Sage Tutorial - SageMath Documentation

Sage Tutorial - SageMath Documentation
Sage Tutorial
Release 7.6
The Sage Development Team
Mar 25, 2017
CONTENTS
1
Introduction
1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Ways to Use Sage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Longterm Goals for Sage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
A Guided Tour
2.1 Assignment, Equality, and Arithmetic
2.2 Getting Help . . . . . . . . . . . . .
2.3 Functions, Indentation, and Counting
2.4 Basic Algebra and Calculus . . . . .
2.5 Plotting . . . . . . . . . . . . . . . .
2.6 Some Common Issues with Functions
2.7 Basic Rings . . . . . . . . . . . . . .
2.8 Linear Algebra . . . . . . . . . . . .
2.9 Polynomials . . . . . . . . . . . . .
2.10 Parents, Conversion and Coercion . .
2.11 Finite Groups, Abelian Groups . . . .
2.12 Number Theory . . . . . . . . . . .
2.13 Some More Advanced Mathematics .
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7
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10
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19
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25
27
30
34
39
41
43
The Interactive Shell
3.1 Your Sage Session . . . . . . . . . . .
3.2 Logging Input and Output . . . . . . .
3.3 Paste Ignores Prompts . . . . . . . . .
3.4 Timing Commands . . . . . . . . . . .
3.5 Other IPython tricks . . . . . . . . . .
3.6 Errors and Exceptions . . . . . . . . .
3.7 Reverse Search and Tab Completion . .
3.8 Integrated Help System . . . . . . . .
3.9 Saving and Loading Individual Objects
3.10 Saving and Loading Complete Sessions
3.11 The Notebook Interface . . . . . . . .
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51
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Interfaces
4.1 GP/PARI
4.2 GAP . .
4.3 Singular .
4.4 Maxima .
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65
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68
Sage, LaTeX and Friends
5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
71
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5.2
5.3
5.4
5.5
5.6
5.7
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72
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77
Programming
6.1 Loading and Attaching Sage files . . . . . . . . . . . .
6.2 Creating Compiled Code . . . . . . . . . . . . . . . . .
6.3 Standalone Python/Sage Scripts . . . . . . . . . . . . .
6.4 Data Types . . . . . . . . . . . . . . . . . . . . . . . .
6.5 Lists, Tuples, and Sequences . . . . . . . . . . . . . . .
6.6 Dictionaries . . . . . . . . . . . . . . . . . . . . . . . .
6.7 Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.8 Iterators . . . . . . . . . . . . . . . . . . . . . . . . . .
6.9 Loops, Functions, Control Statements, and Comparisons
6.10 Profiling . . . . . . . . . . . . . . . . . . . . . . . . .
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Using SageTeX
7.1 An example . . . . . . . . . .
7.2 Make SageTeX known to TeX
7.3 SageTeX documentation . . .
7.4 SageTeX and TeXLive . . . .
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8
Afterword
8.1 Why Python? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2 I would like to contribute somehow. How can I? . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3 How do I reference Sage? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Appendix
9.1 Arithmetical binary operator precedence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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7
Basic Use . . . . . . . . . . . . . . . . . . . . . . .
Customizing LaTeX Generation . . . . . . . . . . .
Customizing LaTeX Processing . . . . . . . . . . .
An Example: Combinatorial Graphs with tkz-graph .
A Fully Capable TeX Installation . . . . . . . . . .
External Programs . . . . . . . . . . . . . . . . . .
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10 Bibliography
101
11 Indices and tables
103
Bibliography
105
ii
Sage Tutorial, Release 7.6
Sage is free, open-source math software that supports research and teaching in algebra, geometry, number theory,
cryptography, numerical computation, and related areas. Both the Sage development model and the technology in Sage
itself are distinguished by an extremely strong emphasis on openness, community, cooperation, and collaboration: we
are building the car, not reinventing the wheel. The overall goal of Sage is to create a viable, free, open-source
alternative to Maple, Mathematica, Magma, and MATLAB.
This tutorial is the best way to become familiar with Sage in only a few hours. You can read it in HTML or PDF
versions, or from the Sage notebook (click Help , then click Tutorial to interactively work through the tutorial
from within Sage).
This work is licensed under a Creative Commons Attribution-Share Alike 3.0 License.
CONTENTS
1
Sage Tutorial, Release 7.6
2
CONTENTS
CHAPTER
ONE
INTRODUCTION
This tutorial should take at most 3-4 hours to fully work through. You can read it in HTML or PDF versions, or from
the Sage notebook click Help , then click Tutorial to interactively work through the tutorial from within Sage.
Though much of Sage is implemented using Python, no Python background is needed to read this tutorial. You will
want to learn Python (a very fun language!) at some point, and there are many excellent free resources for doing so
including [PyT] and [Dive]. If you just want to quickly try out Sage, this tutorial is the place to start. For example:
sage: 2 + 2
4
sage: factor(-2007)
-1 * 3^2 * 223
sage: A = matrix(4,4, range(16)); A
[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]
sage: factor(A.charpoly())
x^2 * (x^2 - 30*x - 80)
sage: m = matrix(ZZ,2, range(4))
sage: m[0,0] = m[0,0] - 3
sage: m
[-3 1]
[ 2 3]
sage: E = EllipticCurve([1,2,3,4,5]);
sage: E
Elliptic Curve defined by y^2 + x*y + 3*y = x^3 + 2*x^2 + 4*x + 5
over Rational Field
sage: E.anlist(10)
[0, 1, 1, 0, -1, -3, 0, -1, -3, -3, -3]
sage: E.rank()
1
sage: k = 1/(sqrt(3)*I + 3/4 + sqrt(73)*5/9); k
36/(20*sqrt(73) + 36*I*sqrt(3) + 27)
sage: N(k)
0.165495678130644 - 0.0521492082074256*I
sage: N(k,30)
# 30 "bits"
0.16549568 - 0.052149208*I
sage: latex(k)
\frac{36}{20 \, \sqrt{73} + 36 i \, \sqrt{3} + 27}
3
Sage Tutorial, Release 7.6
1.1 Installation
If you do not have Sage installed on a computer and just want to try some commands, use online at http://www.sagenb.
org.
See the Sage Installation Guide in the documentation section of the main Sage webpage [SA] for instructions on
installing Sage on your computer. Here we merely make a few comments.
1. The Sage download file comes with “batteries included”. In other words, although Sage uses Python, IPython,
PARI, GAP, Singular, Maxima, NTL, GMP, and so on, you do not need to install them separately as they are
included with the Sage distribution. However, to use certain Sage features, e.g., Macaulay or KASH, you must
install the relevant optional package or at least have the relevant programs installed on your computer already.
Macaulay and KASH are Sage packages (for a list of available optional packages, type sage -optional ,
or browse the “Download” page on the Sage website).
2. The pre-compiled binary version of Sage (found on the Sage web site) may be easier and quicker to install than
the source code version. Just unpack the file and run sage .
3. If you’d like to use the SageTeX package (which allows you to embed the results of Sage computations into
a LaTeX file), you will need to make SageTeX known to your TeX distribution. To do this, see the section
“Make SageTeX known to TeX” in the Sage installation guide (this link should take you to a local copy of the
installation guide). It’s quite easy; you just need to set an environment variable or copy a single file to a directory
that TeX will search.
The documentation for using SageTeX is located in $SAGE_ROOT/local/share/texmf/tex/latex/sagetex/
, where “$SAGE_ROOT ” refers to the directory where you installed Sage – for example, /opt/sage-4.2.1
.
1.2 Ways to Use Sage
You can use Sage in several ways.
• Notebook graphical interface: see the section on the Notebook in the reference manual and The Notebook
Interface below,
• Interactive command line: see The Interactive Shell,
• Programs: By writing interpreted and compiled programs in Sage (see Loading and Attaching Sage files and
Creating Compiled Code), and
• Scripts: by writing stand-alone Python scripts that use the Sage library (see Standalone Python/Sage Scripts).
1.3 Longterm Goals for Sage
• Useful: Sage’s intended audience is mathematics students (from high school to graduate school), teachers, and
research mathematicians. The aim is to provide software that can be used to explore and experiment with
mathematical constructions in algebra, geometry, number theory, calculus, numerical computation, etc. Sage
helps make it easier to interactively experiment with mathematical objects.
• Efficient: Be fast. Sage uses highly-optimized mature software like GMP, PARI, GAP, and NTL, and so is very
fast at certain operations.
• Free and open source: The source code must be freely available and readable, so users can understand what
the system is really doing and more easily extend it. Just as mathematicians gain a deeper understanding of
a theorem by carefully reading or at least skimming the proof, people who do computations should be able to
4
Chapter 1. Introduction
Sage Tutorial, Release 7.6
understand how the calculations work by reading documented source code. If you use Sage to do computations
in a paper you publish, you can rest assured that your readers will always have free access to Sage and all its
source code, and you are even allowed to archive and re-distribute the version of Sage you used.
• Easy to compile: Sage should be easy to compile from source for Linux, OS X and Windows users. This
provides more flexibility for users to modify the system.
• Cooperation: Provide robust interfaces to most other computer algebra systems, including PARI, GAP, Singular, Maxima, KASH, Magma, Maple, and Mathematica. Sage is meant to unify and extend existing math
software.
• Well documented: Tutorial, programming guide, reference manual, and how-to, with numerous examples and
discussion of background mathematics.
• Extensible: Be able to define new data types or derive from built-in types, and use code written in a range of
languages.
• User friendly: It should be easy to understand what functionality is provided for a given object and to view
documentation and source code. Also attain a high level of user support.
1.3. Longterm Goals for Sage
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Chapter 1. Introduction
CHAPTER
TWO
A GUIDED TOUR
This section is a guided tour of some of what is available in Sage. For many more examples, see “Sage Constructions”,
which is intended to answer the general question “How do I construct ...?”. See also the “Sage Reference Manual”,
which has thousands more examples. Also note that you can interactively work through this tour in the Sage notebook
by clicking the Help link.
(If you are viewing the tutorial in the Sage notebook, press shift-enter to evaluate any input cell. You can
even edit the input before pressing shift-enter. On some Macs you might have to press shift-return rather than
shift-enter .)
2.1 Assignment, Equality, and Arithmetic
With some minor exceptions, Sage uses the Python programming language, so most introductory books on Python
will help you to learn Sage.
Sage uses = for assignment. It uses == , <= , >= , < and > for comparison:
sage:
sage:
5
sage:
True
sage:
False
sage:
True
sage:
True
a = 5
a
2 == 2
2 == 3
2 < 3
a == 5
Sage provides all of the basic mathematical operations:
sage:
8
sage:
8
sage:
1
sage:
5/2
sage:
2
sage:
True
2**3
#
** means exponent
2^3
#
^ is a synonym for ** (unlike in Python)
10 % 3
#
for integer arguments, % means mod, i.e., remainder
#
for integer arguments, // returns the integer quotient
10/4
10//4
4 * (10 // 4) + 10 % 4 == 10
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sage: 3^2*4 + 2%5
38
The computation of an expression like 3^2*4 + 2%5 depends on the order in which the operations are applied; this
is specified in the “operator precedence table” in Arithmetical binary operator precedence.
Sage also provides many familiar mathematical functions; here are just a few examples:
sage: sqrt(3.4)
1.84390889145858
sage: sin(5.135)
-0.912021158525540
sage: sin(pi/3)
1/2*sqrt(3)
As the last example shows, some mathematical expressions return ‘exact’ values, rather than numerical approximations. To get a numerical approximation, use either the function n or the method n (and both of these have a longer
name, numerical_approx , and the function N is the same as n )). These take optional arguments prec , which is
the requested number of bits of precision, and digits , which is the requested number of decimal digits of precision;
the default is 53 bits of precision.
sage: exp(2)
e^2
sage: n(exp(2))
7.38905609893065
sage: sqrt(pi).numerical_approx()
1.77245385090552
sage: sin(10).n(digits=5)
-0.54402
sage: N(sin(10),digits=10)
-0.5440211109
sage: numerical_approx(pi, prec=200)
3.1415926535897932384626433832795028841971693993751058209749
Python is dynamically typed, so the value referred to by each variable has a type associated with it, but a given variable
may hold values of any Python type within a given scope:
sage: a = 5
# a is an integer
sage: type(a)
<type 'sage.rings.integer.Integer'>
sage: a = 5/3 # now a is a rational number
sage: type(a)
<type 'sage.rings.rational.Rational'>
sage: a = 'hello' # now a is a string
sage: type(a)
<... 'str'>
The C programming language, which is statically typed, is much different; a variable declared to hold an int can only
hold an int in its scope.
2.2 Getting Help
Sage has extensive built-in documentation, accessible by typing the name of a function or a constant (for example),
followed by a question mark:
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sage: tan?
Type:
Definition:
Docstring:
<class 'sage.calculus.calculus.Function_tan'>
tan( [noargspec] )
The tangent function
EXAMPLES:
sage: tan(pi)
0
sage: tan(3.1415)
-0.0000926535900581913
sage: tan(3.1415/4)
0.999953674278156
sage: tan(pi/4)
1
sage: tan(1/2)
tan(1/2)
sage: RR(tan(1/2))
0.546302489843790
sage: log2?
Type:
<class 'sage.functions.constants.Log2'>
Definition: log2( [noargspec] )
Docstring:
The natural logarithm of the real number 2.
EXAMPLES:
sage: log2
log2
sage: float(log2)
0.69314718055994529
sage: RR(log2)
0.693147180559945
sage: R = RealField(200); R
Real Field with 200 bits of precision
sage: R(log2)
0.69314718055994530941723212145817656807550013436025525412068
sage: l = (1-log2)/(1+log2); l
(1 - log(2))/(log(2) + 1)
sage: R(l)
0.18123221829928249948761381864650311423330609774776013488056
sage: maxima(log2)
log(2)
sage: maxima(log2).float()
.6931471805599453
sage: gp(log2)
0.6931471805599453094172321215
# 32-bit
0.69314718055994530941723212145817656807
# 64-bit
sage: sudoku?
File:
sage/local/lib/python2.5/site-packages/sage/games/sudoku.py
Type:
<... 'function'>
Definition: sudoku(A)
Docstring:
Solve the 9x9 Sudoku puzzle defined by the matrix A.
EXAMPLE:
2.2. Getting Help
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sage: A = matrix(ZZ,9,[5,0,0, 0,8,0, 0,4,9, 0,0,0, 5,0,0,
0,3,0, 0,6,7, 3,0,0, 0,0,1, 1,5,0, 0,0,0, 0,0,0, 0,0,0, 2,0,8, 0,0,0,
0,0,0, 0,0,0, 0,1,8, 7,0,0, 0,0,4, 1,5,0,
0,3,0, 0,0,2,
0,0,0, 4,9,0, 0,5,0, 0,0,3])
sage: A
[5 0 0 0 8 0 0 4 9]
[0 0 0 5 0 0 0 3 0]
[0 6 7 3 0 0 0 0 1]
[1 5 0 0 0 0 0 0 0]
[0 0 0 2 0 8 0 0 0]
[0 0 0 0 0 0 0 1 8]
[7 0 0 0 0 4 1 5 0]
[0 3 0 0 0 2 0 0 0]
[4 9 0 0 5 0 0 0 3]
sage: sudoku(A)
[5 1 3 6 8 7 2 4 9]
[8 4 9 5 2 1 6 3 7]
[2 6 7 3 4 9 5 8 1]
[1 5 8 4 6 3 9 7 2]
[9 7 4 2 1 8 3 6 5]
[3 2 6 7 9 5 4 1 8]
[7 8 2 9 3 4 1 5 6]
[6 3 5 1 7 2 8 9 4]
[4 9 1 8 5 6 7 2 3]
Sage also provides ‘Tab completion’: type the first few letters of a function and then hit the tab key. For example, if
you type ta followed by TAB , Sage will print tachyon,tan,tanh,taylor . This provides a good way to find
the names of functions and other structures in Sage.
2.3 Functions, Indentation, and Counting
To define a new function in Sage, use the def command and a colon after the list of variable names. For example:
sage: def is_even(n):
....:
return n%2 == 0
sage: is_even(2)
True
sage: is_even(3)
False
Note: Depending on which version of the tutorial you are viewing, you may see three dots ....: on the second line
of this example. Do not type them; they are just to emphasize that the code is indented. Whenever this is the case,
press [Return/Enter] once at the end of the block to insert a blank line and conclude the function definition.
You do not specify the types of any of the input arguments. You can specify multiple inputs, each of which may have
an optional default value. For example, the function below defaults to divisor=2 if divisor is not specified.
sage:
....:
sage:
True
sage:
True
sage:
False
10
def is_divisible_by(number, divisor=2):
return number%divisor == 0
is_divisible_by(6,2)
is_divisible_by(6)
is_divisible_by(6, 5)
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You can also explicitly specify one or either of the inputs when calling the function; if you specify the inputs explicitly,
you can give them in any order:
sage: is_divisible_by(6, divisor=5)
False
sage: is_divisible_by(divisor=2, number=6)
True
In Python, blocks of code are not indicated by curly braces or begin and end blocks as in many other languages.
Instead, blocks of code are indicated by indentation, which must match up exactly. For example, the following is a
syntax error because the return statement is not indented the same amount as the other lines above it.
sage: def even(n):
....:
v = []
....:
for i in range(3,n):
....:
if i % 2 == 0:
....:
v.append(i)
....:
return v
Syntax Error:
return v
If you fix the indentation, the function works:
sage: def even(n):
....:
v = []
....:
for i in range(3,n):
....:
if i % 2 == 0:
....:
v.append(i)
....:
return v
sage: even(10)
[4, 6, 8]
Semicolons are not needed at the ends of lines; a line is in most cases ended by a newline. However, you can put
multiple statements on one line, separated by semicolons:
sage: a = 5; b = a + 3; c = b^2; c
64
If you would like a single line of code to span multiple lines, use a terminating backslash:
sage: 2 + \
....:
3
5
In Sage, you count by iterating over a range of integers. For example, the first line below is exactly like for(i=0;
i<3; i++) in C++ or Java:
sage: for i in range(3):
....:
print(i)
0
1
2
The first line below is like for(i=2;i<5;i++) .
sage: for i in range(2,5):
....:
print(i)
2
2.3. Functions, Indentation, and Counting
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3
4
The third argument controls the step, so the following is like for(i=1;i<6;i+=2) .
sage: for i in range(1,6,2):
....:
print(i)
1
3
5
Often you will want to create a nice table to display numbers you have computed using Sage. One easy way to do this
is to use string formatting. Below, we create three columns each of width exactly 6 and make a table of squares and
cubes.
sage: for i in range(5):
....:
print('%6s %6s %6s' % (i, i^2, i^3))
0
0
0
1
1
1
2
4
8
3
9
27
4
16
64
The most basic data structure in Sage is the list, which is – as the name suggests – just a list of arbitrary objects. For
example, the range command that we used creates a list:
sage: range(2,10)
[2, 3, 4, 5, 6, 7, 8, 9]
Here is a more complicated list:
sage: v = [1, "hello", 2/3, sin(x^3)]
sage: v
[1, 'hello', 2/3, sin(x^3)]
List indexing is 0-based, as in many programming languages.
sage: v[0]
1
sage: v[3]
sin(x^3)
Use len(v) to get the length of v , use v.append(obj) to append a new object to the end of v , and use del
v[i] to delete the 𝑖𝑡ℎ entry of v :
sage: len(v)
4
sage: v.append(1.5)
sage: v
[1, 'hello', 2/3, sin(x^3), 1.50000000000000]
sage: del v[1]
sage: v
[1, 2/3, sin(x^3), 1.50000000000000]
Another important data structure is the dictionary (or associative array). This works like a list, except that it can be
indexed with almost any object (the indices must be immutable):
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sage: d = {'hi':-2,
sage: d['hi']
-2
sage: d[e]
pi
3/8:pi,
e:pi}
You can also define new data types using classes. Encapsulating mathematical objects with classes is a powerful
technique that can help to simplify and organize your Sage programs. Below, we define a class that represents the list
of even positive integers up to n; it derives from the builtin type list .
sage: class Evens(list):
....:
def __init__(self, n):
....:
self.n = n
....:
list.__init__(self, range(2, n+1, 2))
....:
def __repr__(self):
....:
return "Even positive numbers up to n."
The __init__ method is called to initialize the object when it is created; the __repr__ method prints the object
out. We call the list constructor method in the second line of the __init__ method. We create an object of class
Evens as follows:
sage: e = Evens(10)
sage: e
Even positive numbers up to n.
Note that e prints using the __repr__ method that we defined. To see the underlying list of numbers, use the list
function:
sage: list(e)
[2, 4, 6, 8, 10]
We can also access the n attribute or treat e like a list.
sage: e.n
10
sage: e[2]
6
2.4 Basic Algebra and Calculus
Sage can perform various computations related to basic algebra and calculus: for example, finding solutions to equations, differentiation, integration, and Laplace transforms. See the Sage Constructions documentation for more examples.
In all these examples, it is important to note that the variables in the functions are defined to be var(...) . As an
example:
sage: u = var('u')
sage: diff(sin(u), u)
cos(u)
If you get a NameError , check to see if you mispelled something, or forgot to define a variable with var(...) .
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2.4.1 Solving Equations
Solving Equations Exactly
The solve function solves equations. To use it, first specify some variables; then the arguments to solve are an
equation (or a system of equations), together with the variables for which to solve:
sage: x = var('x')
sage: solve(x^2 + 3*x + 2, x)
[x == -2, x == -1]
You can solve equations for one variable in terms of others:
sage: x, b, c = var('x b c')
sage: solve([x^2 + b*x + c == 0],x)
[x == -1/2*b - 1/2*sqrt(b^2 - 4*c), x == -1/2*b + 1/2*sqrt(b^2 - 4*c)]
You can also solve for several variables:
sage: x, y = var('x, y')
sage: solve([x+y==6, x-y==4], x, y)
[[x == 5, y == 1]]
The following example of using Sage to solve a system of non-linear equations was provided by Jason Grout: first, we
solve the system symbolically:
sage: var('x y p q')
(x, y, p, q)
sage: eq1 = p+q==9
sage: eq2 = q*y+p*x==-6
sage: eq3 = q*y^2+p*x^2==24
sage: solve([eq1,eq2,eq3,p==1],p,q,x,y)
[[p == 1, q == 8, x == -4/3*sqrt(10) - 2/3, y == 1/6*sqrt(10) - 2/3], [p == 1, q ==
˓→8, x == 4/3*sqrt(10) - 2/3, y == -1/6*sqrt(10) - 2/3]]
For numerical approximations of the solutions, you can instead use:
sage: solns = solve([eq1,eq2,eq3,p==1],p,q,x,y, solution_dict=True)
sage: [[s[p].n(30), s[q].n(30), s[x].n(30), s[y].n(30)] for s in solns]
[[1.0000000, 8.0000000, -4.8830369, -0.13962039],
[1.0000000, 8.0000000, 3.5497035, -1.1937129]]
(The function n prints a numerical approximation, and the argument is the number of bits of precision.)
Solving Equations Numerically
Often times, solve will not be able to find an exact solution to the equation or equations specified. When it fails,
you can use find_root to find a numerical solution. For example, solve does not return anything interesting for
the following equation:
sage: theta = var('theta')
sage: solve(cos(theta)==sin(theta), theta)
[sin(theta) == cos(theta)]
On the other hand, we can use find_root to find a solution to the above equation in the range 0 < 𝜑 < 𝜋/2:
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sage: phi = var('phi')
sage: find_root(cos(phi)==sin(phi),0,pi/2)
0.785398163397448...
2.4.2 Differentiation, Integration, etc.
Sage knows how to differentiate and integrate many functions. For example, to differentiate sin(𝑢) with respect to 𝑢,
do the following:
sage: u = var('u')
sage: diff(sin(u), u)
cos(u)
To compute the fourth derivative of sin(𝑥2 ):
sage: diff(sin(x^2), x, 4)
16*x^4*sin(x^2) - 48*x^2*cos(x^2) - 12*sin(x^2)
To compute the partial derivatives of 𝑥2 + 17𝑦 2 with respect to 𝑥 and 𝑦, respectively:
sage:
sage:
sage:
2*x
sage:
34*y
x, y = var('x,y')
f = x^2 + 17*y^2
f.diff(x)
f.diff(y)
We move on to integrals, both indefinite and definite. To compute
∫︀
𝑥 sin(𝑥2 ) 𝑑𝑥 and
∫︀ 1
𝑥
0 𝑥2 +1
𝑑𝑥
sage: integral(x*sin(x^2), x)
-1/2*cos(x^2)
sage: integral(x/(x^2+1), x, 0, 1)
1/2*log(2)
To compute the partial fraction decomposition of
1
𝑥2 −1 :
sage: f = 1/((1+x)*(x-1))
sage: f.partial_fraction(x)
-1/2/(x + 1) + 1/2/(x - 1)
2.4.3 Solving Differential Equations
You can use Sage to investigate ordinary differential equations. To solve the equation 𝑥′ + 𝑥 − 1 = 0:
sage:
sage:
sage:
sage:
(_C +
t = var('t')
# define a variable t
x = function('x')(t)
# define x to be a function of that variable
DE = diff(x, t) + x - 1
desolve(DE, [x,t])
e^t)*e^(-t)
This uses Sage’s interface to Maxima [Max], and so its output may be a bit different from other Sage output. In this
case, this says that the general solution to the differential equation is 𝑥(𝑡) = 𝑒−𝑡 (𝑒𝑡 + 𝑐).
