networkx/plotly 网络和标签处的间隔节点
spacing nodes at networkx/plotly network and labeling
我使用 networkx 创建了一个网络，情节如下：
edges = df.stack().reset_index()
edges.columns = ['var_1','var_2','correlation']
edges = edges.loc[ (edges['correlation'] < -0.6) | (edges['correlation'] > 0.6) & (edges['var_1'] != edges['var_2']) ].copy()
#create undirected graph with weights corresponding to the correlation magnitude
G0 = nx.from_pandas_edgelist(edges, 'var_1', 'var_2', edge_attr=['correlation'])
mst = G0
# assign colours to edges depending on positive or negative correlation
# assign edge thickness depending on magnitude of correlation
edge_colours = []
edge_width = []
for key, value in nx.get_edge_attributes(mst, 'correlation').items():
edge_colours.append(assign_colour(value))
edge_width.append(assign_thickness(value))
node_size = []
degrees = [val for key, val in dict(G0.degree).items()]
max_deg = max(degrees)
min_deg = min(degrees)
for value in degrees:
node_size.append(assign_node_size(value,min_deg,max_deg))
#draw the network:
nx.draw(mst, pos=nx.fruchterman_reingold_layout(mst),
node_size=15, edge_color=edge_colours, node_colour="black",
edge_width=0.2)
plt.show()
def get_coordinates(G=mst):
"""Returns the positions of nodes and edges in a format for Plotly to draw the network"""
# get list of node positions
pos = nx.fruchterman_reingold_layout(mst)
Xnodes = [pos[n][0] for n in mst.nodes()]
Ynodes = [pos[n][1] for n in mst.nodes()]
Xedges_red = []
Yedges_red = []
Xedges_green = []
Yedges_green = []
def insert_edge(Xedges, Yedges):
Xedges.extend([pos[e[0]][0], pos[e[1]][0], None])
Yedges.extend([pos[e[0]][1], pos[e[1]][1], None])
search_dict = nx.get_edge_attributes(mst, 'correlation')
for e in mst.edges():
correlation = search_dict[(e[0], e[1])]
if correlation <= 0 : # red_edges
insert_edge(Xedges_red, Yedges_red)
else:
insert_edge(Xedges_green, Yedges_green)
# x coordinates of the nodes defining the edge e
return Xnodes, Ynodes, Xedges_red, Yedges_red, Xedges_green, Yedges_green
node_label = list(mst.nodes())
node_label = [fix_string(x) for x in node_label]
# get coordinates for nodes and edges
Xnodes, Ynodes, Xedges_red, Yedges_red, Xedges_green, Yedges_green = get_coordinates()
external_data = [list(x) for x in coding_names_df.values]
external_data = {fix_string(x[0]): x[1] for x in external_data}
external_data2 = [list(y) for y in coding_names_df.values]
external_data2 = {fix_string(y[0]): y[2] for y in external_data2}
external_data3 = [list(z) for z in coding_names_df.values]
external_data3 = {fix_string(z[0]): z[3] for z in external_data3}
external_data4 = [list(s) for s in coding_names_df.values]
external_data4 = {fix_string(s[0]): s[4] for s in external_data4}
# =============================================================================
description = [f"<b>{index}) {node}</b>"
"<br><br>Realm: " +
"<br>" + external_data.get(node, 'No external data found') +
"<br><br>Type: " +
"<br>" + external_data2.get(node, 'No external data found')
for index, node in enumerate(node_label)]
# =============================================================================
# def nodes colours:
node_colour = [assign_node_colour(node, external_data3, coding_names_df) for node in node_label]
node_shape = [assign_node_shape(node, external_data4, coding_names_df) for node in node_label]
# edges
# negative:
tracer_red = go.Scatter(x=Xedges_red, y=Yedges_red,
mode='lines',
line= dict(color="#FA0000", width=1),
hoverinfo='none',
showlegend=False)
# positive:
tracer_green = go.Scatter(x=Xedges_green, y=Yedges_green,
mode='lines',
line= dict(color= "#29A401", width=1),
hoverinfo='none',
showlegend=False)
# nodes
tracer_marker = go.Scatter(x=Xnodes, y=Ynodes,
mode='markers+text',
textposition='top center',
marker=dict(size=node_size,
line=dict(width=0.8, color='black'),
color=node_colour,
symbol=node_shape),
hovertext=description,
hoverinfo='text',
textfont=dict(size=7),
showlegend=False)
axis_style = dict(title='',
titlefont=dict(size=20),
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False)
