Fixed #13634 -- Migrated aggregation tests to use unittest. This removes a...

            msg_prefix + "Template '%s' was used unexpectedly in rendering"

            " the response" % template_name)

    def assertQuerysetEqual(self, qs, values, transform=repr):

        return self.assertEqual(map(transform, qs), values)

def connections_support_transactions():

    """

    Returns True if all connections support transactions.  This is messy

    ``target_status_code`` will be the url and status code for the final

    point of the redirect chain.

.. method:: TestCase.assertQuerysetEqual(response, qs, values, transform=repr)

    Asserts that a queryset ``qs`` returns a particular list of values ``values``.

    The comparison of the contents of ``qs`` and ``values`` is performed using

    the function ``transform``; by default, this means that the ``repr()`` of

    each value is compared. Any other callable can be used if ``repr()`` doesn't

    provide a unique or helpful comparison.

    The comparison is also ordering dependent. If ``qs`` doesn't provide an

    implicit ordering, you will need to apply a ``order_by()`` clause to your

    queryset to ensure that the test will pass reliably.

.. _topics-testing-email:

E-mail services

# coding: utf-8

from django.db import models

class Author(models.Model):

    name = models.CharField(max_length=100)

    age = models.IntegerField()

    def __unicode__(self):

        return self.name

# Tests on 'aggregate'

# Different backends and numbers.

__test__ = {'API_TESTS': """

>>> from django.core import management

>>> from decimal import Decimal

>>> from datetime import date

# Reset the database representation of this app.

# This will return the database to a clean initial state.

>>> management.call_command('flush', verbosity=0, interactive=False)

# Empty Call - request nothing, get nothing.

>>> Author.objects.all().aggregate()

{}

>>> from django.db.models import Avg, Sum, Count, Max, Min

# Single model aggregation

#

# Single aggregate

# Average age of Authors

>>> Author.objects.all().aggregate(Avg('age'))

{'age__avg': 37.4...}

# Multiple aggregates

# Average and Sum of Author ages

>>> Author.objects.all().aggregate(Sum('age'), Avg('age'))

{'age__sum': 337, 'age__avg': 37.4...}

# Aggreates interact with filters, and only

# generate aggregate values for the filtered values

# Sum of the age of those older than 29 years old

>>> Author.objects.all().filter(age__gt=29).aggregate(Sum('age'))

{'age__sum': 254}

# Depth-1 Joins

#

# On Relationships with self

# Average age of the friends of each author

>>> Author.objects.all().aggregate(Avg('friends__age'))

{'friends__age__avg': 34.07...}

# On ManyToMany Relationships

#

# Forward

# Average age of the Authors of Books with a rating of less than 4.5

>>> Book.objects.all().filter(rating__lt=4.5).aggregate(Avg('authors__age'))

{'authors__age__avg': 38.2...}

# Backward

# Average rating of the Books whose Author's name contains the letter 'a'

>>> Author.objects.all().filter(name__contains='a').aggregate(Avg('book__rating'))

{'book__rating__avg': 4.0}

# On OneToMany Relationships

#

# Forward

# Sum of the number of awards of each Book's Publisher

>>> Book.objects.all().aggregate(Sum('publisher__num_awards'))

{'publisher__num_awards__sum': 30}

# Backward

# Sum of the price of every Book that has a Publisher

>>> Publisher.objects.all().aggregate(Sum('book__price'))

{'book__price__sum': Decimal("270.27")}

# Multiple Joins

#

# Forward

>>> Store.objects.all().aggregate(Max('books__authors__age'))

{'books__authors__age__max': 57}

# Backward

# Note that the very long default alias may be truncated

>>> Author.objects.all().aggregate(Min('book__publisher__num_awards'))

{'book__publisher__num_award...': 1}

# Aggregate outputs can also be aliased.

# Average amazon.com Book rating

>>> Store.objects.filter(name='Amazon.com').aggregate(amazon_mean=Avg('books__rating'))

{'amazon_mean': 4.08...}

# Tests on annotate()

# An empty annotate call does nothing but return the same QuerySet

>>> Book.objects.all().annotate().order_by('pk')

[<Book: The Definitive Guide to Django: Web Development Done Right>, <Book: Sams Teach Yourself Django in 24 Hours>, <Book: Practical Django Projects>, <Book: Python Web Development with Django>, <Book: Artificial Intelligence: A Modern Approach>, <Book: Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp>]

