更改 Visdcc 网络图中选定节点的颜色
Change Colors of Selected Nodes in Visdcc Network Graph
我正在使用仪表板分析办公室中所说的话。我目前正忙于我的项目的一部分，构建一个网络图，可视化谁在节目的任何特定剧集中与谁交谈。用户可以选择 select 一季，然后是一集，然后是网络图的 2 个字符。
到目前为止，这是我的代码：
import pandas as pd
import numpy as np
import dash
import os
from dash import dcc
from dash import html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import visdcc
import itertools as it
from sklearn.feature_extraction.text import CountVectorizer
#Load data
sheet_url = 'https://docs.google.com/spreadsheets/d/18wS5AAwOh8QO95RwHLS95POmSNKA2jjzdt0phrxeAE0/edit#gid=747974534'
url = sheet_url.replace('/edit#gid=', '/export?format=csv&gid=')
office_data = pd.read_csv(url)
office_data['season'] = 'Season ' + office_data['season'].astype(str)
office_data['episode'] = 'Episode ' + office_data['episode'].astype(str)
office_data['scene'] = 'Scene ' + office_data['scene'].astype(str)
#-----------Network Graph Prep----------#
#1.) Filter down to just data with main characters
office_data['main_ind'] = np.where(
(office_data['speaker']=='Pam')|
(office_data['speaker']=='Jan')|
(office_data['speaker']=='Kelly')|
(office_data['speaker']=='Phyllis')|
(office_data['speaker']=='Angela')|
(office_data['speaker']=='Erin')|
(office_data['speaker']=='Holly')|
(office_data['speaker']=='Meredith')|
(office_data['speaker']=='Michael')|
(office_data['speaker']=='Jim')|
(office_data['speaker']=='Kevin')|
(office_data['speaker']=='Oscar')|
(office_data['speaker']=='Stanley')|
(office_data['speaker']=='Toby')|
(office_data['speaker']=='Roy')|
(office_data['speaker']=='Ryan')|
(office_data['speaker']=='Andy')|
(office_data['speaker']=='Creed')|
(office_data['speaker']=='Darryl')|
(office_data['speaker']=='Dwight'),
1,0
)
#2.) Filter down to only scenes containing these people
size = office_data.groupby(['season','episode','scene']).size().reset_index()
sums = office_data.groupby(['season','episode','scene']).agg({'main_ind':'sum'}).reset_index()
main_metrics = pd.merge(size,sums,how='left',on=['season','episode','scene'])
main_metrics.rename(columns={0:'count'}, inplace=True )
office_data = pd.merge(office_data,main_metrics,how='left',on=['season','episode','scene'])
office_data['diff'] = office_data['count'] - office_data['main_ind_y']
data_for_ng = office_data[office_data['diff']==0]
#Create a season-character dictionary
season_character_dict = {'Season 1': ['Angela', 'Darryl', 'Dwight', 'Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer'],
'Season 2': ['Angela','Creed', 'Darryl', 'David Wallace', 'Dwight', 'Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer'],
'Season 3': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight', 'Jan', 'Jim','Karen','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer'],
'Season 4': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Holly', 'Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby'],
'Season 5': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Holly', 'Jan', 'Jim','Karen','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby'],
'Season 6': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe','Holly','Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Ryan','Stanley','Toby','Todd Packer'],
'Season 7': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe','Holly','Jan', 'Jim','Karen','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Ryan','Stanley','Toby','Todd Packer'],
'Season 8': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe', 'Jim','Kelly','Kevin','Meredith','Oscar','Pam','Phyllis','Ryan','Stanley','Toby','Todd Packer'],
'Season 9': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe','Jan','Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer']
}
season_episode_dict = {'Season 1': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6'],
'Season 2': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22'],
'Season 3': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23'],
'Season 4': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14'],
'Season 5': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24','Episode 25','Episode 26'],
'Season 6': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24'],
'Season 7': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24'],
'Season 8': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24'],
'Season 9': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23']
}
