Python networkx.number_of_edges() Examples

def test_nondecreasing_degree_sequence(self):
        # Check for lexicographically nondecreasing degree sequences
        # (for fixed number of nodes and edges).
        #
        # There are three exceptions to this rule in the order given in
        # the "Atlas of Graphs" book, so we need to manually exclude
        # those.
        exceptions = [('G55', 'G56'), ('G1007', 'G1008'), ('G1012', 'G1013')]
        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
            for m, group in groupby(group, key=nx.number_of_edges):
                for G1, G2 in pairwise(group):
                    if (G1.name, G2.name) in exceptions:
                        continue
                    d1 = sorted(d for v, d in G1.degree())
                    d2 = sorted(d for v, d in G2.degree())
                    assert_less_equal(d1, d2) 

def test_iterators(self):
        G=self.G()
        G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'), 
                          ('C', 'B'), ('C', 'D')])
        G.add_nodes_from('GJK')
        assert_equal(sorted(G.nodes_iter()),
                     ['A', 'B', 'C', 'D', 'G', 'J', 'K'])
        assert_edges_equal(G.edges_iter(),
        [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'B'), ('C', 'D')])

        assert_equal(sorted([v for k,v in G.degree_iter()]),
                     [0, 0, 0, 2, 2, 3, 3])
        assert_equal(sorted(G.degree_iter(),key=str),
                     [('A', 2), ('B', 3), ('C', 3), ('D', 2), 
                      ('G', 0), ('J', 0), ('K', 0)])
        assert_equal(sorted(G.neighbors_iter('A')),['B', 'C'])
        assert_raises(nx.NetworkXError,G.neighbors_iter,'X')
        G.clear()
        assert_equal(nx.number_of_nodes(G),0)
        assert_equal(nx.number_of_edges(G),0) 

def test_iterators(self):
        G = self.G()
        G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
                          ('C', 'B'), ('C', 'D')])
        G.add_nodes_from('GJK')
        assert_equal(sorted(G.nodes()),
                     ['A', 'B', 'C', 'D', 'G', 'J', 'K'])
        assert_edges_equal(G.edges(),
                           [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'B'), ('C', 'D')])

        assert_equal(sorted([v for k, v in G.degree()]),
                     [0, 0, 0, 2, 2, 3, 3])
        assert_equal(sorted(G.degree(), key=str),
                     [('A', 2), ('B', 3), ('C', 3), ('D', 2),
                      ('G', 0), ('J', 0), ('K', 0)])
        assert_equal(sorted(G.neighbors('A')), ['B', 'C'])
        assert_raises(nx.NetworkXError, G.neighbors, 'X')
        G.clear()
        assert_equal(nx.number_of_nodes(G), 0)
        assert_equal(nx.number_of_edges(G), 0) 

def test_nondecreasing_degree_sequence(self):
        # Check for lexicographically nondecreasing degree sequences
        # (for fixed number of nodes and edges).
        #
        # There are three exceptions to this rule in the order given in
        # the "Atlas of Graphs" book, so we need to manually exclude
        # those.
        exceptions = [('G55', 'G56'), ('G1007', 'G1008'), ('G1012', 'G1013')]
        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
            for m, group in groupby(group, key=nx.number_of_edges):
                for G1, G2 in pairwise(group):
                    if (G1.name, G2.name) in exceptions:
                        continue
                    d1 = sorted(d for v, d in G1.degree())
                    d2 = sorted(d for v, d in G2.degree())
                    assert_less_equal(d1, d2) 

def test_iterators(self):
        G = self.G()
        G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
                          ('C', 'B'), ('C', 'D')])
        G.add_nodes_from('GJK')
        assert_equal(sorted(G.nodes()),
                     ['A', 'B', 'C', 'D', 'G', 'J', 'K'])
        assert_edges_equal(G.edges(),
                           [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'B'), ('C', 'D')])

        assert_equal(sorted([v for k, v in G.degree()]),
                     [0, 0, 0, 2, 2, 3, 3])
        assert_equal(sorted(G.degree(), key=str),
                     [('A', 2), ('B', 3), ('C', 3), ('D', 2),
                      ('G', 0), ('J', 0), ('K', 0)])
        assert_equal(sorted(G.neighbors('A')), ['B', 'C'])
        assert_raises(nx.NetworkXError, G.neighbors, 'X')
        G.clear()
        assert_equal(nx.number_of_nodes(G), 0)
        assert_equal(nx.number_of_edges(G), 0) 

def test_sizes(self):
        G = self.GAG[0]
        assert_equal(G.number_of_nodes(), 0)
        assert_equal(G.number_of_edges(), 0)

