%matplotlib inline
%reload_ext autoreload
%autoreload 2

"""
Explanation: NetworkX tutorial
WNixalo –– 2018/6/7
End of explanation
"""


import networkx as nx
G = nx.Graph()

"""
Explanation: 1. Creating a Graph
empty graph with no nodes and no edges:
End of explanation
"""


G.add_node(1)

"""
Explanation: In NetworkX, nodes can be any hashable object eg: text string, image, XML object, another Graph, a customized node object, etc.
NOTE: Python's None object shouldn't be used as a node – it determines whether optional function args have been assigned in many functions.

2. Nodes
Adding 1 node at a time:
End of explanation
"""


G.add_nodes_from([2,3])

"""
Explanation: Adding a list of nodes:
End of explanation
"""


H = nx.path_graph(10)
G.add_nodes_from(H)

"""
Explanation: Adding any iterable container of nodes. Can also add nodes along w/ node attributes if container yields 2-tuples (node, node_attribute_dict):
End of explanation
"""


G.add_node(H)

"""
Explanation: Note G now contains the nodes of H as nodes of G. In contrast, you could use the graph H as a node in G:
End of explanation
"""


G.add_edge(1,2)
e = (2,3)
G.add_edge(*e)  # unpack edge tuple*

"""
Explanation: Graph G now contains H as a node. This flexibility allows graphs of graphs, graphs of files, graphs of functions, and more. It's worth thinking abt how to structure your application st the nodes are useful entities. Or you cna always use a unique idntifier in G and have a separate dictionary keyed by identifer to the node info.
NOTE: you shouldn't change the node object if the hash depends on its contents.
3. Edges
G can also be grown by adding 1 edge at a time:
End of explanation
"""


G.add_edges_from([(1,2), (1,3)])

"""
Explanation: or adding a list of edges:
End of explanation
"""


G.add_edges_from(H.edges)

"""
Explanation: or adding any ebunch of edges. An ebunch is any iterable container of edge-tuples. An edge-tuple can be a 2-tuple of nodes or a 3-tuple w/ 2 nodes followed by an edge attribute dictionary eg: (2, 3, {'weight': 3.1415}):
End of explanation
"""


G.clear()

"""
Explanation: No complaints when adding existing nodes or edges. After removing all nodes and edges:
End of explanation
"""


G.add_edges_from([(1,2), (1,3)])
G.add_node(1)
G.add_edge(1,2)
G.add_node("vega")       # adds node "spam"
G.add_nodes_from("vega") # adds 4 nodes: 'v', 'e', 'g', 'a'
G.add_edge(3, 'a')

"""
Explanation: we add new nodes/edges and NetworkX quietly ignores any that are already present:
End of explanation
"""


G.number_of_nodes()

G.number_of_edges()

"""
Explanation: At this stage the graph G consists of 8 nodes and 3 edges:
End of explanation
"""


list(G.nodes)

list(G.edges)

list(G.adj[1])  # or list (G.neighbors(1))

G.degree[1]  # the number of edges incident to 1

"""
Explanation: Four basic graph properties facilitate reporting: 
- G.nodes
- G.edges
- G.adj
- G.degree
These are set-like views of the nodes, edges, neighbors (adjacencies), and degrees of nodes in a graph. They offer a continually updated read-only view into graph structure. They're also dict-like in that you can look up node & edge data attributes via the views and iterate w/ data attributes using methods .items() and .data('span').
If you want a specific container type instead of a view, you can speciy one. Here we use lists, though sets, dicts, tuples, and other contianers may be better in other contexts:
End of explanation
"""


G.edges([2, 'a'])

G.degree([2, 3])

"""
Explanation: You can spcify to report edge and dgree frm a subset of all nodes using an nbunch. 
An nbunch is any of:
- None (meaning all nodes)
- a node
- an iteratble container of nodes that's not itself a node in the graph.
End of explanation
"""


G.remove_node(2)
G.remove_nodes_from("vega")
list(G.nodes)

G.remove_edge(1, 3)

"""
Explanation: You can remove nodes and edges from the graph in a similar way to adding. Use methods:
- Graph.remove_node()
- Graph.remove_nodes_from()
- Graph.remove_edge()
- Graph.remove_edges_from()
End of explanation
"""


G.add_edge(1, 2)
H = nx.DiGraph(G)   # create a DiGraph using the connections from G
list(H.edges())

edgelist = [(0, 1), (1, 2), (2, 3)]
H = nx.Graph(edgelist)

"""
Explanation: When creating a graph structure by instantiating one of the graph classes, you can specify data in several formats:
End of explanation
"""


G[1]  # same as G.adj[1]

G[1][2]

G.edges[1, 2]

