How to implement new algebraic structures in Sage #

Sage’s category and coercion framework #

The aim of this tutorial is to explain how one can benefit from Sage’s category framework and coercion model when implementing new algebraic structures. It is based on a worksheet created in 2011.

We illustrate the concepts of Sage’s category framework and coercion model by means of a detailed example, namely a toy implementation of fraction fields. The code is developed step by step, so that the reader can focus on one detail in each part of this tutorial. The complete code can be found in the appendix.

Outline #

Use existing base classes

For using Sage’s coercion system, it is essential to work with sub--classes of sage.structure.parent.Parent or sage.structure.element.Element, respectively. They provide default implementations of many “magical” double-underscore Python methods, which must not be overridden. Instead, the actual implementation should be in single underscore methods, such as _add_ or _mul_.
Turn your parent structure into an object of a category

Declare the category during initialisation-–Your parent structure will inherit further useful methods and consistency tests.
Provide your parent structure with an element class

Assign to it an attribute called Element-–The elements will inherit further useful methods from the category. In addition, some basic conversions will immediately work.
Implement further conversions

Never override a parent’s __call__ method! Provide the method _element_constructor_ instead.
Declare coercions

If a conversion happens to be a morphism, you may consider to turn it into a coercion. It will then implicitly be used in arithmetic operations.
Advanced coercion: Define construction functors for your parent structure

Sage will automatically create new parents for you when needed, by the so--called sage.categories.pushout.pushout() construction.
Run the automatic test suites

Each method should be documented and provide a doc test (we are not giving examples here). In addition, any method defined for the objects or elements of a category should be supported by a test method, that is executed when running the test suite.

Base classes #

In Sage, a “Parent” is an object of a category and contains elements. Parents should inherit from sage.structure.parent.Parent and their elements from sage.structure.element.Element.

Sage provides appropriate sub--classes of Parent and Element for a variety of more concrete algebraic structures, such as groups, rings, or fields, and of their elements. But some old stuff in Sage doesn’t use it. Volunteers for refactoring are welcome!

The parent#

Since we wish to implement a special kind of fields, namely fraction fields, it makes sense to build on top of the base class sage.rings.ring.Field provided by Sage.

sage: from sage.rings.ring import Field

This base class provides a lot more methods than a general parent:

sage: [p for p in dir(Field) if p not in dir(Parent)]
['_CommutativeRing__fraction_field',
 '__iter__',
 '__len__',
 '__rxor__',
 '__xor__',
 '_an_element_impl',
 '_coerce_',
 '_coerce_c',
 '_coerce_impl',
 '_default_category',
 '_gens',
 '_ideal_class_',
 '_ideal_monoid',
 '_latex_names',
 '_list',
 '_one_element',
 '_pseudo_fraction_field',
 '_unit_ideal',
 '_zero_element',
 '_zero_ideal',
 'algebraic_closure',
 'base_extend',
 'class_group',
 'content',
 'derivation',
 'derivation_module',
 'divides',
 'epsilon',
 'extension',
 'fraction_field',
 'frobenius_endomorphism',
 'gcd',
 'gen',
 'gens',
 'ideal',
 'ideal_monoid',
 'integral_closure',
 'is_commutative',
 'is_field',
 'is_integral_domain',
 'is_integrally_closed',
 'is_noetherian',
 'is_prime_field',
 'is_subring',
 'krull_dimension',
 'localization',
 'ngens',
 'one',
 'order',
 'prime_subfield',
 'principal_ideal',
 'random_element',
 'unit_ideal',
 'zero',
 'zero_ideal',
 'zeta',
 'zeta_order']

The following is a very basic implementation of fraction fields, that needs to be complemented later.

sage: from sage.structure.unique_representation import UniqueRepresentation
sage: class MyFrac(UniqueRepresentation, Field):
....:     def __init__(self, base):
....:         if base not in IntegralDomains():
....:             raise ValueError("%s is no integral domain" % base)
....:         Field.__init__(self, base)
....:     def _repr_(self):
....:         return "NewFrac(%s)"%repr(self.base())
....:     def base_ring(self):
....:         return self.base().base_ring()
....:     def characteristic(self):
....:         return self.base().characteristic()

This basic implementation is formed by the following steps:

Any ring in Sage has a base and a base ring. The “usual” fraction field of a ring \(R\) has the base \(R\) and the base ring R.base_ring():
```
sage: Frac(QQ['x']).base(), Frac(QQ['x']).base_ring()
(Univariate Polynomial Ring in x over Rational Field, Rational Field)
```
Declaring the base is easy: We just pass it as an argument to the field constructor.
```
sage: Field(ZZ['x']).base()
Univariate Polynomial Ring in x over Integer Ring
```
We are implementing a separate method returning the base ring.
Python uses double--underscore methods for arithmetic methods and string representations. Sage’s base classes often have a default implementation, and it is requested to implement SINGLE underscore methods _repr_, and similarly _add_, _mul_ etc.
You are encouraged to make your parent “unique”. That’s to say, parents should only evaluate equal if they are identical. Sage provides frameworks to create unique parents. We use here the most easy one: Inheriting from the class sage.structure.unique_representation.UniqueRepresentation is enough. Making parents unique can be quite important for an efficient implementation, because the repeated creation of “the same” parent would take a lot of time.
Fraction fields are only defined for integral domains. Hence, we raise an error if the given ring does not belong to the category of integral domains. This is our first use case of categories.
Last, we add a method that returns the characteristic of the field. We don’t go into details, but some automated tests that we study below implicitly rely on this method.

We see that our basic implementation correctly refuses a ring that is not an integral domain:

sage: MyFrac(ZZ['x'])
NewFrac(Univariate Polynomial Ring in x over Integer Ring)
sage: MyFrac(Integers(15))
Traceback (most recent call last):
...
ValueError: Ring of integers modulo 15 is no integral domain

Note

Inheritance from UniqueRepresentation automatically provides our class with pickling, preserving the unique parent condition. If we had defined the class in some external module or in an interactive session, pickling would work immediately.

However, for making the following example work in Sage’s doctesting framework, we need to assign our class as an attribute of the __main__ module, so that the class can be looked up during unpickling.

sage: import __main__
sage: __main__.MyFrac = MyFrac
sage: loads(dumps(MyFrac(ZZ))) is MyFrac(ZZ)
True

Note

In the following sections, we will successively add or change details of MyFrac. Rather than giving a full class definition in each step, we define new versions of MyFrac by inheriting from the previously defined version of MyFrac. We believe this will help the reader to focus on the single detail that is relevant in each section.

The complete code can be found in the appendix.

The elements#

We use the base class sage.structure.element.FieldElement. Note that in the creation of field elements it is not tested that the given parent is a field:

sage: from sage.structure.element import FieldElement
sage: FieldElement(ZZ)
Generic element of a structure

Our toy implementation of fraction field elements is based on the following considerations:

A fraction field element is defined by numerator and denominator, which both need to be elements of the base. There should be methods returning numerator resp. denominator.
The denominator must not be zero, and (provided that the base is an ordered ring) we can make it non-negative, without loss of generality. By default, the denominator is one.
The string representation is returned by the single--underscore method _repr_. In order to make our fraction field elements distinguishable from those already present in Sage, we use a different string representation.
Arithmetic is implemented in single--underscore method _add_, _mul_, etc. We do not override the default double underscore __add__, __mul__, since otherwise, we could not use Sage’s coercion model.