        G = self.GAG[7]
        assert_equal(G.number_of_nodes(), 3)
        assert_equal(G.number_of_edges(), 3) 

def test_nondecreasing_edges(self):
        # check for nondecreasing number of edges (for fixed number of
        # nodes)
        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
            for m1, m2 in pairwise(map(nx.number_of_edges, group)):
                assert_less_equal(m2, m1 + 1) 

def test_sizes(self):
        G = self.GAG[0]
        assert_equal(G.number_of_nodes(), 0)
        assert_equal(G.number_of_edges(), 0)

        G = self.GAG[7]
        assert_equal(G.number_of_nodes(), 3)
        assert_equal(G.number_of_edges(), 3) 

def test_number_of_edges(self):
        assert_equal(self.G.number_of_edges(), nx.number_of_edges(self.G))
        assert_equal(self.DG.number_of_edges(), nx.number_of_edges(self.DG)) 

def test_order_size(self):
        G = self.G()
        G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
                          ('C', 'B'), ('C', 'D')])
        assert_equal(G.order(), 4)
        assert_equal(G.size(), 5)
        assert_equal(G.number_of_edges(), 5)
        assert_equal(G.number_of_edges('A', 'B'), 1)
        assert_equal(G.number_of_edges('A', 'D'), 0) 

def test_nondecreasing_edges(self):
        # check for nondecreasing number of edges (for fixed number of
        # nodes)
        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
            for m1, m2 in pairwise(map(nx.number_of_edges, group)):
                assert_less_equal(m2, m1 + 1) 

def test_size(self):
        num_nodes = nx.number_of_nodes(self.graph)
        self.assertEqual(num_nodes, 3971, "Should be 3971.")
        num_edges = nx.number_of_edges(self.graph)
        self.assertEqual(num_edges, 28202, "Should be 28202.") 

def test_number_of_edges(self):
        assert_equal(self.G.number_of_edges(), nx.number_of_edges(self.G))
        assert_equal(self.DG.number_of_edges(), nx.number_of_edges(self.DG)) 

def test_order_size(self):
        G = self.G()
        G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
                          ('C', 'B'), ('C', 'D')])
        assert_equal(G.order(), 4)
        assert_equal(G.size(), 5)
        assert_equal(G.number_of_edges(), 5)
        assert_equal(G.number_of_edges('A', 'B'), 1)
        assert_equal(G.number_of_edges('A', 'D'), 0) 

def test_create_graph_state():
    graph = nx.complete_graph(4)
    graph = nx.relabel_nodes(graph, {i: i * 2 for i in range(4)})
    prog = create_graph_state(graph)
    n_czs = 0
    for line in prog.out().splitlines():
        if line.startswith('CZ'):
            n_czs += 1

    assert n_czs == nx.number_of_edges(graph) 

def test_number_of_edges(self):
        assert_equal(self.G.number_of_edges(),nx.number_of_edges(self.G))
        assert_equal(self.DG.number_of_edges(),nx.number_of_edges(self.DG)) 

def test_order_size(self):
        G=self.G()
        G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'), 
                          ('C', 'B'), ('C', 'D')])
        assert_equal(G.order(),4)
        assert_equal(G.size(),5)
        assert_equal(G.number_of_edges(),5)
        assert_equal(G.number_of_edges('A','B'),1)
        assert_equal(G.number_of_edges('A','D'),0) 

def __weight(community):
    """

    :param community: a subgraph/algorithms in the graph
    :return: weight of the algorithms (using the formula mentioned in the paper)
    """
    if nx.number_of_nodes(community) == 0:
        return 0
    else:
        return float(2 * nx.number_of_edges(community) / nx.number_of_nodes(community)) 

def test_union_all_and_compose_all():
    K3 = nx.complete_graph(3)
    P3 = nx.path_graph(3)

    G1 = nx.DiGraph()
    G1.add_edge('A', 'B')
    G1.add_edge('A', 'C')
    G1.add_edge('A', 'D')
    G2 = nx.DiGraph()
    G2.add_edge('1', '2')
    G2.add_edge('1', '3')
    G2.add_edge('1', '4')

    G = nx.union_all([G1, G2])
    H = nx.compose_all([G1, G2])
    assert_edges_equal(G.edges(), H.edges())
    assert_false(G.has_edge('A', '1'))
    assert_raises(nx.NetworkXError, nx.union, K3, P3)
    H1 = nx.union_all([H, G1], rename=('H', 'G1'))
    assert_equal(sorted(H1.nodes()),
                 ['G1A', 'G1B', 'G1C', 'G1D',
                  'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])