You can compute Laplace transforms also; the Laplace transform of 𝑡2 𝑒𝑡 − sin(𝑡) is computed as follows:
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sage: s = var("s")
sage: t = var("t")
sage: f = t^2*exp(t) - sin(t)
sage: f.laplace(t,s)
-1/(s^2 + 1) + 2/(s - 1)^3
Here is a more involved example. The displacement from equilibrium (respectively) for a coupled spring attached to a
wall on the left
|------\/\/\/\/\---|mass1|----\/\/\/\/\/----|mass2|
spring1
spring2
is modeled by the system of 2nd order differential equations
𝑚1 𝑥′′1 + (𝑘1 + 𝑘2 )𝑥1 − 𝑘2 𝑥2 = 0
𝑚2 𝑥′′2 + 𝑘2 (𝑥2 − 𝑥1 ) = 0,
where 𝑚𝑖 is the mass of object i, 𝑥𝑖 is the displacement from equilibrium of mass i, and 𝑘𝑖 is the spring constant for
spring i.
Example: Use Sage to solve the above problem with 𝑚1 = 2, 𝑚2 = 1, 𝑘1 = 4, 𝑘2 = 2, 𝑥1 (0) = 3, 𝑥′1 (0) = 0,
𝑥2 (0) = 3, 𝑥′2 (0) = 0.
Solution: Take the Laplace transform of the first equation (with the notation 𝑥 = 𝑥1 , 𝑦 = 𝑥2 ):
sage: de1 = maxima("2*diff(x(t),t, 2) + 6*x(t) - 2*y(t)")
sage: lde1 = de1.laplace("t","s"); lde1
2*((-%at('diff(x(t),t,1),t=0))+s^2*'laplace(x(t),t,s)-x(0)*s)-2*'laplace(y(t),t,s)+6*
˓→'laplace(x(t),t,s)
This is hard to read, but it says that
−2𝑥′ (0) + 2𝑠2 · 𝑋(𝑠) − 2𝑠𝑥(0) − 2𝑌 (𝑠) + 6𝑋(𝑠) = 0
(where the Laplace transform of a lower case function like 𝑥(𝑡) is the upper case function 𝑋(𝑠)). Take the Laplace
transform of the second equation:
sage: de2 = maxima("diff(y(t),t, 2) + 2*y(t) - 2*x(t)")
sage: lde2 = de2.laplace("t","s"); lde2
(-%at('diff(y(t),t,1),t=0))+s^2*'laplace(y(t),t,s)+2*'laplace(y(t),t,s)-2*
˓→'laplace(x(t),t,s)-y(0)*s
This says
−𝑌 ′ (0) + 𝑠2 𝑌 (𝑠) + 2𝑌 (𝑠) − 2𝑋(𝑠) − 𝑠𝑦(0) = 0.
Plug in the initial conditions for 𝑥(0), 𝑥′ (0), 𝑦(0), and 𝑦 ′ (0), and solve the resulting two equations:
sage: var('s X Y')
(s, X, Y)
sage: eqns = [(2*s^2+6)*X-2*Y == 6*s, -2*X +(s^2+2)*Y == 3*s]
sage: solve(eqns, X,Y)
[[X == 3*(s^3 + 3*s)/(s^4 + 5*s^2 + 4),
Y == 3*(s^3 + 5*s)/(s^4 + 5*s^2 + 4)]]
Now take inverse Laplace transforms to get the answer:
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sage: var('s t')
(s, t)
sage: inverse_laplace((3*s^3 + 9*s)/(s^4 + 5*s^2 + 4),s,t)
cos(2*t) + 2*cos(t)
sage: inverse_laplace((3*s^3 + 15*s)/(s^4 + 5*s^2 + 4),s,t)
-cos(2*t) + 4*cos(t)
Therefore, the solution is
𝑥1 (𝑡) = cos(2𝑡) + 2 cos(𝑡),
𝑥2 (𝑡) = 4 cos(𝑡) − cos(2𝑡).
This can be plotted parametrically using
sage: t = var('t')
sage: P = parametric_plot((cos(2*t) + 2*cos(t), 4*cos(t) - cos(2*t) ),
....:
(t, 0, 2*pi), rgbcolor=hue(0.9))
sage: show(P)
The individual components can be plotted using
sage:
sage:
sage:
sage:
t = var('t')
p1 = plot(cos(2*t) + 2*cos(t), (t,0, 2*pi), rgbcolor=hue(0.3))
p2 = plot(4*cos(t) - cos(2*t), (t,0, 2*pi), rgbcolor=hue(0.6))
show(p1 + p2)
For more on plotting, see Plotting. See section 5.5 of [NagleEtAl2004] for further information on differential equations.
2.4.4 Euler’s Method for Systems of Differential Equations
In the next example, we will illustrate Euler’s method for first and second order ODEs. We first recall the basic idea
for first order equations. Given an initial value problem of the form
𝑦 ′ = 𝑓 (𝑥, 𝑦),
𝑦(𝑎) = 𝑐,
we want to find the approximate value of the solution at 𝑥 = 𝑏 with 𝑏 > 𝑎.
Recall from the definition of the derivative that
𝑦 ′ (𝑥) ≈
𝑦(𝑥 + ℎ) − 𝑦(𝑥)
,
ℎ
where ℎ > 0 is given and small. This and the DE together give 𝑓 (𝑥, 𝑦(𝑥)) ≈
𝑦(𝑥+ℎ)−𝑦(𝑥)
.
ℎ
Now solve for 𝑦(𝑥 + ℎ):
𝑦(𝑥 + ℎ) ≈ 𝑦(𝑥) + ℎ · 𝑓 (𝑥, 𝑦(𝑥)).
If we call ℎ · 𝑓 (𝑥, 𝑦(𝑥)) the “correction term” (for lack of anything better), call 𝑦(𝑥) the “old value of 𝑦”, and call
𝑦(𝑥 + ℎ) the “new value of 𝑦”, then this approximation can be re-expressed as
𝑦𝑛𝑒𝑤 ≈ 𝑦𝑜𝑙𝑑 + ℎ · 𝑓 (𝑥, 𝑦𝑜𝑙𝑑 ).
If we break the interval from 𝑎 to 𝑏 into 𝑛 steps, so that ℎ =
in a table.
𝑥
𝑎
𝑎+ℎ
𝑎 + 2ℎ
...
𝑏 = 𝑎 + 𝑛ℎ
𝑦
𝑐
𝑐 + ℎ · 𝑓 (𝑎, 𝑐)
...
ℎ · 𝑓 (𝑥, 𝑦)
ℎ · 𝑓 (𝑎, 𝑐)
...
???
...
2.4. Basic Algebra and Calculus
𝑏−𝑎
𝑛 ,
then we can record the information for this method
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The goal is to fill out all the blanks of the table, one row at a time, until we reach the ??? entry, which is the Euler’s
method approximation for 𝑦(𝑏).
The idea for systems of ODEs is similar.
Example: Numerically approximate 𝑧(𝑡) at 𝑡 = 1 using 4 steps of Euler’s method, where 𝑧 ′′ + 𝑡𝑧 ′ + 𝑧 = 0, 𝑧(0) = 1,
𝑧 ′ (0) = 0.
We must reduce the 2nd order ODE down to a system of two first order DEs (using 𝑥 = 𝑧, 𝑦 = 𝑧 ′ ) and apply Euler’s
method:
sage: t,x,y = PolynomialRing(RealField(10),3,"txy").gens()
sage: f = y; g = -x - y * t
sage: eulers_method_2x2(f,g, 0, 1, 0, 1/4, 1)
t
x
h*f(t,x,y)
y
0
1
0.00
0
1/4
1.0
-0.062
-0.25
1/2
0.94
-0.12
-0.48
3/4
0.82
-0.16
-0.66
1
0.65
-0.18
-0.74
h*g(t,x,y)
-0.25
-0.23
-0.17
-0.081
0.022
Therefore, 𝑧(1) ≈ 0.65.
We can also plot the points (𝑥, 𝑦) to get an approximate picture of the curve.
The function
eulers_method_2x2_plot will do this; in order to use it, we need to define functions 𝑓 and 𝑔 which takes
one argument with three coordinates: (𝑡, 𝑥, 𝑦).
sage: f = lambda z: z[2]
# f(t,x,y) = y
sage: g = lambda z: -sin(z[1]) # g(t,x,y) = -sin(x)
sage: P = eulers_method_2x2_plot(f,g, 0.0, 0.75, 0.0, 0.1, 1.0)
At this point, P is storing two plots: P[0] , the plot of 𝑥 vs. 𝑡, and P[1] , the plot of 𝑦 vs. 𝑡. We can plot both of
these as follows:
sage: show(P[0] + P[1])
(For more on plotting, see Plotting.)
2.4.5 Special functions
Several orthogonal polynomials and special functions are implemented, using both PARI [GAP] and Maxima [Max].
These are documented in the appropriate sections (“Orthogonal polynomials” and “Special functions”, respectively)
of the Sage reference manual.
sage: x = polygen(QQ, 'x')
sage: chebyshev_U(2,x)
4*x^2 - 1
sage: bessel_I(1,1).n(250)
0.56515910399248502720769602760986330732889962162109200948029448947925564096
sage: bessel_I(1,1).n()
0.565159103992485
sage: bessel_I(2,1.1).n()
0.167089499251049
At this point, Sage has only wrapped these functions for numerical use. For symbolic use, please use the Maxima
interface directly, as in the following example:
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sage: maxima.eval("f:bessel_y(v, w)")
'bessel_y(v,w)'
sage: maxima.eval("diff(f,w)")
'(bessel_y(v-1,w)-bessel_y(v+1,w))/2'
2.5 Plotting
Sage can produce two-dimensional and three-dimensional plots.
2.5.1 Two-dimensional Plots
In two dimensions, Sage can draw circles, lines, and polygons; plots of functions in rectangular coordinates; and also
polar plots, contour plots and vector field plots. We present examples of some of these here. For more examples of
plotting with Sage, see Solving Differential Equations and Maxima, and also the Sage Constructions documentation.
This command produces a yellow circle of radius 1, centered at the origin:
sage: circle((0,0), 1, rgbcolor=(1,1,0))
Graphics object consisting of 1 graphics primitive
You can also produce a filled circle:
sage: circle((0,0), 1, rgbcolor=(1,1,0), fill=True)
Graphics object consisting of 1 graphics primitive
You can also create a circle by assigning it to a variable; this does not plot it:
sage: c = circle((0,0), 1, rgbcolor=(1,1,0))
To plot it, use c.show() or show(c) , as follows:
sage: c.show()
Alternatively, evaluating c.save('filename.png') will save the plot to the given file.
Now, these ‘circles’ look more like ellipses because the axes are scaled differently. You can fix this:
sage: c.show(aspect_ratio=1)
The command show(c,aspect_ratio=1) accomplishes the same thing, or you can save the picture using
c.save('filename.png',aspect_ratio=1) .
It’s easy to plot basic functions:
sage: plot(cos, (-5,5))
Graphics object consisting of 1 graphics primitive
Once you specify a variable name, you can create parametric plots also:
sage: x = var('x')
sage: parametric_plot((cos(x),sin(x)^3),(x,0,2*pi),rgbcolor=hue(0.6))
Graphics object consisting of 1 graphics primitive
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It’s important to notice that the axes of the plots will only intersect if the origin is in the viewing range of the graph,
and that with sufficiently large values scientific notation may be used:
sage: plot(x^2,(x,300,500))
Graphics object consisting of 1 graphics primitive
You can combine several plots by adding them:
sage:
sage:
sage:
sage:
sage:
x = var('x')
p1 = parametric_plot((cos(x),sin(x)),(x,0,2*pi),rgbcolor=hue(0.2))
p2 = parametric_plot((cos(x),sin(x)^2),(x,0,2*pi),rgbcolor=hue(0.4))
p3 = parametric_plot((cos(x),sin(x)^3),(x,0,2*pi),rgbcolor=hue(0.6))
show(p1+p2+p3, axes=false)
A good way to produce filled-in shapes is to produce a list of points (L in the example below) and then use the
polygon command to plot the shape with boundary formed by those points. For example, here is a green deltoid:
sage: L = [[-1+cos(pi*i/100)*(1+cos(pi*i/100)),
....:
2*sin(pi*i/100)*(1-cos(pi*i/100))] for i in range(200)]
sage: p = polygon(L, rgbcolor=(1/8,3/4,1/2))
sage: p
Graphics object consisting of 1 graphics primitive
Type show(p,axes=false) to see this without any axes.
You can add text to a plot:
sage:
....:
sage:
sage:
sage:
L = [[6*cos(pi*i/100)+5*cos((6/2)*pi*i/100),
6*sin(pi*i/100)-5*sin((6/2)*pi*i/100)] for i in range(200)]
p = polygon(L, rgbcolor=(1/8,1/4,1/2))
t = text("hypotrochoid", (5,4), rgbcolor=(1,0,0))
show(p+t)
Calculus teachers draw the following plot frequently on the board: not just one branch of arcsin but rather several of
them: i.e., the plot of 𝑦 = sin(𝑥) for 𝑥 between −2𝜋 and 2𝜋, flipped about the 45 degree line. The following Sage
commands construct this:
sage: v = [(sin(x),x) for x in srange(-2*float(pi),2*float(pi),0.1)]
sage: line(v)
Graphics object consisting of 1 graphics primitive
Since the tangent function has a larger range than sine, if you use the same trick to plot the inverse tangent, you should
change the minimum and maximum coordinates for the x-axis:
sage: v = [(tan(x),x) for x in srange(-2*float(pi),2*float(pi),0.01)]
sage: show(line(v), xmin=-20, xmax=20)
Sage also computes polar plots, contour plots and vector field plots (for special types of functions). Here is an example
of a contour plot:
sage: f = lambda x,y: cos(x*y)
sage: contour_plot(f, (-4, 4), (-4, 4))
Graphics object consisting of 1 graphics primitive
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2.5.2 Three-Dimensional Plots
Sage can also be used to create three-dimensional plots. In both the notebook and the REPL, these plots will be
displayed by default using the open source package [Jmol], which supports interactively rotating and zooming the
figure with the mouse.
Use plot3d to graph a function of the form 𝑓 (𝑥, 𝑦) = 𝑧:
sage: x, y = var('x,y')
sage: plot3d(x^2 + y^2, (x,-2,2), (y,-2,2))
Graphics3d Object
Alternatively, you can use parametric_plot3d to graph a parametric surface where each of 𝑥, 𝑦, 𝑧 is determined by a function of one or two variables (the parameters, typically 𝑢 and 𝑣). The previous plot can be expressed
parametrically as follows:
sage: u, v = var('u, v')
sage: f_x(u, v) = u
sage: f_y(u, v) = v
sage: f_z(u, v) = u^2 + v^2
sage: parametric_plot3d([f_x, f_y, f_z], (u, -2, 2), (v, -2, 2))
Graphics3d Object
The third way to plot a 3D surface in Sage is implicit_plot3d , which graphs a contour of a function like
𝑓 (𝑥, 𝑦, 𝑧) = 0 (this defines a set of points). We graph a sphere using the classical formula:
sage: x, y, z = var('x, y, z')
sage: implicit_plot3d(x^2 + y^2 + z^2 - 4, (x,-2, 2), (y,-2, 2), (z,-2, 2))
Graphics3d Object
Here are some more examples:
Yellow Whitney’s umbrella:
sage: u, v = var('u,v')
sage: fx = u*v
sage: fy = u
sage: fz = v^2
sage: parametric_plot3d([fx, fy, fz], (u, -1, 1), (v, -1, 1),
....:
frame=False, color="yellow")
Graphics3d Object
Cross cap:
sage: u, v = var('u,v')
sage: fx = (1+cos(v))*cos(u)
sage: fy = (1+cos(v))*sin(u)
sage: fz = -tanh((2/3)*(u-pi))*sin(v)
sage: parametric_plot3d([fx, fy, fz], (u, 0, 2*pi), (v, 0, 2*pi),
....:
frame=False, color="red")
Graphics3d Object
Twisted torus:
sage:
sage:
sage:
sage:
u,
fx
fy
fz
v
=
=
=
= var('u,v')
(3+sin(v)+cos(u))*cos(2*v)
(3+sin(v)+cos(u))*sin(2*v)
sin(u)+2*cos(v)
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sage: parametric_plot3d([fx, fy, fz], (u, 0, 2*pi), (v, 0, 2*pi),
....:
frame=False, color="red")
Graphics3d Object
Lemniscate:
sage: x, y, z = var('x,y,z')
sage: f(x, y, z) = 4*x^2 * (x^2 + y^2 + z^2 + z) + y^2 * (y^2 + z^2 - 1)
sage: implicit_plot3d(f, (x, -0.5, 0.5), (y, -1, 1), (z, -1, 1))
Graphics3d Object
2.6 Some Common Issues with Functions
Some aspects of defining functions (e.g., for differentiation or plotting) can be confusing. In this section we try to
address some of the relevant issues.
Here are several ways to define things which might deserve to be called “functions”:
1. Define a Python function, as described in Functions, Indentation, and Counting. These functions can be plotted,
but not differentiated or integrated.
sage: def f(z): return z^2
sage: type(f)
<... 'function'>
sage: f(3)
9
sage: plot(f, 0, 2)
Graphics object consisting of 1 graphics primitive
In the last line, note the syntax. Using plot(f(z),0,2) instead will give a NameError , because z is a dummy
variable in the definition of f and is not defined outside of that definition. In order to be able to use f(z) in the plot
command, z (or whatever is desired) needs to be defined as a variable. We can use the syntax below, or in the next
item in our list.
sage: var('z')
# define z to be a variable
z
sage: f(z)
z^2
sage: plot(f(z), 0, 2)
Graphics object consisting of 1 graphics primitive
At this point, f(z) is a symbolic expression, the next item in our list.
2. Define a “callable symbolic expression”. These can be plotted, differentiated, and integrated.
sage: g(x) = x^2
sage: g
# g sends x to x^2
x |--> x^2
sage: g(3)
9
sage: Dg = g.derivative(); Dg
x |--> 2*x
sage: Dg(3)
6
sage: type(g)
<type 'sage.symbolic.expression.Expression'>
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sage: plot(g, 0, 2)
Graphics object consisting of 1 graphics primitive
Note that while g is a callable symbolic expression, g(x) is a related, but different sort of object, which can also be
plotted, differentated, etc., albeit with some issues: see item 5 below for an illustration.
sage: g(x)
x^2
sage: type(g(x))
<type 'sage.symbolic.expression.Expression'>
sage: g(x).derivative()
2*x
sage: plot(g(x), 0, 2)
Graphics object consisting of 1 graphics primitive
3. Use a pre-defined Sage ‘calculus function’. These can be plotted, and with a little help, differentiated, and integrated.
sage: type(sin)
<class 'sage.functions.trig.Function_sin'>
sage: plot(sin, 0, 2)
Graphics object consisting of 1 graphics primitive
sage: type(sin(x))
<type 'sage.symbolic.expression.Expression'>
sage: plot(sin(x), 0, 2)
Graphics object consisting of 1 graphics primitive
By itself, sin cannot be differentiated, at least not to produce cos .
sage: f = sin
sage: f.derivative()
Traceback (most recent call last):
...
AttributeError: ...
Using f = sin(x) instead of sin works, but it is probably even better to use f(x) = sin(x) to define a
callable symbolic expression.
sage: S(x) = sin(x)
sage: S.derivative()
x |--> cos(x)
Here are some common problems, with explanations:
4. Accidental evaluation.
sage: def h(x):
....:
if x<2:
....:
return 0
....:
else:
....:
return x-2
The issue: plot(h(x),0,4) plots the line 𝑦 = 𝑥 − 2, not the multi-line function defined by h . The reason? In
the command plot(h(x),0,4) , first h(x) is evaluated: this means plugging the symbolic variable x into the
function h . So, the inequality x < 2 evaluates to False first, and hence h(x) evaluates to x -2 . This can be
seen with
sage: bool(x < 2)
False
2.6. Some Common Issues with Functions
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sage: h(x)
x - 2
Note that here there are two different x : the Python variable used to define the function h (which is local to its
definition) and the symbolic variable x which is available on startup in Sage.
The solution: don’t use plot(h(x),0,4) ; instead, use
sage: plot(h, 0, 4)
Graphics object consisting of 1 graphics primitive
5. Accidentally producing a constant instead of a function.
sage: f = x
sage: g = f.derivative()
sage: g
1
The problem: g(3) , for example, returns an error, saying “ValueError: the number of arguments must be less than or
equal to 0.”
sage:
<type
sage:
<type
type(f)
'sage.symbolic.expression.Expression'>
type(g)
'sage.symbolic.expression.Expression'>
g is not a function, it’s a constant, so it has no variables associated to it, and you can’t plug anything into it.
The solution: there are several options.
• Define f initially to be a symbolic expression.
sage: f(x) = x
# instead of 'f = x'
sage: g = f.derivative()
sage: g
x |--> 1
sage: g(3)
1
sage: type(g)
<type 'sage.symbolic.expression.Expression'>
• Or with f as defined originally, define g to be a symbolic expression.
sage: f = x
sage: g(x) = f.derivative() # instead of 'g = f.derivative()'
sage: g
x |--> 1
sage: g(3)
1
sage: type(g)
<type 'sage.symbolic.expression.Expression'>
• Or with f and g as defined originally, specify the variable for which you are substituting.
sage: f = x
sage: g = f.derivative()
sage: g
1
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sage: g(x=3)
1
# instead of 'g(3)'
Finally, here’s one more way to tell the difference between the derivatives of f = x and f(x) = x
sage:
sage:
sage:
()
sage:
(x,)
sage:
sage:
sage:
()
sage:
()
f(x) = x
g = f.derivative()
g.variables() # the variables present in g
g.arguments()
# the arguments which can be plugged into g
f = x
h = f.derivative()
h.variables()
h.arguments()
As this example has been trying to illustrate, h accepts no arguments, and this is why h(3) returns an error.
2.7 Basic Rings
When defining matrices, vectors, or polynomials, it is sometimes useful and sometimes necessary to specify the “ring”
over which it is defined. A ring is a mathematical construction in which there are well-behaved notions of addition
and multiplication; if you’ve never heard of them before, you probably just need to know about these four commonly
used rings:
• the integers {..., −1, 0, 1, 2, ...}, called ZZ in Sage.
• the rational numbers – i.e., fractions, or ratios, of integers – called QQ in Sage.
• the real numbers, called RR in Sage.
• the complex numbers, called CC in Sage.
You may need to know about these distinctions because the same polynomial, for example, can be treated
differently
√
depending on the ring over which it is defined. For instance, the polynomial 𝑥2 − 2 has two roots, ± 2. Those roots
are not rational, so if you are working with polynomials with rational coefficients, the polynomial won’t factor. With
real coefficients, it will. Therefore you may want to specify the ring to insure that you are getting the information you
expect. The following two commands defines the sets of polynomials with rational coefficents and real coefficients,
respectively. The sets are named “ratpoly” and “realpoly”, but these aren’t important here; however, note that the
strings ”.<t>” and ”.<z>” name the variables used in the two cases.
sage: ratpoly.<t> = PolynomialRing(QQ)
sage: realpoly.<z> = PolynomialRing(RR)
Now we illustrate the point about factoring 𝑥2 − 2:
sage: factor(t^2-2)
t^2 - 2
sage: factor(z^2-2)
(z - 1.41421356237310) * (z + 1.41421356237310)
Similar comments apply to matrices: the row-reduced form of a matrix can depend on the ring over which it is defined,
as can its eigenvalues and eigenvectors. For more about constructing polynomials, see Polynomials, and for more about
matrices, see Linear Algebra.
