In [1]:

    

#image below using <img src="files/wgraph5.png">


    

In [2]:

    

import networkx as nx


    

In [3]:

    

import  matplotlib.pyplot as plt


    

In [4]:

    

#inline matplotlib 
%matplotlib inline


    

In [5]:

    

g1 = nx.Graph()


    

In [6]:

    

type(g1)


    

    
Out[6]:





networkx.classes.graph.Graph

In [7]:

    

print (dir(g1))


    

    




['__class__', '__contains__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'add_cycle', 'add_edge', 'add_edges_from', 'add_node', 'add_nodes_from', 'add_path', 'add_star', 'add_weighted_edges_from', 'adj', 'adjacency_iter', 'adjacency_list', 'adjlist_dict_factory', 'clear', 'copy', 'degree', 'degree_iter', 'edge', 'edge_attr_dict_factory', 'edges', 'edges_iter', 'get_edge_data', 'graph', 'has_edge', 'has_node', 'is_directed', 'is_multigraph', 'name', 'nbunch_iter', 'neighbors', 'neighbors_iter', 'node', 'node_dict_factory', 'nodes', 'nodes_iter', 'nodes_with_selfloops', 'number_of_edges', 'number_of_nodes', 'number_of_selfloops', 'order', 'remove_edge', 'remove_edges_from', 'remove_node', 'remove_nodes_from', 'selfloop_edges', 'size', 'subgraph', 'to_directed', 'to_undirected']

In [8]:

    

g1.add_nodes_from(['s','a','b','c','g'])


    

In [9]:

    

g1.nodes()


    

    
Out[9]:





['a', 's', 'g', 'c', 'b']

In [10]:

    

nx.draw(g1)  #http://networkx.github.io/documentation/development/reference/generated/networkx.drawing.nx_pylab.draw.html


    

In [11]:

    

nx.draw_networkx(g1, node_color='r') 
#http://networkx.github.io/documentation/development/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#networkx.drawing.nx_pylab.draw_networkx


    

In [12]:

    

#add edges
g1.add_edge('s','a')
g1.add_edge('s','b')
g1.add_edge('a','b')
g1.add_edge('a','c')
g1.add_edge('a','g')
g1.add_edge('b','c')
g1.add_edge('c','g')


    

In [13]:

    

#draw - note it's a different layout each time, we'll fix that 
nx.draw_networkx(g1)


    

In [14]:

    

#title - note it's in a grid, we will need that positioning 
nx.draw_networkx(g1, with_labels=True)
plt.title("Simple Graph")
plt.show()


    

In [15]:

    

#labels 
g1.add_edge('s','a', weight=1)
g1.add_edge('s','b', weight=4)
g1.add_edge('a','b', weight=2)
g1.add_edge('a','c', weight=5)
g1.add_edge('a','g', weight=12)
g1.add_edge('b','c', weight=2)
g1.add_edge('c','g', weight=3)


    

In [16]:

    

g1.edges(data=True)


    

    
Out[16]:





[('a', 's', {'weight': 1}),
 ('a', 'g', {'weight': 12}),
 ('a', 'c', {'weight': 5}),
 ('a', 'b', {'weight': 2}),
 ('s', 'b', {'weight': 4}),
 ('g', 'c', {'weight': 3}),
 ('c', 'b', {'weight': 2})]

In [17]:

    

from IPython.display import IFrame


    

In [18]:

    

IFrame('http://networkx.readthedocs.org/en/stable/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html?highlight=draw_networkx', width='90%',height=460)


    

    
Out[18]:





        
        

In [19]:

    

for source, sink, data in g1.edges(data=True):   #confirms print out 
    print (source,sink, data )


    

    




a s {'weight': 1}
a g {'weight': 12}
a c {'weight': 5}
a b {'weight': 2}
s b {'weight': 4}
g c {'weight': 3}
c b {'weight': 2}

In [20]:

    

dict([( (source,sink), data['weight']) for source,sink,data in g1.edges(data=True)])


    

    
Out[20]:





{('a', 'b'): 2,
 ('a', 'c'): 5,
 ('a', 'g'): 12,
 ('a', 's'): 1,
 ('c', 'b'): 2,
 ('g', 'c'): 3,
 ('s', 'b'): 4}

In [21]:

    

edge_labs = dict([( (u,v), d['weight']) for u,v,d in g1.edges(data=True)])
print(edge_labs)


    

    




{('a', 's'): 1, ('s', 'b'): 4, ('a', 'g'): 12, ('a', 'c'): 5, ('a', 'b'): 2, ('g', 'c'): 3, ('c', 'b'): 2}

In [22]:

    

#pos = nx.spectral_layout(g1)
pos = nx.spring_layout(g1)


    

In [23]:

    

nx.draw(g1, pos) #since nx.draw no labels as default 
nx.draw_networkx_edge_labels(g1, pos, edge_labels=edge_labs)
plt.title("Great!Weighted Spaghetti Graph")
plt.show()


    

In [24]:

