Python networkx.is_strongly_connected方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def is_connected(C):
        """
        Return `True` if the square connection matrix `C` is connected, i.e., every
        unit is reachable from every other unit, otherwise `False`.

        Note
        ----

        This function only performs the check if the NetworkX package is available:

          https://networkx.github.io/

        """
        if nx is None:
            return True

        G = nx.from_numpy_matrix(C, create_using=nx.DiGraph())
        return nx.is_strongly_connected(G) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def validate_cuts(G, s, t, solnValue, partition, capacity, flow_func):
    assert_true(all(n in G for n in partition[0]),
                msg=msg.format(flow_func.__name__))
    assert_true(all(n in G for n in partition[1]),
                msg=msg.format(flow_func.__name__))
    cutset = compute_cutset(G, partition)
    assert_true(all(G.has_edge(u, v) for (u, v) in cutset),
                msg=msg.format(flow_func.__name__))
    assert_equal(solnValue, sum(G[u][v][capacity] for (u, v) in cutset),
                msg=msg.format(flow_func.__name__))
    H = G.copy()
    H.remove_edges_from(cutset)
    if not G.is_directed():
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
    else:
        assert_false(nx.is_strongly_connected(H),
                     msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def validate_cuts(G, s, t, solnValue, partition, capacity, flow_func):
    assert_true(all(n in G for n in partition[0]),
                msg=msg.format(flow_func.__name__))
    assert_true(all(n in G for n in partition[1]),
                msg=msg.format(flow_func.__name__))
    cutset = compute_cutset(G, partition)
    assert_true(all(G.has_edge(u, v) for (u, v) in cutset),
                msg=msg.format(flow_func.__name__))
    assert_equal(solnValue, sum(G[u][v][capacity] for (u, v) in cutset),
                 msg=msg.format(flow_func.__name__))
    H = G.copy()
    H.remove_edges_from(cutset)
    if not G.is_directed():
        assert_false(nx.is_connected(H), msg=msg.format(flow_func.__name__))
    else:
        assert_false(nx.is_strongly_connected(H),
                     msg=msg.format(flow_func.__name__)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def test_synthetic_network():
    # Load in the GeoJSON as a JSON and convert to a dictionary
    geojson_path = fixture('synthetic_east_bay.geojson')
    with open(geojson_path, 'r') as gjf:
        reference_geojson = json.load(gjf)

    G1 = load_synthetic_network_as_graph(reference_geojson)

    # This fixture gets broken into 15 chunks, so 15 + 1 = 16
    nodes = list(G1.nodes())
    assert len(nodes) == 16

    # And since it is one-directional, it gets the same edges as chunks
    edges = list(G1.edges())
    assert len(edges) == 15

    # Since this is a one-way graph, with no other context, the
    # graph will be weakly connected
    assert nx.is_strongly_connected(G1) is False

    # Go back to the GeoJSON and set optional bidirectional flag
    for i in range(len(reference_geojson['features'])):
        reference_geojson['features'][i]['properties']['bidirectional'] = True

    G2 = load_synthetic_network_as_graph(reference_geojson)

    # We re-use the same stop nodes for both directions
    nodes = list(G2.nodes())
    assert len(nodes) == 16

    # Double the number of edges as before
    edges = list(G2.edges())
    assert len(edges) == 15 * 2

    # But now, by asking for a bidirectional graph, we can assert strong
    assert nx.is_strongly_connected(G2) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def test_synthetic_network_with_custom_stops():
    # Load in the GeoJSON as a JSON and convert to a dictionary
    geojson_path = fixture('synthetic_east_bay.geojson')
    with open(geojson_path, 'r') as gjf:
        reference_geojson = json.load(gjf)

    # Add in specific, custom stops under new properties key
    custom_stops = [[-122.29225158691406, 37.80876678753658],
                    [-122.28886127471924, 37.82341261847038],
                    [-122.2701072692871, 37.83005652796547]]
    reference_geojson['features'][0]['properties']['stops'] = custom_stops

    G1 = load_synthetic_network_as_graph(reference_geojson)

    # Sanity check the outputs against the custom stops input
    assert len(list(G1.nodes())) == (len(custom_stops) + 2)
    assert len(list(G1.edges())) == (len(custom_stops) + 1)

    # Go back to the GeoJSON and set optional bidirectional flag
    reference_geojson['features'][0]['properties']['bidirectional'] = True

    G2 = load_synthetic_network_as_graph(reference_geojson)

    # We re-use the same stop nodes for both directions
    nodes = list(G2.nodes())
    assert len(nodes) == (len(custom_stops) + 2)

    # Double the number of edges as before
    edges = list(G2.edges())
    assert len(edges) == (len(custom_stops) + 1) * 2

    # But now, by asking for a bidirectional graph, we can assert strong
    assert nx.is_strongly_connected(G2) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def is_eulerian(G):
    """Return True if G is an Eulerian graph, False otherwise.