2.7. Basic Rings
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The symbol I represents the square root of −1; i is a synonym for I . Of course, this is not a rational number:
sage: i # square root of -1
I
sage: i in QQ
False
Note: The above code may not work as expected if the variable i has been assigned a different value, for example, if
it was used as a loop variable. If this is the case, type
sage: reset('i')
to get the original complex value of i .
There is one subtlety in defining complex numbers: as mentioned above, the symbol i represents a square root of
−1, but it is a formal or symbolic square root of −1. Calling CC(i) or CC.0 returns the complex square root of
−1. Arithmetic involving different kinds of numbers is possible by so-called coercion, see Parents, Conversion and
Coercion.
sage: i = CC(i)
# floating point complex number
sage: i == CC.0
True
sage: a, b = 4/3, 2/3
sage: z = a + b*i
sage: z
1.33333333333333 + 0.666666666666667*I
sage: z.imag()
# imaginary part
0.666666666666667
sage: z.real() == a
# automatic coercion before comparison
True
sage: a + b
2
sage: 2*b == a
True
sage: parent(2/3)
Rational Field
sage: parent(4/2)
Rational Field
sage: 2/3 + 0.1
# automatic coercion before addition
0.766666666666667
sage: 0.1 + 2/3
# coercion rules are symmetric in SAGE
0.766666666666667
Here are more examples of basic rings in Sage. As noted above, the ring of rational numbers may be referred to using
QQ , or also RationalField() (a field is a ring in which the multiplication is commutative and in which every
nonzero element has a reciprocal in that ring, so the rationals form a field, but the integers don’t):
sage: RationalField()
Rational Field
sage: QQ
Rational Field
sage: 1/2 in QQ
True
The decimal number 1.2 is considered to be in QQ : decimal numbers which happen to also√be rational can be
“coerced” into the rational numbers (see Parents, Conversion and Coercion). The numbers 𝜋 and 2 are not rational,
though:
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sage:
True
sage:
False
sage:
True
sage:
False
sage:
True
1.2 in QQ
pi in QQ
pi in RR
sqrt(2) in QQ
sqrt(2) in CC
For use in higher mathematics, Sage also knows about other rings, such as finite fields, 𝑝-adic integers, the ring of
algebraic numbers, polynomial rings, and matrix rings. Here are constructions of some of these:
sage: GF(3)
Finite Field of size 3
sage: GF(27, 'a') # need to name the generator if not a prime field
Finite Field in a of size 3^3
sage: Zp(5)
5-adic Ring with capped relative precision 20
sage: sqrt(3) in QQbar # algebraic closure of QQ
True
2.8 Linear Algebra
Sage provides standard constructions from linear algebra, e.g., the characteristic polynomial, echelon form, trace,
decomposition, etc., of a matrix.
Creation of matrices and matrix multiplication is easy and natural:
sage: A = Matrix([[1,2,3],[3,2,1],[1,1,1]])
sage: w = vector([1,1,-4])
sage: w*A
(0, 0, 0)
sage: A*w
(-9, 1, -2)
sage: kernel(A)
Free module of degree 3 and rank 1 over Integer Ring
Echelon basis matrix:
[ 1 1 -4]
Note that in Sage, the kernel of a matrix 𝐴 is the “left kernel”, i.e. the space of vectors 𝑤 such that 𝑤𝐴 = 0.
Solving matrix equations is easy, using the method solve_right . Evaluating A.solve_right(Y) returns a
matrix (or vector) 𝑋 so that 𝐴𝑋 = 𝑌 :
sage: Y
sage: X
sage: X
(-2, 1,
sage: A
(0, -4,
= vector([0, -4, -1])
= A.solve_right(Y)
0)
* X
-1)
# checking our answer...
A backslash \ can be used in the place of solve_right ; use A \ Y instead of A.solve_right(Y) .
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sage: A \ Y
(-2, 1, 0)
If there is no solution, Sage returns an error:
sage: A.solve_right(w)
Traceback (most recent call last):
...
ValueError: matrix equation has no solutions
Similarly, use A.solve_left(Y) to solve for 𝑋 in 𝑋𝐴 = 𝑌 .
Sage can also compute eigenvalues and eigenvectors:
sage: A = matrix([[0, 4], [-1, 0]])
sage: A.eigenvalues ()
[-2*I, 2*I]
sage: B = matrix([[1, 3], [3, 1]])
sage: B.eigenvectors_left()
[(4, [
(1, 1)
], 1), (-2, [
(1, -1)
], 1)]
(The syntax for the output of eigenvectors_left is a list of triples: (eigenvalue, eigenvector, multiplicity).)
Eigenvalues and eigenvectors over QQ or RR can also be computed using Maxima (see Maxima below).
As noted in Basic Rings, the ring over which a matrix is defined affects some of its properties. In the following, the
first argument to the matrix command tells Sage to view the matrix as a matrix of integers (the ZZ case), a matrix
of rational numbers (QQ ), or a matrix of reals (RR ):
sage: AZ = matrix(ZZ, [[2,0], [0,1]])
sage: AQ = matrix(QQ, [[2,0], [0,1]])
sage: AR = matrix(RR, [[2,0], [0,1]])
sage: AZ.echelon_form()
[2 0]
[0 1]
sage: AQ.echelon_form()
[1 0]
[0 1]
sage: AR.echelon_form()
[ 1.00000000000000 0.000000000000000]
[0.000000000000000 1.00000000000000]
For computing eigenvalues and eigenvectors of matrices over floating point real or complex numbers, the matrix
should be defined over RDF (Real Double Field) or CDF (Complex Double Field), respectively. If no ring is specified
and floating point real or complex numbers are used then by default the matrix is defined over the RR or CC fields,
respectively, which do not support these computations for all the cases:
sage: ARDF = matrix(RDF, [[1.2, 2], [2, 3]])
sage: ARDF.eigenvalues() # rel tol 8e-16
[-0.09317121994613098, 4.293171219946131]
sage: ACDF = matrix(CDF, [[1.2, I], [2, 3]])
sage: ACDF.eigenvectors_right() # rel tol 3e-15
[(0.8818456983293743 - 0.8209140653434135*I, [(0.7505608183809549, -0.616145932704589
˓→+ 0.2387941530333261*I)], 1),
(3.3181543016706256 + 0.8209140653434133*I, [(0.14559469829270957 + 0.
˓→3756690858502104*I, 0.9152458258662108)], 1)]
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2.8.1 Matrix spaces
We create the space Mat3×3 (Q) of 3 × 3 matrices with rational entries:
sage: M = MatrixSpace(QQ,3)
sage: M
Full MatrixSpace of 3 by 3 dense matrices over Rational Field
(To specify the space of 3 by 4 matrices, you would use MatrixSpace(QQ,3,4) . If the number of columns is
omitted, it defaults to the number of rows, so MatrixSpace(QQ,3) is a synonym for MatrixSpace(QQ,3,3)
.) The space of matrices has a basis which Sage stores as a list:
sage: B = M.basis()
sage: len(B)
9
sage: B[1]
[0 1 0]
[0 0 0]
[0 0 0]
We create a matrix as an element of M .
sage: A = M(range(9)); A
[0 1 2]
[3 4 5]
[6 7 8]
Next we compute its reduced row echelon form and kernel.
sage: A.echelon_form()
[ 1 0 -1]
[ 0 1 2]
[ 0 0 0]
sage: A.kernel()
Vector space of degree 3 and dimension 1 over Rational Field
Basis matrix:
[ 1 -2 1]
Next we illustrate computation of matrices defined over finite fields:
sage: M = MatrixSpace(GF(2),4,8)
sage: A = M([1,1,0,0, 1,1,1,1, 0,1,0,0, 1,0,1,1,
....:
0,0,1,0, 1,1,0,1, 0,0,1,1, 1,1,1,0])
sage: A
[1 1 0 0 1 1 1 1]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 1 1 1 1 1 0]
sage: rows = A.rows()
sage: A.columns()
[(1, 0, 0, 0), (1, 1, 0, 0), (0, 0, 1, 1), (0, 0, 0, 1),
(1, 1, 1, 1), (1, 0, 1, 1), (1, 1, 0, 1), (1, 1, 1, 0)]
sage: rows
[(1, 1, 0, 0, 1, 1, 1, 1), (0, 1, 0, 0, 1, 0, 1, 1),
(0, 0, 1, 0, 1, 1, 0, 1), (0, 0, 1, 1, 1, 1, 1, 0)]
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We make the subspace over F2 spanned by the above rows.
sage: V = VectorSpace(GF(2),8)
sage: S = V.subspace(rows)
sage: S
Vector space of degree 8 and dimension 4 over Finite Field of size 2
Basis matrix:
[1 0 0 0 0 1 0 0]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 0 1 0 0 1 1]
sage: A.echelon_form()
[1 0 0 0 0 1 0 0]
[0 1 0 0 1 0 1 1]
[0 0 1 0 1 1 0 1]
[0 0 0 1 0 0 1 1]
The basis of 𝑆 used by Sage is obtained from the non-zero rows of the reduced row echelon form of the matrix of
generators of 𝑆.
2.8.2 Sparse Linear Algebra
Sage has support for sparse linear algebra over PIDs.
sage: M = MatrixSpace(QQ, 100, sparse=True)
sage: A = M.random_element(density = 0.05)
sage: E = A.echelon_form()
The multi-modular algorithm in Sage is good for square matrices (but not so good for non-square matrices):
sage:
sage:
sage:
sage:
sage:
sage:
M
A
E
M
A
E
=
=
=
=
=
=
MatrixSpace(QQ, 50, 100, sparse=True)
M.random_element(density = 0.05)
A.echelon_form()
MatrixSpace(GF(2), 20, 40, sparse=True)
M.random_element()
A.echelon_form()
Note that Python is case sensitive:
sage: M = MatrixSpace(QQ, 10,10, Sparse=True)
Traceback (most recent call last):
...
TypeError: __classcall__() got an unexpected keyword argument 'Sparse'
2.9 Polynomials
In this section we illustrate how to create and use polynomials in Sage.
2.9.1 Univariate Polynomials
There are three ways to create polynomial rings.
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sage: R = PolynomialRing(QQ, 't')
sage: R
Univariate Polynomial Ring in t over Rational Field
This creates a polynomial ring and tells Sage to use (the string) ‘t’ as the indeterminate when printing to the screen.
However, this does not define the symbol t for use in Sage, so you cannot use it to enter a polynomial (such as 𝑡2 + 1)
belonging to R .
An alternate way is
sage: S = QQ['t']
sage: S == R
True
This has the same issue regarding t .
A third very convenient way is
sage: R.<t> = PolynomialRing(QQ)
or
sage: R.<t> = QQ['t']
or even
sage: R.<t> = QQ[]
This has the additional side effect that it defines the variable t to be the indeterminate of the polynomial ring, so you
can easily construct elements of R , as follows. (Note that the third way is very similar to the constructor notation in
Magma, and just as in Magma it can be used for a wide range of objects.)
sage: poly = (t+1) * (t+2); poly
t^2 + 3*t + 2
sage: poly in R
True
Whatever method you use to define a polynomial ring, you can recover the indeterminate as the 0𝑡ℎ generator:
sage: R = PolynomialRing(QQ, 't')
sage: t = R.0
sage: t in R
True
Note that a similar construction works with the complex numbers: the complex numbers can be viewed as being
generated over the real numbers by the symbol i ; thus we have the following:
sage: CC
Complex Field with 53 bits of precision
sage: CC.0 # 0th generator of CC
1.00000000000000*I
For polynomial rings, you can obtain both the ring and its generator, or just the generator, during ring creation as
follows:
sage: R, t = QQ['t'].objgen()
sage: t
= QQ['t'].gen()
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sage: R, t = objgen(QQ['t'])
sage: t
= gen(QQ['t'])
Finally we do some arithmetic in Q[𝑡].
sage: R, t = QQ['t'].objgen()
sage: f = 2*t^7 + 3*t^2 - 15/19
sage: f^2
4*t^14 + 12*t^9 - 60/19*t^7 + 9*t^4 - 90/19*t^2 + 225/361
sage: cyclo = R.cyclotomic_polynomial(7); cyclo
t^6 + t^5 + t^4 + t^3 + t^2 + t + 1
sage: g = 7 * cyclo * t^5 * (t^5 + 10*t + 2)
sage: g
7*t^16 + 7*t^15 + 7*t^14 + 7*t^13 + 77*t^12 + 91*t^11 + 91*t^10 + 84*t^9
+ 84*t^8 + 84*t^7 + 84*t^6 + 14*t^5
sage: F = factor(g); F
(7) * t^5 * (t^5 + 10*t + 2) * (t^6 + t^5 + t^4 + t^3 + t^2 + t + 1)
sage: F.unit()
7
sage: list(F)
[(t, 5), (t^5 + 10*t + 2, 1), (t^6 + t^5 + t^4 + t^3 + t^2 + t + 1, 1)]
Notice that the factorization correctly takes into account and records the unit part.
If you were to use, e.g., the R.cyclotomic_polynomial function a lot for some research project, in addition
to citing Sage you should make an attempt to find out what component of Sage is being used to actually compute
the cyclotomic polynomial and cite that as well. In this case, if you type R.cyclotomic_polynomial?? to
see the source code, you’ll quickly see a line f = pari.polcyclo(n) which means that PARI is being used for
computation of the cyclotomic polynomial. Cite PARI in your work as well.
Dividing two polynomials constructs an element of the fraction field (which Sage creates automatically).
sage: x = QQ['x'].0
sage: f = x^3 + 1; g = x^2 - 17
sage: h = f/g; h
(x^3 + 1)/(x^2 - 17)
sage: h.parent()
Fraction Field of Univariate Polynomial Ring in x over Rational Field
Using Laurent series, one can compute series expansions in the fraction field of QQ[x] :
sage: R.<x> = LaurentSeriesRing(QQ); R
Laurent Series Ring in x over Rational Field
sage: 1/(1-x) + O(x^10)
1 + x + x^2 + x^3 + x^4 + x^5 + x^6 + x^7 + x^8 + x^9 + O(x^10)
If we name the variable differently, we obtain a different univariate polynomial ring.
sage:
sage:
sage:
False
sage:
False
sage:
x
sage:
x^2 -
32
R.<x> = PolynomialRing(QQ)
S.<y> = PolynomialRing(QQ)
x == y
R == S
R(y)
R(y^2 - 17)
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The ring is determined by the variable. Note that making another ring with variable called x does not return a different
ring.
sage:
sage:
sage:
True
sage:
True
sage:
True
R = PolynomialRing(QQ, "x")
T = PolynomialRing(QQ, "x")
R == T
R is T
R.0 == T.0
Sage also has support for power series and Laurent series rings over any base ring. In the following example, we create
an element of F7 [[𝑇 ]] and divide to create an element of F7 ((𝑇 )).
sage: R.<T> = PowerSeriesRing(GF(7)); R
Power Series Ring in T over Finite Field of size 7
sage: f = T + 3*T^2 + T^3 + O(T^4)
sage: f^3
T^3 + 2*T^4 + 2*T^5 + O(T^6)
sage: 1/f
T^-1 + 4 + T + O(T^2)
sage: parent(1/f)
Laurent Series Ring in T over Finite Field of size 7
You can also create power series rings using a double-brackets shorthand:
sage: GF(7)[['T']]
Power Series Ring in T over Finite Field of size 7
2.9.2 Multivariate Polynomials
To work with polynomials of several variables, we declare the polynomial ring and variables first.
sage: R = PolynomialRing(GF(5),3,"z") # here, 3 = number of variables
sage: R
Multivariate Polynomial Ring in z0, z1, z2 over Finite Field of size 5
Just as for defining univariate polynomial rings, there are alternative ways:
sage: GF(5)['z0, z1, z2']
Multivariate Polynomial Ring in z0, z1, z2 over Finite Field of size 5
sage: R.<z0,z1,z2> = GF(5)[]; R
Multivariate Polynomial Ring in z0, z1, z2 over Finite Field of size 5
Also, if you want the variable names to be single letters then you can use the following shorthand:
sage: PolynomialRing(GF(5), 3, 'xyz')
Multivariate Polynomial Ring in x, y, z over Finite Field of size 5
Next let’s do some arithmetic.
sage: z = GF(5)['z0, z1, z2'].gens()
sage: z
(z0, z1, z2)
sage: (z[0]+z[1]+z[2])^2
z0^2 + 2*z0*z1 + z1^2 + 2*z0*z2 + 2*z1*z2 + z2^2
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You can also use more mathematical notation to construct a polynomial ring.
sage: R = GF(5)['x,y,z']
sage: x,y,z = R.gens()
sage: QQ['x']
Univariate Polynomial Ring in x over Rational Field
sage: QQ['x,y'].gens()
(x, y)
sage: QQ['x'].objgens()
(Univariate Polynomial Ring in x over Rational Field, (x,))
Multivariate polynomials are implemented in Sage using Python dictionaries and the “distributive representation” of a
polynomial. Sage makes some use of Singular [Si], e.g., for computation of gcd’s and Gröbner basis of ideals.
sage:
sage:
sage:
sage:
x^2
R, (x, y) = PolynomialRing(RationalField(), 2, 'xy').objgens()
f = (x^3 + 2*y^2*x)^2
g = x^2*y^2
f.gcd(g)
Next we create the ideal (𝑓, 𝑔) generated by 𝑓 and 𝑔, by simply multiplying (f,g) by R (we could also write
ideal([f,g]) or ideal(f,g) ).
sage: I = (f, g)*R; I
Ideal (x^6 + 4*x^4*y^2 + 4*x^2*y^4, x^2*y^2) of Multivariate Polynomial
Ring in x, y over Rational Field
sage: B = I.groebner_basis(); B
[x^6, x^2*y^2]
sage: x^2 in I
False
Incidentally, the Gröbner basis above is not a list but an immutable sequence. This means that it has a universe, parent,
and cannot be changed (which is good because changing the basis would break other routines that use the Gröbner
basis).
sage: B.universe()
Multivariate Polynomial Ring in x, y over Rational Field
sage: B[1] = x
Traceback (most recent call last):
...
ValueError: object is immutable; please change a copy instead.
Some (read: not as much as we would like) commutative algebra is available in Sage, implemented via Singular. For
example, we can compute the primary decomposition and associated primes of 𝐼:
sage: I.primary_decomposition()
[Ideal (x^2) of Multivariate Polynomial Ring in x, y over Rational Field,
Ideal (y^2, x^6) of Multivariate Polynomial Ring in x, y over Rational Field]
sage: I.associated_primes()
[Ideal (x) of Multivariate Polynomial Ring in x, y over Rational Field,
Ideal (y, x) of Multivariate Polynomial Ring in x, y over Rational Field]
2.10 Parents, Conversion and Coercion
This section may seem more technical than the previous, but we believe that it is important to understand the meaning
of parents and coercion in order to use rings and other algebraic structures in Sage effectively and efficiently.
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Note that we try to explain notions, but we do not show here how to implement them. An implementation-oriented
tutorial is available as a Sage thematic tutorial.
2.10.1 Elements
If one wants to implement a ring in Python, a first approximation is to create a class for the elements X of that ring
and provide it with the required double underscore methods such as __add__ , __sub__ , __mul__ , of course
making sure that the ring axioms hold.
As Python is a strongly typed (yet dynamically typed) language, one might, at least at first, expect that one implements
one Python class for each ring. After all, Python contains one type <int> for the integers, one type <float> for
the reals, and so on. But that approach must soon fail: There are infinitely many rings, and one can not implement
infinitely many classes.
Instead, one may create a hierarchy of classes designed to implement elements of ubiquitous algebraic structures, such
as groups, rings, skew fields, commutative rings, fields, algebras, and so on.
But that means that elements of fairly different rings can have the same type.
sage: P.<x,y> = GF(3)[]
sage: Q.<a,b> = GF(4,'z')[]
sage: type(x)==type(a)
True
On the other hand, one could also have different Python classes providing different implementations of the same
mathematical structure (e.g., dense matrices versus sparse matrices)
sage: P.<a> = PolynomialRing(ZZ)
sage: Q.<b> = PolynomialRing(ZZ, sparse=True)
sage: R.<c> = PolynomialRing(ZZ, implementation='NTL')
sage: type(a); type(b); type(c)
<type 'sage.rings.polynomial.polynomial_integer_dense_flint.Polynomial_integer_dense_
˓→flint'>
<class 'sage.rings.polynomial.polynomial_element_generic.PolynomialRing_integral_
˓→domain_with_category.element_class'>
<type 'sage.rings.polynomial.polynomial_integer_dense_ntl.Polynomial_integer_dense_ntl
˓→'>
That poses two problems: On the one hand, if one has elements that are two instances of the same class, then one may
expect that their __add__ method will allow to add them; but one does not want that, if the elements belong to very
different rings. On the other hand, if one has elements belonging to different implementations of the same ring, then
one wants to add them, but that is not straight forward if they belong to different Python classes.
The solution to these problems is called “coercion” and will be explained below.
However, it is essential that each element knows what it is element of. That is available by the method parent() :
sage: a.parent(); b.parent(); c.parent()
Univariate Polynomial Ring in a over Integer Ring
Sparse Univariate Polynomial Ring in b over Integer Ring
Univariate Polynomial Ring in c over Integer Ring (using NTL)
2.10.2 Parents and categories
Similar to the hierarchy of Python classes addressed to elements of algebraic structures, Sage also provides classes for
the algebraic structures that contain these elements. Structures containing elements are called “parent structures” in
2.10. Parents, Conversion and Coercion
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Sage, and there is a base class for them. Roughly parallel to the hierarchy of mathematical notions, one has a hierarchy
of classes, namely for sets, rings, fields, and so on:
sage:
True
sage:
True
sage:
False
sage:
True
isinstance(QQ,Field)
isinstance(QQ, Ring)
isinstance(ZZ,Field)
isinstance(ZZ, Ring)
In algebra, objects sharing the same kind of algebraic structures are collected in so-called “categories”. So, there is a
rough analogy between the class hierarchy in Sage and the hierarchy of categories. However, this analogy of Python
classes and categories shouldn’t be stressed too much. After all, mathematical categories are implemented in Sage as
well:
sage: Rings()
Category of rings
sage: ZZ.category()
Join of Category of euclidean domains
and Category of infinite enumerated sets
and Category of metric spaces
sage: ZZ.category().is_subcategory(Rings())
True
sage: ZZ in Rings()
True
sage: ZZ in Fields()
False
sage: QQ in Fields()
True
While Sage’s class hierarchy is centered at implementation details, Sage’s category framework is more centered on
mathematical structure. It is possible to implement generic methods and tests independent of a specific implementation
in the categories.
Parent structures in Sage are supposed to be unique Python objects. For example, once a polynomial ring over a certain
base ring and with a certain list of generators is created, the result is cached:
sage: RR['x','y'] is RR['x','y']
True
2.10.3 Types versus parents
The type RingElement does not correspond perfectly to the mathematical notion of a ring element. For example,
although square matrices belong to a ring, they are not instances of RingElement :
sage: M = Matrix(ZZ,2,2); M
[0 0]
[0 0]
sage: isinstance(M, RingElement)
False
While parents are unique, equal elements of a parent in Sage are not necessarily identical. This is in contrast to the
behaviour of Python for some (albeit not all) integers:
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sage: int(1) is int(1) # Python int
True
sage: int(-15) is int(-15)
False
sage: 1 is 1
# Sage Integer
False
It is important to observe that elements of different rings are in general not distinguished by their type, but by their
parent:
sage: a = GF(2)(1)
sage: b = GF(5)(1)
sage: type(a) is type(b)
True
sage: parent(a)
Finite Field of size 2
sage: parent(b)
Finite Field of size 5
Hence, from an algebraic point of view, the parent of an element is more important than its type.
2.10.4 Conversion versus Coercion
In some cases it is possible to convert an element of one parent structure into an element of a different parent structure.
Such conversion can either be explicit or implicit (this is called coercion).
The reader may know the notions type conversion and type coercion from, e.g., the C programming language. There
are notions of conversion and coercion in Sage as well. But the notions in Sage are centered on parents, not on types.
So, please don’t confuse type conversion in C with conversion in Sage!
We give here a rather brief account. For a detailed description and for information on the implementation, we refer to
the section on coercion in the reference manual and to the thematic tutorial.
There are two extremal positions concerning the possibility of doing arithmetic with elements of different rings:
• Different rings are different worlds, and it makes no sense whatsoever to add or multiply elements of different
rings; even 1 + 1/2 makes no sense, since the first summand is an integer and the second a rational.
Or
• If an element r1 of one ring R1 can somehow be interpreted in another ring R2 , then all arithmetic operations
involving r1 and any element of R2 are allowed. The multiplicative unit exists in all fields and many rings,
and they should all be equal.