    

nx.draw_networkx(g1, pos, node_color='r')
nx.draw_networkx_edge_labels(g1, pos, edge_labels=edge_labs)
# nx.draw_networkx_nodes(g1, pos, node_color='r')
plt.title("Boxed in Weighted Graph")
plt.show()


    

In [25]:

    

#pos fixed pos
#set of fixed positions for the node 
fixed_positions = {'s':(0,4),'a':(4,6), 'b':(4,2), 'c':(8,4), 'g':(12,4)}#dict with two of the positions set


    

In [26]:

    

pos = fixed_positions
nx.draw_networkx(g1, pos, with_labels=True, node_color='r')
nx.draw_networkx_edge_labels(g1, pos, edge_labels=edge_labs)
# nx.draw_networkx_nodes(g1, pos, node_color='r')
plt.title("Weighted Graph")
# plt.axis('off')
plt.show()


    

In [27]:

    

#remove axis


    

In [28]:

    

pos = fixed_positions
nx.draw_networkx(g1, pos, with_labels=True, node_color='r')
nx.draw_networkx_edge_labels(g1, pos, edge_labels=edge_labs)
# nx.draw_networkx_nodes(g1, pos, node_color='b')
plt.title("Weighted Graph")
plt.axis('off')   #removes axis
plt.show()


    

In [29]:

    

#little weird..billard balls, would like lite blue... 
pos = fixed_positions
nx.draw_networkx(g1, pos, with_labels=True, node_size=1800, node_color='b', alpha=0.8, font_size=20,
                font_color='w')
nx.draw_networkx_edge_labels(g1, pos, edge_labels=edge_labs)
# nx.draw_networkx_nodes(g1, pos, node_size=600, node_color='w')
plt.title("Weighted Graph")
plt.axis('off')   #removes axis
plt.show()


    

In [30]:

    

from IPython.display import IFrame
IFrame('http://www.color-hex.com/color/a0cbe2',width='100%', height=200)


    

    
Out[30]:





        
        

In [31]:

    

#
LIGHT_BLUE =  '#A0CBE2'  #html hex colors http://www.color-hex.com/color/a0cbe2
pos = fixed_positions
nx.draw_networkx(g1, pos, with_labels=True, node_size=1800, node_color=LIGHT_BLUE, font_size= 19 ) #node_color='b'
nx.draw_networkx_edge_labels(g1, pos, edge_labels=edge_labs) 

#nodes = nx.draw_networkx_nodes(g1,pos)
#nodes.set_edgecolor('g')

# nx.draw_networkx_nodes(g1, pos, node_shape =None, node_color=LIGHT_BLUE , font_size = 20    )
#https://networkx.github.io/documentation/latest/_modules/networkx/drawing/nx_pylab.html 

plt.title("Weighted Graph")
plt.axis('off')   #removes axis
plt.show()


    

In [70]:

    

# Colorize the edges according to the weight as well
colors = [g1[u][v]['weight'] for u,v in g1.edges()]

LIGHT_BLUE =  '#A0CBE2'  #html hex colors http://www.color-hex.com/color/a0cbe2
pos = fixed_positions
nx.draw(g1, pos=pos, with_labels=True, node_size=1800, node_color=LIGHT_BLUE, font_size= 19,
       edge_color=colors, width=4)
nx.draw_networkx_edge_labels(g1, pos, edge_labels=edge_labs)

plt.title("Weighted Graph")
plt.axis('off')   #removes axis
plt.show()


    

In [73]:

    

nx.shortest_path(g1,'s','g')


    

    
Out[73]:





['s', 'a', 'g']

In [74]:

    

nx.dijkstra_path(g1, 's','g')


    

    
Out[74]:





['s', 'a', 'b', 'c', 'g']

In [75]:

    

nx.astar_path(g1,'s', 'g')


    

    
Out[75]:





['s', 'a', 'b', 'c', 'g']

In [76]:

    

nx.astar_path(g1,'s', 'g', heuristic = None )


    

    
Out[76]:





['s', 'a', 'b', 'c', 'g']

In [78]:

    

pos


    

    
Out[78]:





{'a': (4, 6), 'b': (4, 2), 'c': (8, 4), 'g': (12, 4), 's': (0, 4)}

In [79]:

    

import math


    

In [81]:

    

print("a(x)",pos['a'][0], "b(x)",pos['b'][0])
print(math.sqrt(  (pos['a'][0] - pos['b'][0]) **2 ))


    

    




a(x) 4 b(x) 4
0.0

In [83]:

    

def d2(a,b):
    z = math.sqrt( ((pos[b][0]- pos[a][0])**2) + ((pos[b][1]-pos[a][1])**2)  ) 
    print(z)
    return z


    

In [84]:

    

#write a little function... 
nx.astar_path(g1,'s','g',  heuristic=d2 )


    

    




8.246211251235321
8.246211251235321
0.0
4.0






    
Out[84]:





['s', 'a', 'c', 'g']

In [85]:

    

pos['a']


    

    
Out[85]:





(4, 6)

In [86]:

    

pos['a'][0]


    

    
Out[86]:





4

In [88]:

    

pos['a'][0]-pos['b'][0]


    

    
Out[88]:





0

In [ ]: