Python networkx.kamada_kawai_layout方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def apply_network_layout(G, layout='kamada_kawai', verbose=True):

    if layout == 'kamada_kawai':

        if verbose:
            print('Applying the Kamada-Kawai network layout... (may take several minutes)')

        pos = nx.kamada_kawai_layout(G)

    elif layout == 'spring_embedded':

        if verbose:
            print('Applying the spring-embedded network layout... (may take several minutes)')

        pos = nx.spring_layout(G, k=0.2, iterations=100)

    for n in G:
        G.nodes[n]['x'] = pos[n][0]
        G.nodes[n]['y'] = pos[n][1]

    return G 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def draw_kamada_kawai(G, **kwargs):
    """Draw networkx graph with circular layout.

    Parameters
    ----------
    G : graph
       A networkx graph
    kwargs : optional keywords
       See hvplot.networkx.draw() for a description of optional
       keywords, with the exception of the pos parameter which is not
       used by this function.

    Returns
    -------
    graph : holoviews.Graph or holoviews.Overlay
       Graph element or Graph and Labels
    """
    return draw(G, pos=nx.kamada_kawai_layout, **kwargs) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def build_word_ego_graph():
    import networkx as nx
    import matplotlib.pyplot as plt
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 步骤一（替换sans-serif字体）
    plt.rcParams['axes.unicode_minus'] = False  # 步骤二（解决坐标轴负数的负号显示问题）
    from harvesttext import get_sanguo, get_sanguo_entity_dict, get_baidu_stopwords

    ht0 = HarvestText()
    entity_mention_dict, entity_type_dict = get_sanguo_entity_dict()
    ht0.add_entities(entity_mention_dict, entity_type_dict)
    sanguo1 = get_sanguo()[0]
    stopwords = get_baidu_stopwords()
    docs = ht0.cut_sentences(sanguo1)
    G = ht0.build_word_ego_graph(docs,"刘备",min_freq=3,other_min_freq=2,stopwords=stopwords)
    pos = nx.kamada_kawai_layout(G)
    nx.draw(G,pos)
    nx.draw_networkx_labels(G,pos)
    plt.show()
    G = ht0.build_entity_ego_graph(docs, "刘备", min_freq=3, other_min_freq=2)
    pos = nx.spring_layout(G)
    nx.draw(G, pos)
    nx.draw_networkx_labels(G, pos)
    plt.show() 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_smoke_int(self):
        G = self.Gi
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.planar_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.fruchterman_reingold_layout(self.bigG)
        vpos = nx.spectral_layout(G)
        vpos = nx.spectral_layout(G.to_directed())
        vpos = nx.spectral_layout(self.bigG)
        vpos = nx.spectral_layout(self.bigG.to_directed())
        vpos = nx.shell_layout(G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G)
            vpos = nx.kamada_kawai_layout(G, dim=1) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_complete_graph(self, dim):
        """Tests that nodes in a complete graph are located in the unit circle when using the
        Kamada-Kawai layout"""
        graph = nx.complete_graph(dim)
        layout = nx.kamada_kawai_layout(graph)
        coords = plot._node_coords(graph, layout)
        x = coords["x"]
        y = coords["y"]
        radii = [np.sqrt(x[i] ** 2 + y[i] ** 2) for i in range(dim)]

        assert np.allclose(radii, np.ones(dim), atol=1e-5) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_cycle_graph(self, dim):
        """Tests that the length of edges in a circular cycle graph are all of equal length."""
        graph = nx.cycle_graph(dim)
        layout = nx.kamada_kawai_layout(graph)
        coords = plot._edge_coords(graph, layout)
        x = coords["x"]
        y = coords["y"]
        dists = [
            np.sqrt((x[3 * k] - x[3 * k + 1]) ** 2 + (y[3 * k] - y[3 * k + 1]) ** 2)
            for k in range(dim)
        ]
        first_dist = np.sqrt((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2)

        assert np.allclose(dists, first_dist) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def layout_obs_sub_only(obs, scale=1000.0):
    n_sub = obs.n_sub
    n_line = obs.n_line
    or_sub = obs.line_or_to_subid
    ex_sub = obs.line_ex_to_subid
    
    # Create a graph of substations vertices
    G = nx.Graph()
    
    # Set lines edges
    for line_idx in range(n_line):
        lor_sub = or_sub[line_idx]
        lex_sub = ex_sub[line_idx]
        