Sage favours a compromise. If P1 and P2 are parent structures and p1 is an element of P1 , then the user may
explicitly ask for an interpretation of p1 in P2 . This may not be meaningful in all cases or not be defined for all
elements of P1 , and it is up to the user to ensure that it makes sense. We refer to this as conversion:
sage:
sage:
sage:
True
sage:
True
a = GF(2)(1)
b = GF(5)(1)
GF(5)(a) == b
GF(2)(b) == a
However, an implicit (or automatic) conversion will only happen if this can be done thoroughly and consistently.
Mathematical rigour is essential at that point.
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Such an implicit conversion is called coercion. If coercion is defined, then it must coincide with conversion. Two
conditions must be satisfied for a coercion to be defined:
1. A coercion from P1 to P2 must be given by a structure preserving map (e.g., a ring homomorphism). It does
not suffice that some elements of P1 can be mapped to P2 , and the map must respect the algebraic structure of
P1 .
2. The choice of these coercion maps must be consistent: If P3 is a third parent structure, then the composition of
the chosen coercion from P1 to P2 with the coercion from P2 to P3 must coincide with the chosen coercion
from P1 to P3 . In particular, if there is a coercion from P1 to P2 and P2 to P1 , the composition must be
the identity map of P1 .
So, although it is possible to convert each element of GF(2) into GF(5) , there is no coercion, since there is no ring
homomorphism between GF(2) and GF(5) .
The second aspect - consistency - is a bit more difficult to explain. We illustrate it with multivariate polynomial rings.
In applications, it certainly makes most sense to have name preserving coercions. So, we have:
sage:
sage:
sage:
True
sage:
x
sage:
y
R1.<x,y> = ZZ[]
R2 = ZZ['y','x']
R2.has_coerce_map_from(R1)
R2(x)
R2(y)
If there is no name preserving ring homomorphism, coercion is not defined. However, conversion may still be possible,
namely by mapping ring generators according to their position in the list of generators:
sage:
sage:
False
sage:
z
sage:
x
R3 = ZZ['z','x']
R3.has_coerce_map_from(R1)
R3(x)
R3(y)
But such position preserving conversions do not qualify as coercion: By composing a name preserving map from
ZZ['x','y'] to ZZ['y','x'] with a position preserving map from ZZ['y','x'] to ZZ['a','b'] , a
map would result that is neither name preserving nor position preserving, in violation to consistency.
If there is a coercion, it will be used to compare elements of different rings or to do arithmetic. This is often convenient,
but the user should be aware that extending the == -relation across the borders of different parents may easily result
in overdoing it. For example, while == is supposed to be an equivalence relation on the elements of one ring, this
is not necessarily the case if different rings are involved. For example, 1 in ZZ and in a finite field are considered
equal, since there is a canonical coercion from the integers to any finite field. However, in general there is no coercion
between two different finite fields. Therefore we have
sage:
True
sage:
True
sage:
False
sage:
True
GF(5)(1) == 1
1 == GF(2)(1)
GF(5)(1) == GF(2)(1)
GF(5)(1) != GF(2)(1)
Similarly, we have
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sage: R3(R1.1) == R3.1
True
sage: R1.1 == R3.1
False
sage: R1.1 != R3.1
True
Another consequence of the consistency condition is that coercions can only go from exact rings (e.g., the rationals QQ
) to inexact rings (e.g., real numbers with a fixed precision RR ), but not the other way around. The reason is that the
composition of the coercion from QQ to RR with a conversion from RR to QQ is supposed to be the identity on QQ .
But this is impossible, since some distinct rational numbers may very well be treated equal in RR , as in the following
example:
sage: RR(1/10^200+1/10^100) == RR(1/10^100)
True
sage: 1/10^200+1/10^100 == 1/10^100
False
When comparing elements of two parents P1 and P2 , it is possible that there is no coercion between the two rings,
but there is a canonical choice of a parent P3 so that both P1 and P2 coerce into P3 . In this case, coercion will take
place as well. A typical use case is the sum of a rational number and a polynomial with integer coefficients, yielding
a polynomial with rational coefficients:
sage: P1.<x> = ZZ[]
sage: p = 2*x+3
sage: q = 1/2
sage: parent(p)
Univariate Polynomial Ring in x over Integer Ring
sage: parent(p+q)
Univariate Polynomial Ring in x over Rational Field
Note that in principle the result would also make sense in the fraction field of ZZ['x'] . However, Sage tries to
choose a canonical common parent that seems to be most natural (QQ['x'] in our example). If several potential
common parents seem equally natural, Sage will not pick one of them at random, in order to have a reliable result.
The mechanisms which that choice is based upon is explained in the thematic tutorial.
No coercion into a common parent will take place in the following example:
sage: R.<x> = QQ[]
sage: S.<y> = QQ[]
sage: x+y
Traceback (most recent call last):
...
TypeError: unsupported operand parent(s) for +: 'Univariate Polynomial Ring in x over
˓→Rational Field' and 'Univariate Polynomial Ring in y over Rational Field'
The reason is that Sage would not choose one of the potential candidates QQ['x']['y'] , QQ['y']['x'] ,
QQ['x','y'] or QQ['y','x'] , because all of these four pairwise different structures seem natural common
parents, and there is no apparent canonical choice.
2.11 Finite Groups, Abelian Groups
Sage has some support for computing with permutation groups, finite classical groups (such as 𝑆𝑈 (𝑛, 𝑞)), finite matrix
groups (with your own generators), and abelian groups (even infinite ones). Much of this is implemented using the
interface to GAP.
2.11. Finite Groups, Abelian Groups
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For example, to create a permutation group, give a list of generators, as in the following example.
sage: G = PermutationGroup(['(1,2,3)(4,5)', '(3,4)'])
sage: G
Permutation Group with generators [(3,4), (1,2,3)(4,5)]
sage: G.order()
120
sage: G.is_abelian()
False
sage: G.derived_series()
# random-ish output
[Permutation Group with generators [(1,2,3)(4,5), (3,4)],
Permutation Group with generators [(1,5)(3,4), (1,5)(2,4), (1,3,5)]]
sage: G.center()
Subgroup of (Permutation Group with generators [(3,4), (1,2,3)(4,5)]) generated by
˓→[()]
sage: G.random_element()
# random output
(1,5,3)(2,4)
sage: print(latex(G))
\langle (3,4), (1,2,3)(4,5) \rangle
You can also obtain the character table (in LaTeX format) in Sage:
sage: G = PermutationGroup([[(1,2),(3,4)], [(1,2,3)]])
sage: latex(G.character_table())
\left(\begin{array}{rrrr}
1 & 1 & 1 & 1 \\
1 & -\zeta_{3} - 1 & \zeta_{3} & 1 \\
1 & \zeta_{3} & -\zeta_{3} - 1 & 1 \\
3 & 0 & 0 & -1
\end{array}\right)
Sage also includes classical and matrix groups over finite fields:
sage: MS = MatrixSpace(GF(7), 2)
sage: gens = [MS([[1,0],[-1,1]]),MS([[1,1],[0,1]])]
sage: G = MatrixGroup(gens)
sage: G.conjugacy_class_representatives()
(
[1 0] [0 6] [0 4] [6 0] [0 6] [0 4] [0 6] [0 6] [0 6] [4 0]
[0 1], [1 5], [5 5], [0 6], [1 2], [5 2], [1 0], [1 4], [1 3], [0 2],
[5 0]
[0 3]
)
sage: G = Sp(4,GF(7))
sage: G
Symplectic Group of degree 4 over Finite Field of size 7
sage: G.random_element()
# random output
[5 5 5 1]
[0 2 6 3]
[5 0 1 0]
[4 6 3 4]
sage: G.order()
276595200
You can also compute using abelian groups (infinite and finite):
sage: F = AbelianGroup(5, [5,5,7,8,9], names='abcde')
sage: (a, b, c, d, e) = F.gens()
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sage: d * b**2 * c**3
b^2*c^3*d
sage: F = AbelianGroup(3,[2]*3); F
Multiplicative Abelian group isomorphic to
sage: H = AbelianGroup([2,3], names="xy");
Multiplicative Abelian group isomorphic to
sage: AbelianGroup(5)
Multiplicative Abelian group isomorphic to
sage: AbelianGroup(5).order()
+Infinity
C2 x C2 x C2
H
C2 x C3
Z x Z x Z x Z x Z
2.12 Number Theory
Sage has extensive functionality for number theory. For example, we can do arithmetic in Z/𝑁 Z as follows:
sage:
sage:
sage:
33
sage:
2/3
sage:
sage:
50
sage:
97
sage:
True
R = IntegerModRing(97)
a = R(2) / R(3)
a
a.rational_reconstruction()
b = R(47)
b^20052005
b.modulus()
b.is_square()
Sage contains standard number theoretic functions. For example,
sage: gcd(515,2005)
5
sage: factor(2005)
5 * 401
sage: c = factorial(25); c
15511210043330985984000000
sage: [valuation(c,p) for p in prime_range(2,23)]
[22, 10, 6, 3, 2, 1, 1, 1]
sage: next_prime(2005)
2011
sage: previous_prime(2005)
2003
sage: divisors(28); sum(divisors(28)); 2*28
[1, 2, 4, 7, 14, 28]
56
56
Perfect!
Sage’s sigma(n,k) function adds up the 𝑘 𝑡ℎ powers of the divisors of n :
sage: sigma(28,0); sigma(28,1); sigma(28,2)
6
56
1050
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We next illustrate the extended Euclidean algorithm, Euler’s 𝜑-function, and the Chinese remainder theorem:
sage: d,u,v = xgcd(12,15)
sage: d == u*12 + v*15
True
sage: n = 2005
sage: inverse_mod(3,n)
1337
sage: 3 * 1337
4011
sage: prime_divisors(n)
[5, 401]
sage: phi = n*prod([1 - 1/p for p in prime_divisors(n)]); phi
1600
sage: euler_phi(n)
1600
sage: prime_to_m_part(n, 5)
401
We next verify something about the 3𝑛 + 1 problem.
sage: n =
sage: for
....:
....:
....:
....:
38
2005
i in range(1000):
n = 3*odd_part(n) + 1
if odd_part(n)==1:
print(i)
break
Finally we illustrate the Chinese remainder theorem.
sage: x = crt(2, 1, 3, 5); x
11
sage: x % 3 # x mod 3 = 2
2
sage: x % 5 # x mod 5 = 1
1
sage: [binomial(13,m) for m in range(14)]
[1, 13, 78, 286, 715, 1287, 1716, 1716, 1287, 715, 286, 78, 13, 1]
sage: [binomial(13,m)%2 for m in range(14)]
[1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
sage: [kronecker(m,13) for m in range(1,13)]
[1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, 1]
sage: n = 10000; sum([moebius(m) for m in range(1,n)])
-23
sage: Partitions(4).list()
[[4], [3, 1], [2, 2], [2, 1, 1], [1, 1, 1, 1]]
2.12.1 𝑝-adic Numbers
The field of 𝑝-adic numbers is implemented in Sage. Note that once a 𝑝-adic field is created, you cannot change its
precision.
sage: K = Qp(11); K
11-adic Field with capped relative precision 20
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sage: a = K(211/17); a
4 + 4*11 + 11^2 + 7*11^3 + 9*11^5 + 5*11^6 + 4*11^7 + 8*11^8 + 7*11^9
+ 9*11^10 + 3*11^11 + 10*11^12 + 11^13 + 5*11^14 + 6*11^15 + 2*11^16
+ 3*11^17 + 11^18 + 7*11^19 + O(11^20)
sage: b = K(3211/11^2); b
10*11^-2 + 5*11^-1 + 4 + 2*11 + O(11^18)
Much work has been done implementing rings of integers in 𝑝-adic fields and number fields. The interested reader is
invited to read Introduction to the -adics and ask the experts on the sage-support Google group for further details.
A number of related methods are already implemented in the NumberField class.
sage: R.<x> = PolynomialRing(QQ)
sage: K = NumberField(x^3 + x^2 - 2*x + 8, 'a')
sage: K.integral_basis()
[1, 1/2*a^2 + 1/2*a, a^2]
sage: K.galois_group(type="pari")
Galois group PARI group [6, -1, 2, "S3"] of degree 3 of the Number Field
in a with defining polynomial x^3 + x^2 - 2*x + 8
sage: K.polynomial_quotient_ring()
Univariate Quotient Polynomial Ring in a over Rational Field with modulus
x^3 + x^2 - 2*x + 8
sage: K.units()
(3*a^2 + 13*a + 13,)
sage: K.discriminant()
-503
sage: K.class_group()
Class group of order 1 of Number Field in a with
defining polynomial x^3 + x^2 - 2*x + 8
sage: K.class_number()
1
2.13 Some More Advanced Mathematics
2.13.1 Algebraic Geometry
You can define arbitrary algebraic varieties in Sage, but sometimes nontrivial functionality is limited to rings over Q or
finite fields. For example, we compute the union of two affine plane curves, then recover the curves as the irreducible
components of the union.
sage: x, y = AffineSpace(2, QQ, 'xy').gens()
sage: C2 = Curve(x^2 + y^2 - 1)
sage: C3 = Curve(x^3 + y^3 - 1)
sage: D = C2 + C3
sage: D
Affine Plane Curve over Rational Field defined by
x^5 + x^3*y^2 + x^2*y^3 + y^5 - x^3 - y^3 - x^2 - y^2 + 1
sage: D.irreducible_components()
[
Closed subscheme of Affine Space of dimension 2 over Rational Field defined by:
x^2 + y^2 - 1,
Closed subscheme of Affine Space of dimension 2 over Rational Field defined by:
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x^3 + y^3 - 1
]
We can also find all points of intersection of the two curves by intersecting them and computing the irreducible
components.
sage: V = C2.intersection(C3)
sage: V.irreducible_components()
[
Closed subscheme of Affine Space of dimension 2 over Rational Field defined by:
y - 1,
x,
Closed subscheme of Affine Space of dimension 2 over Rational Field defined by:
y,
x - 1,
Closed subscheme of Affine Space of dimension 2 over Rational Field defined by:
x + y + 2,
2*y^2 + 4*y + 3
]
Thus, e.g., (1, 0) and (0, 1) are on both curves (visibly clear), as are certain (quadratic) points whose 𝑦 coordinates
satisfy 2𝑦 2 + 4𝑦 + 3 = 0.
Sage can compute the toric ideal of the twisted cubic in projective 3 space:
sage: R.<a,b,c,d> = PolynomialRing(QQ, 4)
sage: I = ideal(b^2-a*c, c^2-b*d, a*d-b*c)
sage: F = I.groebner_fan(); F
Groebner fan of the ideal:
Ideal (b^2 - a*c, c^2 - b*d, -b*c + a*d) of Multivariate Polynomial Ring
in a, b, c, d over Rational Field
sage: F.reduced_groebner_bases ()
[[-c^2 + b*d, -b*c + a*d, -b^2 + a*c],
[-c^2 + b*d, b^2 - a*c, -b*c + a*d],
[-c^2 + b*d, b*c - a*d, b^2 - a*c, -c^3 + a*d^2],
[c^3 - a*d^2, -c^2 + b*d, b*c - a*d, b^2 - a*c],
[c^2 - b*d, -b*c + a*d, -b^2 + a*c],
[c^2 - b*d, b*c - a*d, -b^2 + a*c, -b^3 + a^2*d],
[c^2 - b*d, b*c - a*d, b^3 - a^2*d, -b^2 + a*c],
[c^2 - b*d, b*c - a*d, b^2 - a*c]]
sage: F.polyhedralfan()
Polyhedral fan in 4 dimensions of dimension 4
2.13.2 Elliptic Curves
Elliptic curve functionality includes most of the elliptic curve functionality of PARI, access to the data in Cremona’s
online tables (this requires an optional database package), the functionality of mwrank, i.e., 2-descents with computation of the full Mordell-Weil group, the SEA algorithm, computation of all isogenies, much new code for curves over
Q, and some of Denis Simon’s algebraic descent software.
The command EllipticCurve for creating an elliptic curve has many forms:
• EllipticCurve([𝑎1 , 𝑎2 , 𝑎3 , 𝑎4 , 𝑎6 ]): Returns the elliptic curve
𝑦 2 + 𝑎1 𝑥𝑦 + 𝑎3 𝑦 = 𝑥3 + 𝑎2 𝑥2 + 𝑎4 𝑥 + 𝑎6 ,
where the 𝑎𝑖 ‘s are coerced into the parent of 𝑎1 . If all the 𝑎𝑖 have parent Z, they are coerced into Q.
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• EllipticCurve([𝑎4 , 𝑎6 ]): Same as above, but 𝑎1 = 𝑎2 = 𝑎3 = 0.
• EllipticCurve(label): Returns the elliptic curve over from the Cremona database with the given (new!) Cremona
label. The label is a string, such as "11a" or "37b2" . The letter must be lower case (to distinguish it from
the old labeling).
• EllipticCurve(j): Returns an elliptic curve with 𝑗-invariant 𝑗.
• EllipticCurve(R, [𝑎1 , 𝑎2 , 𝑎3 , 𝑎4 , 𝑎6 ]): Create the elliptic curve over a ring 𝑅 with given 𝑎𝑖 ‘s as above.
We illustrate each of these constructors:
sage: EllipticCurve([0,0,1,-1,0])
Elliptic Curve defined by y^2 + y = x^3 - x over Rational Field
sage: EllipticCurve([GF(5)(0),0,1,-1,0])
Elliptic Curve defined by y^2 + y = x^3 + 4*x over Finite Field of size 5
sage: EllipticCurve([1,2])
Elliptic Curve defined by y^2
= x^3 + x + 2 over Rational Field
sage: EllipticCurve('37a')
Elliptic Curve defined by y^2 + y = x^3 - x over Rational Field
sage: EllipticCurve_from_j(1)
Elliptic Curve defined by y^2 + x*y = x^3 + 36*x + 3455 over Rational Field
sage: EllipticCurve(GF(5), [0,0,1,-1,0])
Elliptic Curve defined by y^2 + y = x^3 + 4*x over Finite Field of size 5
The pair (0, 0) is a point on the elliptic curve 𝐸 defined by 𝑦 2 + 𝑦 = 𝑥3 − 𝑥. To create this point in Sage type
E([0,0]) . Sage can add points on such an elliptic curve (recall elliptic curves support an additive group structure
where the point at infinity is the zero element and three co-linear points on the curve add to zero):
sage: E = EllipticCurve([0,0,1,-1,0])
sage: E
Elliptic Curve defined by y^2 + y = x^3 - x over Rational Field
sage: P = E([0,0])
sage: P + P
(1 : 0 : 1)
sage: 10*P
(161/16 : -2065/64 : 1)
sage: 20*P
(683916417/264517696 : -18784454671297/4302115807744 : 1)
sage: E.conductor()
37
The elliptic curves over the complex numbers are parameterized by the 𝑗-invariant. Sage computes 𝑗-invariant as
follows:
sage: E = EllipticCurve([0,0,0,-4,2]); E
Elliptic Curve defined by y^2 = x^3 - 4*x + 2 over Rational Field
sage: E.conductor()
2368
sage: E.j_invariant()
110592/37
If we make a curve with the same 𝑗-invariant as that of 𝐸, it need not be isomorphic to 𝐸. In the following example,
the curves are not isomorphic because their conductors are different.
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sage: F = EllipticCurve_from_j(110592/37)
sage: F.conductor()
37
However, the twist of 𝐹 by 2 gives an isomorphic curve.
sage: G = F.quadratic_twist(2); G
Elliptic Curve defined by y^2 = x^3 - 4*x + 2 over Rational Field
sage: G.conductor()
2368
sage: G.j_invariant()
110592/37
We can compute the coefficients 𝑎𝑛 of the 𝐿-series or modular form
computation uses the PARI C-library:
∑︀∞
𝑛=0
𝑎𝑛 𝑞 𝑛 attached to the elliptic curve. This
sage: E = EllipticCurve([0,0,1,-1,0])
sage: E.anlist(30)
[0, 1, -2, -3, 2, -2, 6, -1, 0, 6, 4, -5, -6, -2, 2, 6, -4, 0, -12, 0, -4,
3, 10, 2, 0, -1, 4, -9, -2, 6, -12]
sage: v = E.anlist(10000)
It only takes a second to compute all 𝑎𝑛 for 𝑛 ≤ 105 :
sage: %time v = E.anlist(100000)
CPU times: user 0.98 s, sys: 0.06 s, total: 1.04 s
Wall time: 1.06
Elliptic curves can be constructed using their Cremona labels. This pre-loads the elliptic curve with information about
its rank, Tamagawa numbers, regulator, etc.
sage: E = EllipticCurve("37b2")
sage: E
Elliptic Curve defined by y^2 + y = x^3 + x^2 - 1873*x - 31833 over Rational
Field
sage: E = EllipticCurve("389a")
sage: E
Elliptic Curve defined by y^2 + y = x^3 + x^2 - 2*x over Rational Field
sage: E.rank()
2
sage: E = EllipticCurve("5077a")
sage: E.rank()
3
We can also access the Cremona database directly.
sage: db = sage.databases.cremona.CremonaDatabase()
sage: db.curves(37)
{'a1': [[0, 0, 1, -1, 0], 1, 1], 'b1': [[0, 1, 1, -23, -50], 0, 3]}
sage: db.allcurves(37)
{'a1': [[0, 0, 1, -1, 0], 1, 1],
'b1': [[0, 1, 1, -23, -50], 0, 3],
'b2': [[0, 1, 1, -1873, -31833], 0, 1],
'b3': [[0, 1, 1, -3, 1], 0, 3]}
The objects returned from the database are not of type EllipticCurve . They are elements of a database and have
a couple of fields, and that’s it. There is a small version of Cremona’s database, which is distributed by default with
Sage, and contains limited information about elliptic curves of conductor ≤ 10000. There is also a large optional
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version, which contains extensive data about all curves of conductor up to 120000 (as of October 2005). There is
also a huge (2GB) optional database package for Sage that contains the hundreds of millions of elliptic curves in the
Stein-Watkins database.
2.13.3 Dirichlet Characters
A Dirichlet character is the extension of a homomorphism (Z/𝑁 Z)* → 𝑅* , for some ring 𝑅, to the map Z → 𝑅
obtained by sending those integers 𝑥 with gcd(𝑁, 𝑥) > 1 to 0.
sage: G = DirichletGroup(12)
sage: G.list()
[Dirichlet character modulo 12 of conductor 1 mapping 7 |--> 1, 5 |--> 1,
Dirichlet character modulo 12 of conductor 4 mapping 7 |--> -1, 5 |--> 1,
Dirichlet character modulo 12 of conductor 3 mapping 7 |--> 1, 5 |--> -1,
Dirichlet character modulo 12 of conductor 12 mapping 7 |--> -1, 5 |--> -1]
sage: G.gens()
(Dirichlet character modulo 12 of conductor 4 mapping 7 |--> -1, 5 |--> 1,
Dirichlet character modulo 12 of conductor 3 mapping 7 |--> 1, 5 |--> -1)
sage: len(G)
4
Having created the group, we next create an element and compute with it.
sage: G = DirichletGroup(21)
sage: chi = G.1; chi
Dirichlet character modulo 21 of conductor 7 mapping 8 |--> 1, 10 |--> zeta6
sage: chi.values()
[0, 1, zeta6 - 1, 0, -zeta6, -zeta6 + 1, 0, 0, 1, 0, zeta6, -zeta6, 0, -1,
0, 0, zeta6 - 1, zeta6, 0, -zeta6 + 1, -1]
sage: chi.conductor()
7
sage: chi.modulus()
21
sage: chi.order()
6
sage: chi(19)
-zeta6 + 1
sage: chi(40)
-zeta6 + 1
It is also possible to compute the action of the Galois group Gal(Q(𝜁𝑁 )/Q) on these characters, as well as the direct
product decomposition corresponding to the factorization of the modulus.
sage: chi.galois_orbit()
[Dirichlet character modulo 21 of conductor 7 mapping 8 |--> 1, 10 |--> -zeta6 + 1,
Dirichlet character modulo 21 of conductor 7 mapping 8 |--> 1, 10 |--> zeta6]
sage: go = G.galois_orbits()
sage: [len(orbit) for orbit in go]
[1, 2, 2, 1, 1, 2, 2, 1]
sage: G.decomposition()
[
Group of Dirichlet characters modulo 3 with values in Cyclotomic Field of order 6 and
˓→degree 2,
Group of Dirichlet characters modulo 7 with values in Cyclotomic Field of order 6 and
˓→degree 2
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]
Next, we construct the group of Dirichlet characters mod 20, but with values in Q(𝑖):
sage: K.<i> = NumberField(x^2+1)
sage: G = DirichletGroup(20,K)
sage: G
Group of Dirichlet characters modulo 20 with values in Number Field in i with
˓→defining polynomial x^2 + 1
We next compute several invariants of G :
sage: G.gens()
(Dirichlet character modulo 20 of conductor 4 mapping 11 |--> -1, 17 |--> 1,
Dirichlet character modulo 20 of conductor 5 mapping 11 |--> 1, 17 |--> i)
sage: G.unit_gens()
(11, 17)
sage: G.zeta()
i
sage: G.zeta_order()
4
In this example we create a Dirichlet character with values in a number field. We explicitly specify the choice of root
of unity by the third argument to DirichletGroup below.
sage: x = polygen(QQ, 'x')
sage: K = NumberField(x^4 + 1, 'a'); a = K.0
sage: b = K.gen(); a == b
True
sage: K
Number Field in a with defining polynomial x^4 + 1
sage: G = DirichletGroup(5, K, a); G
Group of Dirichlet characters modulo 5 with values in the group of order 8 generated
˓→by a in Number Field in a with defining polynomial x^4 + 1
sage: chi = G.0; chi
Dirichlet character modulo 5 of conductor 5 mapping 2 |--> a^2
sage: [(chi^i)(2) for i in range(4)]
[1, a^2, -1, -a^2]
Here NumberField(x^4 + 1,'a') tells Sage to use the symbol “a” in printing what K is (a Number Field in
a with defining polynomial 𝑥4 + 1). The name “a” is undeclared at this point. Once a = K.0 (or equivalently a =
K.gen() ) is evaluated, the symbol “a” represents a root of the generating polynomial 𝑥4 + 1.
