# Welcome to Sage Sandpiles’s documentation!¶

## Introduction¶

Sage Sandpiles is a package for calculations involving Dhar’s abelian sandpile model (ASM) using the open-source mathematics software, Sage. A brief introduction to the ASM follows. For a more thorough introduction, the paper Chip-Firing and Rotor-Routing on Directed Graphs [H], by Holroyd et al. is recommended.

To describe the ASM, let be a directed multigraph with a global sink. By multigraph, we mean that each edge of is assigned a nonnegative integer weight, and by global sink, we mean a vertex of out-degree such that every vertex of has a directed path into . We denote the vertices of by and the nonsink vertices by .

### Configurations and divisors¶

A configuration on is an element of , i.e., the assignment of a nonnegative integer to each nonsink vertex. We think of each integer as a number of grains of sand being placed at the corresponding vertex. A divisor on is an element of , i.e., an element in the free abelian group on all of the vertices. In the context of divisors, it is sometimes useful to think of assigning dollars to each vertex, with negative integers signifying a debt.

### Stabilization¶

A configuration is stable at a vertex if , and itself is stable if it is stable at each nonsink vertex. If is unstable at , the vertex can be fired (toppled) by removing grains of sand from and adding grains of sand to the neighbors of sand, determined by the weights of the edges leaving .

Example. Consider the graph:

All edges have weight except for the edge from vertex 1 to vertex 3, which have weight . If we let with the indicated number of grains of sand on vertices 1, 2, and 3, respectively, then only vertex 1, whose out-degree is 4, is unstable. Firing vertex 1 gives a new configuration . Here, grains have left vertex 1. One of these has gone to the sink vertex (and forgotten), one has gone to vertex 1, and two have gone to vertex 2, since the edge from 1 to 2 has weight 2. Vertex 3 in the new configuration is now unstable. The Sage code for this example looks like this:

Create the sandpile:

sage: g = {'sink':{},
1:{'sink':1, 2:1, 3:2},
2:{1:1, 3:1},
3:{1:1, 2:1}}
sage: S = Sandpile(g, 'sink')
sage: S.show(edge_labels=true)  # to display the graph

Create the configuration:

sage: c = {1:5, 2:0, 3:1}
sage: S.out_degree()
{1: 4, 2: 2, 3: 2, 'sink': 0}

Fire vertex one:

sage: S.fire_vertex(1,c)
{1: 1, 2: 1, 3: 3}
sage: c
{1: 1, 2: 1, 3: 3}

Stabilize the rest of the way:

sage: S.stabilize(c)
[{1: 2, 2: 1, 3: 1}, {1: 1, 2: 2, 3: 3}]

Since vertex 3 has become unstable, it can be fired, which causes vertex 2 to become unstable, etc. Since there is a global sink, repeated firings eventually lead to a stable configuration. The last line of the Sage code, above, is a list, the first element of which is the resulting stable configuration, . (The second component records how many times each vertex fired in the stabilization.)

Since here is a global sink, every configuration will stabilize after a finite number of vertex-firings. It is not obvious, but the resulting stabilization is independent of the order in which unstable vertices are fired. Thus, each configuration stabilizes to a unique stable configuration.

### Laplacian¶

Fix an order on the vertices of . The Laplacian of is

where is the diagonal matrix of out-degrees of the vertices and is the adjacency matrix whose -th entry is the weight of the edge from vertex to vertex , which we take to be if there is no edge. The reduced Laplacian, , is the submatrix of the Laplacian formed by removing the row and column corresponding to the sink vertex. Firing a vertex of a configuration is the same as subtracting the row of the reduced Laplacian.

Example. (Continued.)

    sage: S.vertices()  # here is the ordering of the vertices
[1, 2, 3, 'sink']
sage: S.laplacian()
[ 4 -1 -2 -1]
[-1  2 -1  0]
[-1 -1  2  0]
[ 0  0  0  0]
sage: S.reduced_laplacian()
[ 4 -1 -2]
[-1  2 -1]
[-1 -1  2]

The configuration we considered previously:

sage: c = S.list_to_config([5,0,1])
sage: c
{1: 5, 2: 0, 3: 1}

Firing vertex 1 is the same as substracting the
corresponding row from the reduced Laplacian:

sage: S.fire_vertex(1,c)
{1: 1, 2: 1, 3: 3}
sage: S.reduced_laplacian()[0]
(4, -1, -2)
sage: vector([5,0,1]) - vector([4,-1,-2])
(1, 1, 3)

### Recurrent elements¶

Imagine an experiment in which grains of sand are dropped one-at-a-time onto a graph, pausing to allow the configuration to stabilize between drops. Some configurations will only be seen once in this process. For example, for most graphs, once sand is dropped on the graph, no addition of sand+stabilization will result in a graph empty of sand. Other configurations—the so-called recurrent configurations—will be seen infinitely often as the process is repeated indefinitely.

To be precise, a configuration is recurrent if (i) it is stable, and (ii) given any configuration , there is a configuration such that , the stabilization of .

The maximal-stable configuration, denoted is defined by for all nonsink vertices . It is clear that is recurrent. Further, it is not hard to see that a configuration is recurrent if and only if it has the form for some configuration .

Example. (Continued.)

    sage: S.recurrents(verbose=false)
[[3, 1, 1], [2, 1, 1], [3, 1, 0]]
sage: c = S.list_to_config([2,1,1])
sage: c
{1: 2, 2: 1, 3: 1}
sage: S.is_recurrent(c)
True
sage: S.max_stable()
{1: 3, 2: 1, 3: 1}

Adding any configuration to the max-stable configuration and stabilizing
yields a recurrent configuration.

sage: x = S.list_to_config([1,0,0])
{1: 4, 2: 1, 3: 1}
sage: S.stabilize(_)
[{1: 3, 2: 1, 3: 0}, {1: 2, 2: 3, 3: 4}]
sage: c = _[0]
sage: c
{1: 3, 2: 1, 3: 0}
sage: S.is_recurrent(c)
True

### Burning Configuration¶

A burning configuration is a nonnegative integer-linear combination of the rows of the reduced Laplacian matrix having nonnegative entries and such that every vertex has a path from some vertex in its support. The corresponding burning script gives the integer-linear combination needed to obtain the burning configuration. So if is the burning configuration, is its script, and is the reduced Laplacian, then . The minimal burning configuration is the one with the minimal script (its components are no larger than the components of any other script for a burning configuration).

The following are equivalent for a configuration with burning configuration having script :

• is recurrent;
• stabilizes to ;
• the firing vector for the stabilization of is .

The burning configuration and script are computed using a modified version of Speer’s script algorithm. This is a generalization to directed multigraphs of Dhar’s burning algorithm.

Example.

sage: g = {0:{},1:{0:1,3:1,4:1},2:{0:1,3:1,5:1},
3:{2:1,5:1},4:{1:1,3:1},5:{2:1,3:1}}
sage: G = Sandpile(g,0)
sage: G.burning_config()
{1: 2, 2: 0, 3: 1, 4: 1, 5: 0}
sage: G.burning_config().values()
[2, 0, 1, 1, 0]
sage: G.burning_script()
{1: 1, 2: 3, 3: 5, 4: 1, 5: 4}
sage: G.burning_script().values()
[1, 3, 5, 1, 4]
sage: matrix(G.burning_script().values())*G.reduced_laplacian()
[2 0 1 1 0]

### Sandpile group¶

The collection of stable configurations forms a commutative monoid with addition defined as ordinary addition followed by stabilization. The identity element is the all-zero configuration. This monoid is a group exactly when the underlying graph is a DAG (directed acyclic graph).

The recurrent elements form a submonoid which turns out to be a group. This group is called the sandpile group for , denoted . Its identity element is usually not the all-zero configuration (again, only in the case that is a DAG). So find the identity element is an interesting question.

Let and fix an ordering of the nonsink vertices. Let denote the column-span of , the tranpose of the reduced Laplacian. It is a theorem that

Thus, the number of element of the sandpile group is , which by the matrix-tree theorem is the number of weighted trees directed into the sink.

