Python networkx.NetworkXUnfeasible() Examples

def phase3(self):
        # build potential remaining edges and choose with rejection sampling
        potential_edges = combinations(self.remaining_degree, 2)
        # build auxilliary graph of potential edges not already in graph
        H = nx.Graph([(u,v) for (u,v) in potential_edges
                      if not self.graph.has_edge(u,v)])
        while self.remaining_degree:
            if not self.suitable_edge():
                raise nx.NetworkXUnfeasible('no suitable edges left')
            while True:
                u,v = sorted(random.choice(H.edges()))
                if random.random() < self.q(u,v):
                    break
            if random.random() < self.p(u,v): # accept edge
                self.graph.add_edge(u,v)
                self.update_remaining(u,v, aux_graph=H) 

def test_topological_sort1(self):
        DG = nx.DiGraph([(1, 2), (1, 3), (2, 3)])

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_equal(tuple(algorithm(DG)), (1, 2, 3))

        DG.add_edge(3, 2)

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_raises(nx.NetworkXUnfeasible, consume, algorithm(DG))

        DG.remove_edge(2, 3)

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_equal(tuple(algorithm(DG)), (1, 3, 2))

        DG.remove_edge(3, 2)

        assert_in(tuple(nx.topological_sort(DG)), {(1, 2, 3), (1, 3, 2)})
        assert_equal(tuple(nx.lexicographical_topological_sort(DG)), (1, 2, 3)) 

def sorted_nodes(self):
        """Returns a topological sort of the resource graph.

        Notes
        -----
        Topological sorts are not stable. Be wary of depending on order
        where you shouldn't.

        """
        if self._sorted_nodes is None:
            try:
                self._sorted_nodes = list(nx.algorithms.topological_sort(self.graph))
            except nx.NetworkXUnfeasible:
                raise ResourceError(f'The resource pool contains at least one cycle: '
                                    f'{nx.find_cycle(self.graph)}.')
        return self._sorted_nodes 

def resolve_hook_order(self, hook_tuples: List[HookTuple]) -> List[HookTuple]:
        dag = nx.DiGraph()

        for hook_tuple in hook_tuples:
            dag.add_node(hook_tuple.Hook.name, hook_tuple=hook_tuple)
            for dep_name in hook_tuple.Hook.run_after:
                dag.add_edge(hook_tuple.Hook.name, dep_name)
            for successor_name in hook_tuple.Hook.run_before:
                dag.add_edge(successor_name, hook_tuple.Hook.name)

        try:
            order = reversed(list(nx.topological_sort(dag)))
        except nx.NetworkXUnfeasible:
            msg = 'Circular dependency detected between hooks'
            problem_graph = ', '.join(f'{a} -> {b}'
                                      for a, b in nx.find_cycle(dag))
            raise Exception(f'{msg}: {problem_graph}')

        rv = []
        for hook_name in order:
            hook_tuple = dag.nodes[hook_name].get('hook_tuple')
            if hook_tuple:
                rv.append(hook_tuple)
        return rv 

def __init__(self, config: Dict, recipe_folder: str,
                 exclude: List[str] = None, nocatch: bool=False) ->None:
        self.config = config
        self.recipe_folder = recipe_folder
        self.skip = self.load_skips()
        self.exclude = exclude or []
        self.nocatch = nocatch
        self._messages = []

        dag = nx.DiGraph()
        dag.add_nodes_from(str(check) for check in get_checks())
        dag.add_edges_from(
            (str(check), str(check_dep))
            for check in get_checks()
            for check_dep in check.requires
        )
        self.checks_dag = dag

        try:
            self.checks_ordered = nx.topological_sort(dag, reverse=True)
        except nx.NetworkXUnfeasible:
            raise RunTimeError("Cycle in LintCheck requirements!")
        self.reload_checks() 

def test_topological_sort3(self):
        DG = nx.DiGraph()
        DG.add_edges_from([(1, i) for i in range(2, 5)])
        DG.add_edges_from([(2, i) for i in range(5, 9)])
        DG.add_edges_from([(6, i) for i in range(9, 12)])
        DG.add_edges_from([(4, i) for i in range(12, 15)])

        def validate(order):
            ok_(isinstance(order, list))
            assert_equal(set(order), set(DG))
            for u, v in combinations(order, 2):
                assert_false(nx.has_path(DG, v, u))
        validate(list(nx.topological_sort(DG)))

        DG.add_edge(14, 1)
        assert_raises(nx.NetworkXUnfeasible, consume, nx.topological_sort(DG)) 

def bridge_augmentation(G, avail=None, weight=None):
    """Finds the a set of edges that bridge connects G.