Comparisons can be implemented using _richcmp_. This automatically makes the relational operators like == and < work. Inside this method, you can use the richcmp functions and related tools provided by sage.

Note that _richcmp_ should be provided, since otherwise comparison does not work:

sage: class Foo(sage.structure.element.Element):
....:  def __init__(self, parent, x):
....:      self.x = x
....:  def _repr_(self):
....:      return "<%s>" % self.x
sage: a = Foo(ZZ, 1)
sage: b = Foo(ZZ, 2)
sage: a <= b
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'Foo' and 'Foo'

In the single underscore methods, we can assume that both arguments belong to the same parent. This is one benefit of the coercion model.
When constructing new elements as the result of arithmetic operations, we do not directly name our class, but we use self.__class__. Later, this will come in handy.

This gives rise to the following code:

sage: class MyElement(FieldElement):
....:     def __init__(self, parent,n,d=None):
....:         B = parent.base()
....:         if d is None:
....:             d = B.one()
....:         if n not in B or d not in B:
....:             raise ValueError("Numerator and denominator must be elements of %s"%B)
....:         # Numerator and denominator should not just be "in" B,
....:         # but should be defined as elements of B
....:         d = B(d)
....:         n = B(n)
....:         if d==0:
....:             raise ZeroDivisionError("The denominator must not be zero")
....:         if d<0:
....:             self.n = -n
....:             self.d = -d
....:         else:
....:             self.n = n
....:             self.d = d
....:         FieldElement.__init__(self,parent)
....:     def numerator(self):
....:         return self.n
....:     def denominator(self):
....:         return self.d
....:     def _repr_(self):
....:         return "(%s):(%s)"%(self.n,self.d)
....:     def _richcmp_(self, other, op):
....:         from sage.structure.richcmp import richcmp
....:         return richcmp(self.n*other.denominator(), other.numerator()*self.d, op)
....:     def _add_(self, other):
....:         C = self.__class__
....:         D = self.d*other.denominator()
....:         return C(self.parent(), self.n*other.denominator()+self.d*other.numerator(), D)
....:     def _sub_(self, other):
....:         C = self.__class__
....:         D = self.d*other.denominator()
....:         return C(self.parent(), self.n*other.denominator()-self.d*other.numerator(),D)
....:     def _mul_(self, other):
....:         C = self.__class__
....:         return C(self.parent(), self.n*other.numerator(), self.d*other.denominator())
....:     def _div_(self, other):
....:         C = self.__class__
....:         return C(self.parent(), self.n*other.denominator(), self.d*other.numerator())

Features and limitations of the basic implementation#

Thanks to the single underscore methods, some basic arithmetics works, if we stay inside a single parent structure:

sage: P = MyFrac(ZZ)
sage: a = MyElement(P, 3, 4)
sage: b = MyElement(P, 1, 2)
sage: a+b, a-b, a*b, a/b
((10):(8), (2):(8), (3):(8), (6):(4))
sage: a-b == MyElement(P, 1, 4)
True

There is a default implementation of element tests. We can already do

sage: a in P
True

since \(a\) is defined as an element of \(P\). However, we cannot verify yet that the integers are contained in the fraction field of the ring of integers. It does not even give a wrong answer, but results in an error:

sage: 1 in P
Traceback (most recent call last):
...
NotImplementedError: cannot construct elements of NewFrac(Integer Ring)

We will take care of this later.

Categories in Sage #

Sometimes the base classes do not reflect the mathematics: The set of \(m\times n\) matrices over a field forms, in general, not more than a vector space. Hence, this set (called MatrixSpace) is not implemented on top of sage.rings.ring.Ring. However, if \(m=n\), then the matrix space is an algebra, thus, is a ring.

From the point of view of Python base classes, both cases are the same:

sage: MS1 = MatrixSpace(QQ,2,3)
sage: isinstance(MS1, Ring)
False
sage: MS2 = MatrixSpace(QQ,2)
sage: isinstance(MS2, Ring)
False

Sage’s category framework can differentiate the two cases:

sage: Rings()
Category of rings
sage: MS1 in Rings()
False
sage: MS2 in Rings()
True

And indeed, MS2 has more methods than MS1:

sage: import inspect
sage: L1 = len([s for s in dir(MS1) if inspect.ismethod(getattr(MS1,s,None))])
sage: L2 = len([s for s in dir(MS2) if inspect.ismethod(getattr(MS2,s,None))])
sage: L1 < L2
True

This is because the class of MS2 also inherits from the parent class for algebras:

sage: MS1.__class__.__bases__
(<class 'sage.matrix.matrix_space.MatrixSpace'>,
<class 'sage.categories.category.JoinCategory.parent_class'>)
sage: MS2.__class__.__bases__
(<class 'sage.matrix.matrix_space.MatrixSpace'>,
<class 'sage.categories.category.JoinCategory.parent_class'>)

Below, we will explain how this can be taken advantage of.

It is no surprise that our parent \(P\) defined above knows that it belongs to the category of fields, as it is derived from the base class of fields.

sage: P.category()
Category of fields

However, we could choose a smaller category, namely the category of quotient fields.

Why should one choose a category?#

One can provide default methods for all objects of a category, and for all elements of such objects. Hence, the category framework is a way to inherit useful stuff that is not present in the base classes. These default methods do not rely on implementation details, but on mathematical concepts.

In addition, the categories define test suites for their objects and elements-–see the last section. Hence, one also gets basic sanity tests for free.

How does the category framework work?#

Abstract base classes for the objects (“parent_class”) and the elements of objects (“element_class”) are provided by attributes of the category. During initialisation of a parent, the class of the parent is dynamically changed into a sub--class of the category’s parent class. Likewise, sub--classes of the category’s element class are available for the creation of elements of the parent, as explained below.

A dynamic change of classes does not work in Cython. Nevertheless, method inheritance still works, by virtue of a __getattr__ method.

Note

It is strongly recommended to use the category framework both in Python and in Cython.

Let us see whether there is any gain in choosing the category of quotient fields instead of the category of fields:

sage: QuotientFields().parent_class, QuotientFields().element_class
(<class 'sage.categories.quotient_fields.QuotientFields.parent_class'>,
 <class 'sage.categories.quotient_fields.QuotientFields.element_class'>)
sage: [p for p in dir(QuotientFields().parent_class) if p not in dir(Fields().parent_class)]
[]
sage: [p for p in dir(QuotientFields().element_class) if p not in dir(Fields().element_class)]
['_derivative',
 'denominator',
 'derivative',
 'numerator',
 'partial_fraction_decomposition']

So, there is no immediate gain for our fraction fields, but additional methods become available to our fraction field elements. Note that some of these methods are place-holders: There is no default implementation, but it is required (respectively is optional) to implement these methods:

sage: QuotientFields().element_class.denominator
<abstract method denominator at ...>
sage: from sage.misc.abstract_method import abstract_methods_of_class
sage: abstract_methods_of_class(QuotientFields().element_class)['optional']
['_add_', '_mul_']
sage: abstract_methods_of_class(QuotientFields().element_class)['required']
['__bool__', 'denominator', 'numerator']

Hence, when implementing elements of a quotient field, it is required to implement methods returning the denominator and the numerator, and a method that tells whether the element is nonzero, and in addition, it is optional (but certainly recommended) to provide some arithmetic methods. If one forgets to implement the required methods, the test suites of the category framework will complain-–see below.