Example. (Continued.)

    sage: S.group_order()
3
sage: S.elementary_divisors()
[1, 1, 3]
sage: S.reduced_laplacian().dense_matrix().smith_form()

([1 0 0]
[0 1 0]
[0 0 3],
[ 0  0  1]
[ 1  0  0]
[ 0  1 -1],
[3 1 4]
[4 1 6]
[4 1 5])

Adding the identity to any recurrent configuration and stabilizing yields
the same recurrent configuration:

sage: S.identity()
{1: 3, 2: 1, 3: 0}
sage: i = S.identity()
sage: m = S.max_stable()
True

### Self-organized criticality¶

The sandpile model was introduced by Bak, Tang, and Wiesenfeld in the paper, Self-organized criticality: an explanation of 1/ƒ noise [BTW]. The term self-organized criticality has no generally accepted definition, but can be loosely taken to mean like the sandpile model on a grid-graph. The grid graph is just a grid with an extra sink vertex. The vertices on the interior of each side have one edge to the sink, and the corner vertices have an edge of weight . Thus, every nonsink vertex has out-degree .

One property that is taken as characteristic of self-organized criticality is the presence of a power law (by which is meant an exponential law) describing the behavior of some ascpect of the system. For the grid graph, computer experiments—I do not think there is a proof, yet—indicate that the distribution of avalanche sizes obeys a power law. In the example below, the size of an avalanche is taken to be the sum of the number of imes each vertex fires.

Example.

Distribution of avalanche sizes:

sage: S = Sandpile(grid(10,10),'sink')
sage: S.set_config(S.max_stable())
sage:  a = [sum(S.stabilize(S.add_random())[1].values()) for i in range(10000)]
sage: p = list_plot([[log(i+1),log(a.count(i))] for i in [0..max(a)] if a.count(i)])
sage: p.axes_labels(['N','D(N)'])
sage: p

### Discrete Riemann surfaces¶

A reference for this section is Riemann-Roch and Abel-Jacobi theory on a finite graph [BN].

A divisor on is an element of the free abelian group on its vertices, including the sink. Suppose, as above, that the vertices of have been ordered, and that is the column span of the transpose of the Laplacian. A divisor is then identified with an element . Two divisors are linearly equivalent if they differ by an element of . A divisor is effective, written , if for each , i.e., if . The degree of a divisor, , is . The divisors of degree zero modulo linear equivalence form the Picard group, or Jacobian of the graph. For an undirected graph, the Picard group is isomorphic to the sandpile group.

The complete linear system for a divisor , denoted , is the collection of effective divisors linearly equivalent to To describe the Riemann-Roch theorem in this context, suppose that is an undirected, unweighted graph. The dimension, of the linear system is if and otherwise is the greatest integer such that for all effective divisors of degree . Define the canonical divisor by and the genus by . The Riemann-Roch theorem says that for any divisor ,

Example. Some of the following calculations require the installation of 4ti2.

The sandpile on the complete graph on 5 vertices:

sage: Gamma = graphs.CompleteGraph(5)
sage: S = Sandpile(Gamma,0)

The num_edges method counts each undirected edge twice:

sage: g = S.num_edges()/2 - S.num_verts() + 1

A divisor on the graph:

sage: D = S.list_to_div([1,2,2,0,2])

Verify the Riemann-Roch theorem:

sage: degD = sum(D.values())
sage: K = S.all_k_div(2)
sage: F = S.subtract(K,D)
sage: r_of_D = S.r_of_D(D)[0]
0
1
2
sage: r_of_F = S.r_of_D(F)[0]
0
sage: r_of_D - r_of_F == degD + 1 - g
True

The effective divisors linearly equivalent to D:

sage: S.effective_div(D)

[{0: 0, 1: 1, 2: 1, 3: 4, 4: 1},
{0: 4, 1: 0, 2: 0, 3: 3, 4: 0},
{0: 1, 1: 2, 2: 2, 3: 0, 4: 2}]

### Algebraic geometry of sandpiles¶

A reference for the following material is in the works [PP].

#### Affine¶

Let , and fix an ordering on the nonsink vertices of . let denote the column-span of , the tranpose of the reduced Laplacian. Label vertex with the indeterminate , and let . (Here, denotes the sink vertex of .) The sandpile ideal or toppling ideal is the lattice ideal for :

where for .

For each define where and so that . Then, for each , define . It then turns out that

where is the -th standard basis vector and is any burning configuration.

The affine coordinate ring, is isomorphic to the group algebra of the sandpile group,

The standard term-ordering on is graded reverse lexigraphical order with if vertex is further from the sink than vertex . If is the script for a burning configuration (not necessarily minimal), then

is a Groebner basis for .

#### Projective¶

Now let , where corresponds to the sink vertex. The homogeneous sandpile ideal, denoted , is obtaining by homogenizing with respect to . Let be the (full) Laplacian, and be the column span of its transpose, Then is the lattice ideal for :

This ideal can be calculated by saturating the ideal

with respect to the product of the indeterminates: (extending the operator in the obvious way). A Groebner basis with respect to the degree lexicographic order describe above (with the smallest vertex), is obtained by homogenizing each element of the Groebner basis for the non-homogeneous sandpile ideal with respect to

Example.

    sage: g = {0:{},1:{0:1,3:1,4:1},2:{0:1,3:1,5:1},
3:{2:1,5:1},4:{1:1,3:1},5:{2:1,3:1}}
sage: S = Sandpile(g, 0)
sage: S.ring()

//   characteristic : 0
//   number of vars : 6
//        block   1 : ordering dp
//                  : names    x_5 x_4 x_3 x_2 x_1 x_0
//        block   2 : ordering C

The homogeneous sandpile ideal:

sage: S.ideal()
x_2-x_0,
x_3^2-x_5*x_0,
x_5*x_3-x_0^2,
x_4^2-x_3*x_1,
x_5^2-x_3*x_0,
x_1^3-x_4*x_3*x_0,
x_4*x_1^2-x_5*x_0^2

its resolution:

sage: S.resolution()
'R <-- R^7 <-- R^19 <-- R^25 <-- R^16 <-- R^4'

and Betti table:

sage: S.betti()
0     1     2     3     4     5
------------------------------------------
0:     1     1     -     -     -     -
1:     -     4     6     2     -     -
2:     -     2     7     7     2     -
3:     -     -     6    16    14     4
------------------------------------------
total:     1     7    19    25    16     4

The Hilbert function:

sage: S.hilbert_function()
[1, 5, 11, 15]

and its first differences (which counts the number of superstable
configurations in each degree):

sage: S.first_diffs_hilb()
[1, 4, 6, 4]
sage: x = [sum(i) for i in S.superstables(False)]
sage: sorted(x)
[0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3]

The degree in which the Hilbert function equals the Hilbert polynomial, the
latter always being a constant in the case of a sandpile ideal:

sage: S.postulation()
3

#### Zeros¶

The zero set for the sandpile ideal is

the set of simultaneous zeros of the polynomials in Letting denote the unit circle in the complex plane, is a finite subgroup of , isomorphic to the sandpile group. The zero set is actually linearly isomorphic to a faithful representation of the sandpile group on

Example. (Continued.)

     sage: S = Sandpile({0: {}, 1: {2: 2}, 2: {0: 4, 1: 1}}, 0)
sage: S.ideal()
x_1^2-x_2^2,
x_1*x_2^3-x_0^4,
x_2^5-x_1*x_0^4

Approximation to the zero set (setting x_0 = 1):

sage: S.solve()

[[0.707107*I - 0.707107, 0.707107 - 0.707107*I],
[-0.707107*I - 0.707107, 0.707107*I + 0.707107],
[-1*I, -1*I],
[I, I],
[0.707107*I + 0.707107, -0.707107*I - 0.707107],
[0.707107 - 0.707107*I, 0.707107*I - 0.707107],
[1, 1],
[-1, -1]]
sage: len(_) == S.group_order()
True

The zeros are generated as a group by a single vector:

sage: S.points()
[[e^(1/4*I*pi), e^(-3/4*I*pi)]]

#### Resolutions¶

The homogeneous sandpile ideal, , has a free resolution graded by the divisors on modulo linear equivalence. (See the section on Discrete Riemann Surfaces for the language of divisors and linear equivalence.) Let , as above, and let denote the group of divisors modulo rational equivalence. Then is graded by by letting for each monomial . The minimal free resolution of has the form

where the are the Betti numbers for .

