python - 根据标签值垂直重新排列 Sankey 图

Question

我试图在 Sankey diagram 中绘制 3 个集群之间的患者流量.我有一个 pd.DataFrame counts使用 from-to 值，见下文。要重现此 DF，here是 counts dict 应该加载到 pd.DataFrame (这是visualize_cluster_flow_counts 函数的输入)。

    from    to      value
0   C1_1    C1_2    867
1   C1_1    C2_2    405
2   C1_1    C0_2    2
3   C2_1    C1_2    46
4   C2_1    C2_2    458
... ... ... ...
175 C0_20   C0_21   130
176 C0_20   C2_21   1
177 C2_20   C1_21   12
178 C2_20   C0_21   0
179 C2_20   C2_21   96

from和 to DataFrame 中的值表示集群编号(0、1 或 2)和 x 轴的天数(介于 1 和 21 之间)。如果我用这些值绘制桑基图，结果如下:

代码:

import plotly.graph_objects as go

def visualize_cluster_flow_counts(counts):
    all_sources = list(set(counts['from'].values.tolist() + counts['to'].values.tolist()))            
    
    froms, tos, vals, labs = [], [], [], []
    for index, row in counts.iterrows():
        froms.append(all_sources.index(row.values[0]))
        tos.append(all_sources.index(row.values[1]))
        vals.append(row[2])
        labs.append(row[3])
                
    fig = go.Figure(data=[go.Sankey(
        arrangement='snap',
        node = dict(
          pad = 15,
          thickness = 5,
          line = dict(color = "black", width = 0.1),
          label = all_sources,
          color = "blue"
        ),
        link = dict(
          source = froms,
          target = tos,
          value = vals,
          label = labs
      ))])

    fig.update_layout(title_text="Patient flow between clusters over time: 48h (2 days) - 504h (21 days)", font_size=10)
    fig.show()

visualize_cluster_flow_counts(counts)

但是，我想对条形进行垂直排序，以便 C0 是 总是 在顶部，C1 是 总是 在中间，C2 是 总是 在底部(或相反，无关紧要)。我知道我们可以设置 node.x和 node.y至 manually assign the coordinates .因此，我将 x 值设置为天数 *(1/天数范围)，增量为 +- 0.045。我根据集群值设置 y 值:0、0.5 或 1。然后我获得了下面的图像。垂直顺序很好，但条形之间的垂直边距明显偏离；它们应该与第一个结果相似。

产生这个的代码是:

import plotly.graph_objects as go

def find_node_coordinates(sources):
    x_nodes, y_nodes = [], []
    
    for s in sources:
        # Shift each x with +- 0.045
        x = float(s.split("_")[-1]) * (1/21)
        x_nodes.append(x)
        
        # Choose either 0, 0.5 or 1 for the y-value
        cluster_number = s[1]
        if cluster_number == "0": y = 1
        elif cluster_number == "1": y = 0.5
        else: y = 1e-09
        
        y_nodes.append(y)
                
    return x_nodes, y_nodes


def visualize_cluster_flow_counts(counts):
    all_sources = list(set(counts['from'].values.tolist() + counts['to'].values.tolist()))    
        
    node_x, node_y = find_node_coordinates(all_sources)
    
    froms, tos, vals, labs = [], [], [], []
    for index, row in counts.iterrows():
        froms.append(all_sources.index(row.values[0]))
        tos.append(all_sources.index(row.values[1]))
        vals.append(row[2])
        labs.append(row[3])
                
    fig = go.Figure(data=[go.Sankey(
        arrangement='snap',
        node = dict(
          pad = 15,
          thickness = 5,
          line = dict(color = "black", width = 0.1),
          label = all_sources,
          color = "blue",
          x = node_x,
          y = node_y,
        ),
        link = dict(
          source = froms,
          target = tos,
          value = vals,
          label = labs
      ))])

    fig.update_layout(title_text="Patient flow between clusters over time: 48h (2 days) - 504h (21 days)", font_size=10)
    fig.show()
    
    
visualize_cluster_flow_counts(counts)

问题:如何修复条形的边距，使结果看起来像第一个结果？因此，为了清楚起见:应该将条形推到底部。或者还有另一种方法可以让桑基图根据标签值自动对条形图进行垂直重新排序？

