Question

我正在尝试使用Python （最好使用matplotlib和networkx创建一个线性网络图，尽管我会对bokeh感兴趣在概念到下面的一个。

如何使用pos在Python中有效地构建此图表（networkx？）？我想将此用于更复杂的示例，因此我觉得这很难编码这个简单示例的位置不会有用:(。networkx是否有解决方案？

pos (dictionary, optional) – A dictionary with nodes as keys and positions as values. If not specified a spring layout positioning will be computed. See networkx.layout for functions that compute node positions.

我还没有看到关于如何在networkx中实现这一目标的任何教程，这就是为什么我认为这个问题将成为社区的可靠资源。我已经广泛地浏览了networkx tutorials，并且没有像这样的东西。如果不仔细使用networkx参数，pos的布局将无法解释这种类型的网络......我认为这是我唯一的选择。 https://networkx.github.io/documentation/networkx-1.9/reference/drawing.html文档中的所有预先计算的布局都不能很好地处理这种类型的网络结构。

简单示例：

（A）每个外键是图中从左到右移动的迭代（例如迭代0表示样本，迭代1表示组1-3，与迭代2相同，迭代3表示组1-2）等）。（B）内部字典包含该特定迭代的当前分组，以及表示当前组的前一组合并的权重（例如iteration 3具有Group 1和Group 2以及{ {1}}所有iteration 4 iteration 3's已进入Group 2 iteration 4's，但Group 2 iteration 3's已被拆分。权重总是合计为1.

我的代码为上图的连接w /权重：

Group 1

这是我在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} } } 制作图表时发生的事情：

networkx

注意：我能想到的唯一另一种方法是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()])创建一个散点图，每个刻度表示一次迭代（5包括初始样本），然后将点连接到另一个不同的重量。这将是一些非常混乱的代码，特别是试图排列标记与连接的边缘......但是，我不确定这是否和matplotlib是最好的方法，或者如果有一种工具（例如networkx或bokeh）专为此类绘图而设计。

Answer 1

Networkx拥有适合探索性数据的绘图设施分析，它不是制作出版质量数字的工具，由于各种原因，我不想进入这里。我因此从头开始重写了部分代码库，并制作了一个可以找到名为netgraph的独立绘图模块 here（就像原来纯粹基于matplotlib）。 API是非常非常相似且记录良好，所以不应该这样很难达到你的目的。

在此基础上，我得到以下结果：

我选择颜色来表示边缘强度 1）表示负值，和
2）更好地区分小值但是，您也可以将边宽传递给netgraph（请参阅netgraph.draw_edges()）。

分支的不同顺序是您的数据结构（字典）的结果，表示没有固有顺序。您必须修改数据结构和下面的函数_parse_input()以解决该问题。

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

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

1 个答案:

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

1 个答案:

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