    H2 = nx.union_all([H, G2], rename=("H", ""))
    assert_equal(sorted(H2.nodes()),
                 ['1', '2', '3', '4',
                  'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])

    assert_false(H1.has_edge('NB', 'NA'))

    G = nx.compose_all([G, G])
    assert_edges_equal(G.edges(), H.edges())

    G2 = nx.union_all([G2, G2], rename=('', 'copy'))
    assert_equal(sorted(G2.nodes()),
                 ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4'])

    assert_equal(sorted(G2.neighbors('copy4')), [])
    assert_equal(sorted(G2.neighbors('copy1')), ['copy2', 'copy3', 'copy4'])
    assert_equal(len(G), 8)
    assert_equal(nx.number_of_edges(G), 6)

    E = nx.disjoint_union_all([G, G])
    assert_equal(len(E), 16)
    assert_equal(nx.number_of_edges(E), 12)

    E = nx.disjoint_union_all([G1, G2])
    assert_equal(sorted(E.nodes()), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

    G1 = nx.DiGraph()
    G1.add_edge('A', 'B')
    G2 = nx.DiGraph()
    G2.add_edge(1, 2)
    G3 = nx.DiGraph()
    G3.add_edge(11, 22)
    G4 = nx.union_all([G1, G2, G3], rename=("G1", "G2", "G3"))
    assert_equal(sorted(G4.nodes()),
                 ['G1A', 'G1B', 'G21', 'G22',
                  'G311', 'G322']) 

def density(B, nodes):
    """Returns density of bipartite graph B.

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

    nodes: list or container
      Nodes in one node set of the bipartite graph.

    Returns
    -------
    d : float
       The bipartite density

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> G = nx.complete_bipartite_graph(3,2)
    >>> X=set([0,1,2])
    >>> bipartite.density(G,X)
    1.0
    >>> Y=set([3,4])
    >>> bipartite.density(G,Y)
    1.0

    Notes
    -----
    The container of nodes passed as argument must contain all nodes
    in one of the two bipartite node sets to avoid ambiguity in the
    case of disconnected graphs.
    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    color
    """
    n = len(B)
    m = nx.number_of_edges(B)
    nb = len(nodes)
    nt = n - nb
    if m == 0:  # includes cases n==0 and n==1
        d = 0.0
    else:
        if B.is_directed():
            d = m / (2.0 * float(nb * nt))
        else:
            d = m / float(nb * nt)
    return d 

def networkAnalysis_A(dataFrame):
    # print(pd.concat((ChicagoParkBoundaries_Pd.centroid.x, ChicagoParkBoundaries_Pd.centroid.y),axis=1))
    coordi=pd.concat((dataFrame.centroid.x, dataFrame.centroid.y),axis=1) #提取patch中心点坐标，并合并
    #build weights 
    #1.distance gravity weights
    minDistance=pysal.lib.weights.min_threshold_distance(coordi.to_numpy()) #使用pysal库计算最小距离
    # print(minDistance)
    weights=pysal.lib.weights.DistanceBand(coordi,threshold=minDistance*3,binary=False,alpha=-2.) #使用pysal库计算距离权重
    # print(list(weights))
    # print(dataFrame.columns)
    G=nx.Graph() #建立网络图
    G.position={} #存储点位置
    G.perimeter={} #存储patch周长
    G.FRAC={} #存储patch的FRAC景观指数 Fractal Dimension Index
    G.shapeIdx={} #存储patch的形状指数 
    G.shape_area={} #存储patch的面积
    i=0 #用于计数循环一个点到多个点，构建的边edge
    for (index_label, row_series) in dataFrame.iterrows(): #循环dataFrame数据，逐行读取与存储到网络图graph中
        # print(index_label,"______\n",row_series["perimeter"])
        # print("_"*50)
        #add nodes
        node_parkLabel=row_series["label"] #以“label”字段作为点的ID标识
        G.add_node(node_parkLabel)
        #根据计算需要，加入属性值
        G.position[node_parkLabel]=(row_series["centroid"].x,row_series["centroid"].y)
        G.perimeter[node_parkLabel]=row_series["shapelyLength"]
        G.FRAC[node_parkLabel]=row_series["FRAC"]
        G.shapeIdx[node_parkLabel]=row_series["shapeIdx"]
        G.shape_area[node_parkLabel]=row_series["shape_area"]

