This chapter discusses some issues with, and advice for, coding in Sage.

If you are planning to develop some new code for Sage, design is
important. So think about what your program will do and how that fits
into the structure of Sage. In particular, much of Sage is implemented
in the object-oriented language Python, and there is a hierarchy of
classes that organize code and functionality. For example, if you
implement elements of a ring, your class should derive from
`sage.structure.element.RingElement`, rather than starting from
scratch. Try to figure out how your code should fit in with other Sage
code, and design it accordingly.

Functions with leading and trailing double underscores `__XXX__` are
all predefined by Python. Functions with leading and trailing single
underscores `_XXX_` are defined for Sage. Functions with a single
leading underscore are meant to be semi-private, and those with a
double leading underscore are considered really private. Users can
create functions with leading and trailing underscores.

Just as Python has many standard special methods for objects, Sage
also has special methods. They are typically of the form `_XXX_`.
In a few cases, the trailing underscore is not included, but this will
eventually be changed so that the trailing underscore is always
included. This section describes these special methods.

All objects in Sage should derive from the Cython extension class
`SageObject`:

```
from sage.ext.sage_object import SageObject
class MyClass(SageObject,...):
...
```

or from some other already existing Sage class:

```
from sage.rings.ring import Algebra
class MyFavoriteAlgebra(Algebra):
...
```

You should implement the `_latex_` and `_repr_` method for every
object. The other methods depend on the nature of the object.

Every object `x` in Sage should support the command `latex(x)`, so
that any Sage object can be easily and accurately displayed via
LaTeX. Here is how to make a class (and therefore its instances)
support the command `latex`.

- Define a method
`_latex_(self)`that returns a LaTeX representation of your object. It should be something that can be typeset correctly within math mode. Do not include opening and closing $’s. - Often objects are built up out of other Sage objects, and these
components should be typeset using the
`latex`function. For example, if`c`is a coefficient of your object, and you want to typeset`c`using LaTeX, use`latex(c)`instead of`c._latex_()`, since`c`might not have a`_latex_`method, and`latex(c)`knows how to deal with this. - Do not forget to include a docstring and an example that illustrates LaTeX generation for your object.
- You can use any macros included in
`amsmath`,`amssymb`, or`amsfonts`, or the ones defined in`SAGE_ROOT/doc/commontex/macros.tex`.

An example template for a `_latex_` method follows:

```
class X:
...
def _latex_(self):
r"""
Return the LaTeX representation of X.
EXAMPLES::
sage: a = X(1,2)
sage: latex(a)
'\\frac{1}{2}'
"""
return '\\frac{%s}{%s}'%(latex(self.numer), latex(self.denom))
```

As shown in the example, `latex(a)` will produce LaTeX code
representing the object `a`. Calling `view(a)` will display the
typeset version of this.

The standard Python printing method is `__repr__(self)`. In Sage,
that is for objects that derive from `SageObject` (which is
everything in Sage), instead define `_repr_(self)`. This is
preferable because if you only define `_repr_(self)` and not
`__repr__(self)`, then users can rename your object to print however
they like. Also, some objects should print differently depending on
the context.

Here is an example of the `_latex_` and `_repr_` functions for the
`Pi` class. It is from the file
`SAGE_ROOT/devel/sage/sage/functions/constants.py`:

```
class Pi(Constant):
"""
The ratio of a circle's circumference to its diameter.
EXAMPLES::
sage: pi
pi
sage: float(pi) # rel tol 1e-10
3.1415926535897931
"""
...
def _repr_(self):
return "pi"
def _latex_(self):
return "\\pi"
```

Provide a `_matrix_` method for an object that can be coerced to a
matrix over a ring \(R\). Then the Sage function `matrix` will work
for this object.