Answer 1

首先，我认为当前公开的 API 没有办法顺利实现您的目标，您可以查看源代码 here .
尝试更改您的 find_node_coordinates函数如下(请注意，您应该将计数 DataFrame 传递给):

counts = pd.DataFrame(counts_dict) 
def find_node_coordinates(sources, counts):
    x_nodes, y_nodes = [], []

    flat_on_top = False
    range = 1 # The y range
    total_margin_width = 0.15
    y_range = 1 - total_margin_width 
    margin = total_margin_width / 2 # From number of  Cs
    srcs = counts['from'].values.tolist()
    dsts = counts['to'].values.tolist() 
    values = counts['value'].values.tolist() 
    max_acc = 0

    def _calc_day_flux(d=1):
        _max_acc = 0 
        for i in [0,1,2]:
            # The first ones
            from_source = 'C{}_{}'.format(i,d) 
            indices = [i for i, val in enumerate(srcs) if val == from_source]
            for j in indices: 
                _max_acc += values[j]
        
        return _max_acc

    def _calc_node_io_flux(node_str): 
        c,d = int(node_str.split('_')[0][-1]), int(node_str.split('_')[1])
        _flux_src = 0 
        _flux_dst = 0 

        indices_src = [i for i, val in enumerate(srcs) if val == node_str]
        indices_dst = [j for j, val in enumerate(dsts) if val == node_str]
        for j in indices_src: 
            _flux_src += values[j]
        for j in indices_dst: 
            _flux_dst += values[j]

        return max(_flux_dst, _flux_src) 

    max_acc = _calc_day_flux() 
    graph_unit_per_val = y_range / max_acc
    print("Graph Unit per Acc Val", graph_unit_per_val) 
 
    
    for s in sources:
        # Shift each x with +- 0.045
        d = int(s.split("_")[-1])
        x = float(d) * (1/21)
        x_nodes.append(x)
        
        print(s, _calc_node_io_flux(s))
        # Choose either 0, 0.5 or 1 for the y-v alue
        cluster_number = s[1]

        
        # Flat on Top
        if flat_on_top: 
            if cluster_number == "0": 
              y = _calc_node_io_flux('C{}_{}'.format(2, d))*graph_unit_per_val + margin + _calc_node_io_flux('C{}_{}'.format(1, d))*graph_unit_per_val + margin +  _calc_node_io_flux('C{}_{}'.format(0, d))*graph_unit_per_val/2
            elif cluster_number == "1": y = _calc_node_io_flux('C{}_{}'.format(2, d))*graph_unit_per_val + margin +  _calc_node_io_flux('C{}_{}'.format(1, d))*graph_unit_per_val/2
            else: y = 1e-09
        # Flat On Bottom
        else: 
            if cluster_number == "0": y = 1 - (_calc_node_io_flux('C{}_{}'.format(0,d))*graph_unit_per_val / 2)
            elif cluster_number == "1": y = 1 - (_calc_node_io_flux('C{}_{}'.format(0,d))*graph_unit_per_val + margin + _calc_node_io_flux('C{}_{}'.format(1,d)) * graph_unit_per_val /2 )
            elif cluster_number == "2": y = 1 - (_calc_node_io_flux('C{}_{}'.format(0,d))*graph_unit_per_val + margin + _calc_node_io_flux('C{}_{}'.format(1,d)) * graph_unit_per_val + margin + _calc_node_io_flux('C{}_{}'.format(2,d)) * graph_unit_per_val /2 )
            
        y_nodes.append(y)
                
    return x_nodes, y_nodes

桑基图应该通过相应的归一化值来衡量它们的连接宽度，对吗？在这里我也是这样做的，首先计算每个节点的通量，然后通过计算归一化坐标，根据每个节点的通量计算每个节点的中心。
这是带有修改函数的代码的示例输出，请注意，我试图尽可能地遵守您的代码，因此它有点未优化(例如，可以将节点的值存储在每个指定的源节点之上，以避免其通量重新计算)。
带旗 flat_on_top = True
带旗 flat_on_top = False flat_on_bottom中有点不一致我认为是由 Plotly API 的填充或其他内部来源引起的版本。