__ is AA.BB in the 1st example, and BB in the 2nd. If you want the __name__ to be the same, you can do this:
AA = type('AA', (), {'BB': type('AA.BB', (), {})})
Otherwise, as far as I can tell, both examples are functionally equivalent.

Actually, the only difference you get there is the __qualname__ class attribute.
The __qualname__ is created by the code object running the class body, and passed as an ordinary attribute to the metaclass __new__ method (usually type) .
Therefore all you need to get this level of equivalence is to explicitly pass a proper __qualname__ when creating the class:
In [9]: AA = type('AA', (), {'BB': type('BB', (), {'__qualname__': 'AA.BB'})})
In [10]: AA
Out[10]: __main__.AA
In [11]: AA.BB
Out[11]: __main__.AA.BB
This will likely be enough for you, but beware that there are more subtle differences between classes created dynamically. One of the last metaclass questions I answered is exactly about that, but with the opposite approach: the challenge was to actually be able to distinguish classes created with both styles.
Detect if class was defined declarative or functional - possible?
(warning: that contains what likely is the "deepest black magic" Python code I ever put in an answer here)

Dynamically add properties to a django model

I have a Django model where a lot of fields are choices. So I had to write a lot of "is_something" properties of the class to check whether the instance value is equal to some choice value. Something along the lines of:
class MyModel(models.Model):
some_choicefield = models.IntegerField(choices=SOME_CHOICES)
#property
def is_some_value(self):
return self.some_choicefield == SOME_CHOICES.SOME_CHOICE_VALUE
# a lot of these...
In order to automate this and spare me a lot of redundant code, I thought about patching the instance at creation, with a function that adds a bunch of methods that do the checks.
The code became as follows (I'm assuming there's a "normalize" function that makes the label of the choice a usable function name):
def dynamic_add_checks(instance, field):
if hasattr(field, 'choices'):
choices = getattr(field, 'choices')
for (value,label) in choices:
def fun(instance):
return getattr(instance, field.name) == value
normalized_func_name = "is_%s_%s" % (field.name, normalize(label))
setattr(instance, normalized_func_name, fun(instance))
class MyModel(models.Model):
def __init__(self, *args, **kwargs):
super(MyModel).__init__(*args, **kwargs)
dynamic_add_checks(self, self._meta.get_field('some_choicefield')
some_choicefield = models.IntegerField(choices=SOME_CHOICES)
Now, this works but I have the feeling there is a better way to do it. Perhaps at class creation time (with metaclasses or in the new method)? Do you have any thoughts/suggestions about that?

Well I am not sure how to do this in your way, but in such cases I think the way to go is to simply create a new model, where you keep your choices, and change the field to ForeignKey. This is simpler to code and manage.
You can find a lot of information at a basic level in Django docs: Models: Relationships. In there, there are many links to follow expanding on various topics. Beyong that, I believe it just needs a bit of imagination, and maybe trial and error in the beginning.

I came across a similar problem where I needed to write large number of properties at runtime to provide backward compatibility while changing model fields. There are 2 standard ways to handle this -
First is to use a custom metaclass in your models, which inherits from models default metaclass.
Second, is to use class decorators. Class decorators sometimes provides an easy alternative to metaclasses, unless you have to do something before the creation of class, in which case you have to go with metaclasses.