        # Compute edge vertices indices for current graph
        left_v = lor_sub
        right_v = lex_sub
        
        # Register edge in graph
        G.add_edge(left_v, right_v)
        
    # Convert our layout to nx format
    initial_layout = {}
    for sub_idx, sub_name in enumerate(obs.name_sub):
        initial_layout[sub_idx] = obs.grid_layout[sub_name]
        
    # Use kamada_kawai algorithm
    kkl = nx.kamada_kawai_layout(G, scale=scale)
    # Convert back to our layout format
    improved_layout = {}
    for sub_idx, v in kkl.items():
        sub_key = obs.name_sub[sub_idx]
        vx = int(np.round(v[0]))
        vy = int(np.round(v[1]))
        improved_layout[sub_key] = [vx, vy]

    return improved_layout 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_build_word_ego_graph():
    sys.stdout, expected = open(get_current_function_name()+"_current","w"), open(get_current_function_name()+"_expected").read()
    import networkx as nx
    import matplotlib.pyplot as plt
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 步骤一（替换sans-serif字体）
    plt.rcParams['axes.unicode_minus'] = False  # 步骤二（解决坐标轴负数的负号显示问题）
    from harvesttext import get_sanguo, get_sanguo_entity_dict, get_baidu_stopwords

    ht0 = HarvestText()
    entity_mention_dict, entity_type_dict = get_sanguo_entity_dict()
    ht0.add_entities(entity_mention_dict, entity_type_dict)
    sanguo1 = get_sanguo()[0]
    stopwords = get_baidu_stopwords()
    docs = ht0.cut_sentences(sanguo1)
    G = ht0.build_word_ego_graph(docs,"刘备",min_freq=3,other_min_freq=2,stopwords=stopwords)
    pos = nx.kamada_kawai_layout(G)
    nx.draw(G,pos)
    nx.draw_networkx_labels(G,pos)

    G = ht0.build_entity_ego_graph(docs, "刘备", min_freq=3, other_min_freq=2)
    pos = nx.spring_layout(G)
    nx.draw(G, pos)
    nx.draw_networkx_labels(G, pos)


    sys.stdout.close()
    assert open(get_current_function_name() + "_current").read() == expected 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_networkx_graph():
    ng = nx.Graph()
    ng.add_edge(1, 2, weight=1)
    ng.add_edge(1, 3, weight=10)
    return eda.networkx_graph(ng, nx.kamada_kawai_layout(ng),
                              figsize=(5, 5), alpha=1, node_with_labels=True) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_smoke_empty_graph(self):
        G = []
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.planar_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.spectral_layout(G)
        vpos = nx.shell_layout(G)
        vpos = nx.bipartite_layout(G, G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_smoke_string(self):
        G = self.Gs
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.planar_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.spectral_layout(G)
        vpos = nx.shell_layout(G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G)
            vpos = nx.kamada_kawai_layout(G, dim=1) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_scale_and_center_arg(self):
        sc = self.check_scale_and_center
        c = (4, 5)
        G = nx.complete_graph(9)
        G.add_node(9)
        sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5))
        # rest can have 2*scale length: [-scale, scale]
        sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c)
        sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c)
        sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c)
        sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c)
        if self.scipy is not None:
            sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_default_scale_and_center(self):
        sc = self.check_scale_and_center
        c = (0, 0)
        G = nx.complete_graph(9)
        G.add_node(9)
        sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5))
        sc(nx.spring_layout(G), scale=1, center=c)
        sc(nx.spectral_layout(G), scale=1, center=c)
        sc(nx.circular_layout(G), scale=1, center=c)
        sc(nx.shell_layout(G), scale=1, center=c)
        if self.scipy is not None:
            sc(nx.kamada_kawai_layout(G), scale=1, center=c) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_smoke_empty_graph(self):
        G = []
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.spectral_layout(G)
        vpos = nx.shell_layout(G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import kamada_kawai_layout [as 别名]
def test_smoke_int(self):
        G = self.Gi
        vpos = nx.random_layout(G)
        vpos = nx.circular_layout(G)
        vpos = nx.spring_layout(G)
        vpos = nx.fruchterman_reingold_layout(G)
        vpos = nx.fruchterman_reingold_layout(self.bigG)
        vpos = nx.spectral_layout(G)
        vpos = nx.spectral_layout(G.to_directed())
        vpos = nx.spectral_layout(self.bigG)
        vpos = nx.spectral_layout(self.bigG.to_directed())
        vpos = nx.shell_layout(G)
        if self.scipy is not None:
            vpos = nx.kamada_kawai_layout(G)