"""
Explanation: 4. What to use as Nodes and Edges
Nodes and edges are not specified as NetworkX objects. This frees you to use meaningful items as nodes & edges. Most common are numbers or strings, but a node can be any hashable object (except None), and an edge can be assoc'd w/ any object x using G.add_edge(n1, n2, object=x).
Example n1 and n2 could be protein objects from the RCSB Protein Data Bank, and x could refer to an XML record of publications detailing experimental observations of their interaction.
You can use convert_node_labels_to_integers() to get more traditional graph w/ integer labels.
5. Accessing Edges and Neighbors
In addition to views Graph.edges() and Graph.adj(), access to edges and neighbors is possible using subscript notation:
End of explanation
"""


G.add_edge(1, 3)
G[1][3]['color'] = "blue"
G.edges[1, 2]['color'] = "red"

"""
Explanation: You can get/set the attributes of an edge using subscript notation if the edge already exists:
End of explanation
"""


FG = nx.Graph()
FG.add_weighted_edges_from([(1, 2, 0.125),
                            (1, 3, 0.75),
                            (2, 4, 1.2),
                            (3, 4, 0.375)])
for n, nbrs in FG.adj.items():
    for nbr, eattr in nbrs.items():
        wt = eattr['weight']
        if wt < 0.5: print(f"({n}, {nbr}, {wt})")

"""
Explanation: Fast examination of all (node, adjacency) pairs is achieved using G.adjacency() or G.adj.items(). Note that for undirected graphs, adjacency iteration sees each edge twice:
End of explanation
"""


for (u, v, wt) in FG.edges.data('weight'):
    if wt < 0.5: print(f"({u}, {v}, {wt})")

"""
Explanation: Convenient access to all edges is achieved w/ the edges property:
End of explanation
"""


G = nx.Graph(tag="Freitag")
G.graph

"""
Explanation: 6. Adding attributes to graphs, nodes, and edges
Attributes like weights, labels, colors, or any Python object you like, can be attached to graphs, nodes, or edges.
Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (keys must be hashable). By default these are empty, but attributes can be added or changed using add_edge, add_node or direct manupulation of the attribute dictionaries named G.graph, G.nodes, and G.edges, for a graph G.
6.1 Graph attributes
Assign graph attributes when creating a new graph:
End of explanation
"""


G.graph['tag'] = "Montag"
G.graph

"""
Explanation: Or modify attributes later:
End of explanation
"""


G.add_node(1, time='5pm')
G.add_nodes_from([3], time='2pm')
G.nodes[1]

G.nodes[1]['room'] = 714
G.nodes.data()

"""
Explanation: 6.2 Node attributes
Add node attributes using add_node(), add_nodes_from(), or G.nodes:
End of explanation
"""


G.add_edge(1, 2, weight=4.7)
G.add_edges_from([(3, 4), (4, 5)], color='red')
G.add_edges_from([(1, 2, {'color': 'blue'}), (2, 3, {'weight': 0})])
G[1][2]['weight'] = 4.7
G.edges[3, 4]['weight'] = 4.2

"""
Explanation: Note that adding a node to G.nodes doesn't add it to the graph, use G.add_node() to add new nodes. Simlarly for edges.
6.3 Edge attributes
Add/change edge attributes using add_edge(), add_edges_from(), or subscription notation:
End of explanation
"""


DG = nx.DiGraph()
DG.add_weighted_edges_from([(1, 2, 0.5), (3, 1, 0.75)])
DG.out_degree(1, weight='weight')

DG.degree(1, weight='weight')

list(DG.successors(1))

list(DG.neighbors(1))

"""
Explanation: The special attribute weight should be numeric as it's used by algorithms requiring weighted edges.
7. Directed Graphs
The DiGraph class privdes additional properties specific to directed edges, eg: DiGraph.out_edges(), DiGraph.in_degree(), DiGraph.predecessors(), DiGraph.successors(), etc.
To allow to algorithms to work with both classes easily, the directed versions of neighbors() is equivalent to sucessors() while degree reports the sum of in_degree and out_degree even though that may feel inconsistent at times.
End of explanation
"""


H = nx.Graph(G)  # convert G to undirected graph

"""
Explanation: Some algorithms only work for directed graphs and others are not well defined for directed graphs. The tendency to lump un/directed graphs together is dangerous. If you want to treat a directed graph as undirected fro some measurement, you should probably convert it using Graph.to_undirected() or with:
End of explanation
"""


MG = nx.MultiGraph()
MG.add_weighted_edges_from([(1, 2, 0.5), (1, 2, 0.75), (2, 3, 0.5)])
dict(MG.degree(weight='weight'))

GG = nx.Graph()
for n, nbrs in MG.adjacency():
    for nbr, edict in nbrs.items():
        minvalue = min([d['weight'] for d in edict.values()])
        GG.add_edge(n, nbr, weight=minvalue)

nx.shortest_path(GG, 1, 3)

"""
Explanation: 8. Multigraphs
NetworkX provides classes for graphs which allow multiple edges between any pair of nodes. The MultiGraph and MultiDiGraph classes allow you to add the same edge twice, possibly w/ different edge data. This can be powerful for some applications, but many algorithms are not well defined on such graphs. Where results are defined, eg: MultiGraph.degree() NetworkX provides the function. Otherwise you should convert to a standard graph in a way that makes the measurement well defined:
End of explanation
"""


petersen = nx.petersen_graph()
tutte = nx.tutte_graph()
maze = nx.sedgewick_maze_graph()
tet = nx.tetrahedral_graph()