        #add edges with weights
        # print(weights[i])
        #循环加入边
        for w in weights[i]:
            # print(node_parkLabel)
            # print(w)
            # print(ChicagoParkBoundaries_Pd)
            # print(ChicagoParkBoundaries_Pd.iloc[w]["label"])
            # print(weights[i][w])
            G.add_edge(node_parkLabel, dataFrame.iloc[w]["label"], weight=weights[i][w])
        #     print(w)
            
        i+=1
            
    print("_"*50)
    print("Loaded vector containing network info.")
    print("digraph has %d nodes with %d edges"% (nx.number_of_nodes(G), nx.number_of_edges(G)))
    print("_"*50)
    
    return G


#基于networkx库的网络分析 network analysis_B。 参考案例：Giant Component https://networkx.github.io/documentation/stable/auto_examples/drawing/plot_giant_component.html#sphx-glr-auto-examples-drawing-plot-giant-component-py 

def G_display(G):
    # make new graph
    H = nx.Graph()
    for v in G:
        # print(v)
        H.add_node(v)
    weightValue=list(nx.get_edge_attributes(G,'weight').values()) #提取权重
    # weightsForWidth=[G[u][v]['weight'] for u,v in G.edges()] #another way
    # print(weightValue)
    import pysal.viz.mapclassify as mc
    q=mc.Quantiles(weightValue,k=30).bins #计算分位数，用于显示值的提取
    # print(q)
  
    for (u, v, d) in tqdm(G.edges(data=True)):
        # print(u,v,d)
        # print()
        # print(d['weight'])
        if d['weight'] > q[28]:
            H.add_edge(u, v)

    print("H_digraph has %d nodes with %d edges"% (nx.number_of_nodes(H), nx.number_of_edges(H)))
    # draw with matplotlib/pylab
    plt.figure(figsize=(18, 18))
    # m=2
    # fig = figure(figsize=(9*m,9*m)
    # with nodes colored by degree sized by value elected
    node_color = [float(H.degree(v)) for v in H]
    # print(node_color)
    # nx.draw(H, G.position,node_size=[G.perimeter[v] for v in H],node_color=node_color, with_labels=True)
    
    weightsForWidthScale=np.interp(weightValue, (min(weightValue), max(weightValue)), (1, 3000)) #setting the edge width
    scaleNode=1
    
    # sklearn.preprocessing.minmax_scale(X, feature_range=(0, 1), axis=0, copy=True)
    nx.draw(H, G.position,node_size=minmax_scale([G.shape_area[v]*scaleNode for v in H],feature_range=(10, 2200)), node_color=node_color,with_labels=True,edge_cmap=plt.cm.Blues,width=weightsForWidthScale) #edge_cmap=plt.cm.Blues
    # scale the axes equally
    # plt.xlim(-5000, 500)
    # plt.ylim(-2000, 3500)

    plt.show()


#CSV文件转.shp格式，并返回关键信息。使用geopandas库实现 

def test_union_all_and_compose_all():
    K3=nx.complete_graph(3)
    P3=nx.path_graph(3)

    G1=nx.DiGraph()
    G1.add_edge('A','B')
    G1.add_edge('A','C')
    G1.add_edge('A','D')
    G2=nx.DiGraph()
    G2.add_edge('1','2')
    G2.add_edge('1','3')
    G2.add_edge('1','4')

    G=nx.union_all([G1,G2])
    H=nx.compose_all([G1,G2])
    assert_edges_equal(G.edges(),H.edges())
    assert_false(G.has_edge('A','1'))
    assert_raises(nx.NetworkXError, nx.union, K3, P3)
    H1=nx.union_all([H,G1],rename=('H','G1'))
    assert_equal(sorted(H1.nodes()),
        ['G1A', 'G1B', 'G1C', 'G1D',
         'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])

    H2=nx.union_all([H,G2],rename=("H",""))
    assert_equal(sorted(H2.nodes()),
        ['1', '2', '3', '4',
         'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])

    assert_false(H1.has_edge('NB','NA'))

    G=nx.compose_all([G,G])
    assert_edges_equal(G.edges(),H.edges())

    G2=nx.union_all([G2,G2],rename=('','copy'))
    assert_equal(sorted(G2.nodes()),
        ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4'])

    assert_equal(G2.neighbors('copy4'),[])
    assert_equal(sorted(G2.neighbors('copy1')),['copy2', 'copy3', 'copy4'])
    assert_equal(len(G),8)
    assert_equal(nx.number_of_edges(G),6)

    E=nx.disjoint_union_all([G,G])
    assert_equal(len(E),16)
    assert_equal(nx.number_of_edges(E),12)

    E=nx.disjoint_union_all([G1,G2])
    assert_equal(sorted(E.nodes()),[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

    G1=nx.DiGraph()
    G1.add_edge('A','B')
    G2=nx.DiGraph()
    G2.add_edge(1,2)
    G3=nx.DiGraph()
    G3.add_edge(11,22)
    G4=nx.union_all([G1,G2,G3],rename=("G1","G2","G3"))
    assert_equal(sorted(G4.nodes()),
        ['G1A', 'G1B', 'G21', 'G22',
         'G311', 'G322']) 

def density(B, nodes):
    """Return density of bipartite graph B.