For each divisor class , define a simplicial complex,

The Betti number equals the dimension over of the -th reduced homology group of :

        sage: S = Sandpile({0:{},1:{0: 1, 2: 1, 3: 4},2:{3: 5},3:{1: 1, 2: 1}},0)

Representatives of all divisor classes with nontrivial homology:

sage: p = S.betti_complexes()
sage: p[0]
[{0: -8, 1: 5, 2: 4, 3: 1},
Simplicial complex with vertex set (0, 1, 2, 3) and facets {(1, 2), (3,)}]

The homology associated with the first divisor in the list:

sage: D = p[0][0]
sage: S.effective_div(D)
[{0: 0, 1: 1, 2: 1, 3: 0}, {0: 0, 1: 0, 2: 0, 3: 2}]
sage: [S.support(E) for E in S.effective_div(D)]
[[1, 2], [3]]
sage: S.Dcomplex(D)
Simplicial complex with vertex set (0, 1, 2, 3) and facets {(1, 2), (3,)}
sage: S.Dcomplex(D).homology()
{0: Z, 1: 0}

The minimal free resolution:

sage: S.resolution()
'R <-- R^5 <-- R^5 <-- R^1'
sage: S.betti()
0     1     2     3
------------------------------
0:     1     -     -     -
1:     -     5     5     -
2:     -     -     -     1
------------------------------
total:     1     5     5     1
sage: len(p)
11

The degrees and ranks of the homology groups foreach element of the list p (compare with the
Betti table, above):

sage: [[sum(d[0].values()),d[1].betti()] for d in p]

[[2, {0: 1, 1: 0}],
[3, {0: 0, 1: 1, 2: 0}],
[2, {0: 1, 1: 0}],
[3, {0: 0, 1: 1, 2: 0}],
[2, {0: 1, 1: 0}],
[3, {0: 0, 1: 1, 2: 0}],
[2, {0: 1, 1: 0}],
[3, {0: 0, 1: 1}],
[2, {0: 1, 1: 0}],
[3, {0: 0, 1: 1, 2: 0}],
[5, {0: 0, 1: 0, 2: 1}]]

## Installation¶

It is assumed that Sage is already installed. If not, please see the main Sage homepage for installation instructions. To use sandpile.sage:

sage: load sandpile.sage

You may need to give the full path name to sandpile.sage.

Warning

The methods for computing linear systems of divisors and their corresponding simplicial complexes require the installation of 4ti2.

To make 4ti2 usable from Sage Sandpiles:

• open sandpiles.sage in your favorite text editor and edit the following line (near the beginning of the file, near the copyright statement and the start of the definition of the Sandpile class), replacing the path_to_zsolve string with the path to the executables in your 4ti2 directory:
# set the following if 4ti2 is installed
path_to_zsolve = '/home/davidp/math/sandpile/4ti2/linux_x86/'

• start Sage and load sandpiles.sage as described above.

## Usage¶

### Initialization¶

Most of sandpile.sage consists of the definition of the class Sandpile representing the abelian sandpile model on a graph . Initialization has the form

sage: S = Sandpile(graph, sink)

where graph represents a graph and sink is the key for the sink vertex. There are four possible forms for graph:

1. a Python dictionary of dictionaries:
sage: g = {0: {}, 1: {0: 1, 3: 1, 4: 1}, 2: {0: 1, 3: 1, 5: 1},
3: {2: 1, 5: 1}, 4: {1: 1, 3: 1}, 5: {2: 1, 3: 1}}

Each key is the name of a vertex. Next to each vertex name is a dictionary consisting of pairs: vertex: weight. Each pair represents a directed edge emanating from and ending at vertex having (non-negative integer) weight equal to weight. Loops are allowed. In the example above, all of the weights are 1.

1. a Python dictionary of lists:
sage: g = {0: [], 1: [0, 3, 4], 2: [0, 3, 5],
3: [2, 5], 4: [1, 3], 5: [2, 3]}

This is a short-hand when all of the edge-weights are equal to 1. The above example is for the same displayed graph.

1. a Sage graph (of type sage.graphs.graph.Graph):
sage: g = graphs.CompleteGraph(5)
sage: S = Sandpile(g, 0)
sage: type(g)
<class 'sage.graphs.graph.Graph'>
1. a Sage digraph:
sage: S = Sandpile(digraphs.RandomDirectedGNC(6), 0)
sage: S.show()

See http://sagemath.org/doc/reference/sage/graphs/graph_generators.html for more information on the Sage graph library and graph constructors.

Each of these four formats is preprocessed by the Sandpile class so that, internally, the graph is represented by the dictionary of dictionaries format first presented. This internal format is returned by dict():

sage: S  = Sandpile({0:[], 1:[0, 3, 4], 2:[0, 3, 5],
3: [2, 5], 4: [1, 3], 5: [2, 3]},0)
sage: S.dict()

{0: {},
1: {0: 1, 3: 1, 4: 1},
2: {0: 1, 3: 1, 5: 1},
3: {2: 1, 5: 1},
4: {1: 1, 3: 1},
5: {2: 1, 3: 1}}

Note

The user is responsible for assuring that each vertex has a directed path into the designated sink. If the sink has out-edges, these will be ignored for the purposes of sandpile calculations (but not calculations on divisors).

Code for checking whether a given vertex is a sink:

sage:  S  = Sandpile({0:[], 1:[0, 3, 4], 2:[0, 3, 5],
3: [2, 5], 4: [1, 3], 5: [2, 3]},0)
sage: [S.distance(v,0) for v in S.vertices()] # 0 is a sink
[0, 1, 1, 2, 2, 2]
sage: [S.distance(v,1) for v in S.vertices()] # 1 is not a sink
[+Infinity, 0, +Infinity, +Infinity, 1, +Infinity]

### Methods¶

Here is a summary of Sandpile methods. The summary is followed by a list of complete descriptions of these methods. There are many more methods available for a Sandpile, e.g., those inherited from the class DiGraph. To see them all, enter

sage: dir(Sandpile)

Summary of methods.

Complete descriptions of methods.

Returns the sum of c and d without stabilizing.

INPUT:

c, d - dict (configurations or divisors)

OUTPUT:

dict (configuration or divisor)

EXAMPLES:

sage: S = sandlib('generic')
sage: m = S.max_stable()
sage: m
{1: 2, 2: 2, 3: 1, 4: 1, 5: 1}
{1: 4, 2: 4, 3: 2, 4: 2, 5: 2}

sage: D = S.list_to_div([1,-3,2,0,4,-2])
sage: E = S.list_to_div([2, 3,4,1,1,-2])
{0: 3, 1: 0, 2: 6, 3: 1, 4: 5, 5: -4}

Add one grain of sand to a random nonsink vertex. The config argument can be a configuration or a divisor.

INPUT:

config (optional) - dict (a configuration or divisor)

OUTPUT:

None

EXAMPLES:

We compute the ‘sizes’ of the avalanches caused by adding random grains of sand to the maximal stable configuration on a grid graph. The function stabilize() returns the firing vector of the stabilization, a dictionary whose values say how many times each vertex fires in the stabilization.

sage: S = Sandpile(grid(4,4),'sink')
for i in range(1000)]
sage: b = [[log(i+1),log(a.count(i))]
for i in [0..max(a)] if a.count(i)]
sage: list_plot(b)

Also works for divisors:

sage: S = sandlib('generic')
sage: D = S.list_to_div([0,0,0,0,0,0])
sage: D  #random
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1}

all_k_config(k)

The configuration having k grains of sand on each vertex.

INPUT:

k - integer

OUTPUT:

dict (configuration)

EXAMPLES:

sage: S = sandlib('generic')
sage: S.all_k_config(7)
{1: 7, 2: 7, 3: 7, 4: 7, 5: 7}

all_k_div(k)

The divisor having k grains of sand on each vertex.

INPUT:

k - integer

OUTPUT:

dict (divisor)

EXAMPLES:

sage: S = sandlib('generic')
sage: S.all_k_div(7)
{0: 7, 1: 7, 2: 7, 3: 7, 4: 7, 5: 7}

betti(verbose=True)

Computes the Betti table for the homogeneous sandpile ideal. If verbose is True, it prints the standard Betti table, otherwise, it returns a less formated table.

INPUT:

verbose (optional) - boolean

OUTPUT:

Betti numbers for the sandpile

EXAMPLE:

sage: S = sandlib('generic')
sage: S.betti()
0     1     2     3     4     5
------------------------------------------
0:     1     1     -     -     -     -
1:     -     4     6     2     -     -
2:     -     2     7     7     2     -
3:     -     -     6    16    14     4
------------------------------------------
total:     1     7    19    25    16     4

betti_complexes()

Returns a list of all the divisors with nonempty linear systems whose corresponding simplicial complexes have nonzero homology in some dimension. Each such divisors is returned with its corresponding simplicial complex.