    An Eulerian graph is a graph with an Eulerian circuit.

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

    Examples
    --------
    >>> nx.is_eulerian(nx.DiGraph({0:[3], 1:[2], 2:[3], 3:[0, 1]}))
    True
    >>> nx.is_eulerian(nx.complete_graph(5))
    True
    >>> nx.is_eulerian(nx.petersen_graph())
    False

    Notes
    -----
    This implementation requires the graph to be connected
    (or strongly connected for directed graphs).
    """
    if G.is_directed():
        # Every node must have equal in degree and out degree
        for n in G.nodes_iter():
            if G.in_degree(n) != G.out_degree(n):
               return False
        # Must be strongly connected
        if not nx.is_strongly_connected(G):
            return False
    else:
        # An undirected Eulerian graph has no vertices of odd degrees
        for v,d in G.degree_iter():
            if d % 2 != 0:
                return False
        # Must be connected
        if not nx.is_connected(G):
            return False
    return True 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def test_is_strongly_connected(self):
        for G, C in self.gc:
            if len(C) == 1:
                assert_true(nx.is_strongly_connected(G))
            else:
                assert_false(nx.is_strongly_connected(G)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def test_connected_raise(self):
        G=nx.Graph()
        assert_raises(NetworkXNotImplemented, nx.strongly_connected_components, G)
        assert_raises(NetworkXNotImplemented, nx.kosaraju_strongly_connected_components, G)
        assert_raises(NetworkXNotImplemented, nx.strongly_connected_components_recursive, G)
        assert_raises(NetworkXNotImplemented, nx.strongly_connected_component_subgraphs, G)
        assert_raises(NetworkXNotImplemented, nx.is_strongly_connected, G)
        assert_raises(nx.NetworkXPointlessConcept, nx.is_strongly_connected, nx.DiGraph())
        assert_raises(NetworkXNotImplemented, nx.condensation, G) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def is_eulerian(G):
    """Returns True if and only if `G` is Eulerian.

    A graph is *Eulerian* if it has an Eulerian circuit. An *Eulerian
    circuit* is a closed walk that includes each edge of a graph exactly
    once.

    Parameters
    ----------
    G : NetworkX graph
       A graph, either directed or undirected.

    Examples
    --------
    >>> nx.is_eulerian(nx.DiGraph({0: [3], 1: [2], 2: [3], 3: [0, 1]}))
    True
    >>> nx.is_eulerian(nx.complete_graph(5))
    True
    >>> nx.is_eulerian(nx.petersen_graph())
    False

    Notes
    -----
    If the graph is not connected (or not strongly connected, for
    directed graphs), this function returns False.

    """
    if G.is_directed():
        # Every node must have equal in degree and out degree and the
        # graph must be strongly connected
        return (all(G.in_degree(n) == G.out_degree(n) for n in G) and
                nx.is_strongly_connected(G))
    # An undirected Eulerian graph has no vertices of odd degree and
    # must be connected.
    return all(d % 2 == 0 for v, d in G.degree()) and nx.is_connected(G) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def test_is_strongly_connected(self):
        for G, C in self.gc:
            if len(C) == 1:
                assert_true(nx.is_strongly_connected(G))
            else:
                assert_false(nx.is_strongly_connected(G))

    # deprecated 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def test_null_graph(self):
        G = nx.DiGraph()
        assert_equal(list(nx.strongly_connected_components(G)), [])
        assert_equal(list(nx.kosaraju_strongly_connected_components(G)), [])
        assert_equal(list(nx.strongly_connected_components_recursive(G)), [])
        assert_equal(len(nx.condensation(G)), 0)
        assert_raises(nx.NetworkXPointlessConcept, nx.is_strongly_connected, nx.DiGraph()) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def test_connected_raise(self):
        G = nx.Graph()
        assert_raises(NetworkXNotImplemented, nx.strongly_connected_components, G)
        assert_raises(NetworkXNotImplemented, nx.kosaraju_strongly_connected_components, G)
        assert_raises(NetworkXNotImplemented, nx.strongly_connected_components_recursive, G)
        assert_raises(NetworkXNotImplemented, nx.is_strongly_connected, G)
        assert_raises(nx.NetworkXPointlessConcept, nx.is_strongly_connected, nx.DiGraph())
        assert_raises(NetworkXNotImplemented, nx.condensation, G)
        # deprecated
        assert_raises(NetworkXNotImplemented, nx.strongly_connected_component_subgraphs, G)