    Adding these edges to G will make it 2-edge-connected.
    If no constraints are specified the returned set of edges is minimum an
    optimal, otherwise the solution is approximated.

    Notes
    -----
    If there are no constraints the solution can be computed in linear time
    using :func:`unconstrained_bridge_augmentation`. Otherwise, the problem
    becomes NP-hard and is the solution is approximated by
    :func:`weighted_bridge_augmentation`.
    """
    if G.number_of_nodes() < 3:
        raise nx.NetworkXUnfeasible(
            'impossible to bridge connect less than 3 nodes')
    if avail is None:
        return unconstrained_bridge_augmentation(G)
    else:
        return weighted_bridge_augmentation(G, avail, weight=weight)


# --- Algorithms and Helpers --- 

def is_directed_acyclic_graph(G):
    """Return True if the graph `G` is a directed acyclic graph (DAG) or
    False if not.

    Parameters
    ----------
    G : NetworkX graph

    Returns
    -------
    bool
        True if `G` is a DAG, False otherwise
    """
    if not G.is_directed():
        return False
    try:
        consume(topological_sort(G))
        return True
    except nx.NetworkXUnfeasible:
        return False 

def phase3(self):
        # build potential remaining edges and choose with rejection sampling
        potential_edges = combinations(self.remaining_degree, 2)
        # build auxiliary graph of potential edges not already in graph
        H = nx.Graph([(u, v) for (u, v) in potential_edges
                      if not self.graph.has_edge(u, v)])
        rng = self.rng
        while self.remaining_degree:
            if not self.suitable_edge():
                raise nx.NetworkXUnfeasible('no suitable edges left')
            while True:
                u, v = sorted(rng.choice(list(H.edges())))
                if rng.random() < self.q(u, v):
                    break
            if rng.random() < self.p(u, v):  # accept edge
                self.graph.add_edge(u, v)
                self.update_remaining(u, v, aux_graph=H) 

def test_all_topological_sorts_3(self):
        def unfeasible():
            DG = nx.DiGraph([(1, 2), (2, 3), (3, 4), (4, 2), (4, 5)])
            # convert to list to execute generator
            list(nx.all_topological_sorts(DG))

        def not_implemented():
            G = nx.Graph([(1, 2), (2, 3)])
            # convert to list to execute generator
            list(nx.all_topological_sorts(G))

        def not_implemted_2():
            G = nx.MultiGraph([(1, 2), (1, 2), (2, 3)])
            list(nx.all_topological_sorts(G))
        assert_raises(nx.NetworkXUnfeasible, unfeasible)
        assert_raises(nx.NetworkXNotImplemented, not_implemented)
        assert_raises(nx.NetworkXNotImplemented, not_implemted_2) 

def test_topological_sort3(self):
        DG = nx.DiGraph()
        DG.add_edges_from([(1, i) for i in range(2, 5)])
        DG.add_edges_from([(2, i) for i in range(5, 9)])
        DG.add_edges_from([(6, i) for i in range(9, 12)])
        DG.add_edges_from([(4, i) for i in range(12, 15)])

        def validate(order):
            ok_(isinstance(order, list))
            assert_equal(set(order), set(DG))
            for u, v in combinations(order, 2):
                assert_false(nx.has_path(DG, v, u))
        validate(list(nx.topological_sort(DG)))

        DG.add_edge(14, 1)
        assert_raises(nx.NetworkXUnfeasible, consume, nx.topological_sort(DG)) 

def is_directed_acyclic_graph(G):
    """Return True if the graph G is a directed acyclic graph (DAG) or 
    False if not.

