如何使用`networkx`中的`pos`参数创建流程图风格的图表？ （Python 3）

问题描述 投票：0回答：2

我正在尝试使用

Python
（最好使用
matplotlib
和
networkx
，尽管会对
bokeh
感兴趣）创建一个线性网络图，其概念与下面的类似。

如何使用 

pos
在 Python 中有效地构建此图表（
networkx
？）？ 我想将其用于更复杂的示例，所以我觉得对这个简单示例的位置进行硬编码是没有用的:( 。
networkx
有解决办法吗？

pos（字典，可选）——以节点为键的字典， 作为价值观的立场。如果没有指定弹簧布局定位将 被计算。有关计算节点的函数，请参阅 networkx.layout 职位。

我还没有在

networkx
中看到任何关于如何实现这一点的教程，这就是为什么我相信这个问题将成为社区的可靠资源。我已经广泛浏览了 
networkx
教程，但那里没有类似的内容。如果不仔细使用 
networkx
参数，
pos
的布局将使这种类型的网络无法解释......我相信这是我唯一的选择。 https://networkx.github.io/documentation/networkx-1.9/reference/drawing.html文档上的预计算布局似乎都不能很好地处理这种类型的网络结构。

简单示例：

(A) 每个外键都是图中从左向右移动的迭代（例如迭代 0 表示样本，迭代 1 具有组 1 - 3，迭代 2 相同，迭代 3 具有组 1 - 2 等） 。 (B) 内部字典包含该特定迭代中的当前分组，以及代表当前组的先前组合并的权重（例如

iteration 3

 具有
Group 1
 和
Group 2
 以及
iteration 4
 的所有
iteration 3's）
Group 2
 已合并为
iteration 4's
Group 2
，但
iteration 3's
Group 1
 已被拆分。权重之和始终为 1。

我的连接代码与上图的权重：

D_iter_current_previous = { 1: { "Group 1":{"sample_0":0.5, "sample_1":0.5, "sample_2":0, "sample_3":0, "sample_4":0}, "Group 2":{"sample_0":0, "sample_1":0, "sample_2":1, "sample_3":0, "sample_4":0}, "Group 3":{"sample_0":0, "sample_1":0, "sample_2":0, "sample_3":0.5, "sample_4":0.5} }, 2: { "Group 1":{"Group 1":1, "Group 2":0, "Group 3":0}, "Group 2":{"Group 1":0, "Group 2":1, "Group 3":0}, "Group 3":{"Group 1":0, "Group 2":0, "Group 3":1} }, 3: { "Group 1":{"Group 1":0.25, "Group 2":0, "Group 3":0.75}, "Group 2":{"Group 1":0.25, "Group 2":0.75, "Group 3":0} }, 4: { "Group 1":{"Group 1":1, "Group 2":0}, "Group 2":{"Group 1":0.25, "Group 2":0.75} } }

这就是我在networkx

中制作图表时发生的情况：


import networkx
import matplotlib.pyplot as plt

# Create Directed Graph
G = nx.DiGraph()

# Iterate through all connections
for iter_n, D_current_previous in D_iter_current_previous.items():
    for current_group, D_previous_weights in D_current_previous.items():
        for previous_group, weight in D_previous_weights.items():
            if weight > 0:
                # Define connections using `|__|` as a delimiter for the names
                previous_node = "%d|__|%s"%(iter_n - 1, previous_group)
                current_node = "%d|__|%s"%(iter_n, current_group)
                connection = (previous_node, current_node)
                G.add_edge(*connection, weight=weight)

# Draw Graph with labels and width thickness
nx.draw(G, with_labels=True, width=[G[u][v]['weight'] for u,v in G.edges()])






注意：我能想到的唯一其他方法是 
matplotlib
 创建一个散点图，每个刻度代表一次迭代（5 个，包括初始样本），然后用不同的权重将点相互连接。这将是一些非常混乱的代码，特别是试图将标记的边缘与连接对齐...但是，我不确定这和
networkx
是否是最好的方法，或者是否有工具（例如 
bokeh
 或 
plotly
）专为此类绘图而设计。

    



    
    python
    
    matplotlib
    
    plot
    
    graph
    
    networkx
    
    
2个回答

    

      
      
      

        

          

            

              
18
投票

              

            

          

        

        
Networkx 拥有不错的探索性数据绘图工具
分析，它不是制作出版质量数据的工具，
由于各种原因我不想进入这里。我因此
从头开始重写了那部分代码库，并做了一个
可以找到名为 netgraph 的独立绘图模块

here（就像纯粹基于 matplotlib 的原始版本一样）。 API 是
非常非常相似并且有据可查，所以不应该太相似
很难根据您的目的进行塑造。


在此基础上我得到以下结果：






我选择颜色来表示边缘强度，你可以

1) 表示负值，并且

2）更好地区分小值。

但是，您也可以将边缘宽度传递给 netgraph（参见 
netgraph.draw_edges()
）。 



分支的不同顺序是数据结构（字典）的结果，这表明没有固有的顺序。您必须修改您的数据结构和下面的函数
_parse_input()
才能解决该问题。



代码：



import itertools
import numpy as np
import matplotlib.pyplot as plt
import netgraph; reload(netgraph)

def plot_layered_network(weight_matrices,
                         distance_between_layers=2,
                         distance_between_nodes=1,
                         layer_labels=None,
                         **kwargs):
    """
    Convenience function to plot layered network.

