# SageMathCell online  --> https://sagecell.sagemath.org/?q=gdqigr
# 
# 

'''
Main properties of the graph

Z9x16Z117      | Ord.: 1053 / Size: 7371 / Diam.: 3 / Avg.dist: 2.80038 / 14-reg.? True /  Girth: 5   
Z3x337Z344    | Ord.: 1032 / Size: 7224 / Diam.: 3 / Avg.dist: 2.79825 / 14-reg.? True /  Girth: 3   

 Symmetry properties of the graph

Z9x16Z117      | Aut.group.ord.: 1053 /  Cayley ? True ---  vtx.trans. ? True -- edge.trans. ? False
Z3x337Z344    | Aut.group.ord.: 1032 /  Cayley ? True ---  vtx.trans. ? True -- edge.trans. ? False
Degree histogram Z9x16Z117  : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1053]
Degree histogram Z3x337Z344 : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1032]

 Automorphism group structure
 C_n is the cyclic group of order n;  x means direct product; : means semidirect product.

Z9x16Z117  : C13 : (C9 : C9)  |  center order: 9
Z3x337Z344 : C8 x (C43 : C3)  |  center order: 8

Z9x16Z117      distance distrib from vtx. 0: [1, 14, 182, 856]
Z3x337Z344    distance distrib from vtx. 0: [1, 14, 180, 837]

'''


import networkx as nx

Z9x16Z117 = 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            
graphs = [('Z9x16Z117    ', Z9x16Z117 ), ('Z3x337Z344   ', Z3x337Z344 )]

Z9x16Z117nx =Z9x16Z117.networkx_graph()
Z3x337Z344nx =Z3x337Z344.networkx_graph()


def count_k_cycles(G, k):
    count = 0
    visited = set()
    def dfs(path, start, depth):
        nonlocal count
        current = path[-1]
        # Early exit if we?re going too deep
        if depth == k:
            if start in G.neighbors(current):
                # Normalize to avoid duplicates
                cycle = tuple(sorted(path))
                if cycle not in visited:
                    visited.add(cycle)
                    count += 1
            return
        for neighbor in G.neighbors(current):
            if neighbor not in path and neighbor >= start:
                dfs(path + [neighbor], start, depth + 1)
    for v in G.vertices():
        dfs([v], v, 1)
    return count   # each cycle counted twice (once forward, once reverse)

def algebraic_connectivity(G):
    """
    Compute the algebraic connectivity (Fiedler value) of a graph G.
    INPUT:
        - G: a SageMath Graph
    OUTPUT:
        - The second-smallest eigenvalue of the Laplacian matrix of G
    """
    L = G.laplacian_matrix()
    eigenvalues = L.eigenvalues()
    eigenvalues.sort()
    
    if len(eigenvalues) < 2:
        return 0  # Trivial case: empty or isolated vertex graph
    return eigenvalues[1]


def check_all_isomorphisms(graph_list):
    n = len(graph_list)
    print("\n Isomorphism check of all pairs (a dot means the pair ARE isomorphic):")
    for i in range(n):
        label_i, G_i = graph_list[i]
        for j in range(i + 1, n):
            label_j, G_j = graph_list[j]
            if G_i.is_isomorphic(G_j):
#               print(".", end="")
                print(f"{label_i} IS isomorphic to {label_j}")
            else:
                print(f"{label_i} NOT isomorphic to {label_j}")
                
def non_isomorphic(graph_list):
    reps = []
    labels = []

    for label, G in graph_list:
        if not any(G.is_isomorphic(H) for _, H in reps):
            reps.append((label, G))
            labels.append(label.strip())   # remove extra spaces if you want    
    return labels

def isomorphic(graph_list):
    labels = []

    for i, (label, G) in enumerate(graph_list):
        if any(i != j and G.is_isomorphic(H) for j, (_, H) in enumerate(graph_list)):
            labels.append(label.strip())

    return labels

def compare_graphs_list(graphs):
    canon = {}
    count =1
    for label, G in graphs:
        s6 = G.canonical_label().sparse6_string()

#        print(f"{label.strip():15} : {s6[:40]}...")

        canon.setdefault(s6, []).append(label.strip())
    print("\n noniso ->",len(canon))
    print("\nGroups:")
    for group in canon.values():
        if len(group) > 1:
            print(count,"Isomorphic :", ", ".join(group))
            count=count+1
        else:
            print(count,"Unique      :", group[0])
            count=count+1



print("\n START \n")