    Parameters
    ----------
    G : NetworkX graph
        A graph

    Returns
    -------
    is_dag : bool
        True if G is a DAG, false otherwise
    """
    if not G.is_directed():
        return False
    try:
        topological_sort(G, reverse=True)
        return True
    except nx.NetworkXUnfeasible:
        return False 

def test_topological_sort1(self):
        DG = nx.DiGraph([(1, 2), (1, 3), (2, 3)])

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_equal(tuple(algorithm(DG)), (1, 2, 3))

        DG.add_edge(3, 2)

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_raises(nx.NetworkXUnfeasible, consume, algorithm(DG))

        DG.remove_edge(2, 3)

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_equal(tuple(algorithm(DG)), (1, 3, 2))

        DG.remove_edge(3, 2)

        assert_in(tuple(nx.topological_sort(DG)), {(1, 2, 3), (1, 3, 2)})
        assert_equal(tuple(nx.lexicographical_topological_sort(DG)), (1, 2, 3)) 

def test_reverse_topological_sort1(self):
        DG = nx.DiGraph()
        DG.add_edges_from([(1, 2), (1, 3), (2, 3)])
        assert_equal(nx.topological_sort(DG, reverse=True), [3, 2, 1])
        assert_equal(
            nx.topological_sort_recursive(DG, reverse=True), [3, 2, 1])

        DG.add_edge(3, 2)
        assert_raises(nx.NetworkXUnfeasible,
                      nx.topological_sort, DG, reverse=True)
        assert_raises(nx.NetworkXUnfeasible,
                      nx.topological_sort_recursive, DG, reverse=True)

        DG.remove_edge(2, 3)
        assert_equal(nx.topological_sort(DG, reverse=True), [2, 3, 1])
        assert_equal(
            nx.topological_sort_recursive(DG, reverse=True), [2, 3, 1]) 

def test_topological_sort3(self):
        DG = nx.DiGraph()
        DG.add_edges_from([(1, i) for i in range(2, 5)])
        DG.add_edges_from([(2, i) for i in range(5, 9)])
        DG.add_edges_from([(6, i) for i in range(9, 12)])
        DG.add_edges_from([(4, i) for i in range(12, 15)])

        def validate(order):
            ok_(isinstance(order, list))
            assert_equal(set(order), set(DG))
            for u, v in combinations(order, 2):
                assert_false(nx.has_path(DG, v, u))
        validate(nx.topological_sort_recursive(DG))
        validate(nx.topological_sort(DG))

        DG.add_edge(14, 1)
        assert_raises(nx.NetworkXUnfeasible, nx.topological_sort, DG)
        assert_raises(nx.NetworkXUnfeasible, nx.topological_sort_recursive, DG) 

def test_exception(self):
        """Bad input should raise exception."""
        G = self.florentine
        assert_raises(nx.NetworkXUnfeasible,
                      nx.maximal_independent_set, G, ["Smith"])
        assert_raises(nx.NetworkXUnfeasible,
                      nx.maximal_independent_set, G, ["Salviati", "Pazzi"]) 

def test_sum_demands_not_zero(self):
        G = nx.DiGraph()
        G.add_node('s', demand = -5)
        G.add_node('t', demand = 4)
        G.add_edge('s', 'a', weight = 1, capacity = 3)
        G.add_edge('a', 'b', weight = 3)
        G.add_edge('a', 'c', weight = -6)
        G.add_edge('b', 'd', weight = 1)
        G.add_edge('c', 'd', weight = -2)
        G.add_edge('d', 't', weight = 1, capacity = 3)
        assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
        assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G) 

def test_no_flow_satisfying_demands(self):
        G = nx.DiGraph()
        G.add_node('s', demand = -5)
        G.add_node('t', demand = 5)
        G.add_edge('s', 'a', weight = 1, capacity = 3)
        G.add_edge('a', 'b', weight = 3)
        G.add_edge('a', 'c', weight = -6)
        G.add_edge('b', 'd', weight = 1)
        G.add_edge('c', 'd', weight = -2)
        G.add_edge('d', 't', weight = 1, capacity = 3)
        assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
        assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G) 