    Arguments:
    ----------
        weight_matrices: [w1, w2, ..., wn]
            list of weight matrices defining the connectivity between layers;
            each weight matrix is a 2-D ndarray with rows indexing source and columns indexing targets;
            the number of sources has to match the number of targets in the last layer

        distance_between_layers: int

        distance_between_nodes: int

        layer_labels: [str1, str2, ..., strn+1]
            labels of layers

        **kwargs: passed to netgraph.draw()

    Returns:
    --------
        ax: matplotlib axis instance

    """
    nodes_per_layer = _get_nodes_per_layer(weight_matrices)

    node_positions = _get_node_positions(nodes_per_layer,
                                         distance_between_layers,
                                         distance_between_nodes)

    w = _combine_weight_matrices(weight_matrices, nodes_per_layer)

    ax = netgraph.draw(w, node_positions, **kwargs)

    if not layer_labels is None:
        ax.set_xticks(distance_between_layers*np.arange(len(weight_matrices)+1))
        ax.set_xticklabels(layer_labels)
        ax.xaxis.set_ticks_position('bottom')

    return ax

def _get_nodes_per_layer(weight_matrices):
    nodes_per_layer = []
    for w in weight_matrices:
        sources, targets = w.shape
        nodes_per_layer.append(sources)
    nodes_per_layer.append(targets)
    return nodes_per_layer

def _get_node_positions(nodes_per_layer,
                        distance_between_layers,
                        distance_between_nodes):
    x = []
    y = []
    for ii, n in enumerate(nodes_per_layer):
        x.append(distance_between_nodes * np.arange(0., n))
        y.append(ii * distance_between_layers * np.ones((n)))
    x = np.concatenate(x)
    y = np.concatenate(y)
    return np.c_[y,x]

def _combine_weight_matrices(weight_matrices, nodes_per_layer):
    total_nodes = np.sum(nodes_per_layer)
    w = np.full((total_nodes, total_nodes), np.nan, np.float)

    a = 0
    b = nodes_per_layer[0]
    for ii, ww in enumerate(weight_matrices):
        w[a:a+ww.shape[0], b:b+ww.shape[1]] = ww
        a += nodes_per_layer[ii]
        b += nodes_per_layer[ii+1]

    return w

def test():
    w1 = np.random.rand(4,5) #< 0.50
    w2 = np.random.rand(5,6) #< 0.25
    w3 = np.random.rand(6,3) #< 0.75

    import string
    node_labels = dict(zip(range(18), list(string.ascii_lowercase)))

    fig, ax = plt.subplots(1,1)
    plot_layered_network([w1,w2,w3],
                         layer_labels=['start', 'step 1', 'step 2', 'finish'],
                         ax=ax,
                         node_size=20,
                         node_edge_width=2,
                         node_labels=node_labels,
                         edge_width=5,
    )
    plt.show()
    return

def test_example(input_dict):
    weight_matrices, node_labels = _parse_input(input_dict)
    fig, ax = plt.subplots(1,1)
    plot_layered_network(weight_matrices,
                         layer_labels=['', '1', '2', '3', '4'],
                         distance_between_layers=10,
                         distance_between_nodes=8,
                         ax=ax,
                         node_size=300,
                         node_edge_width=10,
                         node_labels=node_labels,
                         edge_width=50,
    )
    plt.show()
    return

def _parse_input(input_dict):
    weight_matrices = []
    node_labels = []

    # initialise sources
    sources = set()
    for v in input_dict[1].values():
        for s in v.keys():
            sources.add(s)
    sources = list(sources)

    for ii in range(len(input_dict)):
        inner_dict = input_dict[ii+1]
        targets = inner_dict.keys()

        w = np.full((len(sources), len(targets)), np.nan, np.float)
        for ii, s in enumerate(sources):
            for jj, t in enumerate(targets):
                try:
                    w[ii,jj] = inner_dict[t][s]
                except KeyError:
                    pass

        weight_matrices.append(w)
        node_labels.append(sources)
        sources = targets

    node_labels.append(targets)
    node_labels = list(itertools.chain.from_iterable(node_labels))
    node_labels = dict(enumerate(node_labels))

    return weight_matrices, node_labels

# --------------------------------------------------------------------------------
# script
# --------------------------------------------------------------------------------

if __name__ == "__main__":