The following is from
`SAGE_ROOT/devel/sage/sage/graphs/graph.py`:

```
class GenericGraph(SageObject):
...
def _matrix_(self, R=None):
if R is None:
return self.am()
else:
return self.am().change_ring(R)
def adjacency_matrix(self, sparse=None, boundary_first=False):
...
```

Similarly, provide a `_vector_` method for an object that can be
coerced to a vector over a ring \(R\). Then the Sage function `vector`
will work for this object. The following is from the file
`SAGE_ROOT/sage/sage/modules/free_module_element.pyx`:

```
cdef class FreeModuleElement(element_Vector): # abstract base class
...
def _vector_(self, R):
return self.change_ring(R)
```

To make Python even more usable interactively, there are a number of
tweaks to the syntax made when you use Sage from the commandline or
via the notebook (but not for Python code in the Sage
library). Technically, this is implemented by a `preparse()`
function that rewrites the input string. Most notably, the following
replacements are made:

Sage supports a special syntax for generating rings or, more generally, parents with named generators:

sage: R.<x,y> = QQ[] sage: preparse('R.<x,y> = QQ[]') "R = QQ['x, y']; (x, y,) = R._first_ngens(2)"

Integer and real literals are Sage integers and Sage floating point numbers. For example, in pure Python these would be an attribute error:

sage: 16.sqrt() 4 sage: 87.factor() 3 * 29

Raw literals are not preparsed, which can be useful from an efficiency point of view. Just like Python ints are denoted by an L, in Sage raw integer and floating literals are followed by an “r” (or “R”) for raw, meaning not preparsed. For example:

sage: a = 393939r sage: a 393939 sage: type(a) <type 'int'> sage: b = 393939 sage: type(b) <type 'sage.rings.integer.Integer'> sage: a == b True

Raw literals can be very useful in certain cases. For instance, Python integers can be more efficient than Sage integers when they are very small. Large Sage integers are much more efficient than Python integers since they are implemented using the GMP C library.

Consult the file `preparser.py` for more details about Sage
preparsing, more examples involving raw literals, etc.

When a file `foo.sage` is loaded in a Sage session, a preparsed
version of `foo.sage` is created and named `foo.py`. The beginning
of `foo.py` states:

`This file was *autogenerated* from the file foo.sage.`

The following files are relevant to preparsing in Sage:

`SAGE_ROOT/spkg/bin/sage``SAGE_ROOT/local/bin/sage-preparse``SAGE_ROOT/devel/sage/sage/misc/preparser.py`

In particular, the file `preparser.py` contains the Sage preparser
code.

The primary goal of coercion is to be able to transparently do arithmetic, comparisons, etc. between elements of distinct sets. For example, when one writes \(3 + 1/2\), one wants to perform arithmetic on the operands as rational numbers, despite the left term being an integer. This makes sense given the obvious and natural inclusion of the integers into the rational numbers. The goal of the coercion system is to facilitate this (and more complicated arithmetic) without having to explicitly map everything over into the same domain, and at the same time being strict enough to not resolve ambiguity or accept nonsense.

The coercion model for Sage is described in detail, with examples, in the Coercion section of the Sage Reference Manual.

Parent structures (e.g. rings, fields, matrix spaces, etc.) should be immutable and globally unique whenever possible. Immutability means, among other things, that properties like generator labels and default coercion precision cannot be changed.

Global uniqueness while not wasting memory is best implemented using the standard Python weakref module, a factory function, and module scope variable.

Certain objects, e.g. matrices, may start out mutable and become
immutable later. See the file
`SAGE_ROOT/src/sage/structure/mutability.py`.

Here is the definition of `__hash__` from the Python reference
manual:

Called by built-in functionhash()and for operations on members of hashed collections including set, frozenset, and dict.__hash__()should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to somehow mix together (e.g. using exclusive or) the hash values for the components of the object that also play a part in comparison of objects. If a class does not define a__cmp__()method it should not define a__hash__()operation either; if it defines__cmp__()or__eq__()but not__hash__(), its instances will not be usable as dictionary keys. If a class defines mutable objects and implements a__cmp__()or__eq__()method, it should not implement__hash__(), since the dictionary implementation requires that a key’s hash value is immutable (if the object’s hash value changes, it will be in the wrong hash bucket).