INPUT:

None

OUTPUT:

list (of pairs [divisors, corresponding simplicial complex])

EXAMPLES:

sage: S = Sandpile({0:{},1:{0: 1, 2: 1, 3: 4},2:{3: 5},3:{1: 1, 2: 1}},0)
sage: p = S.betti_complexes()
sage: p[0]

[{0: -8, 1: 5, 2: 4, 3: 1},
Simplicial complex with vertex set (0, 1, 2, 3) and facets {(1, 2), (3,)}]
sage: S.resolution()
'R <-- R^5 <-- R^5 <-- R^1'
sage: S.betti()
0     1     2     3
------------------------------
0:     1     -     -     -
1:     -     5     5     -
2:     -     -     -     1
------------------------------
total:     1     5     5     1
sage: len(p)
11

burning_config()

A minimal burning configuration.

INPUT:

None

OUTPUT:

dict (configuration)

EXAMPLES:

sage: g = {0:{},1:{0:1,3:1,4:1},2:{0:1,3:1,5:1},
3:{2:1,5:1},4:{1:1,3:1},5:{2:1,3:1}}
sage: G = Sandpile(g,0)
sage: G.burning_config()
{1: 2, 2: 0, 3: 1, 4: 1, 5: 0}
sage: G.config_to_list(G.burning_config())
[2, 0, 1, 1, 0]
sage: G.burning_script()
{1: 1, 2: 3, 3: 5, 4: 1, 5: 4}
sage: script = G.config_to_list(G.burning_script())
sage: script
[1, 3, 5, 1, 4]
sage: matrix(script)*G.reduced_laplacian()
[2 0 1 1 0]

NOTES:

The burning configuration and script are computed using a modified version of Speer’s script algorithm. This is a generalization to directed multigraphs of Dhar’s burning algorithm.

A burning configuration is a nonnegative integer-linear combination of the rows of the reduced Laplacian matrix having nonnegative entries and such that every vertex has a path from some vertex in its support. The corresponding burning script gives the integer-linear combination needed to obtain the burning configuration. So if is the burning configuration, is its script, and is the reduced Laplacian, then . The minimal burning configuration is the one with the minimal script (its components are no larger than the components of any other script for a burning configuration).

The following are equivalent for a configuration with burning configuration having script :

• is recurrent;
• stabilizes to ;
• the firing vector for the stabilization of is .

burning_script()

A script for the minimal burning configuration.

INPUT:

None

OUTPUT:

dict

EXAMPLES:

sage: g = {0:{},1:{0:1,3:1,4:1},2:{0:1,3:1,5:1},
3:{2:1,5:1},4:{1:1,3:1},5:{2:1,3:1}}
sage: G = Sandpile(g,0)
sage: G.burning_config()
{1: 2, 2: 0, 3: 1, 4: 1, 5: 0}
sage: G.config_to_list(G.burning_config())
[2, 0, 1, 1, 0]
sage: G.burning_script()
{1: 1, 2: 3, 3: 5, 4: 1, 5: 4}
sage: script = G.config_to_list(G.burning_script())
sage: script
[1, 3, 5, 1, 4]
sage: matrix(script)*G.reduced_laplacian()
[2 0 1 1 0]

NOTES:

The burning configuration and script are computed using a modified version of Speer’s script algorithm. This is a generalization to directed multigraphs of Dhar’s burning algorithm.

A burning configuration is a nonnegative integer-linear combination of the rows of the reduced Laplacian matrix having nonnegative entries and such that every vertex has a path from some vertex in its support. The corresponding burning script gives the integer-linear combination needed to obtain the burning configuration. So if is the burning configuration, is its script, and is the reduced Laplacian, then . The minimal burning configuration is the one with the minimal script (its components are no larger than the components of any other script for a burning configuration).

The following are equivalent for a configuration with burning configuration having script :

• is recurrent;
• stabilizes to ;
• the firing vector for the stabilization of is .

compare_configs(c, d)

Returns True if each c is at least as large as d at each vertex.

INPUT:

c, d - dict (configurations or divisors)

OUTPUT:

boolean

EXAMPLES:

sage: S = sandlib('generic')
sage: c = S.all_k_config(5)
sage: d = S.all_k_config(4)
sage: S.compare_configs(c,d)
True
sage: S.compare_configs(d,c)
False

config()

The current saved configuration.

INPUT:

None

OUTPUT:

None

EXAMPLES:

sage: G = sandlib('generic')
sage: G.set_config(G.max_stable())
sage: G.config() == G.max_stable()
True
{1: 4, 2: 4, 3: 2, 4: 2, 5: 1}
sage: G.config()
{1: 4, 2: 4, 3: 2, 4: 2, 5: 1}

NOTES:

Each sandpile has a saved configuration (initially the zero configuration) The configuration can be set with the set_config() method and may be affected by certain other methods, e.g., stabilize(), add(), and add_random()—usually those methods that output configurations.

config_to_list(config)

Convert a configuration into a list in the order determined by nonsink_vertices().

INPUT:

config - dict (configuration)

OUTPUT:

list of integers

EXAMPLES:

sage: S = sandlib('generic')
sage: c = S.list_to_config([5,4,3,2,1])
sage: c
{1: 5, 2: 4, 3: 3, 4: 2, 5: 1}
sage: S.config_to_list(c)
[5, 4, 3, 2, 1]

NOTES:

The method checks the length of config but not the types of its entries.

Dcomplex(D)

Returns the simplicial complex determined by the supports of the effective divisors linearly equivalent to D.

INPUT:

D - dict (divisor)

OUTPUT:

simplicial complex

EXAMPLES:

sage: S = sandlib('generic')
sage: p = S.Dcomplex(S.list_to_div([0,1,2,0,0,1]))
sage: p.homology()
{0: 0, 1: Z x Z, 2: 0, 3: 0}
sage: p.f_vector()
[1, 6, 15, 9, 1]
sage: p.betti()
{0: 0, 1: 2, 2: 0, 3: 0}

dict()

Returns a dictionary of dictionaries representing a directed graph.

INPUT:

None

OUTPUT:

dict

EXAMPLES:

sage: G = sandlib('generic')
sage: G.dict()

{0: {},
1: {0: 1, 3: 1, 4: 1},
2: {0: 1, 3: 1, 5: 1},
3: {2: 1, 5: 1},
4: {1: 1, 3: 1},
5: {2: 1, 3: 1}}
sage: G.sink()
0

div_to_list(div)

Convert a divisor into a list in the order determined by vertices().

INPUT:

div - dict (divisor)

OUTPUT:

list of integers

EXAMPLES:

sage: S = sandlib('generic')
sage: c = S.list_to_div([6,5,4,3,2,1])
sage: c
{0: 6, 1: 5, 2: 4, 3: 3, 4: 2, 5: 1}
sage: S.div_to_list(c)
[6, 5, 4, 3, 2, 1]

NOTES:

The method checks the length of div but not the types of its entries.

dualize(config=None)

Returns max_stable - config.

INPUT:

config (optional) - dict (configuration)

OUTPUT:

dict (configuration)

EXAMPLES:

sage: S = sandlib('generic')
sage: S.dualize(S.max_stable())
{1: 0, 2: 0, 3: 0, 4: 0, 5: 0}

effective_div(div):

Returns all effective divisors linearly equivalent to div.

INPUT:

div - dict (divisor)

OUTPUT:

list (of divisors)

EXAMPLES:

sage: S = sandlib('generic')
sage: D = S.list_to_div([0,0,0,0,0,2])
sage: S.effective_div(D)
[{0: 1, 1: 0, 2: 0, 3: 1, 4: 0, 5: 0},
{0: 0, 1: 0, 2: 1, 3: 1, 4: 0, 5: 0},
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 2}]
sage: [S.div_to_list(d) for d in _]
[[1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 2]]

element_order(config=None)

Returns the order of the recurrent element equivalent to config.

INPUT:

config (optional) - configuration

OUTPUT:

integer

EXAMPLES:

sage: S = sandlib('generic')
sage: [S.element_order(r) for r in S.recurrents()]
[1, 3, 15, 5, 5, 15, 5, 15, 3, 15, 15, 5, 15, 15, 15]

elementary_divisors()

The elementary divisors of the sandpile group (a finite abelian group).