layout = dict(title='',
width=1300,
height=900,
autosize=False,
showlegend=False,
xaxis=axis_style,
yaxis=axis_style,
hovermode='closest',
plot_bgcolor = '#fff')
fig = dict(data=[tracer_red, tracer_green, tracer_marker], layout=layout)
display(HTML("""<p>Node sizes are proportional to the size of annualised returns.<br>
Node colours signify positive or negative returns since beginning of the timeframe.</p> """))
plot(fig)
我得到了这个情节：network
我想添加标签，但是太拥挤了（尤其是在中间）
所以我有两个问题：
- 我怎样才能在中间留出间距？ （但仍保持fruchterman_reingold的位置）
- 如何只添加几个特定的标签？
如有帮助，我们将不胜感激！发送:)
您可以尝试在布局算法中设置 k 参数，如 docs 中所述，它设置：
- k: (float (default=None)) – 节点之间的最佳距离。如果 None 距离设置为 1/sqrt(n)，其中 n 是节点数。增加此值可将节点移得更远。
因此，通过稍微调整一下这个值，并相应地增加，我们可以获得更分散的布局并避免节点标签之间的重叠。
这是一个简单的例子来说明行为是什么：
result_set = {('plant','tree'), ('tree','oak'), ('flower', 'rose'), ('flower','daisy'), ('plant','flower'), ('tree','pine'), ('plant','roots'), ('animal','fish'),('animal','bird'), ('bird','robin'), ('bird','falcon'), ('animal', 'homo'),('homo','homo-sapiens'), ('animal','reptile'), ('reptile','snake'),('fungi','mushroom'), ('fungi','mold'), ('fungi','toadstool'),('reptile','crocodile'), ('mushroom','Portabello'), ('mushroom','Shiitake'),('pine','roig'),('pine','pinyer'), ('tree','eucaliptus'),('rose','Floribunda'),('rose','grandiflora')}
G=nx.from_edgelist(result_set)
pos=nx.fruchterman_reingold_layout(G)
plt.figure(figsize=(8,5))
nx.draw(G, pos=pos,
with_labels=True,
nodesize=1000,
node_color='lightgreen')
如果我们将 k 的值增加到 0.5，我们将在布局中很好地分布节点：
pos_spaced=nx.fruchterman_reingold_layout(G, k=0.5, iterations=100)
plt.figure(figsize=(10,6))
nx.draw(G,
pos=pos_spaced,
with_labels=True,
nodesize=1000,
node_color='lightgreen')
How can I add just a few specific labels?
为此，您将 draw 中的 labels 参数设置为包含您要显示的标签的字典。在节点名称与标签相同的情况下，只需创建一个字典映射 node->node 如下：
show_labels = ['plant', 'tree', 'oak', 'eucaliptus']
pos_spaced=nx.fruchterman_reingold_layout(G, k=0.54, iterations=100)
plt.figure(figsize=(10,6))
nx.draw(G,
pos=pos_spaced,
with_labels=True,
nodesize=1000,
labels=dict(zip(show_labels,show_labels)),
node_color='lightgreen')
我使用 networkx 创建了一个网络，情节如下：
edges = df.stack().reset_index()
edges.columns = ['var_1','var_2','correlation']
edges = edges.loc[ (edges['correlation'] < -0.6) | (edges['correlation'] > 0.6) & (edges['var_1'] != edges['var_2']) ].copy()
#create undirected graph with weights corresponding to the correlation magnitude
G0 = nx.from_pandas_edgelist(edges, 'var_1', 'var_2', edge_attr=['correlation'])
mst = G0
# assign colours to edges depending on positive or negative correlation
# assign edge thickness depending on magnitude of correlation
edge_colours = []
edge_width = []
for key, value in nx.get_edge_attributes(mst, 'correlation').items():
edge_colours.append(assign_colour(value))
edge_width.append(assign_thickness(value))
node_size = []
degrees = [val for key, val in dict(G0.degree).items()]
max_deg = max(degrees)
min_deg = min(degrees)
for value in degrees:
node_size.append(assign_node_size(value,min_deg,max_deg))
#draw the network:
nx.draw(mst, pos=nx.fruchterman_reingold_layout(mst),
node_size=15, edge_color=edge_colours, node_colour="black",
edge_width=0.2)
plt.show()
def get_coordinates(G=mst):
"""Returns the positions of nodes and edges in a format for Plotly to draw the network"""
# get list of node positions
pos = nx.fruchterman_reingold_layout(mst)
Xnodes = [pos[n][0] for n in mst.nodes()]
Ynodes = [pos[n][1] for n in mst.nodes()]
Xedges_red = []
Yedges_red = []
Xedges_green = []
Yedges_green = []
def insert_edge(Xedges, Yedges):
Xedges.extend([pos[e[0]][0], pos[e[1]][0], None])
Yedges.extend([pos[e[0]][1], pos[e[1]][1], None])
search_dict = nx.get_edge_attributes(mst, 'correlation')
for e in mst.edges():
correlation = search_dict[(e[0], e[1])]
if correlation <= 0 : # red_edges
insert_edge(Xedges_red, Yedges_red)
else:
insert_edge(Xedges_green, Yedges_green)
# x coordinates of the nodes defining the edge e
return Xnodes, Ynodes, Xedges_red, Yedges_red, Xedges_green, Yedges_green
node_label = list(mst.nodes())
node_label = [fix_string(x) for x in node_label]
