A simple graph library...
... A bit like networkx, just without the overhead...
... similar to graph-tool, without the Python 2.7 legacy...
... with code that you can explain to your boss...
Detailed tutorial evolving in the examples section.
Install:
pip install graph-theory
Upgrade:
pip install graph-theory --upgrade --no-cache
Testing:
pytest tests
Import:
import Graph
g = Graph()
import Graph3d
g3d = Graph3D()
Modules:
| module | description |
|---|---|
from graph import Graph, Graph3D | Elementary methods (see basic methods below) for Graph and Graph3D. |
from graph import ... | All methods available on Graph (see table below) |
from graph.assignment_problem import ... | solvers for assignment problem, the Weapons-Target Assignment Problem, ... |
from graph.hash import ... | graph hash functions: graph hash, merkle tree, flow graph hash |
from graph.random import ... | graph generators for random, 2D and 3D graphs. |
from graph.transshipment_problem import ... | solvers for the transshipment problem |
from graph.traffic_scheduling_problem import ... | solvers for the traffic jams (and slide puzzle) |
from graph.visuals import ... | methods for creating matplotlib plots |
from graph.finite_state_machine import ... | finite state machine |
All module functions are available from Graph and Graph3D (where applicable).
| Graph | Graph3D | methods | returns | example |
|---|---|---|---|---|
| + | + | a in g | assert if g contains node a | |
| + | + | g.add_node(n, [obj]) | adds a node (with a pointer to object obj if given) | |
| + | + | g.copy() | returns a shallow copy of g | |
| + | + | g.node(node1) | returns object attached to node 1 | |
| + | + | g.del_node(node1) | deletes node1 and all it's edges | |
| + | + | g.nodes() | returns a list of nodes | |
| + | + | len(g.nodes()) | returns the number of nodes | |
| + | + | g.nodes(from_node=1) | returns nodes with edges from node 1 | |
| + | + | g.nodes(to_node=2) | returns nodes with edges to node 2 | |
| + | + | g.nodes(in_degree=2) | returns nodes with 2 incoming edges | |
| + | + | g.nodes(out_degree=2) | returns nodes with 2 outgoing edges | |
| + | + | g.add_edge(1,2,3) | adds edge to g for vector (1,2) with value 3 | |
| + | + | g.edge(1,2) | returns value of edge between nodes 1 and 2 | |
| + | + | g.edge(1,2,default=3) | returns default=3 if edge(1,2) doesn't exist.similar to d.get(key, 3) | |
| + | + | g.del_edge(1,2) | removes edge between nodes 1 and 2 | |
| + | + | g.edges() | returns a list of edges | |
| + | + | len(g.edges()) | returns the number of edges | |
| + | + | g.edges(path=[path]) | returns a list of edges (along a path if given). | |
| + | + | same_path(p1,p2) | compares two paths to determine if they contain same sequences ex.: [1,2,3] == [2,3,1] | |
| + | + | g.edges(from_node=1) | returns edges outgoing from node 1 | |
| + | + | g.edges(to_node=2) | returns edges incoming to node 2 | |
| + | + | g.from_dict(d) | updates the graph from a dictionary | |
| + | + | g.to_dict() | returns the graph as a dictionary | |
| + | + | g.from_list(L) | updates the graph from a list | |
| + | + | g.to_list() | return the graph as a list of edges | |
| + | + | g.shortest_path(start,end [, memoize, avoids]) | returns the distance and path for path with smallest edge sum If memoize=True, sub results are cached for faster access if repeated calls.If avoids=set(), then these nodes are not a part of the path. | |
| + | + | g.shortest_path_bidirectional(start,end) | returns distance and path for the path with smallest edge sum using bidrectional search. | |
| + | + | g.is_connected(start,end) | determines if there is a path from start to end | |
| + | + | g.breadth_first_search(start,end) | returns the number of edges and path with fewest edges | |
| + | + | g.breadth_first_walk(start,end) | returns a generator for a BFS walk | |
| + | + | g.degree_of_separation(n1,n2) | returns the distance between two nodes using BFS | |
| + | + | g.distance_map(starts,ends, reverse) | returns a dictionary with the distance from any start to any end (or reverse) | |