# Annotate inserts the alias into the model object with the aggregated result

>>> books = Book.objects.all().annotate(mean_age=Avg('authors__age'))

>>> books.get(pk=1).name

u'The Definitive Guide to Django: Web Development Done Right'

>>> books.get(pk=1).mean_age

34.5

# On ManyToMany Relationships

# Forward

# Average age of the Authors of each book with a rating less than 4.5

>>> books = Book.objects.all().filter(rating__lt=4.5).annotate(Avg('authors__age'))

>>> sorted([(b.name, b.authors__age__avg) for b in books])

[(u'Artificial Intelligence: A Modern Approach', 51.5), (u'Practical Django Projects', 29.0), (u'Python Web Development with Django', 30.3...), (u'Sams Teach Yourself Django in 24 Hours', 45.0)]

# Count the number of authors of each book

>>> books = Book.objects.annotate(num_authors=Count('authors'))

>>> sorted([(b.name, b.num_authors) for b in books])

[(u'Artificial Intelligence: A Modern Approach', 2), (u'Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp', 1), (u'Practical Django Projects', 1), (u'Python Web Development with Django', 3), (u'Sams Teach Yourself Django in 24 Hours', 1), (u'The Definitive Guide to Django: Web Development Done Right', 2)]

# Backward

# Average rating of the Books whose Author's names contains the letter 'a'

>>> authors = Author.objects.all().filter(name__contains='a').annotate(Avg('book__rating'))

>>> sorted([(a.name, a.book__rating__avg) for a in authors])

[(u'Adrian Holovaty', 4.5), (u'Brad Dayley', 3.0), (u'Jacob Kaplan-Moss', 4.5), (u'James Bennett', 4.0), (u'Paul Bissex', 4.0), (u'Stuart Russell', 4.0)]

# Count the number of books written by each author

>>> authors = Author.objects.annotate(num_books=Count('book'))

>>> sorted([(a.name, a.num_books) for a in authors])

[(u'Adrian Holovaty', 1), (u'Brad Dayley', 1), (u'Jacob Kaplan-Moss', 1), (u'James Bennett', 1), (u'Jeffrey Forcier', 1), (u'Paul Bissex', 1), (u'Peter Norvig', 2), (u'Stuart Russell', 1), (u'Wesley J. Chun', 1)]

# On OneToMany Relationships

# Forward

# Annotate each book with the number of awards of each Book's Publisher

>>> books = Book.objects.all().annotate(Sum('publisher__num_awards'))

>>> sorted([(b.name, b.publisher__num_awards__sum) for b in books])

[(u'Artificial Intelligence: A Modern Approach', 7), (u'Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp', 9), (u'Practical Django Projects', 3), (u'Python Web Development with Django', 7), (u'Sams Teach Yourself Django in 24 Hours', 1), (u'The Definitive Guide to Django: Web Development Done Right', 3)]

# Backward

# Annotate each publisher with the sum of the price of all books sold

>>> publishers = Publisher.objects.all().annotate(Sum('book__price'))

>>> sorted([(p.name, p.book__price__sum) for p in publishers])

[(u'Apress', Decimal("59.69")), (u"Jonno's House of Books", None), (u'Morgan Kaufmann', Decimal("75.00")), (u'Prentice Hall', Decimal("112.49")), (u'Sams', Decimal("23.09"))]

# Calls to values() are not commutative over annotate().

# Calling values on a queryset that has annotations returns the output

# as a dictionary

>>> [sorted(o.iteritems()) for o in Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values()]

[[('contact_id', 1), ('id', 1), ('isbn', u'159059725'), ('mean_age', 34.5), ('name', u'The Definitive Guide to Django: Web Development Done Right'), ('pages', 447), ('price', Decimal("30...")), ('pubdate', datetime.date(2007, 12, 6)), ('publisher_id', 1), ('rating', 4.5)]]

>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values('pk', 'isbn', 'mean_age')

[{'pk': 1, 'isbn': u'159059725', 'mean_age': 34.5}]

# Calling values() with parameters reduces the output

>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values('name')

[{'name': u'The Definitive Guide to Django: Web Development Done Right'}]

# An empty values() call before annotating has the same effect as an

# empty values() call after annotating

>>> [sorted(o.iteritems()) for o in Book.objects.filter(pk=1).values().annotate(mean_age=Avg('authors__age'))]

[[('contact_id', 1), ('id', 1), ('isbn', u'159059725'), ('mean_age', 34.5), ('name', u'The Definitive Guide to Django: Web Development Done Right'), ('pages', 447), ('price', Decimal("30...")), ('pubdate', datetime.date(2007, 12, 6)), ('publisher_id', 1), ('rating', 4.5)]]