def test_exceptions(self):
        G = nx.Graph()
        assert_raises(nx.NetworkXNotImplemented, nx.network_simplex, G)
        assert_raises(nx.NetworkXNotImplemented, nx.capacity_scaling, G)
        G = nx.MultiGraph()
        assert_raises(nx.NetworkXNotImplemented, nx.network_simplex, G)
        assert_raises(nx.NetworkXNotImplemented, nx.capacity_scaling, G)
        G = nx.DiGraph()
        assert_raises(nx.NetworkXError, nx.network_simplex, G)
        assert_raises(nx.NetworkXError, nx.capacity_scaling, G)
        G.add_node(0, demand=float('inf'))
        assert_raises(nx.NetworkXError, nx.network_simplex, G)
        assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G)
        G.nodes[0]['demand'] = 0
        G.add_node(1, demand=0)
        G.add_edge(0, 1, weight=-float('inf'))
        assert_raises(nx.NetworkXError, nx.network_simplex, G)
        assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G)
        G[0][1]['weight'] = 0
        G.add_edge(0, 0, weight=float('inf'))
        assert_raises(nx.NetworkXError, nx.network_simplex, G)
        #assert_raises(nx.NetworkXError, nx.capacity_scaling, G)
        G[0][0]['weight'] = 0
        G[0][1]['capacity'] = -1
        assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
        #assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G)
        G[0][1]['capacity'] = 0
        G[0][0]['capacity'] = -1
        assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
        #assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G) 

def test_topological_sort2(self):
        DG = nx.DiGraph({1: [2], 2: [3], 3: [4],
                         4: [5], 5: [1], 11: [12],
                         12: [13], 13: [14], 14: [15]})
        assert_raises(nx.NetworkXUnfeasible, consume, nx.topological_sort(DG))

        assert_false(nx.is_directed_acyclic_graph(DG))

        DG.remove_edge(1, 2)
        consume(nx.topological_sort(DG))
        assert_true(nx.is_directed_acyclic_graph(DG)) 

def test_negcycle_infcap(self):
        G = nx.DiGraph()
        G.add_node('s', demand = -5)
        G.add_node('t', demand = 5)
        G.add_edge('s', 'a', weight = 1, capacity = 3)
        G.add_edge('a', 'b', weight = 3)
        G.add_edge('c', 'a', weight = -6)
        G.add_edge('b', 'd', weight = 1)
        G.add_edge('d', 'c', weight = -2)
        G.add_edge('d', 't', weight = 1, capacity = 3)
        assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
        assert_raises(nx.NetworkXUnbounded, nx.capacity_scaling, G) 

def test_exception(self):
        """Bad input should raise exception."""
        G = self.florentine
        assert_raises(nx.NetworkXUnfeasible,
                      nx.maximal_independent_set, G, ["Smith"])
        assert_raises(nx.NetworkXUnfeasible,
                      nx.maximal_independent_set, G, ["Salviati", "Pazzi"]) 

def __init__(self, degree, seed=None):
        if not nx.is_graphical(degree):
            raise nx.NetworkXUnfeasible('degree sequence is not graphical')
        if seed is not None:
            random.seed(seed)
        self.degree = list(degree)
        # node labels are integers 0,...,n-1
        self.m = sum(self.degree) / 2.0  # number of edges
        try:
            self.dmax = max(self.degree)  # maximum degree
        except ValueError:
            self.dmax = 0 

def has_cycle(G):
    """Decides whether the directed graph has a cycle."""
    try:
        consume(topological_sort(G))
    except nx.NetworkXUnfeasible:
        return True
    else:
        return False 

def _basic_graphical_tests(deg_sequence):
    # Sort and perform some simple tests on the sequence
    if not nx.utils.is_list_of_ints(deg_sequence):
        # check for a type that can be converted to int. Like numpy.int64
        ds = []
        for d in deg_sequence:
            try:
                intd = int(d)
            except ValueError:
                raise nx.NetworkXError("Invalid type in deg_sequence: not an integer")
            if intd != d:
                raise nx.NetworkXError("Invalid type in deg_sequence: not an integer")
            ds.append(intd)
        deg_sequence = ds
    p = len(deg_sequence)
    num_degs = [0] * p
    dmax, dmin, dsum, n = 0, p, 0, 0
    for d in deg_sequence:
        # Reject if degree is negative or larger than the sequence length
        if d < 0 or d >= p:
            raise nx.NetworkXUnfeasible
        # Process only the non-zero integers
        elif d > 0:
            dmax, dmin, dsum, n = max(dmax, d), min(dmin, d), dsum + d, n + 1
            num_degs[d] += 1
    # Reject sequence if it has odd sum or is oversaturated
    if dsum % 2 or dsum > n * (n - 1):
        raise nx.NetworkXUnfeasible
    return dmax, dmin, dsum, n, num_degs 