#    Commented out due to variability on Travis-CI hardware/operating systems
#    def test_linear_time(self):
#        # See Issue #2831
#        count = 100  # base case
#        dg = nx.DiGraph()
#        dg.add_nodes_from([0, 1])
#        for i in range(2, count):
#            dg.add_node(i)
#            dg.add_edge(i, 1)
#            dg.add_edge(0, i)
#        t = time.time()
#        ret = tuple(nx.strongly_connected_components(dg))
#        dt = time.time() - t
#
#        count = 200
#        dg = nx.DiGraph()
#        dg.add_nodes_from([0, 1])
#        for i in range(2, count):
#            dg.add_node(i)
#            dg.add_edge(i, 1)
#            dg.add_edge(0, i)
#        t = time.time()
#        ret = tuple(nx.strongly_connected_components(dg))
#        dt2 = time.time() - t
#        assert_less(dt2, dt * 2.3)  # should be 2 times longer for this graph 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def get_largest_component(G, strongly=False):
    """
    https://github.com/gboeing/osmnx/blob/master/osmnx/utils.py
    Return a subgraph of the largest weakly or strongly connected component
    from a directed graph.
    Parameters
    ----------
    G : networkx multidigraph
    strongly : bool
        if True, return the largest strongly instead of weakly connected
        component
    Returns
    -------
    G : networkx multidigraph
        the largest connected component subgraph from the original graph
    """

    start_time = time.time()
    original_len = len(list(G.nodes()))

    if strongly:
        # if the graph is not connected retain only the largest strongly connected component
        if not nx.is_strongly_connected(G):

            # get all the strongly connected components in graph then identify the largest
            sccs = nx.strongly_connected_components(G)
            largest_scc = max(sccs, key=len)
            G = induce_subgraph(G, largest_scc)

            msg = ('Graph was not connected, retained only the largest strongly '
                   'connected component ({:,} of {:,} total nodes) in {:.2f} seconds')
            print(msg.format(len(list(G.nodes())), original_len, time.time()-start_time))
    else:
        # if the graph is not connected retain only the largest weakly connected component
        if not nx.is_weakly_connected(G):

            # get all the weakly connected components in graph then identify the largest
            wccs = nx.weakly_connected_components(G)
            largest_wcc = max(wccs, key=len)
            G = induce_subgraph(G, largest_wcc)

            msg = ('Graph was not connected, retained only the largest weakly '
                   'connected component ({:,} of {:,} total nodes) in {:.2f} seconds')
            print(msg.format(len(list(G.nodes())), original_len, time.time()-start_time))

    return G 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import is_strongly_connected [as 别名]
def get_largest_component(G, strongly=False):
    """
    Get subgraph of MultiDiGraph's largest weakly/strongly connected component.

    Parameters
    ----------
    G : networkx.MultiDiGraph
        input graph
    strongly : bool
        if True, return the largest strongly instead of weakly connected
        component

    Returns
    -------
    G : networkx.MultiDiGraph
        the largest connected component subgraph from the original graph
    """
    original_len = len(list(G.nodes()))

    if strongly:
        # if the graph is not connected retain only the largest strongly connected component
        if not nx.is_strongly_connected(G):

            # get all the strongly connected components in graph then identify the largest
            sccs = nx.strongly_connected_components(G)
            largest_scc = max(sccs, key=len)
            G = induce_subgraph(G, largest_scc)

            msg = (
                f"Graph was not connected, retained only the largest strongly "
                f"connected component ({len(G)} of {original_len} total nodes)"
            )
            utils.log(msg)
    else:
        # if the graph is not connected retain only the largest weakly connected component
        if not nx.is_weakly_connected(G):

            # get all the weakly connected components in graph then identify the largest
            wccs = nx.weakly_connected_components(G)
            largest_wcc = max(wccs, key=len)
            G = induce_subgraph(G, largest_wcc)

            msg = (
                f"Graph was not connected, retained only the largest weakly "
                f"connected component ({len(G)} of {original_len} total nodes)"
            )
            utils.log(msg)

    return G