I bet you know Django fields with choices provided will automatically have a display function.
Say you have a field defined like this:
category = models.SmallIntegerField(choices=CHOICES)
You can simply call a function called get_category_display() to access the display value. Here is the Django source code of this feature:
https://github.com/django/django/blob/baff4dd37dabfef1ff939513fa45124382b57bf8/django/db/models/base.py#L962
https://github.com/django/django/blob/baff4dd37dabfef1ff939513fa45124382b57bf8/django/db/models/fields/init.py#L704
So we can follow this approach to achieve our dynamically set property goal.
Here is my scenario, a little bit different from yours but down to the end it's the same:
I have two classes, Course and Lesson, class Lesson has a ForeignKey field of Course, and I want to add a property name cached_course to class Lesson which will try to get Course from cache first, and fallback to database if cache misses:
Here is a typical solution:
from django.db import models
class Course(models.Model):
# some fields
class Lesson(models.Model):
course = models.ForeignKey(Course)
#property
def cached_course(self):
key = key_func()
course = cache.get(key)
if not course:
course = get_model_from_db()
cache.set(key, course)
return course
Turns out I have so many ForeignKey fields to cache, so here is the code following the similar approach of Django get_FIELD_display feature:
from django.db import models
from django.utils.functional import curry
class CachedForeignKeyField(models.ForeignKey):
def contribute_to_class(self, cls, name, **kwargs):
super(models.ForeignKey, self).contribute_to_class(cls, name, **kwargs)
setattr(cls, "cached_%s" % self.name,
property(curry(cls._cached_FIELD, field=self)))
class BaseModel(models.Model):
def _cached_FIELD(self, field):
value = getattr(self, field.attname)
Model = field.related_model
return cache.get_model(Model, pk=value)
class Meta:
abstract = True
class Course(BaseModel):
# some fields
class Lesson(BaseModel):
course = CachedForeignKeyField(Course)
By customizing CachedForeignKeyField, and overwrite the contribute_to_class method, along with BaseModel class with a _cached_FIELD method, every CachedForeignKeyField will automatically have a cached_FIELD property accordingly.
Too good to be true, bravo!

Django Manager Chaining

I was wondering if it was possible (and, if so, how) to chain together multiple managers to produce a query set that is affected by both of the individual managers. I'll explain the specific example that I'm working on:
I have multiple abstract model classes that I use to provide small, specific functionality to other models. Two of these models are a DeleteMixin and a GlobalMixin.
The DeleteMixin is defined as such:
class DeleteMixin(models.Model):
deleted = models.BooleanField(default=False)
objects = DeleteManager()
class Meta:
abstract = True
def delete(self):
self.deleted = True
self.save()
Basically it provides a pseudo-delete (the deleted flag) instead of actually deleting the object.
The GlobalMixin is defined as such:
class GlobalMixin(models.Model):
is_global = models.BooleanField(default=True)
objects = GlobalManager()
class Meta:
abstract = True
It allows any object to be defined as either a global object or a private object (such as a public/private blog post).
Both of these have their own managers that affect the queryset that is returned. My DeleteManager filters the queryset to only return results that have the deleted flag set to False, while the GlobalManager filters the queryset to only return results that are marked as global. Here is the declaration for both:
class DeleteManager(models.Manager):
def get_query_set(self):
return super(DeleteManager, self).get_query_set().filter(deleted=False)
class GlobalManager(models.Manager):
def globals(self):
return self.get_query_set().filter(is_global=1)
The desired functionality would be to have a model extend both of these abstract models and grant the ability to only return the results that are both non-deleted and global. I ran a test case on a model with 4 instances: one was global and non-deleted, one was global and deleted, one was non-global and non-deleted, and one was non-global and deleted. If I try to get result sets as such: SomeModel.objects.all(), I get instance 1 and 3 (the two non-deleted ones - great!). If I try SomeModel.objects.globals(), I get an error that DeleteManager doesn't have a globals (this is assuming my model declaration is as such: SomeModel(DeleteMixin, GlobalMixin). If I reverse the order, I don't get the error, but it doesn't filter out the deleted ones). If I change GlobalMixin to attach GlobalManager to globals instead of objects (so the new command would be SomeModel.globals.globals()), I get instances 1 and 2 (the two globals), while my intended result would be to only get instance 1 (the global, non-deleted one).
I wasn't sure if anyone had run into any situation similar to this and had come to a result. Either a way to make it work in my current thinking or a re-work that provides the functionality I'm after would be very much appreciated. I know this post has been a little long-winded. If any more explanation is needed, I would be glad to provide it.
Edit:
I have posted the eventual solution I used to this specific problem below. It is based on the link to Simon's custom QuerySetManager.

See this snippet on Djangosnippets: http://djangosnippets.org/snippets/734/
Instead of putting your custom methods in a manager, you subclass the queryset itself. It's very easy and works perfectly. The only issue I've had is with model inheritance, you always have to define the manager in model subclasses (just: "objects = QuerySetManager()" in the subclass), even though they will inherit the queryset. This will make more sense once you are using QuerySetManager.