    # test()

    input_dict =   {
        1: {
            "Group 1":{"sample_0":0.5, "sample_1":0.5, "sample_2":0, "sample_3":0, "sample_4":0},
            "Group 2":{"sample_0":0, "sample_1":0, "sample_2":1, "sample_3":0, "sample_4":0},
            "Group 3":{"sample_0":0, "sample_1":0, "sample_2":0, "sample_3":0.5, "sample_4":0.5}
            },
        2: {
            "Group 1":{"Group 1":1, "Group 2":0, "Group 3":0},
            "Group 2":{"Group 1":0, "Group 2":1, "Group 3":0},
            "Group 3":{"Group 1":0, "Group 2":0, "Group 3":1}
            },
        3: {
            "Group 1":{"Group 1":0.25, "Group 2":0, "Group 3":0.75},
            "Group 2":{"Group 1":0.25, "Group 2":0.75, "Group 3":0}
            },
        4: {
            "Group 1":{"Group 1":1, "Group 2":0},
            "Group 2":{"Group 1":0.25, "Group 2":0.75}
            }
        }

    test_example(input_dict)

    pass

    



      

      
      
      

      

        

          

            

              
0
投票

              
            

          

        

        
我最近的任务是在实验室中制作一个与此类似的图表，仅使用 
NetworkX
 和 
matplotlib
，但在 Stack Overflow 上没有找到好的答案。在浏览文档后，我发现有一个很好的方法可以使用 
multipartite_layout()
 中的 
NetworkX
 来实现这一点。您甚至可以单独绘制边缘和边缘标签，以获得您在概念中可视化的加权箭头不透明度。


弄清楚这一点后，我认为这将是一个很好的第一个 Stack Overflow 评论，因此我创建了一个 Stack Overflow 帐户和一个
公共 GitHub 存储库，演示如何仅使用 matplotlib
 和 
NetworkX 创建多部分加权有向图/流程图
.


Resulting NetworkX Graph

您可以在公共 GitHub 存储库中找到用于创建演示的代码：


import networkx as nx
import matplotlib.pyplot as plt

D_iter_current_previous =    {
        1: {
            "Group 1":{"sample_0":0.5, "sample_1":0.5, "sample_2":0, "sample_3":0, "sample_4":0},
            "Group 2":{"sample_0":0, "sample_1":0, "sample_2":1, "sample_3":0, "sample_4":0},
            "Group 3":{"sample_0":0, "sample_1":0, "sample_2":0, "sample_3":0.5, "sample_4":0.5}
            },
        2: {
            "Group 1":{"Group 1":1, "Group 2":0, "Group 3":0},
            "Group 2":{"Group 1":0, "Group 2":1, "Group 3":0},
            "Group 3":{"Group 1":0, "Group 2":0, "Group 3":1}
            },
        3: {
            "Group 1":{"Group 1":0.25, "Group 2":0, "Group 3":0.75},
            "Group 2":{"Group 1":0.25, "Group 2":0.75, "Group 3":0}
            },
        4: {
            "Group 1":{"Group 1":1, "Group 2":0},
            "Group 2":{"Group 1":0.25, "Group 2":0.75}
            }
        }

# Create a NextworkX directed graph
g = nx.DiGraph()
g.add_nodes_from(['sample_0', 'sample_1', 'sample_2', 'sample_3', 'sample_4'], subset=0)
g.add_nodes_from(['Group 1.1', 'Group 2.1', 'Group 3.1'], subset=1)
g.add_nodes_from(['Group 1.2', 'Group 2.2', 'Group 3.2'], subset=2)
g.add_nodes_from(['Group 1.3', 'Group 2.3'], subset=3)
g.add_nodes_from(['Group 1.4', 'Group 2.4'], subset=4)

# Add Title to PLot
plt.title('Multipartite Weighted Directed Graph')

# Create a list of the edges and alphas (opacity)
edges = []
alphas = []
for subset, subset_stuff in D_iter_current_previous.items():
    for node, prev_nodes in subset_stuff.items():
        for prev_node, weight in prev_nodes.items():
            if subset > 1:
                edges.append((f'{prev_node}.{subset-1}', f'{node}.{subset}'))
                alphas.append(weight)
            else:
                edges.append((f'{prev_node}', f'{node}.{subset}'))
                alphas.append(weight)

# Make a dict of the edge labels
edge_labels = dict(zip(edges, alphas))

# Draw the nodes with the multipartite layout
pos = nx.multipartite_layout(g, align='vertical')
nx.draw(g, pos, with_labels=True, node_size=1500, font_size=8)

# Draw the edges with their corresponding alphas
nx.draw_networkx_edges(g, pos, edgelist=edges, alpha=alphas, arrows=True, node_size=1500)

# Draw the edge labels with their corresponding alphas
counter = 0
for edge, alpha in edge_labels.items():
    if alpha > 0:
        nx.draw_networkx_edge_labels(g, pos, edge_labels={edge: float(alpha)}, alpha=float(alpha), font_size=7)

# Show the plot
plt.show()

    



      

      
    

  

  

    
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