Implementing the category framework for the parent#

We simply need to declare the correct category by an optional argument of the field constructor, where we provide the possibility to override the default category:

sage: from sage.categories.quotient_fields import QuotientFields
sage: class MyFrac(MyFrac):
....:     def __init__(self, base, category=None):
....:         if base not in IntegralDomains():
....:             raise ValueError("%s is no integral domain" % base)
....:         Field.__init__(self, base, category=category or QuotientFields())

When constructing instances of MyFrac, their class is dynamically changed into a new class called MyFrac_with_category. It is a common sub--class of MyFrac and of the category’s parent class:

sage: P = MyFrac(ZZ)
sage: type(P)
<class '__main__.MyFrac_with_category'>
sage: isinstance(P,MyFrac)
True
sage: isinstance(P,QuotientFields().parent_class)
True

The fraction field \(P\) inherits additional methods. For example, the base class Field does not have a method sum. But \(P\) inherits such method from the category of commutative additive monoids-–see sum():

sage: P.sum.__module__
'sage.categories.additive_monoids'

We have seen above that we can add elements. Nevertheless, the sum method does not work, yet:

sage: a = MyElement(P, 3, 4)
sage: b = MyElement(P, 1, 2)
sage: c = MyElement(P, -1, 2)
sage: P.sum([a, b, c])
Traceback (most recent call last):
...
NotImplementedError: cannot construct elements of NewFrac(Integer Ring)

The reason is that the sum method starts with the return value of P.zero(), which defaults to P(0)-–but the conversion of integers into P is not implemented, yet.

Implementing the category framework for the elements#

Similar to what we have seen for parents, a new class is dynamically created that combines the element class of the parent’s category with the class that we have implemented above. However, the category framework is implemented in a different way for elements than for parents:

We provide the parent \(P\) (or its class) with an attribute called “Element”, whose value is a class.
The parent automatically obtains an attribute P.element_class, that subclasses both P.Element and P.category().element_class.

Hence, for providing our fraction fields with their own element classes, we just need to add a single line to our class:

sage: class MyFrac(MyFrac):
....:     Element = MyElement

This little change provides several benefits:

We can now create elements by simply calling the parent:

sage: P = MyFrac(ZZ)
sage: P(1), P(2,3)
((1):(1), (2):(3))

There is a method zero returning the expected result:
```
sage: P.zero()
(0):(1)
```

The sum method mentioned above suddenly works:

sage: a = MyElement(P, 9, 4)
sage: b = MyElement(P, 1, 2)
sage: c = MyElement(P, -1, 2)
sage: P.sum([a,b,c])
(36):(16)

Exponentiation now works out of the box using the multiplication that we defined:
```
sage: a^3
(729):(64)
```

What did happen behind the scenes to make this work?#

We provided P.Element, and thus obtain P.element_class, which is a lazy attribute. It provides a dynamic class, which is a sub--class of both MyElement defined above and of P.category().element_class:

sage: P.__class__.element_class
<sage.misc.lazy_attribute.lazy_attribute object at ...>
sage: P.element_class
<class '__main__.MyFrac_with_category.element_class'>
sage: type(P.element_class)
<class 'sage.structure.dynamic_class.DynamicInheritComparisonMetaclass'>
sage: issubclass(P.element_class, MyElement)
True
sage: issubclass(P.element_class,P.category().element_class)
True

The default __call__ method of \(P\) passes the given arguments to P.element_class, adding the argument parent=P. This is why we are now able to create elements by calling the parent.

In particular, these elements are instances of that new dynamic class:

sage: type(P(2,3))
<class '__main__.MyFrac_with_category.element_class'>

Note

All elements of \(P\) should use the element class. In order to make sure that this also holds for the result of arithmetic operations, we created them as instances of self.__class__ in the arithmetic methods of MyElement.

P.zero() defaults to returning P(0) and thus returns an instance of P.element_class. Since P.sum([...]) starts the summation with P.zero() and the class of the sum only depends on the first summand, by our implementation, we have:

sage: type(a)
<class '__main__.MyElement'>
sage: isinstance(a,P.element_class)
False
sage: type(P.sum([a,b,c]))
<class '__main__.MyFrac_with_category.element_class'>

The method factor provided by P.category().element_class (see above) simply works:

sage: a; a.factor(); P(6,4).factor()
(9):(4)
2^-2 * 3^2
2^-1 * 3

But that’s surprising: The element \(a\) is just an instance of MyElement, but not of P.element_class, and its class does not know about the factor method. In fact, this is due to a __getattr__ method defined for sage.structure.element.Element.

sage: hasattr(type(a), 'factor')
False
sage: hasattr(P.element_class, 'factor')
True
sage: hasattr(a, 'factor')
True

A first note on performance#

The category framework is sometimes blamed for speed regressions, as in github issue #9138 and github issue #11900. But if the category framework is used properly, then it is fast. For illustration, we determine the time needed to access an attribute inherited from the element class. First, we consider an element that uses the class that we implemented above, but does not use the category framework properly:

sage: type(a)
<class '__main__.MyElement'>
sage: timeit('a.factor',number=1000)     # random
1000 loops, best of 3: 2 us per loop

Now, we consider an element that is equal to \(a\), but uses the category framework properly:

sage: a2 = P(9,4)
sage: a2 == a
True
sage: type(a2)
<class '__main__.MyFrac_with_category.element_class'>
sage: timeit('a2.factor',number=1000)    # random
1000 loops, best of 3: 365 ns per loop

So, don’t be afraid of using categories!

Coercion-–the basics #

Theoretical background#

Coercion is not just type conversion#

“Coercion” in the C programming language means “automatic type conversion”. However, in Sage, coercion is involved if one wants to be able to do arithmetic, comparisons, etc. between elements of distinct parents. Hence, coercion is not about a change of types, but about a change of parents.

As an illustration, we show that elements of the same type may very well belong to rather different parents:

sage: P1 = QQ['v,w']; P2 = ZZ['w,v']
sage: type(P1.gen()) == type(P2.gen())
True
sage: P1 == P2
False

\(P_2\) naturally is a sub--ring of \(P_1\). So, it makes sense to be able to add elements of the two rings-–the result should then live in \(P_1\), and indeed it does:

sage: (P1.gen()+P2.gen()).parent() is P1
True

It would be rather inconvenient if one needed to manually convert an element of \(P_2\) into \(P_1\) before adding. The coercion system does that conversion automatically.

Not every conversion is a coercion#

A coercion happens implicitly, without being explicitly requested by the user. Hence, coercion must be based on mathematical rigour. In our example, any element of \(P_2\) can be naturally interpreted as an element of \(P_1\). We thus have:

sage: P1.has_coerce_map_from(P2)
True
sage: P1.coerce_map_from(P2)
Coercion map:
  From: Multivariate Polynomial Ring in w, v over Integer Ring
  To:   Multivariate Polynomial Ring in v, w over Rational Field

While there is a conversion from \(P_1\) to \(P_2\) (namely restricted to polynomials with integral coefficients), this conversion is not a coercion:

sage: P2.convert_map_from(P1)
Conversion map:
  From: Multivariate Polynomial Ring in v, w over Rational Field
  To:   Multivariate Polynomial Ring in w, v over Integer Ring
sage: P2.has_coerce_map_from(P1)
False
sage: P2.coerce_map_from(P1) is None
True

The four axioms requested for coercions#

A coercion is a morphism in an appropriate category.