INPUT:

None

OUTPUT:

list of integers

EXAMPLES:

sage: S = sandlib('generic')
sage: S.elementary_divisors()
[1, 1, 1, 1, 15]

equivalent_recurrent(config)

Returns the recurrent configuration equivalent to the given configuration and also returns the corresponding firing vector.

INPUT:

config - dict (configuration)

OUTPUT:

[configuration, firing_vector]

EXAMPLES:

sage: S = sandlib('generic')
sage: c = [0,0,0,0,0]
sage: config = S.list_to_config([0,0,0,0,0])
sage: x = S.equivalent_recurrent(config)
sage: x[0] == S.identity()
True
sage: r = [x[0][v] for v in S.nonsink_vertices()]
sage: f = [x[1][v] for v in S.nonsink_vertices()]
sage: vector(r) == vector(c) - vector(f)*S.reduced_laplacian()
True

NOTES:

Let be the reduced laplacian, the initial configuration, the returned configuration, and the firing vector. Then .

equivalent_superstable(config)

Returns the superstable configuration equivalent to the given configuration and also returns the corresponding firing vector.

INPUT:

config - dict (configuration)

OUTPUT:

[configuration, firing_vector]

EXAMPLES:

sage: S = sandlib('generic')
sage: m = S.max_stable()
sage: x = S.equivalent_superstable(m)
sage: S.is_superstable(x[0])
True
sage: s = [x[0][v] for v in S.nonsink_vertices()]
sage: f = [x[1][v] for v in S.nonsink_vertices()]
sage: m = [m[v] for v in S.nonsink_vertices()]
sage: vector(s) == vector(m) - vector(f)*S.reduced_laplacian()

NOTES:

Let be the reduced laplacian, the initial configuration, the returned configuration, and the firing vector. Then .

fire_vertex(v, config=None)

Fire (topple) a given vertex of a configuration.

INPUT:

• v - vertex name
• config - dict

OUTPUT:

dict (configuration)

EXAMPLES:

sage: G = sandlib('generic')
sage: G.unstable(c)
[1, 2, 3, 4]
sage: c
{1: 4, 2: 4, 3: 2, 4: 2, 5: 1}
sage: G.fire_vertex(1,c)
{1: 1, 2: 4, 3: 3, 4: 3, 5: 1}

NOTES:

This method fires vertex v in config provided v is a nonsink, unstable vertex. Returns the result (and modifies self.config). The vertex v is fired only once, even if the result leaves v unstable.

first_diffs_hilb()

Returns the first differences of the Hilbert function of the homogeneous sandpile ideal.

INPUT:

None

OUTPUT:

list of nonnegative integers

EXAMPLES:

sage: S = sandlib('generic')
sage: S.hilbert_function()
[1, 5, 11, 15]
sage: S.first_diffs_hilb()
[1, 4, 6, 4]

groebner()

Returns a Groebner basis for the homogeneous sandpile ideal with respect to the standard sandpile ordering (see ring).

INPUT:

None

OUTPUT:

Groebner basis

EXAMPLES:

sage: S = sandlib('generic')
sage: S.groebner()

x_2-x_0,
x_3^2-x_5*x_0,
x_5*x_3-x_0^2,
x_4^2-x_3*x_1,
x_5^2-x_3*x_0,
x_1^3-x_4*x_3*x_0,
x_4*x_1^2-x_5*x_0^2

group_order()

Returns the size of the sandpile group.

INPUT:

None

OUTPUT:

int

EXAMPLES:

sage: G = sandlib('generic')
sage: G.group_order()
15

hilbert_function()

Returns the Hilbert function of the homogeneous sandpile ideal.

INPUT:

None

OUTPUT:

list of nonnegative integers

EXAMPLES:

sage: S = sandlib('generic')
sage: S.hilbert_function()
[1, 5, 11, 15]

ideal()

The saturated, homogeneous sandpile ideal.

INPUT:

None

OUTPUT:

ideal

EXAMPLES:

sage: S = sandlib('generic')
sage: S.ideal()

x_2-x_0,
x_3^2-x_5*x_0,
x_5*x_3-x_0^2,
x_4^2-x_3*x_1,
x_5^2-x_3*x_0,
x_1^3-x_4*x_3*x_0,
x_4*x_1^2-x_5*x_0^2

identity()

Returns the identity configuration.

INPUT:

None

OUTPUT:

dict (the identity configuration)

EXAMPLES:

sage: G = sandlib('generic')
sage: e = G.identity()
sage: x
[{1: 2, 2: 2, 3: 1, 4: 1, 5: 1}, {1: 1, 2: 5, 3: 7, 4: 1, 5: 6}]
sage: G.config_to_list(x[0])
[2, 2, 1, 1, 1]
sage: G.config_to_list(G.max_stable())
[2, 2, 1, 1, 1]

in_degree(v=None)

Return the in-degree of a vertex or a list of all in-degrees.

INPUT:

v - vertex name or None

OUTPUT:

integer or list of integers

EXAMPLES:

sage: G = sandlib('generic')
sage: G.in_degree(2)
2
sage: G.in_degree()
{0: 2, 1: 1, 2: 2, 3: 4, 4: 1, 5: 2}

is_recurrent(config=None)

Returns True if config is recurrent, i.e., is an element of the sandpile group.

INPUT:

config (optional) - dict (configuration)

OUTPUT:

boolean

EXAMPLES:

sage: S = sandlib('generic')
sage: S.is_recurrent(S.identity())
True
sage: c = S.list_to_config([0,0,0,0,0])
sage: S.is_recurrent(c)
False

is_stable(config=None)

Returns True if config is stable.

INPUT:

config (optional) - dict (configuration)

OUTPUT:

boolean

EXAMPLES:

sage: S = sandlib('generic')
sage: S.is_stable(S.max_stable())
True
sage: S.is_stable(c)
False

is_superstable(config=None)

Returns True if config is superstable, i.e., it does not permit multiset firings.

INPUT:

config (optional) - dict (configuration)

OUTPUT:

boolean

EXAMPLES:

sage: S = sandlib('generic')
sage: S.is_superstable(S.identity())
False
sage: c = S.subtract(S.max_stable(), S.identity())
sage: S.is_superstable(c)
True

is_symmetric(c, orbits)

This function checks if is constant over the vertices in each sublist of orbits.

INPUT:

• c - configuration
• orbit - list of lists of vertices

OUTPUT:

boolean

EXAMPLES:

sage: S = sandlib('kite')
sage: S.dict()

{0: {},
1: {0: 1, 2: 1, 3: 1},
2: {1: 1, 3: 1, 4: 1},
3: {1: 1, 2: 1, 4: 1},
4: {2: 1, 3: 1}}
sage: c = S.list_to_config([1, 2, 2, 3])
sage: S.is_symmetric(c,[[2,3]])
True

laplacian()

Returns the Laplacian matrix of the graph.

INPUT:

None

OUTPUT:

matrix

EXAMPLES:

sage: G = sandlib('generic')
sage: G.laplacian()
[ 0  0  0  0  0  0]
[-1  3  0 -1 -1  0]
[-1  0  3 -1  0 -1]
[ 0  0 -1  2  0 -1]
[ 0 -1  0 -1  2  0]
[ 0  0 -1 -1  0  2]

linear_system(div)

Returns the complete linear system of a divisor.

INPUT:

div - divisor

OUTPUT:

dict - {num_homog: int, homog:list, num_inhomog:int, inhomog:list}

EXAMPLES:

sage: S = sandlib('generic')
sage: D = S.list_to_div([0,0,0,0,0,2])
sage: S.linear_system(D)

{'homog': [[1, 0, 0, 0, 0, 0], [-1, 0, 0, 0, 0, 0]],
'inhomog': [[0, 0, -1, -1, 0, -2], [0, 0, 0, 0, 0, -1], [0, 0, 0, 0, 0, 0]],
'num_homog': 2,
'num_inhomog': 3}

NOTES:

If is the Laplacian, an arbitrary such that has the form where is in inhomg and is in the integer span of homog in the output of linear_system(D).

WARNING:

This method requires 4ti2. After local installation of 4ti2, set the path_to_zsolve at the beginning of sandpile.sage.

list_to_config(L)

Convert a list into a configuration.

INPUT:

L - list of nonnegative integers

OUTPUT:

dict (configuration)

EXAMPLES:

sage: S = sandlib('generic')
sage: S.list_to_config([3,6,4,2,4])
{1: 3, 2: 6, 3: 4, 4: 2, 5: 4}

NOTES:

The method checks the length of L but not the types of its entries.

list_to_div(L)

Convert a list of integers into a divisor.

INPUT:

L - list of integers

OUTPUT:

dict (configuration)

EXAMPLES:

sage: S = sandlib('generic')
sage: S.list_to_div([6,5,4,3,2,1])
{0: 6, 1: 5, 2: 4, 3: 3, 4: 2, 5: 1}

NOTES:

The method checks the length of L but not the types of its entries.

max_stable()

Returns the maximal stable configuration.

INPUT:

None

OUTPUT:

dict (the maximal stable configuration)

EXAMPLES:

sage: G = sandlib('generic')
sage: G.max_stable()
{1: 2, 2: 2, 3: 1, 4: 1, 5: 1}

nonsink_vertices()

The names of the nonsink vertices.

INPUT:

None

OUTPUT:

None

EXAMPLES:

sage: G = sandlib('generic')
sage: G.nonsink_vertices()
[1, 2, 3, 4, 5]

out_degree(v=None)

Return the out-degree of a vertex or a list of all out-degrees.

INPUT:

v (optional) - vertex name

OUTPUT:

integer or list of integers

EXAMPLES:

sage: G = sandlib('generic')
sage: G.out_degree(2)
3
sage: G.out_degree()
{0: 0, 1: 3, 2: 3, 3: 2, 4: 2, 5: 2}

points()

Returns generators for the multiplicative group of zeros of the sandpile ideal.

INPUT:

None

OUTPUT:

list of complex numbers

EXAMPLES:

The sandpile group in this example is cyclic, and hence there is a single generator for the group of solutions.

sage: S = sandlib('generic')
sage: S.points()
[[e^(4/5*I*pi), 1, e^(2/3*I*pi), e^(-34/15*I*pi), e^(-2/3*I*pi)]]

postulation()

Returns the postulation number of the sandpile ideal. This is the largest weight of a superstable configuration of the graph.

INPUT:

None

OUTPUT:

nonnegative integer

EXAMPLES:

sage: S = sandlib('generic')
sage: S.postulation()
3

r_of_D(D)

Computes r(D) and an effective divisor F such that |D - F| is empty.

INPUT:

D - divisor

OUTPUT:

tuple ((integer r(D), divisor F))

EXAMPLES:

sage: S = sandlib('generic')
sage: D = S.list_to_div([0,0,0,0,0,4])
sage: E = S.r_of_D(D)
0
1
sage: E
(1, {0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 0})
sage: F = E[1]
sage: S.div_to_list(S.subtract(D,F))
[0, -1, 0, -1, 0, 4]
sage: S.effective_div(S.subtract(D,F))
[]
sage: S.r_of_D(S.list_to_div([0,0,0,0,0,-4]))
(-1, {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: -4})

recurrent_difference(config1, config2)

Returns a recurrent configuration equivalent to the difference of the two given configurations, config1 - config2.

INPUT:

config1, config2 - dict (configurations)

OUTPUT:

dict (configuration)

EXAMPLES:

sage: S = sandlib('generic')
sage: c = S.recurrent_difference(S.max_stable(), S.max_stable())
sage: c
{1: 2, 2: 2, 3: 1, 4: 1, 5: 0}
sage: c == S.identity()
True

recurrents(verbose=True)

Returns the list of recurrent configurations as dictionaries if verbose is True, otherwise as lists of integers.

INPUT:

verbose (optional) – boolean

OUTPUT:

list (of recurrent configurations)

EXAMPLES:

sage: S = sandlib('generic')
sage: S.recurrents()
[{1: 2, 2: 2, 3: 1, 4: 1, 5: 1},
{1: 2, 2: 2, 3: 0, 4: 1, 5: 1},
{1: 0, 2: 2, 3: 1, 4: 1, 5: 0},
{1: 0, 2: 2, 3: 1, 4: 1, 5: 1},
{1: 1, 2: 2, 3: 1, 4: 1, 5: 1},
{1: 1, 2: 2, 3: 0, 4: 1, 5: 1},
{1: 2, 2: 2, 3: 1, 4: 0, 5: 1},
{1: 2, 2: 2, 3: 0, 4: 0, 5: 1},
{1: 2, 2: 2, 3: 1, 4: 0, 5: 0},
{1: 1, 2: 2, 3: 1, 4: 1, 5: 0},
{1: 1, 2: 2, 3: 1, 4: 0, 5: 0},
{1: 1, 2: 2, 3: 1, 4: 0, 5: 1},
{1: 0, 2: 2, 3: 0, 4: 1, 5: 1},
{1: 2, 2: 2, 3: 1, 4: 1, 5: 0},
{1: 1, 2: 2, 3: 0, 4: 0, 5: 1}]
sage: S.recurrents(false)
[[2, 2, 1, 1, 1],
[2, 2, 0, 1, 1],
[0, 2, 1, 1, 0],
[0, 2, 1, 1, 1],
[1, 2, 1, 1, 1],
[1, 2, 0, 1, 1],
[2, 2, 1, 0, 1],
[2, 2, 0, 0, 1],
[2, 2, 1, 0, 0],
[1, 2, 1, 1, 0],
[1, 2, 1, 0, 0],
[1, 2, 1, 0, 1],
[0, 2, 0, 1, 1],
[2, 2, 1, 1, 0],
[1, 2, 0, 0, 1]]

reduced_laplacian()

Returns the reduced Laplacian matrix of the graph.

INPUT:

None

OUTPUT:

matrix

EXAMPLES:

sage: G = sandlib('generic')
sage: G.laplacian()
[ 0  0  0  0  0  0]
[-1  3  0 -1 -1  0]
[-1  0  3 -1  0 -1]
[ 0  0 -1  2  0 -1]
[ 0 -1  0 -1  2  0]
[ 0  0 -1 -1  0  2]
sage: G.reduced_laplacian()
[ 3  0 -1 -1  0]
[ 0  3 -1  0 -1]
[ 0 -1  2  0 -1]
[-1  0 -1  2  0]
[ 0 -1 -1  0  2]

NOTES:

This is the Laplacian matrix with the row and column indexed by the sink vertex removed.

reset_config(config=None)

Turn config into the zero configuration.

INPUT:

config (optional) - dict

OUTPUT:

None

EXAMPLES:

sage: G = sandlib('generic')
{1: 4, 2: 4, 3: 2, 4: 2, 5: 2}
sage: G.config()
{1: 4, 2: 4, 3: 2, 4: 2, 5: 2}
sage: G.reset_config()
sage: G.config()
{1: 0, 2: 0, 3: 0, 4: 0, 5: 0}

resolution(verbose=False)

This function computes a minimal free resolution of the homogeneous sandpile ideal. If verbose is True, then all of the mappings are returned. Otherwise, the resolution is summarized.

INPUT:

verbose (optional) - boolean

OUTPUT:

free resolution of the sandpile ideal

EXAMPLES:

sage: S = Sandpile({0: {}, 1: {2: 2}, 2: {0: 4, 1: 1}}, 0)
sage: S.resolution()
'R <-- R^2 <-- R^1'
sage: S.resolution(verbose=True)
[1]:
_[1]=x_1^2-x_2^2
_[2]=x_1*x_2^3-x_0^4
[2]:
_[1]=x_1*x_2^3*gen(1)-x_0^4*gen(1)-x_1^2*gen(2)+x_2^2*gen(2)
[3]:
_[1]=0

ring()

The ring containing the homogeneous sandpile ideal.

INPUT:

None

OUTPUT:

ring

EXAMPLES:

sage: S = sandlib('generic')
sage: S.ring()

//   characteristic : 0
//   number of vars : 6
//        block   1 : ordering dp
//                  : names    x_5 x_4 x_3 x_2 x_1 x_0
//        block   2 : ordering C

NOTES:

The indeterminate corresponds to the -th vertex as listed my the method vertices. The term-ordering is degrevlex with indeterminates ordered according to their distance from the sink (larger indeterminates are further from the sink).

set_config(config)

Sets the current configuration to config.

INPUT:

config - dict

OUTPUT:

None

EXAMPLES:

sage: G = sandlib('generic')
sage: G.set_config(G.identity())
sage: G.config() == G.identity()
True

sink()

Returns the identifier for the sink vertex.