2.13.4 Modular Forms
Sage can do some computations related to modular forms, including dimensions, computing spaces of modular symbols, Hecke operators, and decompositions.
There are several functions available for computing dimensions of spaces of modular forms. For example,
sage: dimension_cusp_forms(Gamma0(11),2)
1
sage: dimension_cusp_forms(Gamma0(1),12)
1
sage: dimension_cusp_forms(Gamma1(389),2)
6112
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Next we illustrate computation of Hecke operators on a space of modular symbols of level 1 and weight 12.
sage: M = ModularSymbols(1,12)
sage: M.basis()
([X^8*Y^2,(0,0)], [X^9*Y,(0,0)], [X^10,(0,0)])
sage: t2 = M.T(2)
sage: t2
Hecke operator T_2 on Modular Symbols space of dimension 3 for Gamma_0(1)
of weight 12 with sign 0 over Rational Field
sage: t2.matrix()
[ -24
0
0]
[
0 -24
0]
[4860
0 2049]
sage: f = t2.charpoly('x'); f
x^3 - 2001*x^2 - 97776*x - 1180224
sage: factor(f)
(x - 2049) * (x + 24)^2
sage: M.T(11).charpoly('x').factor()
(x - 285311670612) * (x - 534612)^2
We can also create spaces for Γ0 (𝑁 ) and Γ1 (𝑁 ).
sage: ModularSymbols(11,2)
Modular Symbols space of dimension 3 for Gamma_0(11) of weight 2 with sign
0 over Rational Field
sage: ModularSymbols(Gamma1(11),2)
Modular Symbols space of dimension 11 for Gamma_1(11) of weight 2 with
sign 0 and over Rational Field
Let’s compute some characteristic polynomials and 𝑞-expansions.
sage: M = ModularSymbols(Gamma1(11),2)
sage: M.T(2).charpoly('x')
x^11 - 8*x^10 + 20*x^9 + 10*x^8 - 145*x^7 + 229*x^6 + 58*x^5 - 360*x^4
+ 70*x^3 - 515*x^2 + 1804*x - 1452
sage: M.T(2).charpoly('x').factor()
(x - 3) * (x + 2)^2 * (x^4 - 7*x^3 + 19*x^2 - 23*x + 11)
* (x^4 - 2*x^3 + 4*x^2 + 2*x + 11)
sage: S = M.cuspidal_submodule()
sage: S.T(2).matrix()
[-2 0]
[ 0 -2]
sage: S.q_expansion_basis(10)
[
q - 2*q^2 - q^3 + 2*q^4 + q^5 + 2*q^6 - 2*q^7 - 2*q^9 + O(q^10)
]
We can even compute spaces of modular symbols with character.
sage: G = DirichletGroup(13)
sage: e = G.0^2
sage: M = ModularSymbols(e,2); M
Modular Symbols space of dimension 4 and level 13, weight 2, character
[zeta6], sign 0, over Cyclotomic Field of order 6 and degree 2
sage: M.T(2).charpoly('x').factor()
(x - zeta6 - 2) * (x - 2*zeta6 - 1) * (x + zeta6 + 1)^2
sage: S = M.cuspidal_submodule(); S
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Modular Symbols subspace of dimension 2 of Modular Symbols space of
dimension 4 and level 13, weight 2, character [zeta6], sign 0, over
Cyclotomic Field of order 6 and degree 2
sage: S.T(2).charpoly('x').factor()
(x + zeta6 + 1)^2
sage: S.q_expansion_basis(10)
[
q + (-zeta6 - 1)*q^2 + (2*zeta6 - 2)*q^3 + zeta6*q^4 + (-2*zeta6 + 1)*q^5
+ (-2*zeta6 + 4)*q^6 + (2*zeta6 - 1)*q^8 - zeta6*q^9 + O(q^10)
]
Here is another example of how Sage can compute the action of Hecke operators on a space of modular forms.
sage: T = ModularForms(Gamma0(11),2)
sage: T
Modular Forms space of dimension 2 for Congruence Subgroup Gamma0(11) of
weight 2 over Rational Field
sage: T.degree()
2
sage: T.level()
11
sage: T.group()
Congruence Subgroup Gamma0(11)
sage: T.dimension()
2
sage: T.cuspidal_subspace()
Cuspidal subspace of dimension 1 of Modular Forms space of dimension 2 for
Congruence Subgroup Gamma0(11) of weight 2 over Rational Field
sage: T.eisenstein_subspace()
Eisenstein subspace of dimension 1 of Modular Forms space of dimension 2
for Congruence Subgroup Gamma0(11) of weight 2 over Rational Field
sage: M = ModularSymbols(11); M
Modular Symbols space of dimension 3 for Gamma_0(11) of weight 2 with sign
0 over Rational Field
sage: M.weight()
2
sage: M.basis()
((1,0), (1,8), (1,9))
sage: M.sign()
0
Let 𝑇𝑝 denote the usual Hecke operators (𝑝 prime). How do the Hecke operators 𝑇2 , 𝑇3 , 𝑇5 act on the space of modular
symbols?
sage: M.T(2).matrix()
[ 3 0 -1]
[ 0 -2 0]
[ 0 0 -2]
sage: M.T(3).matrix()
[ 4 0 -1]
[ 0 -1 0]
[ 0 0 -1]
sage: M.T(5).matrix()
[ 6 0 -1]
[ 0 1 0]
[ 0 0 1]
50
Chapter 2. A Guided Tour
CHAPTER
THREE
THE INTERACTIVE SHELL
In most of this tutorial, we assume you start the Sage interpreter using the sage command. This starts a customized
version of the IPython shell, and imports many functions and classes, so they are ready to use from the command
prompt. Further customization is possible by editing the $SAGE_ROOT/ipythonrc file. Upon starting Sage, you
get output similar to the following:
---------------------------------------------------------------------| SAGE Version 3.1.1, Release Date: 2008-05-24
|
| Type notebook() for the GUI, and license() for information.
|
----------------------------------------------------------------------
sage:
To quit Sage either press Ctrl-D or type quit or exit .
sage: quit
Exiting SAGE (CPU time 0m0.00s, Wall time 0m0.89s)
The wall time is the time that elapsed on the clock hanging from your wall. This is relevant, since CPU time does not
track time used by subprocesses like GAP or Singular.
(Avoid killing a Sage process with kill -9 from a terminal, since Sage might not kill child processes, e.g., Maple
processes, or cleanup temporary files from $HOME/.sage/tmp .)
3.1 Your Sage Session
The session is the sequence of input and output from when you start Sage until you quit. Sage logs all Sage input,
via IPython. In fact, if you’re using the interactive shell (not the notebook interface), then at any point you may type
%history (or %hist ) to get a listing of all input lines typed so far. You can type ? at the Sage prompt to find out
more about IPython, e.g., “IPython offers numbered prompts ... with input and output caching. All input is saved and
can be retrieved as variables (besides the usual arrow key recall). The following GLOBAL variables always exist (so
don’t overwrite them!)”:
_: previous input (interactive shell and notebook)
__: next previous input (interactive shell only)
_oh : list of all inputs (interactive shell only)
Here is an example:
sage: factor(100)
_1 = 2^2 * 5^2
sage: kronecker_symbol(3,5)
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_2 = -1
sage: %hist
#This only works from the interactive shell, not the notebook.
1: factor(100)
2: kronecker_symbol(3,5)
3: %hist
sage: _oh
_4 = {1: 2^2 * 5^2, 2: -1}
sage: _i1
_5 = 'factor(ZZ(100))\n'
sage: eval(_i1)
_6 = 2^2 * 5^2
sage: %hist
1: factor(100)
2: kronecker_symbol(3,5)
3: %hist
4: _oh
5: _i1
6: eval(_i1)
7: %hist
We omit the output numbering in the rest of this tutorial and the other Sage documentation.
You can also store a list of input from session in a macro for that session.
sage: E = EllipticCurve([1,2,3,4,5])
sage: M = ModularSymbols(37)
sage: %hist
1: E = EllipticCurve([1,2,3,4,5])
2: M = ModularSymbols(37)
3: %hist
sage: %macro em 1-2
Macro `em` created. To execute, type its name (without quotes).
sage: E
Elliptic Curve defined by y^2 + x*y + 3*y = x^3 + 2*x^2 + 4*x + 5 over
Rational Field
sage: E = 5
sage: M = None
sage: em
Executing Macro...
sage: E
Elliptic Curve defined by y^2 + x*y + 3*y = x^3 + 2*x^2 + 4*x + 5 over
Rational Field
When using the interactive shell, any UNIX shell command can be executed from Sage by prefacing it by an exclamation point ! . For example,
sage: !ls
auto example.sage glossary.tex
t
tmp
tut.log
tut.tex
returns the listing of the current directory.
The PATH has the Sage bin directory at the front, so if you run gp , gap , singular , maxima , etc., you get the
versions included with Sage.
sage: !gp
Reading GPRC: /etc/gprc ...Done.
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GP/PARI CALCULATOR Version 2.2.11 (alpha)
i686 running linux (ix86/GMP-4.1.4 kernel) 32-bit version
...
sage: !singular
SINGULAR
A Computer Algebra System for Polynomial Computations
/
/
Development
version 3-0-1
\
October 2005
0<
by: G.-M. Greuel, G. Pfister, H. Schoenemann
FB Mathematik der Universitaet, D-67653 Kaiserslautern
\
3.2 Logging Input and Output
Logging your Sage session is not the same as saving it (see Saving and Loading Complete Sessions for that). To log
input (and optionally output) use the logstart command. Type logstart? for more details. You can use this
command to log all input you type, all output, and even play back that input in a future session (by simply reloading
the log file).
[email protected]:~$ sage
---------------------------------------------------------------------| SAGE Version 3.0.2, Release Date: 2008-05-24
|
| Type notebook() for the GUI, and license() for information.
|
---------------------------------------------------------------------sage: logstart setup
Activating auto-logging. Current session state plus future input saved.
Filename
: setup
Mode
: backup
Output logging : False
Timestamping
: False
State
: active
sage: E = EllipticCurve([1,2,3,4,5]).minimal_model()
sage: F = QQ^3
sage: x,y = QQ['x,y'].gens()
sage: G = E.gens()
sage:
Exiting SAGE (CPU time 0m0.61s, Wall time 0m50.39s).
[email protected]:~$ sage
---------------------------------------------------------------------| SAGE Version 3.0.2, Release Date: 2008-05-24
|
| Type notebook() for the GUI, and license() for information.
|
---------------------------------------------------------------------sage: load("setup")
Loading log file <setup> one line at a time...
Finished replaying log file <setup>
sage: E
Elliptic Curve defined by y^2 + x*y = x^3 - x^2 + 4*x + 3 over Rational
Field
sage: x*y
x*y
sage: G
[(2 : 3 : 1)]
If you use Sage in the Linux KDE terminal konsole then you can save your session as follows: after starting Sage
in konsole , select “settings”, then “history...”, then “set unlimited”. When you are ready to save your session, select
3.2. Logging Input and Output
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“edit” then “save history as...” and type in a name to save the text of your session to your computer. After saving this
file, you could then load it into an editor, such as xemacs, and print it.
3.3 Paste Ignores Prompts
Suppose you are reading a session of Sage or Python computations and want to copy them into Sage. But there are
annoying >>> or sage: prompts to worry about. In fact, you can copy and paste an example, including the prompts
if you want, into Sage. In other words, by default the Sage parser strips any leading >>> or sage: prompt before
passing it to Python. For example,
sage: 2^10
1024
sage: sage: sage: 2^10
1024
sage: >>> 2^10
1024
3.4 Timing Commands
If you place the %time command at the beginning of an input line, the time the command takes to run will be
displayed after the output. For example, we can compare the running time for a certain exponentiation operation in
several ways. The timings below will probably be much different on your computer, or even between different versions
of Sage. First, native Python:
sage: %time a = int(1938)^int(99484)
CPU times: user 0.66 s, sys: 0.00 s, total: 0.66 s
Wall time: 0.66
This means that 0.66 seconds total were taken, and the “Wall time”, i.e., the amount of time that elapsed on your wall
clock, is also 0.66 seconds. If your computer is heavily loaded with other programs, the wall time may be much larger
than the CPU time.
It’s also possible to use the timeit function to try to get timing over a large number of iterations of a command.
This gives slightly different information, and requires the input of a string with the command you want to time.
sage: timeit("int(1938)^int(99484)")
5 loops, best of 3: 44.8 ms per loop
Next we time exponentiation using the native Sage Integer type, which is implemented (in Cython) using the GMP
library:
sage: %time a = 1938^99484
CPU times: user 0.04 s, sys: 0.00 s, total: 0.04 s
Wall time: 0.04
Using the PARI C-library interface:
sage: %time a = pari(1938)^pari(99484)
CPU times: user 0.05 s, sys: 0.00 s, total: 0.05 s
Wall time: 0.05
GMP is better, but only slightly (as expected, since the version of PARI built for Sage uses GMP for integer arithmetic).
You can also time a block of commands using the cputime command, as illustrated below:
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sage:
sage:
sage:
sage:
sage:
0.64
t = cputime()
a = int(1938)^int(99484)
b = 1938^99484
c = pari(1938)^pari(99484)
cputime(t)
# somewhat random output
sage: cputime?
...
Return the time in CPU second since SAGE started, or with optional
argument t, return the time since time t.
INPUT:
t -- (optional) float, time in CPU seconds
OUTPUT:
float -- time in CPU seconds
The walltime command behaves just like the cputime command, except that it measures wall time.
We can also compute the above power in some of the computer algebra systems that Sage includes. In each case we
execute a trivial command in the system, in order to start up the server for that program. The most relevant time is the
wall time. However, if there is a significant difference between the wall time and the CPU time then this may indicate
a performance issue worth looking into.
sage: time 1938^99484;
CPU times: user 0.01 s, sys: 0.00 s, total:
Wall time: 0.01
sage: gp(0)
0
sage: time g = gp('1938^99484')
CPU times: user 0.00 s, sys: 0.00 s, total:
Wall time: 0.04
sage: maxima(0)
0
sage: time g = maxima('1938^99484')
CPU times: user 0.00 s, sys: 0.00 s, total:
Wall time: 0.30
sage: kash(0)
0
sage: time g = kash('1938^99484')
CPU times: user 0.00 s, sys: 0.00 s, total:
Wall time: 0.04
sage: mathematica(0)
0
sage: time g = mathematica('1938^99484')
CPU times: user 0.00 s, sys: 0.00 s, total:
Wall time: 0.03
sage: maple(0)
0
sage: time g = maple('1938^99484')
CPU times: user 0.00 s, sys: 0.00 s, total:
Wall time: 0.11
sage: gap(0)
0
sage: time g = gap.eval('1938^99484;;')
CPU times: user 0.00 s, sys: 0.00 s, total:
Wall time: 1.02
Note
that
GAP
and
Maxima
3.4. Timing Commands
are
the
slowest
0.01 s
0.00 s
0.00 s
0.00 s
0.00 s
0.00 s
0.00 s
in
this
test
(this
was
run
on
the
machine
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sage.math.washington.edu ). Because of the pexpect interface overhead, it is perhaps unfair to compare these to Sage, which is the fastest.
3.5 Other IPython tricks
As noted above, Sage uses IPython as its front end, and so you can use any of IPython’s commands and features. You
can read the full IPython documentation. Meanwhile, here are some fun tricks – these are called “Magic commands”
in IPython:
• You can use %bg to run a command in the background, and then use jobs to access the results, as follows. (The
comments not tested are here because the %bg syntax doesn’t work well with Sage’s automatic testing
facility. If you type this in yourself, it should work as written. This is of course most useful with commands
which take a while to complete.)
sage: def quick(m): return 2*m
sage: %bg quick(20) # not tested
Starting job # 0 in a separate thread.
sage: jobs.status() # not tested
Completed jobs:
0 : quick(20)
sage: jobs[0].result # the actual answer, not tested
40
Note that jobs run in the background don’t use the Sage preparser – see The Pre-Parser: Differences between
Sage and Python for more information. One (perhaps awkward) way to get around this would be to run
sage: %bg eval(preparse('quick(20)')) # not tested
It is safer and easier, though, to just use %bg on commands which don’t require the preparser.
• You can use %edit (or %ed or ed ) to open an editor, if you want to type in some complex code. Before
you start Sage, make sure that the EDITOR environment variable is set to your favorite editor (by putting
export EDITOR=/usr/bin/emacs or export EDITOR=/usr/bin/vim or something similar in
the appropriate place, like a .profile file). From the Sage prompt, executing %edit will open up the
named editor. Then within the editor you can define a function:
def some_function(n):
return n**2 + 3*n + 2
Save and quit from the editor. For the rest of your Sage session, you can then use some_function . If you
want to modify it, type %edit some_function from the Sage prompt.
• If you have a computation and you want to modify its output for another use, perform the computation and type
%rep : this will place the output from the previous command at the Sage prompt, ready for you to edit it.
sage: f(x) = cos(x)
sage: f(x).derivative(x)
-sin(x)
At this point, if you type %rep at the Sage prompt, you will get a new Sage prompt, followed by -sin(x) ,
with the cursor at the end of the line.
For more, type %quickref to get a quick reference guide to IPython. As of this writing (April 2011), Sage uses
version 0.9.1 of IPython, and the documentation for its magic commands is available online. Various slightly advanced
aspects of magic command system are documented here in IPython.
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3.6 Errors and Exceptions
When something goes wrong, you will usually see a Python “exception”. Python even tries to suggest what raised the
exception. Often you see the name of the exception, e.g., NameError or ValueError (see the Python Reference
Manual [Py] for a complete list of exceptions). For example,
sage: 3_2
-----------------------------------------------------------File "<console>", line 1
ZZ(3)_2
^
SyntaxError: invalid syntax
sage: EllipticCurve([0,infinity])
-----------------------------------------------------------Traceback (most recent call last):
...
TypeError: Unable to coerce Infinity (<class 'sage...Infinity'>) to Rational
The interactive debugger is sometimes useful for understanding what went wrong. You can toggle it on or off using
%pdb (the default is off). The prompt ipdb> appears if an exception is raised and the debugger is on. From within
the debugger, you can print the state of any local variable, and move up and down the execution stack. For example,
sage: %pdb
Automatic pdb calling has been turned ON
sage: EllipticCurve([1,infinity])
--------------------------------------------------------------------------<type 'exceptions.TypeError'>
Traceback (most recent call last)
...
ipdb>
For a list of commands in the debugger, type ? at the ipdb> prompt:
ipdb> ?
Documented commands (type help <topic>):
========================================
EOF
break commands
debug
h
a
bt
condition disable help
alias c
cont
down
ignore
args
cl
continue
enable
j
b
clear d
exit
jump
whatis where
l
list
n
next
p
pdef
pdoc
pinfo
pp
q
quit
r
return
s
step
tbreak
u
unalias
up
w
Miscellaneous help topics:
==========================
exec pdb
Undocumented commands:
======================
retval rv
Type Ctrl-D or quit to return to Sage.
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3.7 Reverse Search and Tab Completion
Reverse search: Type the beginning of a command, then Ctrl-p (or just hit the up arrow key) to go back to each
line you have entered that begins in that way. This works even if you completely exit Sage and restart later. You can
also do a reverse search through the history using Ctrl-r . All these features use the readline package, which is
available on most flavors of Linux.
To illustrate tab completion, first create the three dimensional vector space 𝑉 = Q3 as follows:
sage: V = VectorSpace(QQ,3)
sage: V
Vector space of dimension 3 over Rational Field
You can also use the following more concise notation:
sage: V = QQ^3
Then it is easy to list all member functions for 𝑉 using tab completion. Just type V. , then type the [tab key] key
on your keyboard:
sage: V.[tab key]
V._VectorSpace_generic__base_field
...
V.ambient_space
V.base_field
V.base_ring
V.basis
V.coordinates
...
V.zero_vector
If you type the first few letters of a function, then [tab key] , you get only functions that begin as indicated.
sage: V.i[tab key]
V.is_ambient V.is_dense
V.is_full
V.is_sparse
If you wonder what a particular function does, e.g., the coordinates function, type V.coordinates? for help or
V.coordinates?? for the source code, as explained in the next section.
3.8 Integrated Help System
Sage features an integrated help facility. Type a function name followed by ? for the documentation for that function.
sage: V = QQ^3
sage: V.coordinates?
Type:
instancemethod
Base Class:
<type 'instancemethod'>
String Form:
<bound method FreeModule_ambient_field.coordinates of Vector
space of dimension 3 over Rational Field>
Namespace:
Interactive
File:
/home/was/s/local/lib/python2.4/site-packages/sage/modules/f
ree_module.py
Definition:
V.coordinates(self, v)
Docstring:
Write v in terms of the basis for self.
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Returns a list c such that if B is the basis for self, then
sum c_i B_i = v.
If v is not in self, raises an ArithmeticError exception.
EXAMPLES:
sage: M = FreeModule(IntegerRing(), 2); M0,M1=M.gens()
sage: W = M.submodule([M0 + M1, M0 - 2*M1])
sage: W.coordinates(2*M0-M1)
[2, -1]
As shown above, the output tells you the type of the object, the file in which it is defined, and a useful description of
the function with examples that you can paste into your current session. Almost all of these examples are regularly
automatically tested to make sure they work and behave exactly as claimed.
Another feature that is very much in the spirit of the open source nature of Sage is that if f is a Python function, then
typing f?? displays the source code that defines f . For example,
sage: V = QQ^3
sage: V.coordinates??
Type:
instancemethod
...
Source:
def coordinates(self, v):
"""
Write $v$ in terms of the basis for self.
...
"""
return self.coordinate_vector(v).list()
This tells us that all the coordinates function does is call the coordinate_vector function and change the
result into a list. What does the coordinate_vector function do?
sage: V = QQ^3
sage: V.coordinate_vector??
...
def coordinate_vector(self, v):
...
return self.ambient_vector_space()(v)
The coordinate_vector function coerces its input into the ambient space, which has the effect of computing
the vector of coefficients of 𝑣 in terms of 𝑉 . The space 𝑉 is already ambient since it’s just Q3 . There is also a
coordinate_vector function for subspaces, and it’s different. We create a subspace and see:
sage: V = QQ^3; W = V.span_of_basis([V.0, V.1])
sage: W.coordinate_vector??
...
def coordinate_vector(self, v):
"""
...
"""
# First find the coordinates of v wrt echelon basis.
w = self.echelon_coordinate_vector(v)
# Next use transformation matrix from echelon basis to
# user basis.
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T = self.echelon_to_user_matrix()
return T.linear_combination_of_rows(w)
(If you think the implementation is inefficient, please sign up to help optimize linear algebra.)
You may also type help(command_name) or help(class) for a manpage-like help file about a given class.
sage: help(VectorSpace)
Help on class VectorSpace ...
class VectorSpace(__builtin__.object)
| Create a Vector Space.
|
| To create an ambient space over a field with given dimension
| using the calling syntax ...