This first axiom has two implications:
1. A coercion is defined on all elements of a parent.
  
  A polynomial of degree zero over the integers can be interpreted as an integer-–but the attempt to convert a polynomial of non-zero degree would result in an error:
```
sage: ZZ(P2.one())
1
sage: ZZ(P2.gen(1))
Traceback (most recent call last):
...
TypeError: not a constant polynomial
```
  Hence, we only have a partial map. This is fine for a conversion, but a partial map does not qualify as a coercion.
2. Coercions are structure preserving.
  
  Any real number can be converted to an integer, namely by rounding. However, such a conversion is not useful in arithmetic operations, since the underlying algebraic structure is not preserved:
```
sage: int(1.6)+int(2.7) == int(1.6+2.7)
False
```
  The structure that is to be preserved depends on the category of the involved parents. For example, the coercion from the integers into the rational field is a homomorphism of euclidean domains:
```
sage: QQ.coerce_map_from(ZZ).category_for()
Join of Category of euclidean domains and Category of infinite sets
and Category of metric spaces
```
There is at most one coercion from one parent to another

In addition, if there is a coercion from \(P_2\) to \(P_1\), then a conversion from \(P_2\) to \(P_1\) is defined for all elements of \(P_2\) and coincides with the coercion. Nonetheless, user-exposed maps are copies of the internally used maps whence the lack of identity between different instantiations:
```
sage: P1.coerce_map_from(P2) is P1.convert_map_from(P2)
False
```
For internally used maps, the maps are identical:
```
sage: P1._internal_coerce_map_from(P2) is P1._internal_convert_map_from(P2)
True
```
Coercions can be composed

If there is a coercion \(\varphi: P_1 \to P_2\) and another coercion \(\psi: P_2 \to P_3\), then the composition of \(\varphi\) followed by \(\psi\) must yield the unique coercion from \(P_1\) to \(P_3\).
The identity is a coercion

Together with the two preceding axioms, it follows: If there are coercions from \(P_1\) to \(P_2\) and from \(P_2\) to \(P_1\), then they are mutually inverse.

Implementing a conversion#

We have seen above that some conversions into our fraction fields became available after providing the attribute Element. However, we cannot convert elements of a fraction field into elements of another fraction field, yet:

sage: P(2/3)
Traceback (most recent call last):
...
ValueError: Numerator and denominator must be elements of Integer Ring

For implementing a conversion, the default __call__ method should (almost) never be overridden. Instead, we implement the method _element_constructor_, that should return an instance of the parent’s element class. Some old parent classes violate that rule-–please help to refactor them!

sage: class MyFrac(MyFrac):
....:     def _element_constructor_(self, *args, **kwds):
....:         if len(args)!=1:
....:             return self.element_class(self, *args, **kwds)
....:         x = args[0]
....:         try:
....:             P = x.parent()
....:         except AttributeError:
....:             return self.element_class(self, x, **kwds)
....:         if P in QuotientFields() and P != self.base():
....:             return self.element_class(self, x.numerator(), x.denominator(), **kwds)
....:         return self.element_class(self, x, **kwds)

In addition to the conversion from the base ring and from pairs of base ring elements, we now also have a conversion from the rationals to our fraction field of \(\ZZ\):

sage: P = MyFrac(ZZ)
sage: P(2); P(2,3); P(3/4)
(2):(1)
(2):(3)
(3):(4)

Recall that above, the test \(1 \in P\) failed with an error. We try again and find that the error has disappeared. This is because we are now able to convert the integer \(1\) into \(P\). But the containment test still yields a wrong answer:

sage: 1 in P
False

The technical reason: We have a conversion \(P(1)\) of \(1\) into \(P\), but this is not known as a coercion-–yet!

sage: P.has_coerce_map_from(ZZ), P.has_coerce_map_from(QQ)
(False, False)

Establishing a coercion#

There are two main ways to make Sage use a particular conversion as a coercion:

One can use sage.structure.parent.Parent.register_coercion(), normally during initialisation of the parent (see documentation of the method).
A more flexible way is to provide a method _coerce_map_from_ for the parent.

Let \(P\) and \(R\) be parents. If P._coerce_map_from_(R) returns False or None, then there is no coercion from \(R\) to \(P\). If it returns a map with domain \(R\) and codomain \(P\), then this map is used for coercion. If it returns True, then the conversion from \(R\) to \(P\) is used as coercion.

Note that in the following implementation, we need a special case for the rational field, since QQ.base() is not the ring of integers.

sage: class MyFrac(MyFrac):
....:     def _coerce_map_from_(self, S):
....:         if self.base().has_coerce_map_from(S):
....:             return True
....:         if S in QuotientFields():
....:             if self.base().has_coerce_map_from(S.base()):
....:                 return True
....:             if hasattr(S,'ring_of_integers') and self.base().has_coerce_map_from(S.ring_of_integers()):
....:                 return True

By the method above, a parent coercing into the base ring will also coerce into the fraction field, and a fraction field coerces into another fraction field if there is a coercion of the corresponding base rings. Now, we have:

sage: P = MyFrac(QQ['x'])
sage: P.has_coerce_map_from(ZZ['x']), P.has_coerce_map_from(Frac(ZZ['x'])), P.has_coerce_map_from(QQ)
(True, True, True)

We can now use coercion from \(\ZZ[x]\) and from \(\QQ\) into \(P\) for arithmetic operations between the two rings:

sage: 3/4+P(2)+ZZ['x'].gen(), (P(2)+ZZ['x'].gen()).parent() is P
((4*x + 11):(4), True)

Equality and element containment#

Recall that above, the test \(1 \in P\) gave a wrong answer. Let us repeat the test now:

sage: 1 in P
True

Why is that?

The default element containment test \(x \in P\) is based on the interplay of three building blocks: conversion, coercion, and equality test.

Clearly, if the conversion \(P(x)\) raises an error, then \(x\) cannot be seen as an element of \(P\). On the other hand, a conversion \(P(x)\) can generally do very nasty things. So, the fact that \(P(x)\) works without error is necessary, but not sufficient for \(x \in P\).
If \(P\) is the parent of \(x\), then the conversion \(P(x)\) will not change \(x\) (at least, that’s the default). Hence, we will have \(x=P(x)\).
Sage uses coercion not only for arithmetic operations, but also for comparison: If there is a coercion from the parent of \(x\) to \(P\), then the equality test x==P(x) reduces to P(x)==P(x). Otherwise, x==P(x) will evaluate as false.

That leads to the following default implementation of element containment testing:

Note

\(x \in P\) holds if and only if the test x==P(x) does not raise an error and evaluates as true.

If the user is not happy with that behaviour, the “magical” Python method __contains__ can be overridden.

