traversing graph and creating dynamic variable

Question

I have a simple graph with few nodes and these nodes have attributes such as "type" and "demand".

def mygraph():
    G = nx.Graph()
    G.add_nodes_from([("N1", {"type":"parent","demand": 10}),
    ("N2"{"type":"parent","demand": 12}),
    ("N3", {"type":"parent","demand": 25}), 
    ("S1", {"type":"server","demand": 12}), 
    ("S2,{"type":"server","demand": 20})])

I am passing this graph to another function in pyomo library. The dummy pyomo function is as follows:

def mymodel():
    g=mygraph()
    **VARIABLES**
    model.const1 = Constraint(my constraint1)
    model.const2 = Constraint(my constraint2)
    model.obj1 = Objective(my objective)
    status = SolverFactory('glpk')
    results = status.solve(model)
    assert_optimal_termination(results)
    model.display()
mymodel()

I am trying to:

1. In graph function mygraph():, I need to find the total number of nodes in the graph G with attribute type==parent.

2. In pyomo function mymodel():, I need to create the number new of VARIABLES equal to the number of nodes with attribute type==parent. So in the case above, my program must create 3 new variables, since 3 nodes have attribute type==parent in my graph function. The values of these newly created variables will be accessed from the demand attribute of the same node thus, it should be something like this;

new_var1=demand of node1 (i.e., node1_demand=10 in this case)
new_var2=demand of node2 (i.e., node2_demand=12)
new_var3=demand of node3 (i.e., node2_demand=25)

Answer 1

For the first part you can loop over the nodes:

sum(1 for n,attr in G.nodes(data=True) if attr['type']=='parent')
# 3

# or to get all types
from collections import Counter
c = Counter(attr['type'] for n,attr in G.nodes(data=True))
# {'parent': 3, 'server': 2}
c['parent']
# 3
c['server']
# 2

For the second part (which also gives you the answer of the first part of you check the length):

{n: attr['demand'] for n,attr in G.nodes(data=True) if attr['type']=='parent'}

# or
[attr['demand'] for n,attr in G.nodes(data=True) if attr['type']=='parent']

Output:

{'N1': 10, 'N2': 12, 'N3': 25}

# or
[10, 12, 25]

instanciating attributes

def mymodel():
    g = mygraph()
    nodes = [attr['demand']
             for n,attr in G.nodes(data=True)
             if attr['type']=='parent']

    # initialize model?

    for i,n in enumerate(nodes, start=1):
        setattr(model, f'const{1}', Constraint(something with n))
    
    # ...