INPUT:

None

OUTPUT: Object (name for the sink vertex)

EXAMPLES:

sage: G = sandlib('generic')
sage: G.sink()
0
sage: H = Sandpile(grid(2,2),'sink')
sage: H.sink()
'sink'
sage: type(H.sink())
<type 'str'>

solve()

Computes approximations of the complex affine zeros of the sandpile ideal.

INPUT:

None

OUTPUT:

list of complex numbers

EXAMPLES:

sage: S = Sandpile({0: {}, 1: {2: 2}, 2: {0: 4, 1: 1}}, 0)
sage: S.solve()

[[0.707107*I - 0.707107, 0.707107 - 0.707107*I],
[-0.707107*I - 0.707107, 0.707107*I + 0.707107],
[-1*I, -1*I],
[I, I],
[0.707107*I + 0.707107, -0.707107*I - 0.707107],
[0.707107 - 0.707107*I, 0.707107*I - 0.707107],
[1, 1],
[-1, -1]]
sage: len(_)
8
sage: S.group_order()
8

NOTES:

The solutions form a multiplicative group isomorphic to the sandpile group. Generators for this group are given exactly by points().

stabilize(config=None)

Returns the stabilized configuration and its firing vector.

INPUT:

config (optional) - dict

OUTPUT:

list - [out_config, firing_vector]

EXAMPLES:

sage: S = sandlib('generic')
sage: S.stabilize(c)
[{1: 2, 2: 2, 3: 1, 4: 1, 5: 1}, {1: 1, 2: 5, 3: 7, 4: 1, 5: 6}]

The Sandpiles current configuration is modified:

sage: S.config()
{1: 2, 2: 2, 3: 1, 4: 1, 5: 1}

stabilize_one_step(config=None)

Fire each unstable vertex in config, returning a list [out_config, firing_vector] where out_config is the modified configuration.

INPUT:

config (optional) - dict

OUTPUT:

list of the form [dict, dict]

EXAMPLES:

sage: G = sandlib('generic')
sage: c
{1: 4, 2: 4, 3: 2, 4: 2, 5: 1}
sage: G.unstable(c)
[1, 2, 3, 4]
sage: G.stabilize_one_step(c)
[{1: 2, 2: 2, 3: 3, 4: 1, 5: 3}, {1: 1, 2: 1, 3: 1, 4: 1, 5: 0}]

Returns the stabilization and corresponding firing vector of the sum of two configurations.

INPUT:

config1, config2 - dict (configurations)

OUTPUT:

list ([stabilized sum, firing_vector])

EXAMPLES:

sage: S = sandlib('generic')
[{1: 2, 2: 2, 3: 0, 4: 1, 5: 1}, {1: 1, 2: 7, 3: 10, 4: 1, 5: 9}]

subtract(c, d)

Returns the difference, c - d.

INPUT:

c, d - dict (configurations or divisors)

OUTPUT:

dict (configuration or divisor)

EXAMPLES:

Subtracting configurations:

sage: S = sandlib('generic')
sage: m = S.max_stable()
sage: m
{1: 2, 2: 2, 3: 1, 4: 1, 5: 1}
sage: S.subtract(m, m)
{1: 0, 2: 0, 3: 0, 4: 0, 5: 0}

Subtracting divisors:

sage: D = S.list_to_div([1,-3,2,0,4,-2])
sage: E = S.list_to_div([2, 3,4,1,1,-2])
sage: S.subtract(D,E)
{0: -1, 1: -6, 2: -2, 3: -1, 4: 3, 5: 0}

superstables(verbose=True)

Returns the list of superstable configurations as dictionaries if verbose is True, otherwise as lists of integers. The superstables are also known as -parking functions.

INPUT:

verbose - boolean

OUTPUT:

list (of superstable elements)

EXAMPLES:

sage: S = sandlib('generic')
sage: S.superstables()
[{1: 0, 2: 0, 3: 0, 4: 0, 5: 0},
{1: 0, 2: 0, 3: 1, 4: 0, 5: 0},
{1: 2, 2: 0, 3: 0, 4: 0, 5: 1},
{1: 2, 2: 0, 3: 0, 4: 0, 5: 0},
{1: 1, 2: 0, 3: 0, 4: 0, 5: 0},
{1: 1, 2: 0, 3: 1, 4: 0, 5: 0},
{1: 0, 2: 0, 3: 0, 4: 1, 5: 0},
{1: 0, 2: 0, 3: 1, 4: 1, 5: 0},
{1: 0, 2: 0, 3: 0, 4: 1, 5: 1},
{1: 1, 2: 0, 3: 0, 4: 0, 5: 1},
{1: 1, 2: 0, 3: 0, 4: 1, 5: 1},
{1: 1, 2: 0, 3: 0, 4: 1, 5: 0},
{1: 2, 2: 0, 3: 1, 4: 0, 5: 0},
{1: 0, 2: 0, 3: 0, 4: 0, 5: 1},
{1: 1, 2: 0, 3: 1, 4: 1, 5: 0}]
sage: S.superstables(false)
[[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[2, 0, 0, 0, 1],
[2, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 1, 1, 0],
[0, 0, 0, 1, 1],
[1, 0, 0, 0, 1],
[1, 0, 0, 1, 1],
[1, 0, 0, 1, 0],
[2, 0, 1, 0, 0],
[0, 0, 0, 0, 1],
[1, 0, 1, 1, 0]]

support(D)

The input is a dictionary of integers. The output is a list of keys of nonzero values of the dictionary.

INPUT:

D - dict (configuration or divisor)

OUTPUT:

list - support of the configuration or divisor

EXAMPLES:

sage: S = sandlib('generic')
sage: c = S.identity()
sage: S.config_to_list(c)
[2, 2, 1, 1, 0]
sage: S.support(c)
[1, 2, 3, 4]
sage: S.vertices()
[0, 1, 2, 3, 4, 5]

symmetric_recurrents(orbits)

Returns the list of symmetric recurrent configurations.

INPUT:

orbits - list of lists partitioning the vertices

OUTPUT:

list of recurrent configurations

EXAMPLES:

sage: S = sandlib('kite')
sage: S.dict()
{0: {},
1: {0: 1, 2: 1, 3: 1},
2: {1: 1, 3: 1, 4: 1},
3: {1: 1, 2: 1, 4: 1},
4: {2: 1, 3: 1}}
sage: S.symmetric_recurrents([[1],[2,3],[4]])
[{1: 2, 2: 2, 3: 2, 4: 1}, {1: 2, 2: 2, 3: 2, 4: 0}]
sage: S.recurrents()
[{1: 2, 2: 2, 3: 2, 4: 1},
{1: 2, 2: 2, 3: 2, 4: 0},
{1: 2, 2: 1, 3: 2, 4: 0},
{1: 2, 2: 2, 3: 0, 4: 1},
{1: 2, 2: 0, 3: 2, 4: 1},
{1: 2, 2: 2, 3: 1, 4: 0},
{1: 2, 2: 1, 3: 2, 4: 1},
{1: 2, 2: 2, 3: 1, 4: 1}]

NOTES:

The user is responsible for ensuring that the list of orbits comes from a group of symmetries of the underlying graph.

unsaturated_ideal()

The unsaturated, homogeneous sandpile ideal.

INPUT:

None

OUTPUT:

ideal

EXAMPLES:

sage: S = sandlib('generic')
sage: S.unsaturated_ideal()
x_1^3-x_4*x_3*x_0,
x_2^3-x_5*x_3*x_0,
x_3^2-x_5*x_2,
x_4^2-x_3*x_1,
x_5^2-x_3*x_2
sage: S.ideal()
x_2-x_0,
x_3^2-x_5*x_0,
x_5*x_3-x_0^2,
x_4^2-x_3*x_1,
x_5^2-x_3*x_0,
x_1^3-x_4*x_3*x_0,
x_4*x_1^2-x_5*x_0^2

unstable(config=None)

The list of unstable vertices in config.

INPUT:

config (optional) - dict

OUTPUT:

list of vertex names

EXAMPLES:

sage: G = sandlib('generic')
sage: G.unstable(c)
[1, 2, 3, 4]
sage: G.stabilize(c);
sage: G.unstable(c)
[]

version()

Returns the version number of Sage Sandpiles.

INPUT:

None

OUTPUT:

string

EXAMPLES:

sage: S = sandlib('generic')
sage: S.version()
Sage Sandpiles Version 1.5

vertices(boundary_first=False))

Return a list of the vertices.

INPUT:

• boundary_first - Return the boundary vertices first.