:
:
When you type q to exit the help system, your session appears just as it was. The help listing does not clutter up your session, unlike the output of function_name? sometimes does. It’s particularly helpful to type
help(module_name) . For example, vector spaces are defined in sage.modules.free_module , so type
help(sage.modules.free_module) for documentation about that whole module. When viewing documentation using help, you can search by typing / and in reverse by typing ? .
3.9 Saving and Loading Individual Objects
Suppose you compute a matrix or worse, a complicated space of modular symbols, and would like to save it for later
use. What can you do? There are several approaches that computer algebra systems take to saving individual objects.
1. Save your Game: Only support saving and loading of complete sessions (e.g., GAP, Magma).
2. Unified Input/Output: Make every object print in a way that can be read back in (GP/PARI).
3. Eval: Make it easy to evaluate arbitrary code in the interpreter (e.g., Singular, PARI).
Because Sage uses Python, it takes a different approach, which is that every object can be serialized, i.e., turned into
a string from which that object can be recovered. This is in spirit similar to the unified I/O approach of PARI, except
it doesn’t have the drawback that objects print to screen in too complicated of a way. Also, support for saving and
loading is (in most cases) completely automatic, requiring no extra programming; it’s simply a feature of Python that
was designed into the language from the ground up.
Almost all Sage objects x can be saved in compressed form to disk using save(x,filename) (or in many cases
x.save(filename) ). To load the object back in, use load(filename) .
sage:
sage:
[ 15
[ 42
[ 69
sage:
A = MatrixSpace(QQ,3)(range(9))^2
A
18 21]
54 66]
90 111]
save(A, 'A')
You should now quit Sage and restart. Then you can get A back:
sage: A = load('A')
sage: A
[ 15 18 21]
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[ 42
[ 69
54 66]
90 111]
You can do the same with more complicated objects, e.g., elliptic curves. All data about the object that is cached is
stored with the object. For example,
sage:
sage:
sage:
sage:
E = EllipticCurve('11a')
v = E.anlist(100000)
save(E, 'E')
quit
# takes a while
The saved version of E takes 153 kilobytes, since it stores the first 100000 𝑎𝑛 with it.
~/tmp$ ls -l E.sobj
-rw-r--r-- 1 was was 153500 2006-01-28 19:23 E.sobj
~/tmp$ sage [...]
sage: E = load('E')
sage: v = E.anlist(100000)
# instant!
(In Python, saving and loading is accomplished using the cPickle module. In particular, a Sage object x can be
saved via cPickle.dumps(x,2) . Note the 2 !)
Sage cannot save and load individual objects created in some other computer algebra systems, e.g., GAP, Singular,
Maxima, etc. They reload in a state marked “invalid”. In GAP, though many objects print in a form from which they
can be reconstructed, many don’t, so reconstructing from their print representation is purposely not allowed.
sage: a = gap(2)
sage: a.save('a')
sage: load('a')
Traceback (most recent call last):
...
ValueError: The session in which this object was defined is no longer
running.
GP/PARI objects can be saved and loaded since their print representation is enough to reconstruct them.
sage: a = gp(2)
sage: a.save('a')
sage: load('a')
2
Saved objects can be re-loaded later on computers with different architectures or operating systems, e.g., you could
save a huge matrix on 32-bit OS X and reload it on 64-bit Linux, find the echelon form, then move it back. Also, in
many cases you can even load objects into versions of Sage that are different than the versions they were saved in, as
long as the code for that object isn’t too different. All the attributes of the objects are saved, along with the class (but
not source code) that defines the object. If that class no longer exists in a new version of Sage, then the object can’t be
reloaded in that newer version. But you could load it in an old version, get the objects dictionary (with x.__dict__
), and save the dictionary, and load that into the newer version.
3.9.1 Saving as Text
You can also save the ASCII text representation of objects to a plain text file by simply opening a file in write mode
and writing the string representation of the object (you can write many objects this way as well). When you’re done
writing objects, close the file.
3.9. Saving and Loading Individual Objects
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sage:
sage:
sage:
sage:
sage:
R.<x,y> = PolynomialRing(QQ,2)
f = (x+y)^7
o = open('file.txt','w')
o.write(str(f))
o.close()
3.10 Saving and Loading Complete Sessions
Sage has very flexible support for saving and loading complete sessions.
The command save_session(sessionname) saves all the variables you’ve defined in the current session as a
dictionary in the given sessionname . (In the rare case when a variable does not support saving, it is simply not
saved to the dictionary.) The resulting file is an .sobj file and can be loaded just like any other object that was
saved. When you load the objects saved in a session, you get a dictionary whose keys are the variables names and
whose values are the objects.
You can use the load_session(sessionname) command to load the variables defined in sessionname into
the current session. Note that this does not wipe out variables you’ve already defined in your current session; instead,
the two sessions are merged.
First we start Sage and define some variables.
sage:
sage:
sage:
sage:
_4 =
E = EllipticCurve('11a')
M = ModularSymbols(37)
a = 389
t = M.T(2003).matrix(); t.charpoly().factor()
(x - 2004) * (x - 12)^2 * (x + 54)^2
Next we save our session, which saves each of the above variables into a file. Then we view the file, which is about
3K in size.
sage: save_session('misc')
Saving a
Saving M
Saving t
Saving E
sage: quit
[email protected]:~/tmp$ ls -l misc.sobj
-rw-r--r-- 1 was was 2979 2006-01-28 19:47 misc.sobj
Finally we restart Sage, define an extra variable, and load our saved session.
sage: b = 19
sage: load_session('misc')
Loading a
Loading M
Loading E
Loading t
Each saved variable is again available. Moreover, the variable b was not overwritten.
sage: M
Full Modular Symbols space for Gamma_0(37) of weight 2 with sign 0
and dimension 5 over Rational Field
sage: E
Elliptic Curve defined by y^2 + y = x^3 - x^2 - 10*x - 20 over Rational
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Field
sage: b
19
sage: a
389
3.11 The Notebook Interface
The Sage notebook is run by typing
sage: notebook()
on the command line of Sage. This starts the Sage notebook and opens your default web browser to view it. The
server’s state files are stored in $HOME/.sage/sage\_notebook .
Other options include:
sage: notebook("directory")
which starts a new notebook server using files in the given directory, instead of the default directory
$HOME/.sage/sage_notebook . This can be useful if you want to have a collection of worksheets associated
with a specific project, or run several separate notebook servers at the same time.
When you start the notebook, it first creates the following files in $HOME/.sage/sage_notebook :
nb.sobj
objects/
worksheets/
(the notebook SAGE object file)
(a directory containing SAGE objects)
(a directory containing SAGE worksheets).
After creating the above files, the notebook starts a web server.
A “notebook” is a collection of user accounts, each of which can have any number of worksheets. When you create
a new worksheet, the data that defines it is stored in the worksheets/username/number directories. In each
such directory there is a plain text file worksheet.txt - if anything ever happens to your worksheets, or Sage, or
whatever, that human-readable file contains everything needed to reconstruct your worksheet.
From within Sage, type notebook? for much more about how to start a notebook server.
The following diagram illustrates the architecture of the Sage Notebook:
---------------------|
|
|
|
|
firefox/safari
|
|
|
|
javascript
|
|
program
|
|
|
|
|
---------------------|
^
| AJAX |
V
|
---------------------|
|
|
sage
|
3.11. The Notebook Interface
SAGE process 1
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|
web
| ------------>
|
server
|
pexpect
|
|
|
|
----------------------
SAGE process 2
SAGE process 3
.
.
.
(Python processes)
For help on a Sage command, cmd , in the notebook browser box, type cmd?
<shift-enter> ).
and now hit <esc> (not
For help on the keyboard shortcuts available in the notebook interface, click on the Help link.
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CHAPTER
FOUR
INTERFACES
A central facet of Sage is that it supports computation with objects in many different computer algebra systems “under
one roof” using a common interface and clean programming language.
The console and interact methods of an interface do very different things. For example, using GAP as an example:
1. gap.console() : This opens the GAP console - it transfers control to GAP. Here Sage is serving as nothing
more than a convenient program launcher, similar to the Linux bash shell.
2. gap.interact() : This is a convenient way to interact with a running GAP instance that may be “full of”
Sage objects. You can import Sage objects into this GAP session (even from the interactive interface), etc.
4.1 GP/PARI
PARI is a compact, very mature, highly optimized C program whose primary focus is number theory. There are two
very distinct interfaces that you can use in Sage:
• gp - the “G o P ARI” interpreter, and
• pari - the PARI C library.
For example, the following are two ways of doing the same thing. They look identical, but the output is actually
different, and what happens behind the scenes is drastically different.
sage: gp('znprimroot(10007)')
Mod(5, 10007)
sage: pari('znprimroot(10007)')
Mod(5, 10007)
In the first case, a separate copy of the GP interpreter is started as a server, and the string 'znprimroot(10007)'
is sent to it, evaluated by GP, and the result is assigned to a variable in GP (which takes up space in the child GP
processes memory that won’t be freed). Then the value of that variable is displayed. In the second case, no separate
program is started, and the string 'znprimroot(10007)' is evaluated by a certain PARI C library function. The
result is stored in a piece of memory on the Python heap, which is freed when the variable is no longer referenced.
The objects have different types:
sage: type(gp('znprimroot(10007)'))
<class 'sage.interfaces.gp.GpElement'>
sage: type(pari('znprimroot(10007)'))
<type 'sage.libs.cypari2.gen.Gen'>
So which should you use? It depends on what you’re doing. The GP interface can do absolutely anything you could do
in the usual GP/PARI command line program, since it is running that program. In particular, you can load complicated
PARI programs and run them. In contrast, the PARI interface (via the C library) is much more restrictive. First, not all
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member functions have been implemented. Second, a lot of code, e.g., involving numerical integration, won’t work
via the PARI interface. That said, the PARI interface can be significantly faster and more robust than the GP one.
(If the GP interface runs out of memory evaluating a given input line, it will silently and automatically double the
stack size and retry that input line. Thus your computation won’t crash if you didn’t correctly anticipate the amount
of memory that would be needed. This is a nice trick the usual GP interpreter doesn’t seem to provide. Regarding the
PARI C library interface, it immediately copies each created object off of the PARI stack, hence the stack never grows.
However, each object must not exceed 100MB in size, or the stack will overflow when the object is being created.
This extra copying does impose a slight performance penalty.)
In summary, Sage uses the PARI C library to provide functionality similar to that provided by the GP/PARI interpreter,
except with different sophisticated memory management and the Python programming language.
First we create a PARI list from a Python list.
sage: v = pari([1,2,3,4,5])
sage: v
[1, 2, 3, 4, 5]
sage: type(v)
<type 'sage.libs.cypari2.gen.Gen'>
Every PARI object is of type Gen . The PARI type of the underlying object can be obtained using the type member
function.
sage: v.type()
't_VEC'
In PARI, to create an elliptic curve we enter ellinit([1,2,3,4,5]) . Sage is similar, except that ellinit is
a method that can be called on any PARI object, e.g., our t_VEC 𝑣.
sage: e = v.ellinit()
sage: e.type()
't_VEC'
sage: pari(e)[:13]
[1, 2, 3, 4, 5, 9, 11, 29, 35, -183, -3429, -10351, 6128487/10351]
Now that we have an elliptic curve object, we can compute some things about it.
sage: e.elltors()
[1, [], []]
sage: e.ellglobalred()
[10351, [1, -1, 0, -1], 1, [11, 1; 941, 1], [[1, 5, 0, 1], [1, 5, 0, 1]]]
sage: f = e.ellchangecurve([1,-1,0,-1])
sage: f[:5]
[1, -1, 0, 4, 3]
4.2 GAP
Sage comes with GAP for computational discrete mathematics, especially group theory.
Here’s an example of GAP’s IdGroup function, which uses the optional small groups database that has to be installed
separately, as explained below.
sage: G = gap('Group((1,2,3)(4,5), (3,4))')
sage: G
Group( [ (1,2,3)(4,5), (3,4) ] )
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sage: G.Center()
Group( () )
sage: G.IdGroup()
[ 120, 34 ]
sage: G.Order()
120
# optional - database_gap
We can do the same computation in Sage without explicitly invoking the GAP interface as follows:
sage: G = PermutationGroup([[(1,2,3),(4,5)],[(3,4)]])
sage: G.center()
Subgroup of (Permutation Group with generators [(3,4), (1,2,3)(4,5)]) generated by
˓→[()]
sage: G.group_id()
# optional - database_gap
[120, 34]
sage: n = G.order(); n
120
For some GAP functionality, you should install two optional Sage packages. This can be done with the command:
sage -i gap_packages database_gap
4.3 Singular
Singular provides a massive and mature library for Gröbner bases, multivariate polynomial gcds, bases of RiemannRoch spaces of a plane curve, and factorizations, among other things. We illustrate multivariate polynomial factorization using the Sage interface to Singular (do not type the ....: ):
sage: R1 = singular.ring(0, '(x,y)', 'dp')
sage: R1
polynomial ring, over a field, global ordering
//
characteristic : 0
//
number of vars : 2
//
block
1 : ordering dp
//
: names
x y
//
block
2 : ordering C
sage: f = singular('9*y^8 - 9*x^2*y^7 - 18*x^3*y^6 - 18*x^5*y^6 +'
....:
'9*x^6*y^4 + 18*x^7*y^5 + 36*x^8*y^4 + 9*x^10*y^4 - 18*x^11*y^2 -'
....:
'9*x^12*y^3 - 18*x^13*y^2 + 9*x^16')
Now that we have defined 𝑓 , we print it and factor.
sage: f
9*x^16-18*x^13*y^2-9*x^12*y^3+9*x^10*y^4-18*x^11*y^2+36*x^8*y^4+18*x^7*y^5-18*x^5*y^
˓→6+9*x^6*y^4-18*x^3*y^6-9*x^2*y^7+9*y^8
sage: f.parent()
Singular
sage: F = f.factorize(); F
[1]:
_[1]=9
_[2]=x^6-2*x^3*y^2-x^2*y^3+y^4
_[3]=-x^5+y^2
[2]:
1,1,2
4.3. Singular
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sage: F[1][2]
x^6-2*x^3*y^2-x^2*y^3+y^4
As with the GAP example in GAP, we can compute the above factorization without explicitly using the Singular
interface (however, behind the scenes Sage uses the Singular interface for the actual computation). Do not type the
....: :
sage:
sage:
....:
....:
sage:
(9) *
x, y = QQ['x, y'].gens()
f = (9*y^8 - 9*x^2*y^7 - 18*x^3*y^6 - 18*x^5*y^6 + 9*x^6*y^4
+ 18*x^7*y^5 + 36*x^8*y^4 + 9*x^10*y^4 - 18*x^11*y^2 - 9*x^12*y^3
- 18*x^13*y^2 + 9*x^16)
factor(f)
(-x^5 + y^2)^2 * (x^6 - 2*x^3*y^2 - x^2*y^3 + y^4)
4.4 Maxima
Maxima is included with Sage, as well as a Lisp implementation. The gnuplot package (which Maxima uses by default
for plotting) is distributed as a Sage optional package. Among other things, Maxima does symbolic manipulation.
Maxima can integrate and differentiate functions symbolically, solve 1st order ODEs, most linear 2nd order ODEs,
and has implemented the Laplace transform method for linear ODEs of any degree. Maxima also knows about a wide
range of special functions, has plotting capabilities via gnuplot, and has methods to solve and manipulate matrices
(such as row reduction, eigenvalues and eigenvectors), and polynomial equations.
We illustrate the Sage/Maxima interface by constructing the matrix whose 𝑖, 𝑗 entry is 𝑖/𝑗, for 𝑖, 𝑗 = 1, . . . , 4.
sage: f = maxima.eval('ij_entry[i,j] := i/j')
sage: A = maxima('genmatrix(ij_entry,4,4)'); A
matrix([1,1/2,1/3,1/4],[2,1,2/3,1/2],[3,3/2,1,3/4],[4,2,4/3,1])
sage: A.determinant()
0
sage: A.echelon()
matrix([1,1/2,1/3,1/4],[0,0,0,0],[0,0,0,0],[0,0,0,0])
sage: A.eigenvalues()
[[0,4],[3,1]]
sage: A.eigenvectors()
[[[0,4],[3,1]],[[[1,0,0,-4],[0,1,0,-2],[0,0,1,-4/3]],[[1,2,3,4]]]]
Here’s another example:
sage: A = maxima("matrix ([1, 0, 0], [1, -1, 0], [1, 3, -2])")
sage: eigA = A.eigenvectors()
sage: V = VectorSpace(QQ,3)
sage: eigA
[[[-2,-1,1],[1,1,1]],[[[0,0,1]],[[0,1,3]],[[1,1/2,5/6]]]]
sage: v1 = V(sage_eval(repr(eigA[1][0][0]))); lambda1 = eigA[0][0][0]
sage: v2 = V(sage_eval(repr(eigA[1][1][0]))); lambda2 = eigA[0][0][1]
sage: v3 = V(sage_eval(repr(eigA[1][2][0]))); lambda3 = eigA[0][0][2]
sage:
sage:
sage:
sage:
True
sage:
sage:
68
M = MatrixSpace(QQ,3,3)
AA = M([[1,0,0],[1, - 1,0],[1,3, - 2]])
b1 = v1.base_ring()
AA*v1 == b1(lambda1)*v1
b2 = v2.base_ring()
AA*v2 == b2(lambda2)*v2
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True
sage: b3 = v3.base_ring()
sage: AA*v3 == b3(lambda3)*v3
True
Finally, we give an example of using Sage to plot using openmath . Many of these were modified from the Maxima
reference manual.
A 2D plot of several functions (do not type the ....: ):
sage: maxima.plot2d('[cos(7*x),cos(23*x)^4,sin(13*x)^3]','[x,0,1]',
....:
'[plot_format,openmath]')
# not tested
A “live” 3D plot which you can move with your mouse (do not type the ....: ):
sage: maxima.plot3d ("2^(-u^2 + v^2)", "[u, -3, 3]", "[v, -2, 2]", # not tested
....:
'[plot_format, openmath]')
sage: maxima.plot3d("atan(-x^2 + y^3/4)", "[x, -4, 4]", "[y, -4, 4]", # not tested
....:
"[grid, 50, 50]",'[plot_format, openmath]')
The next plot is the famous Möbius strip (do not type the ....: ):
sage: maxima.plot3d("[cos(x)*(3 + y*cos(x/2)), sin(x)*(3 + y*cos(x/2)), y*sin(x/2)]",
˓→ # not tested
....:
"[x, -4, 4]", "[y, -4, 4]", '[plot_format, openmath]')
The next plot is the famous Klein bottle (do not type the ....: ):
sage: maxima("expr_1: 5*cos(x)*(cos(x/2)*cos(y) + sin(x/2)*sin(2*y)+ 3.0) - 10.0")
5*cos(x)*(sin(x/2)*sin(2*y)+cos(x/2)*cos(y)+3.0)-10.0
sage: maxima("expr_2: -5*sin(x)*(cos(x/2)*cos(y) + sin(x/2)*sin(2*y)+ 3.0)")
-5*sin(x)*(sin(x/2)*sin(2*y)+cos(x/2)*cos(y)+3.0)
sage: maxima("expr_3: 5*(-sin(x/2)*cos(y) + cos(x/2)*sin(2*y))")
5*(cos(x/2)*sin(2*y)-sin(x/2)*cos(y))
sage: maxima.plot3d ("[expr_1, expr_2, expr_3]", "[x, -%pi, %pi]", # not tested
....:
"[y, -%pi, %pi]", "['grid, 40, 40]", '[plot_format, openmath]')
4.4. Maxima
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CHAPTER
FIVE
SAGE, LATEX AND FRIENDS
AUTHOR: Rob Beezer (2010-05-23)
Sage and the LaTeX dialect of TeX have an intensely synergistic relationship. This section aims to introduce the
variety of interactions, beginning with the most basic and proceeding to the more unusual and arcane. (So you may
not want to read this entire section on your first pass through this tutorial.)
5.1 Overview
It may be easiest to understand the various uses of LaTeX with a brief overview of the mechanics of the three principal
methods employed by Sage.
1. Every “object” in Sage is required to have a LaTeX representation. You can access this representation by
executing, in the notebook or at the sage command line, latex(foo) where foo is some object in Sage.
The output is a string that should render a reasonably accurate representation of foo when used in TeX’s
math-mode (for example, when enclosed between a pair of single dollar signs). Some examples of this follow
below.
In this way, Sage can be used effectively for constructing portions of a LaTeX document: create or compute an
object in Sage, print latex() of the object and cut/paste it into your document.
2. The notebook interface is configured to use MathJax to render mathematics cleanly in a web browser. MathJax is
an open source JavaScript display engine for mathematics that works in all modern browsers. It is able to render
a large, but not totally complete, subset of TeX. It has no support for things like complicated tables, sectioning or
document management, as it is oriented towards accurately rendering “snippets” of TeX. Seemingly automatic
rendering of math in the notebook is provided by converting the latex() representation of an object (as
described above) into a form of HTML palatable to MathJax.
Since MathJax uses its own scalable fonts, it is superior to other methods that rely on converting equations, or
other snippets of TeX, into static inline images.
3. At the Sage command-line, or in the notebook when LaTeX code is more involved than MathJax can handle, a
system-wide installation of LaTeX can be employed. Sage includes almost everything you need to build and use
Sage, but a significant exception is TeX itself. So in these situations you need to have TeX installed, along with
some associated conversion utilities, to utilize the full power.
Here we demonstrate some basic uses of the latex() function.
sage: var('z')
z
sage: latex(z^12)
z^{12}
sage: latex(integrate(z^4, z))
\frac{1}{5} \, z^{5}
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sage: latex('a string')
\text{\texttt{a{ }string}}
sage: latex(QQ)
\Bold{Q}
sage: latex(matrix(QQ, 2, 3, [[2,4,6],[-1,-1,-1]]))
\left(\begin{array}{rrr}
2 & 4 & 6 \\
-1 & -1 & -1
\end{array}\right)
Basic MathJax functionality is largely automatic in the notebook, but we can partially demonstrate this support with
the MathJax class. The eval function of this class converts a Sage object to its LaTeX representation and then
wraps it in HTML that invokes the CSS “math” class, which then employs MathJax.
sage: from sage.misc.latex import MathJax
sage: mj = MathJax()
sage: var('z')
z
sage: mj(z^12)
<html><script type="math/tex; mode=display">\newcommand{\Bold}[1]{\mathbf{#1}}z^{12}</
˓→script></html>
sage: mj(QQ)
<html><script type="math/tex; mode=display">\newcommand{\Bold}[1]{\mathbf{#1}}\Bold{Q}
˓→</script></html>
sage: mj(ZZ['x'])
<html><script type="math/tex; mode=display">\newcommand{\Bold}[1]{\mathbf{#1}}\Bold{Z}
˓→[x]</script></html>
sage: mj(integrate(z^4, z))
<html><script type="math/tex; mode=display">\newcommand{\Bold}[1]{\mathbf{#1}}\frac{1}
˓→{5} \, z^{5}</script></html>
5.2 Basic Use
As indicated in the overview, the simplest way to exploit Sage’s support of LaTeX is to use the latex() function to
create legitimate LaTeX code to represent mathematical objects. These strings can then be incorporated into standalone
LaTeX documents. This works identically in the notebook and at the Sage command line.
At the other extreme is the view() command. At the Sage command line the command view(foo) will create
the LaTeX representation of foo , incorporate this into a simple LaTeX document, and then process that document
with your system-wide TeX installation. Finally, the appropriate viewer will be called to display the output from the
TeX command. Which version of TeX is used, and therefore the nature of the output and associated viewer, can be
customized (see Customizing LaTeX Processing).
In the notebook, the view(foo) command creates the appropriate combination of HTML and CSS so that MathJax
will render the LaTeX representation properly in the worksheet. To the user, it simply creates a nicely formatted
version of the output, distinct from the default ASCII output of Sage. Not every mathematical object in Sage has
a LaTeX representation amenable to the limited capabilities of MathJax. In these cases, the MathJax interpretation
can be bypassed, the system-wide TeX called instead, and the subsequent output converted to a graphic image for
display in the worksheet. Affecting and controlling this process is discussed below in the section Customizing LaTeX
Generation.
The notebook has two other features for employing TeX. The first is the “Typeset” button just above the first cell of a
worksheet, to the right of the four drop-down boxes. When checked, any subsequent evaluations of cells will result in
output interpreted by MathJax, hence of a typeset quality. Note that this effect is not retroactive – previously evaluated
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cells need to be re-evaluated. Essentially, checking the “Typeset” button is identical to wrapping the output of each
cell in the view() command.