Coercion-–the advanced parts #

So far, we are able to add integers and rational numbers to elements of our new implementation of the fraction field of \(\ZZ\).

sage: P = MyFrac(ZZ)

sage: 1/2+P(2,3)+1
(13):(6)

Surprisingly, we can even add a polynomial over the integers to an element of \(P\), even though the result lives in a new parent, namely in a polynomial ring over \(P\):

sage: P(1/2) + ZZ['x'].gen(), (P(1/2) + ZZ['x'].gen()).parent() is P['x']
((1):(1)*x + (1):(2), True)

In the next, seemingly more easy example, there “obviously” is a coercion from the fraction field of \(\ZZ\) to the fraction field of \(\ZZ[x]\). However, Sage does not know enough about our new implementation of fraction fields. Hence, it does not recognise the coercion:

sage: Frac(ZZ['x']).has_coerce_map_from(P)
False

Two obvious questions arise:

How / why has the new ring been constructed in the example above?
How can we establish a coercion from \(P\) to \(\mathrm{Frac}(\ZZ[x])\)?

The key to answering both question is the construction of parents from simpler pieces, that we are studying now. Note that we will answer the second question not by providing a coercion from \(P\) to \(\mathrm{Frac}(\ZZ[x])\), but by teaching Sage to automatically construct \(\mathrm{MyFrac}(\ZZ[x])\) and coerce both \(P\) and \(\mathrm{Frac}(\ZZ[x])\) into it.

If we are lucky, a parent can tell how it has been constructed:

sage: Poly,R = QQ['x'].construction()
sage: Poly,R
(Poly[x], Rational Field)
sage: Fract,R = QQ.construction()
sage: Fract,R
(FractionField, Integer Ring)

In both cases, the first value returned by construction() is a mathematical construction, called construction functor-–see ConstructionFunctor. The second return value is a simpler parent to which the construction functor is applied.

Being functors, the same construction can be applied to different objects of a category:

sage: Poly(QQ) is QQ['x']
True
sage: Poly(ZZ) is ZZ['x']
True
sage: Poly(P) is P['x']
True
sage: Fract(QQ['x'])
Fraction Field of Univariate Polynomial Ring in x over Rational Field

Let us see on which categories these construction functors are defined:

sage: Poly.domain()
Category of rings
sage: Poly.codomain()
Category of rings
sage: Fract.domain()
Category of integral domains
sage: Fract.codomain()
Category of fields

In particular, the construction functors can be composed:

sage: Poly*Fract
Poly[x](FractionField(...))
sage: (Poly*Fract)(ZZ) is QQ['x']
True

In addition, it is often assumed that we have a coercion from input to output of the construction functor:

sage: ((Poly*Fract)(ZZ)).coerce_map_from(ZZ)
Composite map:
  From: Integer Ring
  To:   Univariate Polynomial Ring in x over Rational Field
  Defn:   Natural morphism:
          From: Integer Ring
          To:   Rational Field
        then
          Polynomial base injection morphism:
          From: Rational Field
          To:   Univariate Polynomial Ring in x over Rational Field

Construction functors do not necessarily commute:

sage: (Fract*Poly)(ZZ)
Fraction Field of Univariate Polynomial Ring in x over Integer Ring

The pushout of construction functors#

We can now formulate our problem. We have parents \(P_1\), \(P_2\) and \(R\), and construction functors \(F_1\), \(F_2\), such that \(P_1 = F_1(R)\) and \(P_2 = F_2(R)\). We want to find a new construction functor \(F_3\), such that both \(P_1\) and \(P_2\) coerce into \(P_3 = F_3(R)\).

In analogy to a notion of category theory, \(P_3\) is called the pushout() of \(P_1\) and \(P_2\); and similarly \(F_3\) is called the pushout of \(F_1\) and \(F_2\).

sage: from sage.categories.pushout import pushout
sage: pushout(Fract(ZZ),Poly(ZZ))
Univariate Polynomial Ring in x over Rational Field

\(F_1\circ F_2\) and \(F_2\circ F_1\) are natural candidates for the pushout of \(F_1\) and \(F_2\). However, the order of the functors must rely on a canonical choice. “Indecomposable” construction functors have a rank, and this allows to order them canonically:

Note

If F1.rank is smaller than F2.rank, then the pushout is \(F_2\circ F_1\) (hence, \(F_1\) is applied first).

We have

sage: Fract.rank, Poly.rank
(5, 9)

and thus the pushout is

sage: Fract.pushout(Poly), Poly.pushout(Fract)
(Poly[x](FractionField(...)), Poly[x](FractionField(...)))

This is why the example above has worked.

However, only “elementary” construction functors have a rank:

sage: (Fract*Poly).rank
Traceback (most recent call last):
...
AttributeError: 'CompositeConstructionFunctor' object has no attribute 'rank'...

Shuffling composite construction functors#

If composed construction fuctors \(...\circ F_2\circ F_1\) and \(...\circ G_2\circ G_1\) are given, then Sage determines their pushout by shuffling the constituents:

If F1.rank < G1.rank then we apply \(F_1\) first, and continue with \(...\circ F_3\circ F_2\) and \(...\circ G_2\circ G_1\).
If F1.rank > G1.rank then we apply \(G_1\) first, and continue with \(...\circ F_2\circ F_1\) and \(...\circ G_3\circ G_2\).

If F1.rank == G1.rank, then the tie needs to be broken by other techniques (see below).

As an illustration, we first get us some functors and then see how chains of functors are shuffled.

sage: AlgClos, R = CC.construction(); AlgClos
AlgebraicClosureFunctor

sage: Compl, R = RR.construction(); Compl
Completion[+Infinity, prec=53]

sage: Matr, R = (MatrixSpace(ZZ,3)).construction(); Matr
MatrixFunctor

sage: AlgClos.rank, Compl.rank, Fract.rank, Poly.rank, Matr.rank
(3, 4, 5, 9, 10)

When we apply Fract, AlgClos, Poly and Fract to the ring of integers, we obtain:

sage: (Fract*Poly*AlgClos*Fract)(ZZ)
Fraction Field of Univariate Polynomial Ring in x over Algebraic Field

When we apply Compl, Matr and Poly to the ring of integers, we obtain:

sage: (Poly*Matr*Compl)(ZZ)
Univariate Polynomial Ring in x over Full MatrixSpace of 3 by 3 dense matrices over Integer Ring

Applying the shuffling procedure yields

sage: (Poly*Matr*Fract*Poly*AlgClos*Fract*Compl)(ZZ)
Univariate Polynomial Ring in x over Full MatrixSpace of 3 by 3 dense matrices over Fraction Field of Univariate Polynomial Ring in x over Algebraic Field

and this is indeed equal to the pushout found by Sage:

sage: pushout((Fract*Poly*AlgClos*Fract)(ZZ), (Poly*Matr*Compl)(ZZ))
Univariate Polynomial Ring in x over Full MatrixSpace of 3 by 3 dense matrices over Fraction Field of Univariate Polynomial Ring in x over Algebraic Field

Breaking the tie#

If F1.rank==G1.rank then Sage’s pushout constructions offers two ways to proceed:

Construction functors have a method merge() that either returns None or returns a construction functor-–see below. If either F1.merge(G1) or G1.merge(F1) returns a construction functor \(H_1\), then we apply \(H_1\) and continue with \(...\circ F_3\circ F_2\) and \(...\circ G_3\circ G_2\).
Construction functors have a method commutes(). If either F1.commutes(G1) or G1.commutes(F1) returns True, then we apply both \(F_1\) and \(G_1\) in any order, and continue with \(...\circ F_3\circ F_2\) and \(...\circ G_3\circ G_2\).

