如何在networkx中使用pos参数创建流程图样式的图？（Python 3）

如何在networkx中使用pos参数创建流程图样式的图？（Python 3）

Networkx拥有用于探索性数据分析的不错的绘图工具，它不是制作出版物质量数据的工具，出于各种原因，我不想在这里讨论。因此，我从头开始重写了代码库的那一部分，并制作了一个称为netgraph的独立绘图模块，可以在此处找到它（就像纯粹基于matplotlib的原始绘图模块
一样）。该API非常非常相似，并且文档齐全，因此按照您的目的进行建模应该不会太困难。

在此基础上，我得到以下结果：

我选择颜色来表示边缘强度，因为您可以
1）表示负值，
2）更好地区分较小的值。
但是，您也可以将边缘宽度传递给netgraph（请参阅参考资料

netgraph.draw_edges()

）。

分支的不同顺序是数据结构（字典）的结果，它表示没有固有顺序。您必须修改数据结构和

_parse_input()

下面的功能以解决该问题。

码：

import itertoolsimport numpy as npimport matplotlib.pyplot as pltimport 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 axdef _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_layerdef _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 wdef 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()    returndef 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()    returndef _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