Notice the phrase, “The only required property is that objects which compare equal have the same hash value.” This is an assumption made by the Python language, which in Sage we simply cannot make (!), and violating it has consequences. Fortunately, the consequences are pretty clearly defined and reasonably easy to understand, so if you know about them they do not cause you trouble. The following example illustrates them pretty well:

```
sage: v = [Mod(2,7)]
sage: 9 in v
True
sage: v = set([Mod(2,7)])
sage: 9 in v
False
sage: 2 in v
True
sage: w = {Mod(2,7):'a'}
sage: w[2]
'a'
sage: w[9]
Traceback (most recent call last):
...
KeyError: 9
```

Here is another example:

```
sage: R = RealField(10000)
sage: a = R(1) + R(10)^-100
sage: a == RDF(1) # because the a gets coerced down to RDF
True
```

but `hash(a)` should not equal `hash(1)`.

Unfortunately, in Sage we simply cannot require

`(#) "a == b ==> hash(a) == hash(b)"`

because serious mathematics is simply too complicated for this
rule. For example, the equalities `z == Mod(z, 2)` and
`z == Mod(z, 3)` would force `hash()` to be constant on the
integers.

The only way we could “fix” this problem for good would be to abandon
using the `==` operator for “Sage equality”, and implement Sage
equality as a new method attached to each object. Then we could follow
Python rules for `==` and our rules for everything else, and all
Sage code would become completely unreadable (and for that matter
unwritable). So we just have to live with it.

So what is done in Sage is to attempt to satisfy `(#)` when it is
reasonably easy to do so, but use judgment and not go overboard.
For example,

```
sage: hash(Mod(2,7))
2
```

The output 2 is better than some random hash that also involves the
moduli, but it is of course not right from the Python point of view,
since `9 == Mod(2,7)`. The goal is to make a hash function that is
fast, but within reason respects any obvious natural inclusions and
coercions.

Please avoid catch-all code like this:

```
try:
some_code()
except: # bad
more_code()
```

If you do not have any exceptions explicitly listed (as a tuple), your
code will catch absolutely anything, including `ctrl-C`, typos in
the code, and alarms, and this will lead to confusion. Also, this
might catch real errors which should be propagated to the user.

To summarize, only catch specific exceptions as in the following example:

```
try:
return self.__coordinate_ring
except (AttributeError, OtherExceptions) as msg: # good
more_code_to_compute_something()
```

Note that the syntax in `except` is to list all the exceptions that
are caught as a tuple, followed by an error message.

We mention two issues with importing: circular imports and importing large third-party modules.

First, you must avoid circular imports. For example, suppose that the
file `SAGE_ROOT/src/sage/algebras/steenrod_algebra.py`
started with a line:

```
from sage.sage.algebras.steenrod_algebra_bases import *
```

and that the file
`SAGE_ROOT/src/sage/algebras/steenrod_algebra_bases.py`
started with a line:

```
from sage.sage.algebras.steenrod_algebra import SteenrodAlgebra
```

This sets up a loop: loading one of these files requires the other, which then requires the first, etc.

With this set-up, running Sage will produce an error:

```
Exception exceptions.ImportError: 'cannot import name SteenrodAlgebra'
in 'sage.rings.polynomial.polynomial_element.
Polynomial_generic_dense.__normalize' ignored
-------------------------------------------------------------------
ImportError Traceback (most recent call last)
...
ImportError: cannot import name SteenrodAlgebra
```

Instead, you might replace the `import *` line at the top of the
file by more specific imports where they are needed in the code. For
example, the `basis` method for the class `SteenrodAlgebra` might
look like this (omitting the documentation string):

```
def basis(self, n):
from steenrod_algebra_bases import steenrod_algebra_basis
return steenrod_algebra_basis(n, basis=self._basis_name, p=self.prime)
```

Second, do not import at the top level of your module a third-party module that will take a long time to initialize (e.g. matplotlib). As above, you might instead import specific components of the module when they are needed, rather than at the top level of your file.