By default, F1.merge(G1) returns F1 if F1==G1, and returns None otherwise. The commutes() method exists, but it seems that so far nobody has implemented two functors of the same rank that commute.

Establishing a default implementation#

The typical application of merge() is to provide a coercion between different implementations of the same algebraic structure.

Note

If F1(P) and F2(P) are different implementations of the same thing, then F1.merge(F2)(P) should return the default implementation.

We want to boldly turn our toy implementation of fraction fields into the new default implementation. Hence:

Next, we implement a new version of the “usual” fraction field functor, having the same rank, but returning our new implementation.
We make our new implementation the default, by virtue of a merge method.
Since our fraction fields accept an optional argument category, we pass the optional arguments to the construction functor, which will in turn use it to create a fraction field.

Warning

Do not override the default __call__ method of ConstructionFunctor-–implement _apply_functor instead.
Declare domain and codomain of the functor during initialisation.

sage: from sage.categories.pushout import ConstructionFunctor
sage: class MyFracFunctor(ConstructionFunctor):
....:     rank = 5
....:     def __init__(self, args=None, kwds=None):
....:         self.args = args or ()
....:         self.kwds = kwds or {}
....:         ConstructionFunctor.__init__(self, IntegralDomains(), Fields())
....:     def _apply_functor(self, R):
....:         return MyFrac(R,*self.args,**self.kwds)
....:     def merge(self, other):
....:         if isinstance(other, (type(self), sage.categories.pushout.FractionField)):
....:             return self

sage: MyFracFunctor()
MyFracFunctor

We verify that our functor can really be used to construct our implementation of fraction fields, and that it can be merged with either itself or the usual fraction field constructor:

sage: MyFracFunctor()(ZZ)
NewFrac(Integer Ring)

sage: MyFracFunctor().merge(MyFracFunctor())
MyFracFunctor

sage: MyFracFunctor().merge(Fract)
MyFracFunctor

There remains to let our new fraction fields know about the new construction functor. The arguments that were used when creating the fraction field are stored as an attribute—this is a feature provided by CachedRepresentation. We pass all but the first of these arguments to the construction functor, such that the construction functor is able to reconstruct the fraction field.

sage: class MyFrac(MyFrac):
....:     def construction(self):
....:         return MyFracFunctor(self._reduction[1][1:], self._reduction[2]), self.base()

sage: MyFrac(ZZ['x']).construction()
(MyFracFunctor, Univariate Polynomial Ring in x over Integer Ring)

Due to merging, we have:

sage: pushout(MyFrac(ZZ['x']), Frac(QQ['x']))
NewFrac(Univariate Polynomial Ring in x over Rational Field)

A second note on performance#

Being able to do arithmetics involving elements of different parents, with the automatic creation of a pushout to contain the result, is certainly convenient-–but one should not rely on it, if speed matters. Simply the conversion of elements into different parents takes time. Moreover, by github issue #14058, the pushout may be subject to Python’s cyclic garbage collection. Hence, if one does not keep a strong reference to it, the same parent may be created repeatedly, which is a waste of time. In the following example, we illustrate the slow--down resulting from blindly relying on coercion:

sage: ZZxy = ZZ['x','y']
sage: a = ZZxy('x')
sage: b = 1/2
sage: timeit("c = a+b")    # random
10000 loops, best of 3: 172 us per loop
sage: QQxy = QQ['x','y']
sage: timeit("c2 = QQxy(a)+QQxy(b)") # random
10000 loops, best of 3: 168 us per loop
sage: a2 = QQxy(a)
sage: b2 = QQxy(b)
sage: timeit("c2 = a2+b2") # random
100000 loops, best of 3: 10.5 us per loop

Hence, if one avoids the explicit or implicit conversion into the pushout, but works in the pushout right away, one can get a more than 10--fold speed--up.

The test suites of the category framework #

The category framework does not only provide functionality but also a test framework. This section logically belongs to the section on categories, but without the bits that we have implemented in the section on coercion, our implementation of fraction fields would not have passed the tests yet.

“Abstract” methods#

We have already seen above that a category can require/suggest certain parent or element methods, that the user must/should implement. This is in order to smoothly blend with the methods that already exist in Sage.

The methods that ought to be provided are called abstract_method(). Let us see what methods are needed for quotient fields and their elements:

sage: from sage.misc.abstract_method import abstract_methods_of_class

sage: abstract_methods_of_class(QuotientFields().parent_class)['optional']
[]
sage: abstract_methods_of_class(QuotientFields().parent_class)['required']
['__contains__']

Hence, the only required method (that is actually required for all parents that belong to the category of sets) is an element containment test. That’s fine, because the base class Parent provides a default containment test.

The elements have to provide more:

sage: abstract_methods_of_class(QuotientFields().element_class)['optional']
['_add_', '_mul_']
sage: abstract_methods_of_class(QuotientFields().element_class)['required']
['__bool__', 'denominator', 'numerator']

Hence, the elements must provide denominator() and numerator() methods, and must be able to tell whether they are zero or not. The base class Element provides a default __bool__() method. In addition, the elements may provide Sage’s single underscore arithmetic methods (actually any ring element should provide them).

The `_test_...` methods#

If a parent or element method’s name start with “_test_”, it gives rise to a test in the automatic test suite. For example, it is tested

whether a parent \(P\) actually is an instance of the parent class of the category of \(P\),
whether the user has implemented the required abstract methods,
whether some defining structural properties (e.g., commutativity) hold.

For example, if one forgets to implement required methods, one obtains the following error:

sage: class Foo(Parent):
....:  Element = sage.structure.element.Element
....:  def __init__(self):
....:      Parent.__init__(self, category=QuotientFields())
sage: Bar = Foo()
sage: bar = Bar.element_class(Bar)
sage: bar._test_not_implemented_methods()
Traceback (most recent call last):
...
AssertionError: Not implemented method: denominator

Here are the tests that form the test suite of quotient fields:

sage: [t for t in dir(QuotientFields().parent_class) if t.startswith('_test_')]
['_test_additive_associativity',
 '_test_an_element',
 '_test_associativity',
 '_test_cardinality',
 '_test_characteristic',
 '_test_characteristic_fields',
 '_test_construction',
 '_test_distributivity',
 '_test_divides',
 '_test_elements',
 '_test_elements_eq_reflexive',
 '_test_elements_eq_symmetric',
 '_test_elements_eq_transitive',
 '_test_elements_neq',
 '_test_euclidean_degree',
 '_test_fraction_field',
 '_test_gcd_vs_xgcd',
 '_test_one',
 '_test_prod',
 '_test_quo_rem',
 '_test_some_elements',
 '_test_zero',
 '_test_zero_divisors']

We have implemented all abstract methods (or inherit them from base classes), we use the category framework, and we have implemented coercions. So, we are confident that the test suite runs without an error. In fact, it does!