| + | + | g.network_size(n1, degree_of_separation) | returns the nodes within the range given by degree_of_separation | |
| + | + | g.topological_sort(key) | returns a generator that yields node in order from a non-cyclic graph. | |
| + | + | g.critical_path() | returns the distance of the critical path and a list of Tasks. | Example |
| + | + | g.critical_path_minimize_for_slack() | returns graph with artificial dependencies that minimises slack. | Example |
| + | + | g.phase_lines() | returns a dictionary with the phase_lines for a non-cyclic graph. | |
| + | + | g.sources(n) | returns the source_tree of node n | |
| + | + | g.depth_first_search(start,end) | returns path using DFS and backtracking | |
| + | + | g.depth_scan(start, criteria) | returns set of nodes where criteria is True | |
| + | + | g.distance_from_path(path) | returns the distance for path. | |
| + | + | g.maximum_flow(source,sink) | finds the maximum flow between a source and a sink | |
| + | + | g.maximum_flow_min_cut(source,sink) | finds the maximum flow minimum cut between a source and a sink | |
| + | + | g.minimum_cost_flow(inventory, capacity) | finds the total cost and flows of the capacitated minimum cost flow. | |
| + | + | g.solve_tsp() | solves the traveling salesman problem for the graph. Available methods: 'greedy' (default) and 'bnb | |
| + | + | g.subgraph_from_nodes(nodes) | returns the subgraph of g involving nodes | |
| + | + | g.is_subgraph(g2) | determines if graph g2 is a subgraph in g | |
| + | + | g.is_partite(n) | determines if graph is n-partite | |
| + | + | g.has_cycles() | determines if there are any cycles in the graph | |
| + | + | g.components() | returns set of nodes in each component in g | |
| + | + | g.same_path(p1,p2) | compares two paths, returns True if they're the same | |
| + | + | g.adjacency_matrix() | returns the adjacency matrix for the graph | |
| + | + | g.all_pairs_shortest_paths() | finds the shortest path between all nodes | |
| + | + | g.minsum() | finds the node(s) with shortest total distance to all other nodes | |
| + | + | g.minmax() | finds the node(s) with shortest maximum distance to all other nodes | |
| + | + | g.shortest_tree_all_pairs() | finds the shortest tree for all pairs | |
| + | + | g.has_path(p) | asserts whether a path p exists in g | |
| + | + | g.all_simple_paths(start,end) | finds all simple paths between 2 nodes | |
| + | + | g.all_paths(start,end) | finds all combinations of paths between 2 nodes | |
| - | + | g3d.distance(n1,n2) | returns the spatial distance between n1 and n2 | |
| - | + | g3d.n_nearest_neigur(n1, [n]) | returns the n nearest neigurs to node n1 | |
| - | + | g3d.plot() | returns matplotlib plot of the graph. |
| want to... | doesn't work... | do instead... | ...but why? |
|---|---|---|---|
| have multiple edges between two nodes | Graph(from_list=[(1,2,3), (1,2,4)] | Add dummy nodes[(1,a,3), (a,2,0),(1,b,4),(b,2,0)] | Explicit is better than implicit. |
| multiple values on an edge | g.add_edge(1,2,{'a':3, 'b':4}) | Have two graphsg_a.add_edge(1,2,3)g_b.add_edge(1,2,4) | Most graph algorithms don't work with multiple values |
| do repeated calls to shortest path | g.shortest_path(a,b) is slow | Use g.shortest_path(a,b,memoize=True) instead | memoize uses bidirectional search and caches sub-results along the shortest path for future retrievals |
- Arturo Soucase for packaging and testing.
- Peter Norvig for inspiration on TSP from pytudes.
- Harry Darby for the mountain river map.
- Kyle Downey for depth_scan algorithm.
- Ross Blandford for munich firebrigade centre -, traffic jam - and slide puzzle - test cases.
- Avi Kelman for type-tolerant search, and a number of micro optimizations.
- Joshua Crestone for all simple paths test.
- CodeMartyLikeYou for detecting a bug in
@memoize - Tom Carroll for detecting the bug in del_edge and inspiration for topological sort.
- Sappique for discovering bugs in
__eq__,copyandhas_cycles. - joshinils for discovering bug where
graph.edges(from_node=0)was interpreted asFalse.