"""
Explanation: 9. Graph generators and graph operations
In addition to constructing graphs node-by-node or edge-by-edge, they can also be generated by:


Applying classic graph operations such as:
subgraph(G, nbunch)      - induced subgraph view of G nodes in nbunch
union(G1,G2)             - graph union
disjoint_union(G1,G2)    - graph union assuming all nodes are different
cartesian_product(G1,G2) - return Cartesian product graph
compose(G1,G2)           - combine graphs identifying nodes common to both
complement(G)            - graph complement
create_empty_copy(G)     - return an empty copy fo the same graph class
convert_to_undirected(G) - return an undirected representation of G
convert_to_directed(G)   - return a directed representation of G


Using a call to one of the classic small graphs, eg:
End of explanation
"""


K_5 = nx.complete_graph(5)
K_3_5 = nx.complete_bipartite_graph(3, 5)
barbell = nx.barbell_graph(10, 10)
lollipip = nx.lollipop = nx.lollipop_graph(10, 20)

"""
Explanation: Using a (constructive) generator for a classic graph, eg:
End of explanation
"""


er = nx.erdos_renyi_graph(100, 0.15)
ws = nx.watts_strogatz_graph(30, 3, 0.1)
ba = nx.barabasi_albert_graph(100, 5)
red = nx.random_lobster(100, 0.9, 0.9)

"""
Explanation: using a stochastic graph generator, eg:
End of explanation
"""


# nx.write_gml(red, "path.to.file")
# mygraph = nx.read_gml("path.to.file")

"""
Explanation: Reading a graph stored in a file using common graph formats, such edge lists, adjacency lists, GML, GraphML, pickle, LEDA, and others.
End of explanation
"""


G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3)])
G.add_node("vega")  # adds node "vega"
list(nx.connected_components(G))

sorted(d for n,d in G.degree())

nx.clustering(G)

"""
Explanation: Details on graph formats: Reading and writing graphs
Details on graph generator functions: Graph generators
10. Analyzing Graphs
The structure of G can be analyzed using various graph-theoretic functions such as:
End of explanation
"""


sp = dict(nx.all_pairs_shortest_path(G))
sp[3]

"""
Explanation: Some functions w/ large output iterate over (node, value) 2-tuples. These are easily stored in a dict structure if you want:
End of explanation
"""


import matplotlib.pyplot as plt

"""
Explanation: See Algorithms for details on graph algorithms supported.
11. Drawing Graphs
NetworkX is not primarily a graph drawing package, but basic drawing with Matplotlib as well as an interface to use the open source Graphviz package are included. These are part of the network.drawing module and will be imported if possible.
First import Matplotlib's plot interface (pylab works too):
End of explanation
"""


G = nx.petersen_graph()

plt.subplot(121)
nx.draw(G, with_labels=True, font_weight='bold')

plt.subplot(122)
nx.draw_shell(G, nlist=[range(5, 10), range(5)], 
              with_labels=True, font_weight='bold')

"""
Explanation: You can interactively test code using ipython -pylab, which combines the power of ipython and matplotlib and provides a convenient interactive mode.
To test if if the import of networkx.drawing was successful, draw G using one of:
End of explanation
"""


options = {'node_color': 'black',
           'node_size' : 100,
           'width'     : 3,
          }
plt.subplot(221)
nx.draw_random(G, **options)

plt.subplot(222)
nx.draw_circular(G, **options)

plt.subplot(223)
nx.draw_spectral(G, **options)

plt.subplot(224)
nx.draw_shell(G, nlist=[range(5, 10), range(5)], **options)

"""
Explanation: when drawing to an interactive display. Note that you may need to issue a Matplotlib plt.show() command if you're not suing matplotlib interactively. Matplotlib FAQ.
End of explanation
"""


G = nx.dodecahedral_graph()
shells = [[2, 3, 4, 5, 6], 
          [8, 1, 0, 19, 18, 17, 16, 15, 14, 7], 
          [9, 10, 11, 12, 13]]
nx.draw_shell(G, nlist=shells, **options)

"""
Explanation: You can find additional options via draw_networkx() and layouts via layout. You can use multiple shells with draw_shell():
End of explanation
"""


# nx.draw(G)
# plt.savefig("path.png")

"""
Explanation: To save drawings to a file:
End of explanation
"""


# from networkx.drawing.nx_pydot import write_dot
# pos = nx.nx_agraph.graphviz_layout(G)
# nx.draw(G, pos=pos)
# write_dot(G, 'file.dot')

"""
Explanation: You can also use nx_agraph.graphviz_layout(G) or nx_pydot.graphviz_layout(G) to get node positions or write the graph in dot format for further procesing if Graphviz and PyGraphviz or pydot are available.
End of explanation
"""