Note

The following trick with the __main__ module is only needed in doctests, not in an interactive session or when defining the classes externally.

sage: __main__.MyFrac = MyFrac
sage: __main__.MyElement = MyElement
sage: P = MyFrac(ZZ['x'])
sage: TestSuite(P).run()

Let us see what tests are actually performed:

sage: TestSuite(P).run(verbose=True)
running ._test_additive_associativity() . . . pass
running ._test_an_element() . . . pass
running ._test_associativity() . . . pass
running ._test_cardinality() . . . pass
running ._test_category() . . . pass
running ._test_characteristic() . . . pass
running ._test_characteristic_fields() . . . pass
running ._test_construction() . . . pass
running ._test_distributivity() . . . pass
running ._test_divides() . . . pass
running ._test_elements() . . .
  Running the test suite of self.an_element()
  running ._test_category() . . . pass
  running ._test_eq() . . . pass
  running ._test_new() . . . pass
  running ._test_nonzero_equal() . . . pass
  running ._test_not_implemented_methods() . . . pass
  running ._test_pickling() . . . pass
  pass
running ._test_elements_eq_reflexive() . . . pass
running ._test_elements_eq_symmetric() . . . pass
running ._test_elements_eq_transitive() . . . pass
running ._test_elements_neq() . . . pass
running ._test_eq() . . . pass
running ._test_euclidean_degree() . . . pass
running ._test_fraction_field() . . . pass
running ._test_gcd_vs_xgcd() . . . pass
running ._test_new() . . . pass
running ._test_not_implemented_methods() . . . pass
running ._test_one() . . . pass
running ._test_pickling() . . . pass
running ._test_prod() . . . pass
running ._test_quo_rem() . . . pass
running ._test_some_elements() . . . pass
running ._test_zero() . . . pass
running ._test_zero_divisors() . . . pass

Implementing a new category with additional tests#

As one can see, tests are also performed on elements. There are methods that return one element or a list of some elements, relying on “typical” elements that can be found in most algebraic structures.

sage: P.an_element(); P.some_elements()
(2):(1)
[(2):(1)]

Unfortunately, the list of elements that is returned by the default method is of length one, and that single element could also be a bit more interesting. The method an_element relies on a method _an_element_(), so, we implement that. We also override the some_elements method.

sage: class MyFrac(MyFrac):
....:     def _an_element_(self):
....:         a = self.base().an_element()
....:         b = self.base_ring().an_element()
....:         if (a+b)!=0:
....:             return self(a)**2/(self(a+b)**3)
....:         if b != 0:
....:             return self(a)/self(b)**2
....:         return self(a)**2*self(b)**3
....:     def some_elements(self):
....:         return [self.an_element(),self(self.base().an_element()),self(self.base_ring().an_element())]

sage: P = MyFrac(ZZ['x'])
sage: P.an_element(); P.some_elements()
(x^2):(x^3 + 3*x^2 + 3*x + 1)
[(x^2):(x^3 + 3*x^2 + 3*x + 1), (x):(1), (1):(1)]

Now, as we have more interesting elements, we may also add a test for the “factor” method. Recall that the method was inherited from the category, but it appears that it is not tested.

Normally, a test for a method defined by a category should be provided by the same category. Hence, since factor is defined in the category of quotient fields, a test should be added there. But we won’t change source code here and will instead create a sub--category.

Apparently, If \(e\) is an element of a quotient field, the product of the factors returned by e.factor() should be equal to \(e\). For forming the product, we use the prod method, that, no surprise, is inherited from another category:

sage: P.prod.__module__
'sage.categories.monoids'

When we want to create a sub--category, we need to provide a method super_categories(), that returns a list of all immediate super categories (here: category of quotient fields).

Warning

A sub--category \(S\) of a category \(C\) is not implemented as a sub--class of C.__class__! \(S\) becomes a sub--category of \(C\) only if S.super_categories() returns (a sub--category of) \(C\)!

The parent and element methods of a category are provided as methods of classes that are the attributes ParentMethods and Element Methods of the category, as follows:

sage: from sage.categories.category import Category
sage: class QuotientFieldsWithTest(Category): # do *not* inherit from QuotientFields, but ...
....:     def super_categories(self):
....:         return [QuotientFields()]       # ... declare QuotientFields as a super category!
....:     class ParentMethods:
....:         pass
....:     class ElementMethods:
....:         def _test_factorisation(self, **options):
....:             P = self.parent()
....:             assert self == P.prod([P(b)**e for b,e in self.factor()])

We provide an instance of our quotient field implementation with that new category. Note that categories have a default _repr_ method, that guesses a good string representation from the name of the class: QuotientFieldsWithTest becomes “quotient fields with test”.

Note

The following trick with the __main__ module is only needed in doctests, not in an interactive session or when defining the classes externally.

sage: __main__.MyFrac = MyFrac
sage: __main__.MyElement = MyElement
sage: __main__.QuotientFieldsWithTest = QuotientFieldsWithTest
sage: P = MyFrac(ZZ['x'], category=QuotientFieldsWithTest())
sage: P.category()
Category of quotient fields with test

The new test is inherited from the category. Since an_element() is returning a complicated element, _test_factorisation is a serious test:

sage: P.an_element()._test_factorisation
<bound method QuotientFieldsWithTest.ElementMethods._test_factorisation of (x^2):(x^3 + 3*x^2 + 3*x + 1)>

sage: P.an_element().factor()
(x + 1)^-3 * x^2

Last, we observe that the new test has automatically become part of the test suite. We remark that the existing tests became more serious as well, since we made sage.structure.parent.Parent.an_element() return something more interesting.

sage: TestSuite(P).run(verbose=True)
running ._test_additive_associativity() . . . pass
running ._test_an_element() . . . pass
running ._test_associativity() . . . pass
running ._test_cardinality() . . . pass
running ._test_category() . . . pass
running ._test_characteristic() . . . pass
running ._test_characteristic_fields() . . . pass
running ._test_construction() . . . pass
running ._test_distributivity() . . . pass
running ._test_divides() . . . pass
running ._test_elements() . . .
  Running the test suite of self.an_element()
  running ._test_category() . . . pass
  running ._test_eq() . . . pass
  running ._test_factorisation() . . . pass
  running ._test_new() . . . pass
  running ._test_nonzero_equal() . . . pass
  running ._test_not_implemented_methods() . . . pass
  running ._test_pickling() . . . pass
  pass
running ._test_elements_eq_reflexive() . . . pass
running ._test_elements_eq_symmetric() . . . pass
running ._test_elements_eq_transitive() . . . pass
running ._test_elements_neq() . . . pass
running ._test_eq() . . . pass
running ._test_euclidean_degree() . . . pass
running ._test_fraction_field() . . . pass
running ._test_gcd_vs_xgcd() . . . pass
running ._test_new() . . . pass
running ._test_not_implemented_methods() . . . pass
running ._test_one() . . . pass
running ._test_pickling() . . . pass
running ._test_prod() . . . pass
running ._test_quo_rem() . . . pass
running ._test_some_elements() . . . pass
running ._test_zero() . . . pass
running ._test_zero_divisors() . . . pass

