import networkx as nx

# Make a graph where edges do not have direction.
graph = nx.Graph()

# Make a graph where edges have direction.
directed_graph = nx.DiGraph()

# Make a graph where two nodes can have multiple connecting edges without direction.
multi_graph = nx.MultiGraph()

# Make a graph where two nodes can have multiple connecting edges with direction.
# For example, this kind of graph can represent cities that are connected by more than one highway.
multi_directed_graph = nx.MultiDiGraph()

# Generate a random graph.
degree = 2
node_count = 10
random_graph = nx.random_regular_graph(degree, node_count)

graph = nx.Graph()
# Add single nodes
graph.add_node(100)
graph.add_node('lettuce')
# Add multiple nodes
graph.add_nodes_from(['carrot', 'zucchini'])
# List nodes
graph.nodes()

# Print the first three letters of each node using the nodes_iter() iterator,
# which is more efficient than nodes() because it does not create an itermediate list
[str(node)[:3] for node in graph.nodes_iter()]

% matplotlib inline
nx.draw(graph)

graph = nx.Graph()
# Add single edges
graph.add_edge('City Hall', 'Union Square')
graph.add_edge('Union Square', 'Grand Central')
graph.add_edge('Grand Central', 'Times Square')
# Add multiple edges
graph.add_edges_from([
    ('Penn Station', 'Times Square'),
    ('Times Square', '72nd Street and Broadway'),
    ('72nd Street and Broadway', '96th Street and Broadway'),
])
# List nodes
graph.nodes()

# List edges
graph.edges()

# List edges using the edges_iter() iterator, which is more efficient than edges()
# because it does not create an itermediate list
[(node1, node2) for node1, node2 in graph.edges_iter()]

%matplotlib inline
nx.draw(graph)

# Add graph attributes
graph = nx.Graph(name='phone records')
graph.graph['start_index'] = 500
graph.graph['end_index'] = 10000
graph.graph

# Add node attributes
graph.add_node('18008662453', name='TMobile customer service')
graph.add_node('18883336651', name='AT&T customer service')
graph.add_node('18009220204', name='Verizon customer service')
graph.add_node('18668667509', name='Sprint customer service')
graph.node['18008662453']['name'] = 'T-Mobile customer service'
graph.node['18008662453']

# Look at nodes and their attributes
graph.nodes(data=True)

# Add attributes to an edge
graph.add_edge('18883336651', '18009220204', weight=0.5, confidence=0.9)
graph.add_edge('18009220204', '18668667509', weight=0.3, confidence=0.4)
graph.edge['18883336651']['18009220204']['weight'] = 1.0
graph.edge['18883336651']['18009220204']

# Look at edges and their attributes
graph.edges(data=True)

%matplotlib inline
nx.draw_circular(graph)

graph = nx.Graph()
graph.add_edge('City Hall', 'Union Square', duration=10)
graph.add_edge('Union Square', 'Grand Central', duration=10)
graph.add_edge('Grand Central', 'Times Square', duration=5)
graph.add_edge('Penn Station', 'Times Square', duration=5)
graph.add_edge('Times Square', '72nd Street and Broadway', duration=10)
graph.add_edge('72nd Street and Broadway', '96th Street and Broadway', duration=10)
graph.add_edge('Grand Central', '86th Street and Lexington', duration=10)
# What is the fastest way to get from City Hall to 86th Street and Lexington?
print(nx.dijkstra_path(graph, 'City Hall', '86th Street and Lexington', weight='duration'))
# How long will it take?
print('%s minutes' % nx.dijkstra_path_length(graph, 'City Hall', '86th Street and Lexington', weight='duration'))

path = '/tmp/graph.pkl'
# Save
nx.write_gpickle(graph, path)
# Load
graph = nx.read_gpickle(path)
graph.edges(data=True)