ntaff/pyGraphs

A homemade Python library for manipulation of graphs

pyGraphs library

This repository contain some functions for manipulating graphs objects.

The librairy now contain the following files :

1. vect.py: Contains primitives for the treatment of vectors and matrices (with python's lists)

2. basicsNO.py: Contains basics functions to manipulate non oriented graphs

   • nbSommets(G) Return the number of nodes from G graph
   • nbArete(G) Return the number of edges from G graph
   • ajouteArete(G, i, j) Add an edge between nodes i and j in G graph
   • enleveArete(G, i, j) Delete an edge between nodes i and j in G graph
   • deg(G, i) Return i's degree
   • degre(G) Return G degree
   • listeToMatrice(G) Transform adjacency list in adjacency matrix
   • nonOriente(G) Verify adjacency list symetry (to verify if G is non-oriented)
   • kuratowski(n) Return adjacency list Vector from the n'th Kuratowski graph
   • areteToListe(n, L) Transform edges list in adjacency list
   • matriceToListe(M) Transform adjacency matrix in adjacency list
   • sousG(G,i) Return a copy of G without i

3. basicsO.py: Contains basics functions to manipulate oriented graphs

   • nbSommets(G) Return the number of nodes from G graph
   • nbArcs(G) Return the number of edges from G graph
   • ajouteArc(G, i, j) Add an edge between nodes i and j in G graph
   • enleveArc(G, i, j) Delete an edge between nodes i and j in G graph
   • degS(G, i) Return i's out-degree
   • degreS(G) Return G degree
   • degE(G, i) Return i's in-degree
   • listeToMatrice(G) Transform adjacency list in adjacency matrix
   • voisinage(G, i) Return i's neighborhood
   • areteToListe(n, L) Transform edges list in adjacency list
   • matToListe(M) Transform adjacency matrix in adjacency list

4. BreadthFirstSearch.py: Implement simple and generalized BF search in graphs

   • largeur(G, i) Return nodes visit list for the simple BF search from the i node
   • largeurG(G) Return nodes visit list for the generalized BF search

5. DepthFirstSearch.py: Implement simple and generalized DF search in graphs

   • profRec(G, i, Visite, ordreVisite) Recursive function that do a simple DF search from a node i
   • profond(G, i) Return nodes visit list for the simple DF search from the i node
   • profondG(G) Return nodes visit list for the generalized DF search

6. searchApplicationsNO.py: Implement BF and DF applications in non oriented graphs

   • isConnexe(G): Return true if G is connected
   • cyclicRec(G, pere, visite, cycle): Recursive cycles detection function
   • isCyclic(G): Return true if G have, at least, one cycle
   • isArbre(G): Return true if G is a tree (connected graph without cycles)
   • plusCourtChemin(G, i): Return the distance between node i and all others nodes in G, and the predecessor of each one
   • is_biparti(G): Return true if G is bipartite, false either
   • TCC(G,i): Return the number of nodes in i's connected component

7. searchApplicationsO.py: Implement BF and DF applications in oriented graphs

   • cyclicRec(G, i, Visite, cycle): Recursive Cycles detection function
   • isCyclic(G): Return true if G have one cycle or more

8. coloring.py: Implement some coloring algorithms

   • mini(L): Return the smaller integer who's not in L
   • colorNaive(G): Implement naive coloring algorithm
   • noyau(L, G): Compute kernel of a graph G
   • colorGlouton(G): Implement greedy coloring algorithm
   • colorWP(G): Implement Welsh and Powell coloring algorithm
   • is_valid(G, i, solution): Verify if neighborhood of i have different colors
   • backtracking_rec(G, colors, i, solution, solutionList): Recursive backtracking function
   • backtracking(G, colors=None): Implement Backtracking coloring algorithm

9. topologicalSort.py: Implement topological and level sorting algorithms

   • triTopologique(G): Implement topological sorting
   • triNiveaux(G): Implement level sorting

10. MaxFlow.py: Implement Ford-Fulkerson algorithm

About the authors {#about}

This library was written by Nicolas Taffoureau.