A second feature of the notebook is entering TeX as part of annotating a worksheet. When the cursor is placed between
cells of a worksheet so that a blue bar appears, then a shift-click will open a mini-word-processor, TinyMCE. This
allows for the entry of text, using a WSIWYG editor to create HTML and CSS command for styled text. So it is
possible to add formatted text as commentary within a worksheet. However, text between pairs of dollar signs, or pairs
of double dollar signs is interpreted by MathJax as inline or display math (respectively).
5.3 Customizing LaTeX Generation
There are several ways to customize the actual LaTeX code generated by the latex() command. In the notebook
and at the Sage command-line there is a pre-defined object named latex which has several methods, which you can
list by typing latex. , followed by the tab key (note the period).
A good example is the latex.matrix_delimiters method. It can be used to change the notation surrounding
a matrix – large parentheses, brackets, braces, vertical bars. No notion of style is enforced, you can mix and match as
you please. Notice how the backslashes needed in LaTeX require an extra slash so they are escaped properly within
the Python string.
sage: A = matrix(ZZ, 2, 2, range(4))
sage: latex(A)
\left(\begin{array}{rr}
0 & 1 \\
2 & 3
\end{array}\right)
sage: latex.matrix_delimiters(left='[', right=']')
sage: latex(A)
\left[\begin{array}{rr}
0 & 1 \\
2 & 3
\end{array}\right]
sage: latex.matrix_delimiters(left='\\{', right='\\}')
sage: latex(A)
\left\{\begin{array}{rr}
0 & 1 \\
2 & 3
\end{array}\right\}
The latex.vector_delimiters method works similarly.
The way common rings and fields (integers, rational, reals, etc.) are typeset can be controlled by the
latex.blackboard_bold method. These sets are by default typeset in bold, but may optionally be written
in a double-struck fashion as sometimes done in written work. This is accomplished by redefining the \Bold{}
macro which is built-in to Sage.
sage: latex(QQ)
\Bold{Q}
sage: from sage.misc.latex import MathJax
sage: mj=MathJax()
sage: mj(QQ)
<html><script type="math/tex; mode=display">\newcommand{\Bold}[1]{\mathbf{#1}}\Bold{Q}
˓→</script></html>
sage: latex.blackboard_bold(True)
sage: mj(QQ)
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<html><script type="math/tex; mode=display">\newcommand{\Bold}[1]{\mathbb{#1}}\Bold{Q}
˓→</script></html>
sage: latex.blackboard_bold(False)
It is possible to take advantage of the extensible nature of TeX by adding in new macros and new packages. First,
individual macros can be added so that they are used when MathJax interprets a snippet of TeX in the notebook.
sage: latex.extra_macros()
''
sage: latex.add_macro("\\newcommand{\\foo}{bar}")
sage: latex.extra_macros()
'\\newcommand{\\foo}{bar}'
sage: var('x y')
(x, y)
sage: latex(x+y)
x + y
sage: from sage.misc.latex import MathJax
sage: mj=MathJax()
sage: mj(x+y)
<html><script type="math/tex; mode=display">\newcommand{\Bold}[1]{\mathbf{#1}}
˓→\newcommand{\foo}{bar}x + y</script></html>
Additional macros added this way will also be used in the event that the system-wide version of TeX is called on
something larger than MathJax can handle. The command latex_extra_preamble is used to build the preamble
of a complete LaTeX document, so the following illustrates how this is accomplished. As usual note the need for the
double-backslashes in the Python strings.
sage: latex.extra_macros('')
sage: latex.extra_preamble('')
sage: from sage.misc.latex import latex_extra_preamble
sage: print(latex_extra_preamble())
\newcommand{\ZZ}{\Bold{Z}}
...
\newcommand{\Bold}[1]{\mathbf{#1}}
sage: latex.add_macro("\\newcommand{\\foo}{bar}")
sage: print(latex_extra_preamble())
\newcommand{\ZZ}{\Bold{Z}}
...
\newcommand{\Bold}[1]{\mathbf{#1}}
\newcommand{\foo}{bar}
Again, for larger or more complicated LaTeX expressions, it is possible to add packages (or anything else) to the
preamble of the LaTeX file. Anything may be incorporated into the preamble with the latex.add_to_preamble
command, and the specialized command latex.add_package_to_preamble_if_available will first
check if a certain package is actually available before trying to add it to the preamble.
Here we add the geometry package to the preamble and use it to set the size of the region on the page that TeX will
use (effectively setting the margins). As usual, note the need for the double-backslashes in the Python strings.
sage: from sage.misc.latex import latex_extra_preamble
sage: latex.extra_macros('')
sage: latex.extra_preamble('')
sage: latex.add_to_preamble('\\usepackage{geometry}')
sage: latex.add_to_preamble('\\geometry{letterpaper,total={8in,10in}}')
sage: latex.extra_preamble()
'\\usepackage{geometry}\\geometry{letterpaper,total={8in,10in}}'
sage: print(latex_extra_preamble())
\usepackage{geometry}\geometry{letterpaper,total={8in,10in}}
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\newcommand{\ZZ}{\Bold{Z}}
...
\newcommand{\Bold}[1]{\mathbf{#1}}
A particular package may be added along with a check on its existence, as follows. As an example, we just illustrate
an attempt to add to the preamble a package that presumably does not exist.
sage: latex.extra_preamble('')
sage: latex.extra_preamble()
''
sage: latex.add_to_preamble('\\usepackage{foo-bar-unchecked}')
sage: latex.extra_preamble()
'\\usepackage{foo-bar-unchecked}'
sage: latex.add_package_to_preamble_if_available('foo-bar-checked')
sage: latex.extra_preamble()
'\\usepackage{foo-bar-unchecked}'
5.4 Customizing LaTeX Processing
It is also possible to control which variant of TeX is used for system-wide invocations, thus also influencing the nature
of the output. Similarly, it is also possible to control when the notebook will use MathJax (simple TeX snippets) or the
system-wide TeX installation (more complicated LaTeX expressions).
The latex.engine() command can be used to control if the system-wide executables latex , pdflatex or
xelatex are employed for more complicated LaTeX expressions. When view() is called from the sage commandline and the engine is set to latex , a dvi file is produced and Sage will use a dvi viewer (like xdvi) to display the
result. In contrast, using view() at the Sage command-line, when the engine is set to pdflatex , will produce a
PDF as the result and Sage will call your system’s utility for displaying PDF files (acrobat, okular, evince, etc.).
In the notebook, it is necessary to intervene in the decision as to whether MathJax will interpret a snippet of TeX, or
if the LaTeX is complicated enough that the system-wide installation of TeX should do the work instead. The device
is a list of strings, which if any one is discovered in a piece of LaTeX code signal the notebook to bypass MathJax
and invoke latex (or whichever executable is set by the latex.engine() command). This list is managed by the
latex.add_to_mathjax_avoid_list and latex.mathjax_avoid_list commands.
sage: latex.mathjax_avoid_list([])
sage: latex.mathjax_avoid_list()
[]
sage: latex.mathjax_avoid_list(['foo', 'bar'])
sage: latex.mathjax_avoid_list()
['foo', 'bar']
sage: latex.add_to_mathjax_avoid_list('tikzpicture')
sage: latex.mathjax_avoid_list()
['foo', 'bar', 'tikzpicture']
sage: latex.mathjax_avoid_list([])
sage: latex.mathjax_avoid_list()
[]
Suppose a LaTeX expression is produced in the notebook with view() or while the “Typeset” button is checked,
and then recognized as requiring the external LaTeX installation through the “mathjax avoid list.” Then the selected
executable (as specified by latex.engine() ) will process the LaTeX. However, instead of then spawning an
external viewer (which is the command-line behavior), Sage will attempt to convert the result into a single, tightlycropped image, which is then inserted into the worksheet as the output of the cell.
Just how this conversion proceeds depends on several factors – mostly which executable you have specified as the
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engine and which conversion utilities are available on your system. Four useful converters that will cover all eventualities are dvips , ps2pdf , dvipng and from the ImageMagick suite, convert . The goal is to produce a PNG
file as the output for inclusion back into the worksheet. When a LaTeX expression can be converted successfully to
a dvi by the latex engine, then dvipng should accomplish the conversion. If the LaTeX expression and chosen engine
creates a dvi with specials that dvipng cannot handle, then dvips will create a PostScript file. Such a PostScript file,
or a PDF file created by an engine such as pdflatex , is then processed into a PNG with the convert utility. The
presence of two of these converters can be tested with the have_dvipng() and have_convert() routines.
These conversions are done automatically if you have the necessary converters installed; if not, then an error message
is printed telling you what’s missing and where to download it.
For a concrete example of how complicated LaTeX expressions can be processed, see the example in the next section (An Example: Combinatorial Graphs with tkz-graph) for using the LaTeX tkz-graph package to produce
high-quality renderings of combinatorial graphs. For other examples, there are some pre-packaged test cases. To use
these, it is necessary to import the sage.misc.latex.latex_examples object, which is an instance of the
sage.misc.latex.LatexExamples class, as illustrated below. This class currently has examples of commutative diagrams, combinatorial graphs, knot theory and pstricks, which respectively exercise the following packages:
xy, tkz-graph, xypic, pstricks. After the import, use tab-completion on latex_examples to see the pre-packaged
examples. Calling each example will give you back some explanation about what is required to make the example
render properly. To actually see the examples, it is necessary to use view() (once the preamble, engine, etc are all
set properly).
sage: from sage.misc.latex import latex_examples
sage: latex_examples.diagram()
LaTeX example for testing display of a commutative diagram produced
by xypic.
To use, try to view this object -- it won't work. Now try
'latex.add_to_preamble("\\usepackage[matrix,arrow,curve,cmtip]{xy}")',
and try viewing again -- it should work in the command line but not
from the notebook. In the notebook, run
'latex.add_to_mathjax_avoid_list("xymatrix")' and try again -- you
should get a picture (a part of the diagram arising from a filtered
chain complex).
5.5 An Example: Combinatorial Graphs with tkz-graph
High-quality illustrations of combinatorial graphs (henceforth just “graphs”) are possible with the tkz-graph package. This package is built on top of the tikz front-end to the pgf library. So all of these components need to be
part of a system-wide TeX installation, and it may be possible that these components may not be at their most current
versions as packaged in some TeX implementations. So for best results, it could be necessary or advisable to install
these as part of your personal texmf tree. Creating, maintaining and customizing a system-wide or personal TeX installation is beyond the scope of this document, but it should be easy to find instructions. The necessary files are listed
in A Fully Capable TeX Installation.
Thus, to start we need to insure that the relevant packages are included by adding them to the preamble of the
eventual LaTeX document. The images of graphs do not form properly when a dvi file is used as an intermediate format, so it is best to set the latex engine to the pdflatex executable. At this point a command like
view(graphs.CompleteGraph(4)) should succeed at the Sage command-line and produce a PDF with an
appropriate image of the complete graph 𝐾4 .
For a similar experience in the notebook, it is necessary to disable MathJax processing of the LaTeX code for the graph
by using the “mathjax avoid list.” Graphs are included with a tikzpicture environment, so this is a good choice
for a string to include in the avoidance list. Now, view(graphs.CompleteGraph(4)) in a worksheet should
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call pdflatex to create a PDF and then the convert utility will extract a PNG graphic to insert into the output cell of
the worksheet. The following commands illustrate the steps to get graphs processed by LaTeX in the notebook.
sage: from sage.graphs.graph_latex import setup_latex_preamble
sage: setup_latex_preamble()
sage: latex.extra_preamble() # random - depends on system's TeX installation
'\\usepackage{tikz}\n\\usepackage{tkz-graph}\n\\usepackage{tkz-berge}\n'
sage: latex.engine('pdflatex')
sage: latex.add_to_mathjax_avoid_list('tikzpicture')
sage: latex.mathjax_avoid_list()
['tikz', 'tikzpicture']
At this point, a command like view(graphs.CompleteGraph(4)) should produce a graphic version of the
graph pasted into the notebook, having used pdflatex to process tkz-graph commands to realize the graph.
Note that there is a variety of options to affect how a graph is rendered in LaTeX via tkz-graph , which is again
outside the scope of this section, see the section of the Reference manual titled “LaTeX Options for Graphs” for
instructions and details.
5.6 A Fully Capable TeX Installation
Many of the more advanced features of the integration of TeX with Sage requires a system-wide installation of TeX.
Many versions of Linux have base TeX packages based on TeX-live, for OSX there is TeXshop and for Windows there
is MikTeX. The convert utility is part of the ImageMagick suite (which should be a package or an easy download),
and the three programs dvipng , ps2pdf , and dvips may be included with your TeX distribution. The first two
may also be obtained, respectively, from http://sourceforge.net/projects/dvipng/ and as part of Ghostscript.
Rendering combinatorial graphs requires a recent version of the PGF library, and the files tkz-graph.sty ,
tkz-arith.sty and perhaps tkz-berge.sty , all from the Altermundus site.
5.7 External Programs
There are three programs available to further integrate TeX and Sage. The first is sagetex. A concise description of
sagetex is that it is a collection of TeX macros that allow a LaTeX document to include instructions to have Sage
compute various objects and/or format objects using the latex() support built in to Sage. So as an intermediate
step of compiling a LaTeX document, all of the computational and LaTeX-formatting features of Sage can be handled
automatically. As an example, a mathematics examination can maintain a correct correspondence between questions
and answers by using sagetex to have Sage compute one from the other. See Using SageTeX for more information.
tex2sws begins with a LaTeX document, but defines extra environments for the placement of Sage code. When
processed with the right tools, the result is a Sage worksheet, with content properly formatted for MathJax and the
Sage code incorporated as input cells. So a textbook or article can be authored in LaTeX, blocks of Sage code included,
and the whole document can be transformed into a Sage worksheet where the mathematical text is nicely formatted
and the blocks of Sage code are “live.” Currently in development, see tex2sws @ BitBucket for more information.
sws2tex reverses the process by beginning with a Sage worksheet and converting it to legitimate LaTeX for subsequent
processing with all the tools available for LaTeX documents. Currently in development, see sws2tex @ BitBucket for
more information.
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CHAPTER
SIX
PROGRAMMING
6.1 Loading and Attaching Sage files
Next we illustrate how to load programs written in a separate file into Sage. Create a file called example.sage
with the following content:
print("Hello World")
print(2^3)
You can read in and execute example.sage file using the load command.
sage: load("example.sage")
Hello World
8
You can also attach a Sage file to a running session using the attach command:
sage: attach("example.sage")
Hello World
8
Now if you change example.sage and enter one blank line into Sage (i.e., hit return ), then the contents of
example.sage will be automatically reloaded into Sage.
In particular, attach automatically reloads a file whenever it changes, which is handy when debugging code,
whereas load only loads a file once.
When Sage loads example.sage it converts it to Python, which is then executed by the Python interpreter.
This conversion is minimal; it mainly involves wrapping integer literals in Integer() floating point literals in
RealNumber() , replacing ^ ‘s by ** ‘s, and replacing e.g., R.2 by R.gen(2) . The converted version of
example.sage is contained in the same directory as example.sage and is called example.sage.py . This
file contains the following code:
print("Hello World")
print(Integer(2)**Integer(3))
Integer literals are wrapped and the ^ is replaced by a ** . (In Python ^ means “exclusive or” and ** means
“exponentiation”.)
This preparsing is implemented in sage/misc/interpreter.py .)
You can paste multi-line indented code into Sage as long as there are newlines to make new blocks (this is not necessary
in files). However, the best way to enter such code into Sage is to save it to a file and use attach , as described above.
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6.2 Creating Compiled Code
Speed is crucial in mathematical computations. Though Python is a convenient very high-level language, certain
calculations can be several orders of magnitude faster than in Python if they are implemented using static types in a
compiled language. Some aspects of Sage would have been too slow if it had been written entirely in Python. To deal
with this, Sage supports a compiled “version” of Python called Cython ([Cyt] and [Pyr]). Cython is simultaneously
similar to both Python and C. Most Python constructions, including list comprehensions, conditional expressions, code
like += are allowed; you can also import code that you have written in other Python modules. Moreover, you can
declare arbitrary C variables, and arbitrary C library calls can be made directly. The resulting code is converted to C
and compiled using a C compiler.
In order to make your own compiled Sage code, give the file an .spyx extension (instead of .sage ). If you are
working with the command-line interface, you can attach and load compiled code exactly like with interpreted code (at
the moment, attaching and loading Cython code is not supported with the notebook interface). The actual compilation
is done “behind the scenes” without your having to do anything explicit. The compiled shared object library is stored
under $HOME/.sage/temp/hostname/pid/spyx . These files are deleted when you exit Sage.
NO Sage preparsing is applied to spyx files, e.g., 1/3 will result in 0 in a spyx file instead of the rational number 1/3.
If foo is a function in the Sage library, to use it from a spyx file import sage.all and use sage.all.foo .
import sage.all
def foo(n):
return sage.all.factorial(n)
6.2.1 Accessing C Functions in Separate Files
It is also easy to access C functions defined in separate *.c files. Here’s an example. Create files test.c and
test.spyx in the same directory with contents:
The pure C code: test.c
int add_one(int n) {
return n + 1;
}
The Cython code: test.spyx :
cdef extern from "test.c":
int add_one(int n)
def test(n):
return add_one(n)
Then the following works:
sage: attach("test.spyx")
Compiling (...)/test.spyx...
sage: test(10)
11
If an additional library foo is needed to compile the C code generated from a Cython file, add the line clib foo to
the Cython source. Similarly, an additional C file bar can be included in the compilation with the declaration cfile
bar .
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6.3 Standalone Python/Sage Scripts
The following standalone Sage script factors integers, polynomials, etc:
#!/usr/bin/env sage
import sys
from sage.all import *
if len(sys.argv) != 2:
print("Usage: %s <n>" % sys.argv[0])
print("Outputs the prime factorization of n.")
sys.exit(1)
print(factor(sage_eval(sys.argv[1])))
In order to use this script, your SAGE_ROOT must be in your PATH. If the above script is called factor , here is an
example usage:
bash $
2 * 17
bash $
(2*x -
./factor 2006
* 59
./factor "32*x^5-1"
1) * (16*x^4 + 8*x^3 + 4*x^2 + 2*x + 1)
6.4 Data Types
Every object in Sage has a well-defined type. Python has a wide range of basic built-in types, and the Sage library
adds many more. Some built-in Python types include strings, lists, tuples, ints and floats, as illustrated:
sage: s = "sage"; type(s)
<... 'str'>
sage: s = 'sage'; type(s)
# you can use either single or double quotes
<... 'str'>
sage: s = [1,2,3,4]; type(s)
<... 'list'>
sage: s = (1,2,3,4); type(s)
<... 'tuple'>
sage: s = int(2006); type(s)
<... 'int'>
sage: s = float(2006); type(s)
<... 'float'>
To this, Sage adds many other types. E.g., vector spaces:
sage: V = VectorSpace(QQ, 1000000); V
Vector space of dimension 1000000 over Rational Field
sage: type(V)
<class 'sage.modules.free_module.FreeModule_ambient_field_with_category'>
Only certain functions can be called on V . In other math software systems, these would be called using the “functional”
notation foo(V,...) . In Sage, certain functions are attached to the type (or class) of V , and are called using an
object-oriented syntax like in Java or C++, e.g., V.foo(...) . This helps keep the global namespace from being
polluted with tens of thousands of functions, and means that many different functions with different behavior can be
named foo, without having to use type-checking of arguments (or case statements) to decide which to call. Also, if you
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reuse the name of a function, that function is still available (e.g., if you call something zeta , then want to compute
the value of the Riemann-Zeta function at 0.5, you can still type s=.5; s.zeta() ).
sage: zeta = -1
sage: s=.5; s.zeta()
-1.46035450880959
In some very common cases, the usual functional notation is also supported for convenience and because mathematical
expressions might look confusing using object-oriented notation. Here are some examples.
sage: n = 2; n.sqrt()
sqrt(2)
sage: sqrt(2)
sqrt(2)
sage: V = VectorSpace(QQ,2)
sage: V.basis()
[
(1, 0),
(0, 1)
]
sage: basis(V)
[
(1, 0),
(0, 1)
]
sage: M = MatrixSpace(GF(7), 2); M
Full MatrixSpace of 2 by 2 dense matrices over Finite Field of size 7
sage: A = M([1,2,3,4]); A
[1 2]
[3 4]
sage: A.charpoly('x')
x^2 + 2*x + 5
sage: charpoly(A, 'x')
x^2 + 2*x + 5
To list all member functions for 𝐴, use tab completion. Just type A. , then type the [tab] key on your keyboard, as
explained in Reverse Search and Tab Completion.
6.5 Lists, Tuples, and Sequences
The list data type stores elements of arbitrary type. Like in C, C++, etc. (but unlike most standard computer algebra
systems), the elements of the list are indexed starting from 0:
sage: v = [2, 3, 5, 'x', SymmetricGroup(3)]; v
[2, 3, 5, 'x', Symmetric group of order 3! as a permutation group]
sage: type(v)
<... 'list'>
sage: v[0]
2
sage: v[2]
5
(When indexing into a list, it is OK if the index is not a Python int!) A Sage Integer (or Rational, or anything with an
__index__ method) will work just fine.
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sage:
sage:
3
sage:
sage:
3
sage:
3
v = [1,2,3]
v[2]
n = 2
v[n]
# SAGE Integer
# Perfectly OK!
v[int(n)]
# Also OK.
The range function creates a list of Python int’s (not Sage Integers):
sage: range(1, 15)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
This is useful when using list comprehensions to construct lists:
sage: L = [factor(n) for n in range(1, 15)]
sage: L
[1, 2, 3, 2^2, 5, 2 * 3, 7, 2^3, 3^2, 2 * 5, 11, 2^2 * 3, 13, 2 * 7]
sage: L[12]
13
sage: type(L[12])
<class 'sage.structure.factorization_integer.IntegerFactorization'>
sage: [factor(n) for n in range(1, 15) if is_odd(n)]
[1, 3, 5, 7, 3^2, 11, 13]
For more about how to create lists using list comprehensions, see [PyT].
List slicing is a wonderful feature. If L is a list, then L[m:n] returns the sublist of L obtained by starting at the 𝑚𝑡ℎ
element and stopping at the (𝑛 − 1)𝑠𝑡 element, as illustrated below.
sage: L = [factor(n) for n in range(1, 20)]
sage: L[4:9]
[5, 2 * 3, 7, 2^3, 3^2]
sage: L[:4]
[1, 2, 3, 2^2]
sage: L[14:4]
[]
sage: L[14:]
[3 * 5, 2^4, 17, 2 * 3^2, 19]
Tuples are similar to lists, except they are immutable, meaning once they are created they can’t be changed.
sage: v = (1,2,3,4); v
(1, 2, 3, 4)
sage: type(v)
<... 'tuple'>
sage: v[1] = 5
Traceback (most recent call last):
...
TypeError: 'tuple' object does not support item assignment
Sequences are a third list-oriented Sage type. Unlike lists and tuples, Sequence is not a built-in Python type. By default,
a sequence is mutable, but using the Sequence class method set_immutable , it can be set to be immutable, as
the following example illustrates. All elements of a sequence have a common parent, called the sequences universe.
sage: v = Sequence([1,2,3,4/5])
sage: v
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[1, 2, 3, 4/5]
sage: type(v)
<class 'sage.structure.sequence.Sequence_generic'>
sage: type(v[1])
<type 'sage.rings.rational.Rational'>
sage: v.universe()
Rational Field
sage: v.is_immutable()
False
sage: v.set_immutable()
sage: v[0] = 3
Traceback (most recent call last):
...
ValueError: object is immutable; please change a copy instead.
Sequences derive from lists and can be used anywhere a list can be used:
sage: v = Sequence([1,2,3,4/5])
sage: isinstance(v, list)
True
sage: list(v)
[1, 2, 3, 4/5]
sage: type(list(v))
<... 'list'>
As another example, basis for vector spaces are immutable sequences, since it’s important that you don’t change them.
sage: V = QQ^3; B = V.basis(); B
[
(1, 0, 0),
(0, 1, 0),
(0, 0, 1)
]
sage: type(B)
<class 'sage.structure.sequence.Sequence_generic'>
sage: B[0] = B[1]
Traceback (most recent call last):
...
ValueError: object is immutable; please change a copy instead.
sage: B.universe()
Vector space of dimension 3 over Rational Field
6.6 Dictionaries
A dictionary (also sometimes called an associative array) is a mapping from ‘hashable’ objects (e.g., strings, numbers,
and tuples of such; see the Python documentation http://docs.python.org/tut/node7.html and http://docs.python.org/
lib/typesmapping.html for details) to arbitrary objects.
sage: d = {1:5, 'sage':17, ZZ:GF(7)}
sage: type(d)
<... 'dict'>
sage: d.keys()
[1, 'sage', Integer Ring]
sage: d['sage']
17
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sage: d[ZZ]
Finite Field of size 7
sage: d[1]
5
The third key illustrates that the indexes of a dictionary can be complicated, e.g., the ring of integers.