Here is the specific solution to my problem using the custom QuerySetManager by Simon that Scott linked to.
from django.db import models
from django.contrib import admin
from django.db.models.query import QuerySet
from django.core.exceptions import FieldError
class MixinManager(models.Manager):
def get_query_set(self):
try:
return self.model.MixinQuerySet(self.model).filter(deleted=False)
except FieldError:
return self.model.MixinQuerySet(self.model)
class BaseMixin(models.Model):
admin = models.Manager()
objects = MixinManager()
class MixinQuerySet(QuerySet):
def globals(self):
try:
return self.filter(is_global=True)
except FieldError:
return self.all()
class Meta:
abstract = True
class DeleteMixin(BaseMixin):
deleted = models.BooleanField(default=False)
class Meta:
abstract = True
def delete(self):
self.deleted = True
self.save()
class GlobalMixin(BaseMixin):
is_global = models.BooleanField(default=True)
class Meta:
abstract = True
Any mixin in the future that wants to add extra functionality to the query set simply needs to extend BaseMixin (or have it somewhere in its heirarchy). Any time I try to filter the query set down, I wrapped it in a try-catch in case that field doesn't actually exist (ie, it doesn't extend that mixin). The global filter is invoked using globals(), while the delete filter is automatically invoked (if something is deleted, I never want it to show). Using this system allows for the following types of commands:
TemporaryModel.objects.all() # If extending DeleteMixin, no deleted instances are returned
TemporaryModel.objects.all().globals() # Filter out the private instances (non-global)
TemporaryModel.objects.filter(...) # Ditto about excluding deleteds
One thing to note is that the delete filter won't affect admin interfaces, because the default Manager is declared first (making it the default). I don't remember when they changed the admin to use Model._default_manager instead of Model.objects, but any deleted instances will still appear in the admin (in case you need to un-delete them).

I spent a while trying to come up with a way to build a nice factory to do this, but I'm running into a lot of problems with that.
The best I can suggest to you is to chain your inheritance. It's not very generic, so I'm not sure how useful it is, but all you would have to do is:
class GlobalMixin(DeleteMixin):
is_global = models.BooleanField(default=True)
objects = GlobalManager()
class Meta:
abstract = True
class GlobalManager(DeleteManager):
def globals(self):
return self.get_query_set().filter(is_global=1)
If you want something more generic, the best I can come up with is to define a base Mixin and Manager that redefines get_query_set() (I'm assuming you only want to do this once; things get pretty complicated otherwise) and then pass a list of fields you'd want added via Mixins.
It would look something like this (not tested at all):
class DeleteMixin(models.Model):
deleted = models.BooleanField(default=False)
class Meta:
abstract = True
def create_mixin(base_mixin, **kwargs):
class wrapper(base_mixin):
class Meta:
abstract = True
for k in kwargs.keys():
setattr(wrapper, k, kwargs[k])
return wrapper
class DeleteManager(models.Manager):
def get_query_set(self):
return super(DeleteManager, self).get_query_set().filter(deleted=False)
def create_manager(base_manager, **kwargs):
class wrapper(base_manager):
pass
for k in kwargs.keys():
setattr(wrapper, k, kwargs[k])
return wrapper
Ok, so this is ugly, but what does it get you? Essentially, it's the same solution, but much more dynamic, and a little more DRY, though more complex to read.
First you create your manager dynamically:
def globals(inst):
return inst.get_query_set().filter(is_global=1)
GlobalDeleteManager = create_manager(DeleteManager, globals=globals)
This creates a new manager which is a subclass of DeleteManager and has a method called globals.
Next, you create your mixin model:
GlobalDeleteMixin = create_mixin(DeleteMixin,
is_global=models.BooleanField(default=False),
objects = GlobalDeleteManager())
Like I said, it's ugly. But it means you don't have to redefine globals(). If you want a different type of manager to have globals(), you just call create_manager again with a different base. And you can add as many new methods as you like. Same for the manager, you just keep adding new functions that will return different querysets.
So, is this really practical? Maybe not. This answer is more an exercise in (ab)using Python's flexibility. I haven't tried using this, though I do use some of the underlying principals of dynamically extending classes to make things easier to access.
Let me know if anything is unclear and I'll update the answer.

Another option worth considering is the PassThroughManager:
https://django-model-utils.readthedocs.org/en/latest/managers.html#passthroughmanager

You should use QuerySet instead of Manager.
See Documentation here.