def test_deadend(self):
        """Check if one-node cycles are handled properly. Taken from ticket
        #2906 from @sshraven."""
        G = nx.DiGraph()

        G.add_nodes_from(range(5), demand=0)
        G.node[4]['demand'] = -13
        G.node[3]['demand'] = 13

        G.add_edges_from([(0,2), (0, 3), (2, 1)], capacity=20, weight=0.1)
        assert_raises(nx.NetworkXUnfeasible, nx.min_cost_flow, G) 

def test_no_flow_satisfying_demands(self):
        G = nx.DiGraph()
        G.add_node('s', demand=-5)
        G.add_node('t', demand=5)
        G.add_edge('s', 'a', weight=1, capacity=3)
        G.add_edge('a', 'b', weight=3)
        G.add_edge('a', 'c', weight=-6)
        G.add_edge('b', 'd', weight=1)
        G.add_edge('c', 'd', weight=-2)
        G.add_edge('d', 't', weight=1, capacity=3)
        assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
        assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G) 

def test_random_degree_sequence_graph_raise():
    z = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
    assert_raises(nx.NetworkXUnfeasible, nx.random_degree_sequence_graph, z) 

def test_sum_demands_not_zero(self):
        G = nx.DiGraph()
        G.add_node('s', demand=-5)
        G.add_node('t', demand=4)
        G.add_edge('s', 'a', weight=1, capacity=3)
        G.add_edge('a', 'b', weight=3)
        G.add_edge('a', 'c', weight=-6)
        G.add_edge('b', 'd', weight=1)
        G.add_edge('c', 'd', weight=-2)
        G.add_edge('d', 't', weight=1, capacity=3)
        assert_raises(nx.NetworkXUnfeasible, nx.network_simplex, G)
        assert_raises(nx.NetworkXUnfeasible, nx.capacity_scaling, G) 

def test_antichains(self):
        antichains = nx.algorithms.dag.antichains
        G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
        solution = [[], [4], [3], [2], [1]]
        self._check_antichains(list(antichains(G)), solution)
        G = nx.DiGraph([(1, 2), (2, 3), (2, 4), (3, 5), (5, 6), (5, 7)])
        solution = [[], [4], [7], [7, 4], [6], [6, 4], [6, 7], [6, 7, 4],
                    [5], [5, 4], [3], [3, 4], [2], [1]]
        self._check_antichains(list(antichains(G)), solution)
        G = nx.DiGraph([(1, 2), (1, 3), (3, 4), (3, 5), (5, 6)])
        solution = [[], [6], [5], [4], [4, 6], [4, 5], [3], [2], [2, 6],
                    [2, 5], [2, 4], [2, 4, 6], [2, 4, 5], [2, 3], [1]]
        self._check_antichains(list(antichains(G)), solution)
        G = nx.DiGraph({0: [1, 2], 1: [4], 2: [3], 3: [4]})
        solution = [[], [4], [3], [2], [1], [1, 3], [1, 2], [0]]
        self._check_antichains(list(antichains(G)), solution)
        G = nx.DiGraph()
        self._check_antichains(list(antichains(G)), [[]])
        G = nx.DiGraph()
        G.add_nodes_from([0, 1, 2])
        solution = [[], [0], [1], [1, 0], [2], [2, 0], [2, 1], [2, 1, 0]]
        self._check_antichains(list(antichains(G)), solution)

        def f(x): return list(antichains(x))
        G = nx.Graph([(1, 2), (2, 3), (3, 4)])
        assert_raises(nx.NetworkXNotImplemented, f, G)
        G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
        assert_raises(nx.NetworkXUnfeasible, f, G)