Appendix: The complete code #

# Importing base classes, ...
import sage
from sage.rings.ring import Field
from sage.structure.element import FieldElement
from sage.categories.category import Category
# ... the UniqueRepresentation tool,
from sage.structure.unique_representation import UniqueRepresentation
# ... some categories, and ...
from sage.categories.fields import Fields
from sage.categories.quotient_fields import QuotientFields
from sage.categories.integral_domains import IntegralDomains
# construction functors
from sage.categories.pushout import ConstructionFunctor

# Fraction field elements
class MyElement(FieldElement):
    def __init__(self, parent, n, d=None):
        if parent is None:
            raise ValueError("The parent must be provided")
        B = parent.base()
        if d is None:
            # The default denominator is one
            d = B.one()
        # verify that both numerator and denominator belong to the base
        if n not in B or d not in B:
            raise ValueError("Numerator and denominator must be elements of %s"%B)
        # Numerator and denominator should not just be "in" B,
        # but should be defined as elements of B
        d = B(d)
        n = B(n)
        # the denominator must not be zero
        if d==0:
            raise ZeroDivisionError("The denominator must not be zero")
        # normalize the denominator: WLOG, it shall be non-negative.
        if d<0:
            self.n = -n
            self.d = -d
        else:
            self.n = n
            self.d = d
        FieldElement.__init__(self,parent)

    # Methods required by the category of fraction fields:
    def numerator(self):
        return self.n
    def denominator(self):
        return self.d

    # String representation (single underscore!)
    def _repr_(self):
        return "(%s):(%s)"%(self.n,self.d)

    # Comparison: We can assume that both arguments are coerced
    # into the same parent, which is a fraction field. Hence, we
    # are allowed to use the denominator() and numerator() methods
    # on the second argument.
    def _richcmp_(self, other, op):
        from sage.structure.richcmp import richcmp
        return richcmp(self.n*other.denominator(), other.numerator()*self.d, op)

    # Arithmetic methods, single underscore. We can assume that both
    # arguments are coerced into the same parent.
    # We return instances of self.__class__, because self.__class__ will
    # eventually be a sub-class of MyElement.
    def _add_(self, other):
        C = self.__class__
        D = self.d*other.denominator()
        return C(self.parent(), self.n*other.denominator()+self.d*other.numerator(),D)
    def _sub_(self, other):
        C = self.__class__
        D = self.d*other.denominator()
        return C(self.parent(), self.n*other.denominator()-self.d*other.numerator(),D)
    def _mul_(self, other):
        C = self.__class__
        return C(self.parent(), self.n*other.numerator(), self.d*other.denominator())
    def _div_(self, other):
        C = self.__class__
        return C(self.parent(), self.n*other.denominator(), self.d*other.numerator())

# Inheritance from UniqueRepresentation implements the unique parent
# behaviour. Moreover, it implements pickling (provided that Python
# succeeds to look up the class definition).
class MyFrac(UniqueRepresentation, Field):
    # Implement the category framework for elements, which also
    # makes some basic conversions work.
    Element = MyElement

    # Allow to pass to a different category, by an optional argument
    def __init__(self, base, category=None):
        # Fraction fields only exist for integral domains
        if base not in IntegralDomains():
            raise ValueError("%s is no integral domain" % base)
        # Implement the category framework for the parent
        Field.__init__(self, base, category=category or QuotientFields())

    # Single-underscore method for string representation
    def _repr_(self):
        return "NewFrac(%s)"%repr(self.base())

    # Two methods that are implicitly used in some tests
    def base_ring(self):
        return self.base().base_ring()
    def characteristic(self):
        return self.base().characteristic()

    # Implement conversions. Do not override __call__!
    def _element_constructor_(self, *args, **kwds):
        if len(args)!=1:
           return self.element_class(self, *args, **kwds)
        x = args[0]
        try:
            P = x.parent()
        except AttributeError:
            return self.element_class(self, x, **kwds)
        if P in QuotientFields() and P != self.base():
            return self.element_class(self, x.numerator(), x.denominator(), **kwds)
        return self.element_class(self, x, **kwds)

    # Implement coercion from the base and from fraction fields
    # over a ring that coerces into the base
    def _coerce_map_from_(self, S):
        if self.base().has_coerce_map_from(S):
            return True
        if S in QuotientFields():
            if self.base().has_coerce_map_from(S.base()):
                return True
            if hasattr(S,'ring_of_integers') and self.base().has_coerce_map_from(S.ring_of_integers()):
                return True
    # Tell how this parent was constructed, in order to enable pushout constructions
    def construction(self):
        return MyFracFunctor(), self.base()

    # return some elements of this parent
    def _an_element_(self):
        a = self.base().an_element()
        b = self.base_ring().an_element()
        if (a+b)!=0:
            return self(a)**2/(self(a+b)**3)
        if b != 0:
            return self(a)/self(b)**2
        return self(a)**2*self(b)**3
    def some_elements(self):
        return [self.an_element(),self(self.base().an_element()),self(self.base_ring().an_element())]


# A construction functor for our implementation of fraction fields
class MyFracFunctor(ConstructionFunctor):
    # The rank is the same for Sage's original fraction field functor
    rank = 5
    def __init__(self):
        # The fraction field construction is a functor
        # from the category of integral domains into the category of
        # fields
        # NOTE: We could actually narrow the codomain and use the
        # category QuotientFields()
        ConstructionFunctor.__init__(self, IntegralDomains(), Fields())
    # Applying the functor to an object. Do not override __call__!
    def _apply_functor(self, R):
        return MyFrac(R)
    # Note: To apply the functor to morphisms, implement
    #       _apply_functor_to_morphism

    # Make sure that arithmetic involving elements of Frac(R) and
    # MyFrac(R) works and yields elements of MyFrac(R)
    def merge(self, other):
        if isinstance(other, (type(self), sage.categories.pushout.FractionField)):
            return self

# A quotient field category with additional tests.
# Notes:
# - Category inherits from UniqueRepresentation. Hence, there
#   is only one category for given arguments.
# - Since QuotientFieldsWithTest is a singleton (there is only
#   one instance of this class), we could inherit from
#   sage.categories.category_singleton.Category_singleton
#   rather than from sage.categories.category.Category
class QuotientFieldsWithTest(Category):
    # Our category is a sub-category of the category of quotient fields,
    # by means of the following method.
    def super_categories(self):
        return [QuotientFields()]

    # Here, we could implement methods that are available for
    # all objects in this category.
    class ParentMethods:
        pass

    # Here, we add a new test that is available for all elements
    # of any object in this category.
    class ElementMethods:
        def _test_factorisation(self, **options):
            P = self.parent()
            # The methods prod() and factor() are inherited from
            # some other categories.
            assert self == P.prod([P(b)**e for b,e in self.factor()])

The parent#

The elements#

Features and limitations of the basic implementation#

Why should one choose a category?#

How does the category framework work?#

Implementing the category framework for the parent#

Implementing the category framework for the elements#

What did happen behind the scenes to make this work?#

A first note on performance#

Theoretical background#

Coercion is not just type conversion#

Not every conversion is a coercion#

The four axioms requested for coercions#

Implementing a conversion#

Establishing a coercion#

Equality and element containment#

The pushout of construction functors#

Shuffling composite construction functors#

Breaking the tie#

Establishing a default implementation#

A second note on performance#

“Abstract” methods#

The _test_... methods#

Implementing a new category with additional tests#

The `_test_...` methods#