# get coordinates for nodes and edges
Xnodes, Ynodes, Xedges_red, Yedges_red, Xedges_green, Yedges_green = get_coordinates()
external_data = [list(x) for x in coding_names_df.values]
external_data = {fix_string(x[0]): x[1] for x in external_data}
external_data2 = [list(y) for y in coding_names_df.values]
external_data2 = {fix_string(y[0]): y[2] for y in external_data2}
external_data3 = [list(z) for z in coding_names_df.values]
external_data3 = {fix_string(z[0]): z[3] for z in external_data3}
external_data4 = [list(s) for s in coding_names_df.values]
external_data4 = {fix_string(s[0]): s[4] for s in external_data4}
# =============================================================================
description = [f"<b>{index}) {node}</b>"
"<br><br>Realm: " +
"<br>" + external_data.get(node, 'No external data found') +
"<br><br>Type: " +
"<br>" + external_data2.get(node, 'No external data found')
for index, node in enumerate(node_label)]
# =============================================================================
# def nodes colours:
node_colour = [assign_node_colour(node, external_data3, coding_names_df) for node in node_label]
node_shape = [assign_node_shape(node, external_data4, coding_names_df) for node in node_label]
# edges
# negative:
tracer_red = go.Scatter(x=Xedges_red, y=Yedges_red,
mode='lines',
line= dict(color="#FA0000", width=1),
hoverinfo='none',
showlegend=False)
# positive:
tracer_green = go.Scatter(x=Xedges_green, y=Yedges_green,
mode='lines',
line= dict(color= "#29A401", width=1),
hoverinfo='none',
showlegend=False)
# nodes
tracer_marker = go.Scatter(x=Xnodes, y=Ynodes,
mode='markers+text',
textposition='top center',
marker=dict(size=node_size,
line=dict(width=0.8, color='black'),
color=node_colour,
symbol=node_shape),
hovertext=description,
hoverinfo='text',
textfont=dict(size=7),
showlegend=False)
axis_style = dict(title='',
titlefont=dict(size=20),
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False)
layout = dict(title='',
width=1300,
height=900,
autosize=False,
showlegend=False,
xaxis=axis_style,
yaxis=axis_style,
hovermode='closest',
plot_bgcolor = '#fff')
fig = dict(data=[tracer_red, tracer_green, tracer_marker], layout=layout)
display(HTML("""<p>Node sizes are proportional to the size of annualised returns.<br>
Node colours signify positive or negative returns since beginning of the timeframe.</p> """))
plot(fig)
我得到了这个情节：network
我想添加标签，但是太拥挤了（尤其是在中间） 所以我有两个问题：
- 我怎样才能在中间留出间距？ （但仍保持fruchterman_reingold的位置）
- 如何只添加几个特定的标签？
如有帮助，我们将不胜感激！发送:)
您可以尝试在布局算法中设置 k 参数，如 docs 中所述，它设置：
- k: (float (default=None)) – 节点之间的最佳距离。如果 None 距离设置为 1/sqrt(n)，其中 n 是节点数。增加此值可将节点移得更远。
因此，通过稍微调整一下这个值，并相应地增加，我们可以获得更分散的布局并避免节点标签之间的重叠。 这是一个简单的例子来说明行为是什么：
result_set = {('plant','tree'), ('tree','oak'), ('flower', 'rose'), ('flower','daisy'), ('plant','flower'), ('tree','pine'), ('plant','roots'), ('animal','fish'),('animal','bird'), ('bird','robin'), ('bird','falcon'), ('animal', 'homo'),('homo','homo-sapiens'), ('animal','reptile'), ('reptile','snake'),('fungi','mushroom'), ('fungi','mold'), ('fungi','toadstool'),('reptile','crocodile'), ('mushroom','Portabello'), ('mushroom','Shiitake'),('pine','roig'),('pine','pinyer'), ('tree','eucaliptus'),('rose','Floribunda'),('rose','grandiflora')}
G=nx.from_edgelist(result_set)
pos=nx.fruchterman_reingold_layout(G)
plt.figure(figsize=(8,5))
nx.draw(G, pos=pos,
with_labels=True,
nodesize=1000,
node_color='lightgreen')
如果我们将 k 的值增加到 0.5，我们将在布局中很好地分布节点：
pos_spaced=nx.fruchterman_reingold_layout(G, k=0.5, iterations=100)
plt.figure(figsize=(10,6))
nx.draw(G,
pos=pos_spaced,
with_labels=True,
nodesize=1000,
node_color='lightgreen')
How can I add just a few specific labels?
为此，您将 draw 中的 labels 参数设置为包含您要显示的标签的字典。在节点名称与标签相同的情况下，只需创建一个字典映射 node->node 如下：
show_labels = ['plant', 'tree', 'oak', 'eucaliptus']
pos_spaced=nx.fruchterman_reingold_layout(G, k=0.54, iterations=100)
plt.figure(figsize=(10,6))
nx.draw(G,
pos=pos_spaced,
with_labels=True,
nodesize=1000,
labels=dict(zip(show_labels,show_labels)),
node_color='lightgreen')