def tsp_gurobi(edges):
    from gurobipy import Model, GRB, quicksum

    edges = populate_edge_weights(edges)
    incoming, outgoing, nodes = node_to_edge(edges)
    idx = dict((n, i) for i, n in enumerate(nodes))
    nedges = len(edges)
    n = len(nodes)

    m = Model()

    step = lambda x: "u_{0}".format(x)
    # Create variables
    vars = {}
    for i, (a, b, w) in enumerate(edges):
        vars[i] = m.addVar(obj=w, vtype=GRB.BINARY, name=str(i))
    for u in nodes[1:]:
        u = step(u)
        vars[u] = m.addVar(obj=0, vtype=GRB.INTEGER, name=u)
    m.update()

    # Bounds for step variables
    for u in nodes[1:]:
        u = step(u)
        vars[u].lb = 1
        vars[u].ub = n - 1

    # Add degree constraint
    for v in nodes:
        incoming_edges = incoming[v]
        outgoing_edges = outgoing[v]
        m.addConstr(quicksum(vars[x] for x in incoming_edges) == 1)
        m.addConstr(quicksum(vars[x] for x in outgoing_edges) == 1)

    # Subtour elimination
    edge_store = dict(((idx[a], idx[b]), i) for i, (a, b, w) in enumerate(edges))

    # Given a list of edges, finds the shortest subtour
    def subtour(s_edges):
        visited = [False] * n
        cycles = []
        lengths = []
        selected = [[] for i in range(n)]
        for x, y in s_edges:
            selected[x].append(y)
        while True:
            current = visited.index(False)
            thiscycle = [current]
            while True:
                visited[current] = True
                neighbors = [x for x in selected[current] if not visited[x]]
                if len(neighbors) == 0:
                    break
                current = neighbors[0]
                thiscycle.append(current)
            cycles.append(thiscycle)
            lengths.append(len(thiscycle))
            if sum(lengths) == n:
                break
        return cycles[lengths.index(min(lengths))]

    def subtourelim(model, where):
        if where != GRB.callback.MIPSOL:
            return
        selected = []
        # make a list of edges selected in the solution
        sol = model.cbGetSolution([model._vars[i] for i in range(nedges)])
        selected = [edges[i] for i, x in enumerate(sol) if x > .5]
        selected = [(idx[a], idx[b]) for a, b, w in selected]
        # find the shortest cycle in the selected edge list
        tour = subtour(selected)
        if len(tour) == n:
            return
        # add a subtour elimination constraint
        c = tour
        incident = [edge_store[a, b] for a, b in pairwise(c + [c[0]])]
        model.cbLazy(quicksum(model._vars[x] for x in incident) <= len(tour) - 1)

    m.update()

    m._vars = vars
    m.params.LazyConstraints = 1
    m.optimize(subtourelim)

    selected = [v.varName for v in m.getVars() if v.x > .5]
    selected = [int(x) for x in selected if x[:2] != "u_"]
    results = sorted(x for i, x in enumerate(edges) if i in selected) \
                    if selected else None
    return results
All shapes should rasterize to the default value
dataset/ETHPy150Open tanghaibao/jcvi/algorithms/lpsolve.py/tsp_gurobi--crossover with--dataset/ETHPy150Open mapbox/rasterio/tests/test_features.py/test_rasterize_default_value