EXAMPLE:

sage: P = graphs.PetersenGraph()
sage: P.vertices()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Note that the output of the vertices() function is always sorted. This is sub-optimal, speedwise, but note the following optimizations:

sage: timeit V = P.vertices()                     # not tested
100000 loops, best of 3: 8.85 [micro]s per loop
sage: timeit V = list(P.vertex_iterator())        # not tested
100000 loops, best of 3: 5.74 [micro]s per loop
sage: timeit V = list(P._nxg.adj.iterkeys())      # not tested
100000 loops, best of 3: 3.45 [micro]s per loop

In other words, if you want a fast vertex iterator, call the dictionary directly.

### Utility methods¶

The following methods create Sandpiles. (See the examples in the complete descriptions.)

Complete descriptions of methods.

aztec(n)

The aztec diamond graph.

INPUT:

n - integer

OUTPUT:

dictionary for the aztec diamond graph

EXAMPLES:

sage: aztec(2)

{(-3/2, -1/2): {},
(-3/2, 1/2): {},
(-1/2, -3/2): {'sink': 2, (-1/2, -1/2): 1, (1/2, -3/2): 1},
(-1/2, -1/2): {(-3/2, -1/2): 1,
(-1/2, -3/2): 1,
(-1/2, 1/2): 1,
(1/2, -1/2): 1},
(-1/2, 1/2): {(-3/2, 1/2): 1, (-1/2, -1/2): 1, (-1/2, 3/2): 1, (1/2, 1/2): 1},
(-1/2, 3/2): {},
(1/2, -3/2): {},
(1/2, -1/2): {(-1/2, -1/2): 1, (1/2, -3/2): 1, (1/2, 1/2): 1, (3/2, -1/2): 1},
(1/2, 1/2): {(-1/2, 1/2): 1, (1/2, -1/2): 1, (1/2, 3/2): 1, (3/2, 1/2): 1},
(1/2, 3/2): {},
(3/2, -1/2): {},
(3/2, 1/2): {}}
sage: Sandpile(aztec(2),'sink').group_order()
4542720

NOTES:

This is the aztec diamond graph with a sink vertex added. Boundary vertices have edges to the sink so that each vertex has degree 4.

sandlib(selector=None)

Returns the sandpile identified by selector. If no argument is given, a description of the sandpiles in the sandlib is printed.

INPUT:

selector - identifier or None

OUTPUT:

sandpile or description

EXAMPLES:

sage: sandlib()

Sandpiles in the sandlib:
kite : generic undirected graphs with 5 vertices
generic : generic digraph with 6 vertices
ci1 : complete intersection, non-DAG but equivalent to a DAG
riemann-roch1 : directed graph with postulation 9 and 3 maximal weight superstables
riemann-roch2 : directed graph with a superstable not majorized by a maximal superstable
gor : Gorenstein but not a complete intersection

sage: S = sandlib('gor')
sage: S.resolution()
'R <-- R^5 <-- R^5 <-- R^1'

grid(m, n)

The mxn grid graph. Each nonsink vertex has degree 4.

INPUT: m, n - positive integers

OUTPUT: dictionary for a sandpile with sink named sink.

EXAMPLE:

sage: grid(3,4)
{'sink': {},
(1, 1): {'sink': 2, (1, 2): 1, (2, 1): 1},
(1, 2): {'sink': 1, (1, 1): 1, (1, 3): 1, (2, 2): 1},
(1, 3): {'sink': 1, (1, 2): 1, (1, 4): 1, (2, 3): 1},
(1, 4): {'sink': 2, (1, 3): 1, (2, 4): 1},
(2, 1): {'sink': 1, (1, 1): 1, (2, 2): 1, (3, 1): 1},
(2, 2): {(1, 2): 1, (2, 1): 1, (2, 3): 1, (3, 2): 1},
(2, 3): {(1, 3): 1, (2, 2): 1, (2, 4): 1, (3, 3): 1},
(2, 4): {'sink': 1, (1, 4): 1, (2, 3): 1, (3, 4): 1},
(3, 1): {'sink': 2, (2, 1): 1, (3, 2): 1},
(3, 2): {'sink': 1, (2, 2): 1, (3, 1): 1, (3, 3): 1},
(3, 3): {'sink': 1, (2, 3): 1, (3, 2): 1, (3, 4): 1},
(3, 4): {'sink': 2, (2, 4): 1, (3, 3): 1}}
sage: S = Sandpile(grid(3,4),'sink')
sage: S.group_order()
4140081

random_graph(num_verts, p=1/2, directed=True, weight_max=1)

A random weighted digraph with a directed spanning tree rooted at . If directed = False, the only difference is that if is an edge with tail , head , and weight , then appears also. The result is returned as a Sage digraph.

INPUT:

• num_verts - number of vertices
• p - probability edges occur
• directed - True if directed
• weight_max - integer maximum for random weights

OUTPUT:

random graph

EXAMPLES:

sage: g = random_graph(6,0.2,True,3)
sage: S = Sandpile(g,0)
sage: S.show(edge_labels = True)

random_DAG(num_verts, p=1/2, weight_max=1)

Returns a random directed acyclic graph with num_verts vertices. The method starts with the sink vertex and adds vertices one at a time. Each vertex is connected only to only previously defined vertices, and the probability of each possible connection is given by the argument p. The weight of an edge is a random integer between 1 and weight_max.

INPUT:

• num_verts - positive integer
• p - number between and
• weight_max – integer greater than

OUTPUT:

directed acyclic graph with sink

EXAMPLES:

sage: S = random_DAG(5, 0.3)

glue_graphs(g, h, glue_g, glue_h)

Glue two graphs together.

INPUT:

• g, h - dictionaries for directed multigraphs
• glue_h, glue_g - dictionaries for a vertex

OUTPUT:

dictionary for a directed multigraph

EXAMPLES:

sage: x = {0: {}, 1: {0: 1}, 2: {0: 1, 1: 1}, 3: {0: 1, 1: 1, 2: 1}}
sage: y = {0: {}, 1: {0: 2}, 2: {1: 2}, 3: {0: 1, 2: 1}}
sage: glue_x = {1: 1, 3: 2}
sage: glue_y = {0: 1, 1: 2, 3: 1}
sage: z = glue_graphs(x,y,glue_x,glue_y)
sage: z

{0: {},
'x0': {0: 1, 'x1': 1, 'x3': 2, 'y1': 2, 'y3': 1},
'x1': {'x0': 1},
'x2': {'x0': 1, 'x1': 1},
'x3': {'x0': 1, 'x1': 1, 'x2': 1},
'y1': {0: 2},
'y2': {'y1': 2},
'y3': {0: 1, 'y2': 1}}
sage: S = Sandpile(z,0)
sage: S.first_diffs_hilb()
[1, 6, 17, 31, 41, 41, 31, 17, 6, 1]
sage: S.resolution()
'R <-- R^7 <-- R^21 <-- R^35 <-- R^35 <-- R^21 <-- R^7 <-- R^1'

NOTES:

This method makes a dictionary for a graph by combining those for g and h. The sink of g is replaced by a vertex that is connected to the vertices of g as specified by glue_g the vertices of h as specified in glue_h. The sink of the glued graph is .

Both glue_g and glue_h are dictionaries with entries of the form v:w where v is the vertex to be connected to and w is the weight of the connecting edge.

### Help¶

Documentation for each method is available through the Sage online help system:

sage: Sandpile.fire_vertex?
Namespace:      Interactive
File:           /home/davidp/.sage/temp/xyzzy/7883/_home_davidp_math_sandpile_sage_sage_sandpile1_4_sandpile_sage_47.py
Definition:     Sandpile.fire_vertex(self, v, config=None)
Docstring:

Fire (topple) a given vertex of a configuration.

INPUT:

- v - vertex name
- config - dict

OUTPUT:

dict (configuration)

EXAMPLES::

sage: G = sandlib('generic')
sage: G.unstable(c)
[1, 2, 3, 4]
sage: c
{1: 4, 2: 4, 3: 2, 4: 2, 5: 1}
sage: G.fire_vertex(1,c)

NOTES:

This method fires vertex v in config provided v is a
nonsink, unstable vertex.  Returns the result (and modifies
self.config).  The vertex v is fired only once, even if the
result leaves v unstable.

Note

An alternative to Sandpile.fire_vertex? in the preceding code example would be S.fire_vertex?, if S is any sandpile.

General Sage documentation can be found at http://sagemath.org/doc/.