根据标签值垂直重新排序桑基图
Reorder Sankey diagram vertically based on label value
我正在尝试在 Sankey diagram. I have a pd.DataFrame counts with from-to values, see below. To reproduce this DF, here 中绘制 3 个集群之间的患者流量是 counts 应该加载到 pd.DataFrame 中的字典（这是 visualize_cluster_flow_counts函数）。
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
DataFrame 中的 from 和 to 值表示簇号（0、1 或 2）和 x-axis 的天数（介于 1 和21).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，我想垂直排列条形，这样 C0 总是总是在最上面，C1 总是总是在中间，而 C2 总是 在底部（或者相反，没关系）。我知道我们可以将 node.x 和 node.y 设置为 manually assign the coordinates。因此，我将 x-values 设置为天数 *（1/天数范围），增量为 +- 0.045。我根据簇值设置 y-values：0、0.5 或 1。然后我获得了下图。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。
生成此代码的代码是：
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：如何固定条形图的边距，使结果看起来像第一个结果？因此，为清楚起见：应该将条形图推到底部。或者有另一种方法可以让桑基图根据标签值自动垂直 re-order 条形图吗？
首先，我认为目前公开的 API 没有办法顺利实现您的目标，您可以查看源代码 here。
尝试按如下方式更改 find_node_coordinates 函数（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
Sankey 图应该通过相应的归一化值来权衡它们的连接宽度，对吗？这里我也是这样做的，首先，它计算每个节点的通量，然后通过计算每个节点的中心根据它们的通量计算归一化坐标。
这里是你的代码修改函数的示例输出，请注意，我尽量遵守你的代码，所以它有点未优化（例如，可以将节点的值存储在每个指定的上方源节点以避免重新计算其通量）。
带标志flat_on_top = True
带标志flat_on_top = False
flat_on_bottom 版本中存在一些不一致，我认为这是由填充或 Plotly 的其他内部来源引起的 API。
我正在尝试在 Sankey diagram. I have a pd.DataFrame counts with from-to values, see below. To reproduce this DF, here 中绘制 3 个集群之间的患者流量是 counts 应该加载到 pd.DataFrame 中的字典（这是 visualize_cluster_flow_counts函数）。
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
DataFrame 中的 from 和 to 值表示簇号（0、1 或 2）和 x-axis 的天数（介于 1 和21).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，我想垂直排列条形，这样 C0 总是总是在最上面，C1 总是总是在中间，而 C2 总是 在底部（或者相反，没关系）。我知道我们可以将 node.x 和 node.y 设置为 manually assign the coordinates。因此，我将 x-values 设置为天数 *（1/天数范围），增量为 +- 0.045。我根据簇值设置 y-values：0、0.5 或 1。然后我获得了下图。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。
生成此代码的代码是：
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：如何固定条形图的边距，使结果看起来像第一个结果？因此，为清楚起见：应该将条形图推到底部。或者有另一种方法可以让桑基图根据标签值自动垂直 re-order 条形图吗？
首先，我认为目前公开的 API 没有办法顺利实现您的目标，您可以查看源代码 here。
尝试按如下方式更改 find_node_coordinates 函数（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
Sankey 图应该通过相应的归一化值来权衡它们的连接宽度，对吗？这里我也是这样做的，首先，它计算每个节点的通量，然后通过计算每个节点的中心根据它们的通量计算归一化坐标。
这里是你的代码修改函数的示例输出，请注意，我尽量遵守你的代码，所以它有点未优化（例如，可以将节点的值存储在每个指定的上方源节点以避免重新计算其通量）。
带标志flat_on_top = True
带标志flat_on_top = False
flat_on_bottom 版本中存在一些不一致，我认为这是由填充或 Plotly 的其他内部来源引起的 API。