file.py

 def build_graph_from_current_solution(self):
    varsdict = {}
    for v in self.santa.variables():
        if v.varValue == 1:
            varsdict[v.name] = v.varValue

    G = nx.Graph()
    #   Add nodes to the Graph
    for index_a, row_a in self.cities.iterrows():
        lat_a = row_a["lat"]
        lng_a = row_a["lng"]
        G.add_node(index_a, pos=(lat_a, lng_a))

    #   Add edges according to solution
    for k in varsdict:
        tmp_node = self.variables_dict[k]
        G.add_edge(tmp_node[0], tmp_node[1])

    return G


def add_subtour_constraints(self):
    #   Generate the graph from the current solution
    varsdict = {}
    for v in self.santa.variables():
        if v.varValue == 1:
            varsdict[v.name] = v.varValue

    G = nx.Graph()
    #   Add nodes to the Graph
    for index_a, row_a in self.cities.iterrows():
        lat_a = row_a["lat"]
        lng_a = row_a["lng"]
        G.add_node(index_a, pos=(lat_a, lng_a))

    #   Add edges according to solution
    for k in varsdict:
        tmp_node = self.variables_dict[k]
        G.add_edge(tmp_node[0], tmp_node[1])

    #   If the number of connected components is 1, we found the optimal path
    nr_connected_components = nx.number_connected_components(G)
    if nr_connected_components == 1:
        return True
    #   Otherwise we need to add the SEC equations

    #   Get all the connected components. If there are more than 1, then there are subtours
    components = nx.connected_components(G)
    for c in tqdm(components, total=nr_connected_components):
        self.santa += lpSum([self.x[(a, b)] for a in c for b in c if a != b]) <= len(c) - 1

    print("ok")