# Calling annotate() on a ValuesQuerySet annotates over the groups of

# fields to be selected by the ValuesQuerySet.

# Note that an extra parameter is added to each dictionary. This

# parameter is a queryset representing the objects that have been

# grouped to generate the annotation

>>> Book.objects.all().values('rating').annotate(n_authors=Count('authors__id'), mean_age=Avg('authors__age')).order_by('rating')

[{'rating': 3.0, 'n_authors': 1, 'mean_age': 45.0}, {'rating': 4.0, 'n_authors': 6, 'mean_age': 37.1...}, {'rating': 4.5, 'n_authors': 2, 'mean_age': 34.5}, {'rating': 5.0, 'n_authors': 1, 'mean_age': 57.0}]

# If a join doesn't match any objects, an aggregate returns None

>>> authors = Author.objects.all().annotate(Avg('friends__age')).order_by('id')

>>> len(authors)

9

>>> sorted([(a.name, a.friends__age__avg) for a in authors])

[(u'Adrian Holovaty', 32.0), (u'Brad Dayley', None), (u'Jacob Kaplan-Moss', 29.5), (u'James Bennett', 34.0), (u'Jeffrey Forcier', 27.0), (u'Paul Bissex', 31.0), (u'Peter Norvig', 46.0), (u'Stuart Russell', 57.0), (u'Wesley J. Chun', 33.6...)]

# The Count aggregation function allows an extra parameter: distinct.

# This restricts the count results to unique items

>>> Book.objects.all().aggregate(Count('rating'))

{'rating__count': 6}

>>> Book.objects.all().aggregate(Count('rating', distinct=True))

{'rating__count': 4}

# Retreiving the grouped objects

# When using Count you can also omit the primary key and refer only to

# the related field name if you want to count all the related objects

# and not a specific column

>>> explicit = list(Author.objects.annotate(Count('book__id')))

>>> implicit = list(Author.objects.annotate(Count('book')))

>>> explicit == implicit

True

# Ordering is allowed on aggregates

>>> Book.objects.values('rating').annotate(oldest=Max('authors__age')).order_by('oldest', 'rating')

[{'rating': 4.5, 'oldest': 35}, {'rating': 3.0, 'oldest': 45}, {'rating': 4.0, 'oldest': 57}, {'rating': 5.0, 'oldest': 57}]

>>> Book.objects.values('rating').annotate(oldest=Max('authors__age')).order_by('-oldest', '-rating')

[{'rating': 5.0, 'oldest': 57}, {'rating': 4.0, 'oldest': 57}, {'rating': 3.0, 'oldest': 45}, {'rating': 4.5, 'oldest': 35}]

# It is possible to aggregate over anotated values

>>> Book.objects.all().annotate(num_authors=Count('authors__id')).aggregate(Avg('num_authors'))

{'num_authors__avg': 1.66...}

# You can filter the results based on the aggregation alias.

# Lets add a publisher to test the different possibilities for filtering

>>> p = Publisher(name='Expensive Publisher', num_awards=0)

>>> p.save()

>>> Book(name='ExpensiveBook1', pages=1, isbn='111', rating=3.5, price=Decimal("1000"), publisher=p, contact_id=1, pubdate=date(2008,12,1)).save()

>>> Book(name='ExpensiveBook2', pages=1, isbn='222', rating=4.0, price=Decimal("1000"), publisher=p, contact_id=1, pubdate=date(2008,12,2)).save()

>>> Book(name='ExpensiveBook3', pages=1, isbn='333', rating=4.5, price=Decimal("35"), publisher=p, contact_id=1, pubdate=date(2008,12,3)).save()

# Publishers that have:

# (i) more than one book

>>> Publisher.objects.annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk')

[<Publisher: Apress>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>]

# (ii) a book that cost less than 40

>>> Publisher.objects.filter(book__price__lt=Decimal("40.0")).order_by('pk')

[<Publisher: Apress>, <Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>]

# (iii) more than one book and (at least) a book that cost less than 40

>>> Publisher.objects.annotate(num_books=Count('book__id')).filter(num_books__gt=1, book__price__lt=Decimal("40.0")).order_by('pk')

[<Publisher: Apress>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>]

# (iv) more than one book that costs less than $40

>>> Publisher.objects.filter(book__price__lt=Decimal("40.0")).annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk')

[<Publisher: Apress>]

# Now a bit of testing on the different lookup types

#

>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__range=[1, 3]).order_by('pk')

[<Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Morgan Kaufmann>, <Publisher: Expensive Publisher>]

>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__range=[1, 2]).order_by('pk')

[<Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Morgan Kaufmann>]

>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__in=[1, 3]).order_by('pk')

[<Publisher: Sams>, <Publisher: Morgan Kaufmann>, <Publisher: Expensive Publisher>]

>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__isnull=True)

[]

>>> p.delete()

# Does Author X have any friends? (or better, how many friends does author X have)

>> Author.objects.filter(pk=1).aggregate(Count('friends__id'))

{'friends__id__count': 2.0}

# Give me a list of all Books with more than 1 authors

>>> Book.objects.all().annotate(num_authors=Count('authors__name')).filter(num_authors__ge=2).order_by('pk')

[<Book: The Definitive Guide to Django: Web Development Done Right>, <Book: Artificial Intelligence: A Modern Approach>]

# Give me a list of all Authors that have no friends

>>> Author.objects.all().annotate(num_friends=Count('friends__id', distinct=True)).filter(num_friends=0).order_by('pk')

[<Author: Brad Dayley>]

# Give me a list of all publishers that have published more than 1 books

>>> Publisher.objects.all().annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk')

[<Publisher: Apress>, <Publisher: Prentice Hall>]

# Give me a list of all publishers that have published more than 1 books that cost less than 40

>>> Publisher.objects.all().filter(book__price__lt=Decimal("40.0")).annotate(num_books=Count('book__id')).filter(num_books__gt=1)

[<Publisher: Apress>]

# Give me a list of all Books that were written by X and one other author.

>>> Book.objects.all().annotate(num_authors=Count('authors__id')).filter(authors__name__contains='Norvig', num_authors__gt=1)

[<Book: Artificial Intelligence: A Modern Approach>]

# Give me the average rating of all Books that were written by X and one other author.

#(Aggregate over objects discovered using membership of the m2m set)

# Adding an existing author to another book to test it the right way

>>> a = Author.objects.get(name__contains='Norvig')

>>> b = Book.objects.get(name__contains='Done Right')

>>> b.authors.add(a)

>>> b.save()

# This should do it

>>> Book.objects.all().annotate(num_authors=Count('authors__id')).filter(authors__name__contains='Norvig', num_authors__gt=1).aggregate(Avg('rating'))

{'rating__avg': 4.25}

>>> b.authors.remove(a)

# Give me a list of all Authors that have published a book with at least one other person

# (Filters over a count generated on a related object)

#

# Cheating: [a for a in Author.objects.all().annotate(num_coleagues=Count('book__authors__id'), num_books=Count('book__id', distinct=True)) if a.num_coleagues - a.num_books > 0]

# F-Syntax is required. Will be fixed after F objects are available

# Aggregates also work on dates, times and datetimes

>>> Publisher.objects.annotate(earliest_book=Min('book__pubdate')).exclude(earliest_book=None).order_by('earliest_book').values()

[{'earliest_book': datetime.date(1991, 10, 15), 'num_awards': 9, 'id': 4, 'name': u'Morgan Kaufmann'}, {'earliest_book': datetime.date(1995, 1, 15), 'num_awards': 7, 'id': 3, 'name': u'Prentice Hall'}, {'earliest_book': datetime.date(2007, 12, 6), 'num_awards': 3, 'id': 1, 'name': u'Apress'}, {'earliest_book': datetime.date(2008, 3, 3), 'num_awards': 1, 'id': 2, 'name': u'Sams'}]

>>> Store.objects.aggregate(Max('friday_night_closing'), Min("original_opening"))

{'friday_night_closing__max': datetime.time(23, 59, 59), 'original_opening__min': datetime.datetime(1945, 4, 25, 16, 24, 14)}

# values_list() can also be used

>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('pk', 'isbn', 'mean_age')

[(1, u'159059725', 34.5)]

>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('isbn')

[(u'159059725',)]

>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('mean_age')

[(34.5,)]

>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('mean_age', flat=True)

[34.5]

>>> qs = Book.objects.values_list('price').annotate(count=Count('price')).order_by('-count', 'price')

>>> list(qs) == [(Decimal('29.69'), 2), (Decimal('23.09'), 1), (Decimal('30'), 1), (Decimal('75'), 1), (Decimal('82.8'), 1)]

True

"""}