It is important to try to make `from sage.all import *` as fast as
possible, since this is what dominates the Sage startup time, and
controlling the top-level imports helps to do this. One important
mechanism in Sage are lazy imports, which don’t actually perform the
import but delay it until the object is actually used. See
`sage.misc.lazy_import` for more details of lazy imports, and
*Files and Directory Structure* for an example using lazy imports
for a new module.

Sooner or later you will find places in the Sage library that are, in hindsight, not designed as well as they could be. Of course you want to improve the overall state, but at the same time we don’t want to pull out the carpet under our users’ feet. The process of removing old code is called deprecation.

Note

Before removing any functionality, you should keep a deprecation warning in place for at least one year (if possible). The deprecation must include the trac ticket number where it was introduced.

For example, let’s say you run across the following while working on a module in the Sage library:

```
class Foo(SageObject):
def terrible_idea(self):
return 1
def bad_name(self):
return 1
def f(self, weird_keyword=True):
return self._f_implementation(weird_keyword=weird_keyword)
def _f_implementation(self, weird_keyword=True):
return 1
```

You note that the `terrible_idea()` method does not make any sense,
and should be removed altogether. You open the trac ticket number 3333
(say), and replace the code with:

```
def terrible_idea(self):
from sage.misc.superseded import deprecation
deprecation(3333, 'You can just call f() instead')
return 1
```

Later, you come up with a much better name for the second method. You open the trac ticket number 4444, and replace it with:

```
def much_better_name(self):
return 1
bad_name = deprecated_function_alias(4444, much_better_name)
```

Finally, you like the `f()` method name but you don’t like the
`weird_keyword` name. You fix this by opening the trac ticket 5555,
and replacing it with:

```
@rename_keyword(deprecation=5555, weird_keyword='nice_keyword')
def f(self, nice_keyword=True):
return self._f_implementation(nice_keyword=nice_keyword)
def _f_implementation(self, nice_keyword=True):
return 1
```

Note that the underscore-method `_f_implementation` is, by
convention, not something that ought to be used by others. So we do
not need to deprecate anything when we change it.

Now, any user that still relies on the deprecated functionality will be informed that this is about to change, yet the deprecated commands still work. With all necessary imports, the final result looks like this:

```
sage: from sage.misc.superseded import deprecation, deprecated_function_alias
sage: from sage.misc.decorators import rename_keyword
sage: class Foo(SageObject):
....:
....: def terrible_idea(self):
....: deprecation(3333, 'You can just call f() instead')
....: return 1
....:
....: def much_better_name(self):
....: return 1
....:
....: bad_name = deprecated_function_alias(4444, much_better_name)
....:
....: @rename_keyword(deprecation=5555, weird_keyword='nice_keyword')
....: def f(self, nice_keyword=True):
....: return self._f_implementation(nice_keyword=nice_keyword)
....:
....: def _f_implementation(self, nice_keyword=True):
....: return 1
sage: foo = Foo()
sage: foo.terrible_idea()
doctest:...: DeprecationWarning: You can just call f() instead
See http://trac.sagemath.org/3333 for details.
1
sage: foo.bad_name()
doctest:...: DeprecationWarning: bad_name is deprecated. Please use much_better_name instead.
See http://trac.sagemath.org/4444 for details.
1
sage: foo.f(weird_keyword=False)
doctest:...: DeprecationWarning: use the option 'nice_keyword' instead of 'weird_keyword'
See http://trac.sagemath.org/5555 for details.
1
```

If a function requires an optional package, that function should fail
gracefully—perhaps using a `try`-`except` block—when the
optional package is not available, and should give a hint about how to
install it. For example, typing `sage -optional` gives a list of all
optional packages, so it might suggest to the user that they type
that. The command `optional_packages()` from within Sage also
returns this list.