#noniso = non_isomorphic(graphs)
#print(len(noniso), " non-isomorphic -> ",noniso)

#iso=isomorphic(graphs)
#print(len(iso), " isomorphic -> ",iso,"\n")


compare_graphs_list(graphs)


nonisographs = graphs



# Print properties for each graph in the list
print("\n Main properties of the graph\n")
for label, graph in nonisographs:
    print(f"{label} | Ord.: {graph.order()} / Size: {graph.size()} / Diam.: {graph.diameter()} / Avg.dist: {graph.average_distance().n(digits=6)} / 14-reg.? {graph.is_regular(k=14)} /  Girth: {graph.girth()}   ")#/  Alg.conn. {algebraic_connectivity(graph).n(digits=6)} ")# /   Domin. number: {graph.dominating_set(value_only=True)} ")

print("\n Symmetry properties of the graph\n")
for label, graph in nonisographs:
    print(f"{label} | Aut.group.ord.: {graph.automorphism_group().order()} /  Cayley ? {graph.is_cayley()} ---  vtx.trans. ? {graph.is_vertex_transitive()} -- edge.trans. ? {graph.is_edge_transitive()}" )

print("Degree histogram Z9x16Z117  :", nx.degree_histogram(Z9x16Z117nx) )  
print("Degree histogram Z3x337Z344 :", nx.degree_histogram(Z3x337Z344nx) )  
    
'''    
print("\n Properties of the graphs as at arXiv\n")
for label, graph in nonisographs:
    print(f"{label} & {graph.average_distance().n(digits=6)}  & {graph.girth()}  &  {algebraic_connectivity(graph).n(digits=6)}   &  {graph.automorphism_group().order()} \\\\ ")
''' 
    
# Check isomorphisms
# print(f"Are isomorphic G5 and G6? {G5.is_isomorphic(G6)}")

#check_all_isomorphisms(nonisographs)

print("\n")


# automorphism group structure
print(" Automorphism group structure")
print(' C_n is the cyclic group of order n;  x means direct product; : means semidirect product.\n')

AZ9x16Z117  = Z9x16Z117.automorphism_group()
AZ3x337Z344  =Z3x337Z344.automorphism_group()
print('Z9x16Z117  :', AZ9x16Z117.structure_description(), ' |  center order:',AZ9x16Z117.center().order())
print('Z3x337Z344 :', AZ3x337Z344.structure_description(), ' |  center order:',AZ3x337Z344.center().order())

print("\n")



# Compute the distance distribution from a given vertex v in graph G
# Returns a list where the i-th element is the number of vertices at distance i from v
def distance_distribution(G, v):
    from collections import Counter    
    distances = G.shortest_path_lengths(v)
    distribution = Counter(distances.values())
    result = [distribution[d] for d in sorted(distribution)]    
    return result

print("\n")
for label, graph in nonisographs:
    print(f"{label} distance distrib from vtx. 0: {distance_distribution(graph, 0)}")


# Counting k-cycles for each graph
print("\nNumber of k-cycles for k=3 up to 4")
for label, graph in nonisographs:
    print(f"{label} ","  &  ".join(str(count_k_cycles(graph, k)) for k in range(3, 5)))




'''
# from 0 versio Molodtsov†

for name, G in nonisographs:
    fname = name.strip() + "_adjlst.txt"
    s = "; ".join(
        "{}-{}".format(v, ",".join(map(str, G.neighbors(v))))
        if G.neighbors(v) else str(v)
        for v in G.vertices(sort=True)
    )
    with open(fname, "w") as f:
        f.write(s)

'''
# from 0
       
for label, _ in nonisographs:
    base = label.strip()            
    G = globals()[base + "nx"]      

    with open(f"{base}_edges.txt", "w") as f:
        f.write(",".join(
            f"{{{u},{v}}}"
            for u, v in sorted((min(u, v), max(u, v)) for u, v in G.edges())
        ))

'''
# from 1
        
for label, _ in nonisographs:
    base = label.strip()   
    G = globals()[base + "nx"] 

    with open(f"{base}_Zedges.txt", "w") as f:
        f.write(",".join(
            f"{{{u+1},{v+1}}}"
            for u, v in sorted((min(u, v), max(u, v)) for u, v in G.edges())
        ))
'''       
print("\n DONE \n")        
##
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