You can turn the above dictionary into a list with the same data:
sage: list(d.items())
[(1, 5), ('sage', 17), (Integer Ring, Finite Field of size 7)]
A common idiom is to iterate through the pairs in a dictionary:
sage: d = {2:4, 3:9, 4:16}
sage: [a*b for a, b in d.items()]
[8, 27, 64]
A dictionary is unordered, as the last output illustrates.
6.7 Sets
Python has a built-in set type. The main feature it offers is very fast lookup of whether an element is in the set or not,
along with standard set-theoretic operations.
sage: X = set([1,19,'a']);
Y = set([1,1,1, 2/3])
sage: X
# random sort order
{1, 19, 'a'}
sage: X == set(['a', 1, 1, 19])
True
sage: Y
{2/3, 1}
sage: 'a' in X
True
sage: 'a' in Y
False
sage: X.intersection(Y)
{1}
Sage also has its own set type that is (in some cases) implemented using the built-in Python set type, but has a little bit
of extra Sage-related functionality. Create a Sage set using Set(...) . For example,
sage: X = Set([1,19,'a']);
Y = Set([1,1,1, 2/3])
sage: X
# random sort order
{'a', 1, 19}
sage: X == Set(['a', 1, 1, 19])
True
sage: Y
{1, 2/3}
sage: X.intersection(Y)
{1}
sage: print(latex(Y))
\left\{1, \frac{2}{3}\right\}
sage: Set(ZZ)
Set of elements of Integer Ring
6.7. Sets
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6.8 Iterators
Iterators are a recent addition to Python that are particularly useful in mathematics applications. Here are several
examples; see [PyT] for more details. We make an iterator over the squares of the nonnegative integers up to 10000000.
sage:
sage:
0
sage:
1
sage:
4
v = (n^2 for n in xrange(10000000))
next(v)
next(v)
next(v)
We create an iterate over the primes of the form 4𝑝 + 1 with 𝑝 also prime, and look at the first few values.
sage: w = (4*p + 1 for p in Primes() if is_prime(4*p+1))
sage: w
# in the next line, 0xb0853d6c is a random 0x number
<generator object at 0xb0853d6c>
sage: next(w)
13
sage: next(w)
29
sage: next(w)
53
Certain rings, e.g., finite fields and the integers have iterators associated to them:
sage: [x for x in GF(7)]
[0, 1, 2, 3, 4, 5, 6]
sage: W = ((x,y) for x in ZZ for y in ZZ)
sage: next(W)
(0, 0)
sage: next(W)
(0, 1)
sage: next(W)
(0, -1)
6.9 Loops, Functions, Control Statements, and Comparisons
We have seen a few examples already of some common uses of for loops. In Python, a for loop has an indented
structure, such as
>>> for i in range(5):
...
print(i)
...
0
1
2
3
4
Note the colon at the end of the for statement (there is no “do” or “od” as in GAP or Maple), and the indentation before
the “body” of the loop, namely print(i) . This indentation is important. In Sage, the indentation is automatically
put in for you when you hit enter after a ”:”, as illustrated below.
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sage: for i in range(5):
....:
print(i) # now hit enter twice
....:
0
1
2
3
4
The symbol = is used for assignment. The symbol == is used to check for equality:
sage: for i in range(15):
....:
if gcd(i,15) == 1:
....:
print(i)
....:
1
2
4
7
8
11
13
14
Keep in mind how indentation determines the block structure for if , for , and while statements:
sage: def legendre(a,p):
....:
is_sqr_modp=-1
....:
for i in range(p):
....:
if a % p == i^2 % p:
....:
is_sqr_modp=1
....:
return is_sqr_modp
sage: legendre(2,7)
1
sage: legendre(3,7)
-1
Of course this is not an efficient implementation of the Legendre symbol! It is meant to illustrate various aspects
of Python/Sage programming. The function {kronecker}, which comes with Sage, computes the Legendre symbol
efficiently via a C-library call to PARI.
Finally, we note that comparisons, such as == , != , <= , >= , > , < , between numbers will automatically convert both
numbers into the same type if possible:
sage: 2 < 3.1; 3.1 <= 1
True
False
sage: 2/3 < 3/2;
3/2 < 3/1
True
True
Use bool for symbolic inequalities:
sage: x < x + 1
x < x + 1
sage: bool(x < x + 1)
True
6.9. Loops, Functions, Control Statements, and Comparisons
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When comparing objects of different types in Sage, in most cases Sage tries to find a canonical coercion of both
objects to a common parent (see Parents, Conversion and Coercion for more details). If successful, the comparison
is performed between the coerced objects; if not successful, the objects are considered not equal. For testing whether
two variables reference the same object use is . As we see in this example, the Python int 1 is unique, but the Sage
Integer 1 is not:
sage:
False
sage:
True
sage:
False
sage:
True
1 is 2/2
int(1) is int(2)/int(2)
# optional - python2
1 is 1
1 == 2/2
In the following two lines, the first equality is False because there is no canonical morphism Q → F5 , hence no
canonical way to compare the 1 in F5 to the 1 ∈ Q. In contrast, there is a canonical map Z → F5 , hence the second
comparison is True . Note also that the order doesn’t matter.
sage: GF(5)(1) == QQ(1); QQ(1) == GF(5)(1)
False
False
sage: GF(5)(1) == ZZ(1); ZZ(1) == GF(5)(1)
True
True
sage: ZZ(1) == QQ(1)
True
WARNING: Comparison in Sage is more restrictive than in Magma, which declares the 1 ∈ F5 equal to 1 ∈ Q.
sage: magma('GF(5)!1 eq Rationals()!1')
true
# optional - magma
6.10 Profiling
Section Author: Martin Albrecht ([email protected])
“Premature optimization is the root of all evil.” - Donald Knuth
Sometimes it is useful to check for bottlenecks in code to understand which parts take the most computational time;
this can give a good idea of which parts to optimize. Python and therefore Sage offers several profiling–as this process
is called–options.
The simplest to use is the prun command in the interactive shell. It returns a summary describing which functions
took how much computational time. To profile (the currently slow! - as of version 1.0) matrix multiplication over
finite fields, for example, do:
sage: k,a = GF(2**8, 'a').objgen()
sage: A = Matrix(k,10,10,[k.random_element() for _ in range(10*10)])
sage: %prun B = A*A
32893 function calls in 1.100 CPU seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
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12127
2000
1000
1903
1900
900
1
2105
...
0.160
0.150
0.120
0.120
0.090
0.080
0.070
0.070
0.000
0.000
0.000
0.000
0.000
0.000
0.070
0.000
0.160
0.280
0.370
0.200
0.220
0.260
1.100
0.070
0.000
0.000
0.000
0.000
0.000
0.000
1.100
0.000
:0(isinstance)
matrix.py:2235(__getitem__)
finite_field_element.py:392(__mul__)
finite_field_element.py:47(__init__)
finite_field_element.py:376(__compat)
finite_field_element.py:380(__add__)
matrix.py:864(__mul__)
matrix.py:282(ncols)
Here ncalls is the number of calls, tottime is the total time spent in the given function (and excluding time
made in calls to sub-functions), percall is the quotient of tottime divided by ncalls . cumtime is the
total time spent in this and all sub-functions (i.e., from invocation until exit), percall is the quotient of cumtime
divided by primitive calls, and filename:lineno(function) provides the respective data of each function.
The rule of thumb here is: The higher the function in that listing, the more expensive it is. Thus it is more interesting
for optimization.
As usual, prun? provides details on how to use the profiler and understand the output.
The profiling data may be written to an object as well to allow closer examination:
sage: %prun -r A*A
sage: stats = _
sage: stats?
Note: entering stats = prun -r A\*A displays a syntax error message because prun is an IPython shell command, not a regular function.
For a nice graphical representation of profiling data, you can use the hotshot profiler, a small script called
hotshot2cachetree and the program kcachegrind (Unix only). The same example with the hotshot profiler:
sage:
sage:
sage:
sage:
sage:
k,a = GF(2**8, 'a').objgen()
A = Matrix(k,10,10,[k.random_element() for _ in range(10*10)])
import hotshot
filename = "pythongrind.prof"
prof = hotshot.Profile(filename, lineevents=1)
sage: prof.run("A*A")
<hotshot.Profile instance at 0x414c11ec>
sage: prof.close()
This results in a file pythongrind.prof in the current working directory. It can now be converted to the
cachegrind format for visualization.
On a system shell, type
hotshot2calltree -o cachegrind.out.42 pythongrind.prof
The output file cachegrind.out.42 can now be examined with kcachegrind . Please note that the naming
convention cachegrind.out.XX needs to be obeyed.
6.10. Profiling
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CHAPTER
SEVEN
USING SAGETEX
The SageTeX package allows you to embed the results of Sage computations into a LaTeX document. To use it, you
will need to “install” it first (see Make SageTeX known to TeX).
7.1 An example
Here is a very brief example of using SageTeX. The full documentation can be found in
SAGE_ROOT/local/share/doc/sagetex , where SAGE_ROOT
is the directory where your
Sage installation is located.
That directory contains the documentation and an example file.
See
SAGE_ROOT/local/share/texmf/tex/latex/sagetex for some possibly useful Python scripts.
To see how SageTeX works, follow the directions for installing SageTeX (in Make SageTeX known to TeX) and copy
the following text into a file named, say, st_example.tex :
Warning: The text below will have several errors about unknown control sequences if you are viewing this in the
“live” help. Use the static version to see the correct text.
\documentclass{article}
\usepackage{sagetex}
\begin{document}
Using Sage\TeX, one can use Sage to compute things and put them into
your \LaTeX{} document. For example, there are
$\sage{number_of_partitions(1269)}$ integer partitions of $1269$.
You don't need to compute the number yourself, or even cut and paste
it from somewhere.
Here's some Sage code:
\begin{sageblock}
f(x) = exp(x) * sin(2*x)
\end{sageblock}
The second derivative of $f$ is
\[
\frac{\mathrm{d}^{2}}{\mathrm{d}x^{2}} \sage{f(x)} =
\sage{diff(f, x, 2)(x)}.
\]
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Here's a plot of $f$ from $-1$ to $1$:
\sageplot{plot(f, -1, 1)}
\end{document}
Run LaTeX on st_example.tex as usual. Note that LaTeX will have some complaints, which will include:
Package sagetex Warning: Graphics file
sage-plots-for-st_example.tex/plot-0.eps on page 1 does not exist. Plot
command is on input line 25.
Package sagetex Warning: There were undefined Sage formulas and/or
plots. Run Sage on st_example.sagetex.sage, and then run LaTeX on
st_example.tex again.
Notice that, in addition to the usual collection of files produced by LaTeX, there is a file called
st_example.sagetex.sage . That is a Sage script produced when you run LaTeX on st_example.tex
. The warning message told you to run Sage on st_example.sagetex.sage , so take its advice and do that.
It will tell you to run LaTeX on st_example.tex again, but before you do that, notice that a new file has been
created: st_example.sagetex.sout . That file contains the results of Sage’s computations, in a format that
LaTeX can use to insert into your text. A new directory containing an EPS file of your plot has also been created. Run
LaTeX again and you’ll see that everything that Sage computed and plotted is now included in your document.
The different macros used above should be pretty easy to understand. A sageblock environment typesets your code
verbatim and also executes the code when you run Sage. When you do \sage{foo} , the result put into your document is whatever you get from running latex(foo) inside Sage. Plot commands are a bit more complicated, but in
their simplest form, \sageplot{foo} inserts the image you get from doing foo.save('filename.eps') .
In general, the mantra is:
• run LaTeX on your .tex file;
• run Sage on the generated .sage file;
• run LaTeX again.
You can omit running Sage if you haven’t changed around any Sage commands in your document.
There’s a lot more to SageTeX, and since both Sage and LaTeX are complex, powerful tools, it’s a good idea to read
the documentation for SageTeX, which is in SAGE_ROOT/local/share/doc/sagetex .
7.2 Make SageTeX known to TeX
Sage is largely self-contained, but some parts do need some intervention to work properly. SageTeX is one such part.
The SageTeX package allows one to embed computations and plots from Sage into a LaTeX document. SageTeX is
installed in Sage by default, but to use SageTeX with your LaTeX documents, you need to make your TeX installation
aware of it before it will work.
The key to this is that TeX needs to be able to find sagetex.sty , which can be found in
SAGE_ROOT/local/share/texmf/tex/latex/sagetex/ , where SAGE_ROOT is the directory where
you built or installed Sage. If TeX can find sagetex.sty , then SageTeX will work. There are several ways to
accomplish this.
• The first and simplest way is simply to copy sagetex.sty into the same directory as your LaTeX document.
Since the current directory is always searched when typesetting a document, this will always work.
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There are a couple small problems with this, however: the first is that you will end up with many unnecessary
copies of sagetex.sty scattered around your computer. The second and more serious problem is that if you
upgrade Sage and get a new version of SageTeX, the Python code and LaTeX code for SageTeX may no longer
match, causing errors.
• The second way is to use the TEXINPUTS environment variable. If you are using the bash shell, you can do
export TEXINPUTS="SAGE_ROOT/local/share/texmf//:"
where SAGE_ROOT is the location of your Sage installation. Note that the double slash and colon at the end of
that line are important. Thereafter, TeX and friends will find the SageTeX style file. If you want to make this
change permanent, you can add the above line to your .bashrc file. If you are using a different shell, you may
have to modify the above command to make the environment variable known; see your shell’s documentation
for how to do that.
One flaw with this method is that if you use applications like TeXShop, Kile, or Emacs/AucTeX, they will not
necessarily pick up the environment variable, since when they run LaTeX, they may do so outside your usual
shell environment.
If you ever move your Sage installation, or install a new version into a new directory, you’ll need to update the
above command to reflect the new value of SAGE_ROOT .
• The third (and best) way to make TeX aware of sagetex.sty is to copy that file into a convenient place in
your home directory. In most TeX distributions, the texmf directory in your home directory is automatically
searched for packages. To find out exactly what this directory is, do the following on the command line:
kpsewhich -var-value=TEXMFHOME
which will print out a directory, such as /home/drake/texmf or /Users/drake/Library/texmf .
Copy the tex/ directory from SAGE_ROOT/local/share/texmf/ into your home texmf directory
with a command like
cp -R SAGE_ROOT/local/share/texmf/tex TEXMFHOME
where SAGE_ROOT is, as usual, replaced with the location of your Sage installation and TEXMFHOME is the
result of the kpsewhich command above.
If you upgrade Sage and discover that SageTeX no longer works, you can simply repeat these steps and the Sage
and TeX parts of SageTeX will again be synchronized.
• For installation on a multiuser system, you just modify the above instructions appropriately to copy
sagetex.sty into a systemwide TeX directory. Instead of the directory TEXMFHOME , probably the best
choice is to use the result of
kpsewhich -var-value=TEXMFLOCAL
which will likely produce something like /usr/local/share/texmf . Copy the tex directory as above
into the TEXMFLOCAL directory. Now you need to update TeX’s database of packages, which you can do
simply by running
texhash TEXMFLOCAL
as root, replacing TEXMFLOCAL appropriately. Now all users of your system will have access to the LaTeX
package, and if they can also run Sage, they will be able to use SageTeX.
7.2. Make SageTeX known to TeX
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Warning: it’s very important that the file sagetex.sty that LaTeX uses when typesetting your document
match the version of SageTeX that Sage is using. If you upgrade your Sage installation, you really should delete
all the old versions of sagetex.sty floating around.
Because of this problem, we recommend copying the SageTeX files into your home directory’s texmf directory
(the third method above). Then there is only one thing you need to do (copy a directory) when you upgrade Sage
to insure that SageTeX will work properly.
7.3 SageTeX documentation
While not strictly part of installation, it bears mentioning here that the documentation for SageTeX is maintained
in SAGE_ROOT/local/share/doc/sagetex/sagetex.pdf . There is also an example file in the same
directory – see example.tex and example.pdf , the pre-built result of typesetting that file with LaTeX and
Sage. You can also get those files from the SageTeX bitbucket page.
7.4 SageTeX and TeXLive
One potentially confusing issue is that the popular TeX distribution TeXLive 2009 includes SageTeX. This may seem
nice, but with SageTeX, it’s important that the Sage bits and LaTeX bits be synchronized – which is a problem in
this case, since both Sage and SageTeX are updated frequently, and TeXLive is not. While at the time of this writing
(March 2013), many Linux distributions have moved on to more recent releases of TeXLive, the 2009 release lingers
and is, in fact, the source of most bug reports about SageTeX!
Because of this, it is strongly recommended that you always install the LaTeX part of SageTeX from Sage, as described
above. The instructions above will insure that both halves of SageTeX are compatible and will work properly. Using
TeXLive to provide the LaTeX side of SageTeX is not supported.
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CHAPTER
EIGHT
AFTERWORD
8.1 Why Python?
8.1.1 Advantages of Python
The primary implementation language of Sage is Python (see [Py]), though code that must be fast is implemented in
a compiled language. Python has several advantages:
• Object saving is well-supported in Python. There is extensive support in Python for saving (nearly) arbitrary
objects to disk files or a database.
• Excellent support for documentation of functions and packages in the source code, including automatic extraction of documentation and automatic testing of all examples. The examples are automatically tested regularly
and guaranteed to work as indicated.
• Memory management: Python now has a well thought out and robust memory manager and garbage collector
that correctly deals with circular references, and allows for local variables in files.
• Python has many packages available now that might be of great interest to users of Sage: numerical analysis
and linear algebra, 2D and 3D visualization, networking (for distributed computations and servers, e.g., via
twisted), database support, etc.
• Portability: Python is easy to compile from source on most platforms in minutes.
• Exception handling: Python has a sophisticated and well thought out system of exception handling, whereby
programs gracefully recover even if errors occur in code they call.
• Debugger: Python includes a debugger, so when code fails for some reason, the user can access an extensive
stack trace, inspect the state of all relevant variables, and move up and down the stack.
• Profiler: There is a Python profiler, which runs code and creates a report detailing how many times and for how
long each function was called.
• A Language: Instead of writing a new language for mathematics as was done for Magma, Maple, Mathematica,
Matlab, GP/PARI, GAP, Macaulay 2, Simath, etc., we use the Python language, which is a popular computer
language that is being actively developed and optimized by hundreds of skilled software engineers. Python is a
major open-source success story with a mature development process (see [PyDev]).
8.1.2 The Pre-Parser: Differences between Sage and Python
Some mathematical aspects of Python can be confusing, so Sage behaves differently from Python in several ways.
• Notation for exponentiation: ** versus ^ . In Python, ^ means “xor”, not exponentiation, so in Python we
have
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>>> 2^8
10
>>> 3^2
1
>>> 3**2
9
This use of ^ may appear odd, and it is inefficient for pure math research, since the “exclusive or” function
is rarely used. For convenience, Sage pre-parses all command lines before passing them to Python, replacing
instances of ^ that are not in strings with ** :
sage: 2^8
256
sage: 3^2
9
sage: "3^2"
'3^2'
The bitwise xor operator in Sage is ^^ . This also works for the inplace operator ^^= :
sage:
1
sage:
sage:
sage:
10
3^^2
a = 2
a ^^= 8
a
• Integer division: The Python expression 2/3 does not behave the way mathematicians might expect. In
Python2, if m and n are ints, then m/n is also an int, namely the quotient of m divided by n . Therefore
2/3=0 . In Python3, 2/3 returns the floating point number 0.6666... . In both Python2 and Python3, //
is the Euclidean division and 2//3 returns 0 .
We deal with this in the Sage interpreter, by wrapping integer literals in Integer( ) and making division a
constructor for rational numbers. For example:
sage: 2/3
2/3
sage: (2/3).parent()
Rational Field
sage: 2//3
0
sage: int(2)/int(3) # not tested, python2
0
• Long integers: Python has native support for arbitrary precision integers, in addition to C-int’s. These are
significantly slower than what GMP provides, and have the property that they print with an L at the end to
distinguish them from int’s (and this won’t change any time soon). Sage implements arbitrary precision integers
using the GMP C-library, and these print without an L .
Rather than modifying the Python interpreter (as some people have done for internal projects), we use the Python
language exactly as is, and write a pre-parser for IPython so that the command line behavior of IPython is what a
mathematician expects. This means any existing Python code can be used in Sage. However, one must still obey the
standard Python rules when writing packages that will be imported into Sage.
(To install a Python library, for example that you have found on the Internet, follow the directions, but run sage
-python instead of python . Very often this means typing sage -python setup.py install .)
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8.2 I would like to contribute somehow. How can I?
If you would like to contribute to Sage, your help will be greatly appreciated! It can range from substantial code
contributions to adding to the Sage documentation to reporting bugs.
Browse the Sage web page for information for developers; among other things, you can find a long list of Sage-related
projects ordered by priority and category. The Sage Developer’s Guide has helpful information, as well, and you can
also check out the sage-devel Google group.
8.3 How do I reference Sage?
If you write a paper using Sage, please reference computations done with Sage by including
[Sage] William A. Stein et al., Sage Mathematics Software (Version 4.3).
The Sage Development Team, 2009, http://www.sagemath.org.
in your bibliography (replacing 4.3 with the version of Sage you used). Moreover, please attempt to track down
what components of Sage are used for your computation, e.g., PARI?, GAP?, Singular? Maxima? and also cite those
systems. If you are in doubt about what software your computation uses, feel free to ask on the sage-devel Google
group. See Univariate Polynomials for further discussion of this point.
If you happen to have just read straight through this tutorial, and have some sense of how long it took you, please let
us know on the sage-devel Google group.
Have fun with Sage!
8.2. I would like to contribute somehow. How can I?
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CHAPTER
NINE
APPENDIX
9.1 Arithmetical binary operator precedence
What is 3^2*4 + 2%5 ? The value (38) is determined by this “operator precedence table”. The table below is based
on the table in § 5.14 of the Python Language Reference Manual by G. Rossum and F. Drake. the operations are listed
here in increasing order of precedence.
Operators
or
and
not
in, not in
is, is not
>, <=, >, >=, ==, !=
+, *, /, %
**, ^
Description
boolean or
boolean and
boolean not
membership
identity test
comparison
addition, subtraction
multiplication, division, remainder
exponentiation
Therefore, to compute 3^2*4 + 2%5 , Sage brackets the computation this way: ((3^2)*4) + (2%5) . Thus,
first compute 3^2 , which is 9 , then compute both (3^2)*4 and 2%5 , and finally add these.
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CHAPTER
TEN
BIBLIOGRAPHY
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Chapter 10. Bibliography
CHAPTER
ELEVEN
INDICES AND TABLES
• genindex
• modindex
• search
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Chapter 11. Indices and tables
BIBLIOGRAPHY
[Cyt] Cython, http://www.cython.org.
[Dive] Dive into Python, Freely available online at http://www.diveintopython.net/.
[GAP] The GAP Group, GAP - Groups, Algorithms, and Programming, Version 4.4; 2005, http://www.gap-system.
org
[GAPkg] GAP Packages, http://www.gap-system.org/Packages/packages.html
[GP] PARI/GP http://pari.math.u-bordeaux.fr/.
[Ip] The IPython shell http://ipython.scipy.org.
[Jmol] Jmol: an open-source Java viewer for chemical structures in 3D http://www.jmol.org/.
[Mag] Magma http://magma.maths.usyd.edu.au/magma/.
[Max] Maxima http://maxima.sf.net/
[NagleEtAl2004] Nagle, Saff, and Snider. Fundamentals of Differential Equations. 6th edition, Addison-Wesley,
2004.
[Py] The Python language http://www.python.org/ Reference Manual http://docs.python.org/ref/ref.html.
[PyDev] Python Developer’s Guide https://docs.python.org/devguide/.
[Pyr] Pyrex, http://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/.
[PyT] The Python Tutorial http://www.python.org/.
[SA] Sage web site http://www.sagemath.org/.
[Si] G.-M. Greuel, G. Pfister, and H. Schönemann. Singular 3.0. A Computer Algebra System for Polynomial Computations. Center for Computer Algebra, University of Kaiserslautern (2005). http://www.singular.uni-kl.de.
[SJ] William Stein, David Joyner, Sage: System for Algebra and Geometry Experimentation, Comm. Computer Algebra {39}(2005)61-64.
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Sage Tutorial, Release 7.6
106
Bibliography
INDEX
E
EDITOR, 56
environment variable
EDITOR, 56
107
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