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

    nodes: list or container
      Nodes in one set of the bipartite graph.

    Returns
    -------
    d : float
       The bipartite density

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> G = nx.complete_bipartite_graph(3,2)
    >>> X=set([0,1,2])
    >>> bipartite.density(G,X)
    1.0
    >>> Y=set([3,4])
    >>> bipartite.density(G,Y)
    1.0

    See Also
    --------
    color
    """
    n=len(B)
    m=nx.number_of_edges(B)
    nb=len(nodes)
    nt=n-nb
    if m==0: # includes cases n==0 and n==1
        d=0.0
    else:
        if B.is_directed():
            d=m/(2.0*float(nb*nt))
        else:
            d= m/float(nb*nt)
    return d 

def density(B, nodes):
    """Return density of bipartite graph B.

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

    nodes: list or container
      Nodes in one node set of the bipartite graph.

    Returns
    -------
    d : float
       The bipartite density

    Examples
    --------
    >>> from networkx.algorithms import bipartite
    >>> G = nx.complete_bipartite_graph(3,2)
    >>> X=set([0,1,2])
    >>> bipartite.density(G,X)
    1.0
    >>> Y=set([3,4])
    >>> bipartite.density(G,Y)
    1.0

    Notes
    -----
    The container of nodes passed as argument must contain all nodes
    in one of the two bipartite node sets to avoid ambiguity in the
    case of disconnected graphs.
    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
    for further details on how bipartite graphs are handled in NetworkX.

    See Also
    --------
    color
    """
    n=len(B)
    m=nx.number_of_edges(B)
    nb=len(nodes)
    nt=n-nb
    if m==0: # includes cases n==0 and n==1
        d=0.0
    else:
        if B.is_directed():
            d=m/(2.0*float(nb*nt))
        else:
            d= m/float(nb*nt)
    return d 

def test_union_all_and_compose_all():
    K3=nx.complete_graph(3)
    P3=nx.path_graph(3)

    G1=nx.DiGraph()
    G1.add_edge('A','B')
    G1.add_edge('A','C')
    G1.add_edge('A','D')
    G2=nx.DiGraph()
    G2.add_edge('1','2')
    G2.add_edge('1','3')
    G2.add_edge('1','4')

    G=nx.union_all([G1,G2])
    H=nx.compose_all([G1,G2])
    assert_edges_equal(G.edges(),H.edges())
    assert_false(G.has_edge('A','1'))
    assert_raises(nx.NetworkXError, nx.union, K3, P3)
    H1=nx.union_all([H,G1],rename=('H','G1'))
    assert_equal(sorted(H1.nodes()),
        ['G1A', 'G1B', 'G1C', 'G1D',
         'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])

    H2=nx.union_all([H,G2],rename=("H",""))
    assert_equal(sorted(H2.nodes()),
        ['1', '2', '3', '4',
         'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD'])

    assert_false(H1.has_edge('NB','NA'))

    G=nx.compose_all([G,G])
    assert_edges_equal(G.edges(),H.edges())

    G2=nx.union_all([G2,G2],rename=('','copy'))
    assert_equal(sorted(G2.nodes()),
        ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4'])

    assert_equal(sorted(G2.neighbors('copy4')),[])
    assert_equal(sorted(G2.neighbors('copy1')),['copy2', 'copy3', 'copy4'])
    assert_equal(len(G),8)
    assert_equal(nx.number_of_edges(G),6)

    E=nx.disjoint_union_all([G,G])
    assert_equal(len(E),16)
    assert_equal(nx.number_of_edges(E),12)

    E=nx.disjoint_union_all([G1,G2])
    assert_equal(sorted(E.nodes()),[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

    G1=nx.DiGraph()
    G1.add_edge('A','B')
    G2=nx.DiGraph()
    G2.add_edge(1,2)
    G3=nx.DiGraph()
    G3.add_edge(11,22)
    G4=nx.union_all([G1,G2,G3],rename=("G1","G2","G3"))
    assert_equal(sorted(G4.nodes()),
        ['G1A', 'G1B', 'G21', 'G22',
         'G311', 'G322'])