character_choices = office_data['speaker'].sort_values().unique()
season_choices = office_data['season'].sort_values().unique()
episode_choices = office_data['episode'].sort_values().unique()
app = dash.Dash(__name__,assets_folder=os.path.join(os.curdir,"assets"))
server = app.server
app.layout = html.Div([
dbc.Row([
dbc.Col(
dcc.Dropdown(
id='dropdown4',
options=[{'label': i, 'value': i} for i in season_choices],
value=season_choices[0]
), width=3
),
dbc.Col(
dcc.Dropdown(
id='dropdown7',
options=[{'label': i, 'value': i} for i in episode_choices],
value=episode_choices[0]
), width=3
),
dbc.Col(
dcc.Dropdown(
id='dropdown5',
options=[{'label': i, 'value': i} for i in character_choices],
value=character_choices[0]
), width=3
),
dbc.Col(
dcc.Dropdown(
id='dropdown6',
options=[{'label': i, 'value': i} for i in character_choices],
value=character_choices[1]
), width=3
)
]),
dbc.Row([
dbc.Col(
visdcc.Network(
id='net',
options = dict(
height='600px',
width='100%',
physics={'barnesHut': {'avoidOverlap': 0.5}},
maxVelocity=0,
stabilization={
'enabled': 'true',
'iterations': 15,
'updateInterval': 50,
'onlyDynamicEdges': 'false',
'fit': 'true'
},
)
)
)
])
])
@app.callback(
Output('dropdown5', 'options'),
Output('dropdown5', 'value'),
Input('dropdown4', 'value') #--> choose season
)
def set_character_options2(selected_season):
return [{'label': i, 'value': i} for i in season_character_dict[selected_season]], season_character_dict[selected_season][0],
@app.callback(
Output('dropdown6', 'options'),
Output('dropdown6', 'value'),
Input('dropdown4', 'value') #--> choose season
)
def set_character_options2(selected_season):
return [{'label': i, 'value': i} for i in season_character_dict[selected_season]], season_character_dict[selected_season][1],
@app.callback(
Output('dropdown7', 'options'), #--> filter episodes
Output('dropdown7', 'value'),
Input('dropdown4', 'value') #--> choose season
)
def set_episode_options(selected_season):
return [{'label': i, 'value': i} for i in season_episode_dict[selected_season]], season_episode_dict[selected_season][0],
@app.callback(
Output('net','data'),
Input('dropdown4','value'),
Input('dropdown7','value'),
Input('dropdown5','value'),
Input('dropdown6','value'),
)
def network(season_select, episode_select, character_select1, character_select2):
filtered = data_for_ng[['season','episode','scene','speaker']]
filtered = filtered[filtered['season']==season_select]
filtered = filtered[filtered['episode']==episode_select]
def assets_pairs(speakers):
unique_speakers = set(speakers)
if len(unique_speakers) == 1:
x = speakers.iat[0] # get the only unique asset
pairs = [[x, x]]
else:
pairs = it.permutations(unique_speakers, r=2) # get all the unique pairs without repeated elements
return pd.DataFrame(pairs, columns=['Source', 'Target'])
df_pairs = (
filtered.groupby(['season', 'episode', 'scene'])['speaker']
.apply(assets_pairs) # create asset pairs per group
.groupby(['Source', 'Target'], as_index=False) # compute the weights by
.agg(Weights = ('Source', 'size')) # counting the unique ('Source', 'Target') pairs
)
new_df = df_pairs[(df_pairs['Source']==character_select1)|(df_pairs['Source']==character_select2)]
node_list = list(
set(new_df['Source'].unique().tolist()+new_df['Target'].unique().tolist())
)
nodes = [{
'id': node_name,
'label': node_name,
#'color':#i_dont_know_what_to_put_here,
'shape':'dot',
'size':15
}
for i, node_name in enumerate(node_list)]
#Create edges from df
edges=[]
for row in new_df.to_dict(orient='records'):
source, target = row['Source'], row['Target']
edges.append({
'id':source + "__" + target,
'from': source,
'to': target,
'width': 2
})
data = {'nodes':nodes, 'edges': edges}
return data
app.run_server(host='0.0.0.0',port='8051')
我遇到的问题是，当有很多连接时，很难看到源节点在哪里（2 个 selected 字符）。所以，我希望能够更改这些节点的颜色，使这些图表更易于理解。但是，我还没有想出一种方法来更改特定节点的颜色 - 到目前为止，似乎只能更改所有节点的颜色。
我找到了网络功能的文档页面，但不确定如何实现我在此处找到的一些内容：https://visjs.github.io/vis-network/docs/network/nodes.html#
谁能帮我弄清楚如何让图表的这一小部分正常工作？如有任何帮助，我们将不胜感激！
谢谢！
您只需要将源节点和目标节点与其他节点区分开即可。
一个快速解决方法是在创建节点列表时添加一个 if 条件，如下所示：
nodes = [
({
'id': node_name,
'label': node_name,
'color': "red",
'shape':'dot',
'size':15
})
if node_name == character_select1 or node_name == character_select2
else
({
'id': node_name,
'label': node_name,
'color': "green",
'shape':'dot',
'size':15
})
for _, node_name in enumerate(node_list)
]
这会将指定的节点着色为红色，intermediate/irrelevant 的节点着色为绿色。
我正在使用仪表板分析办公室中所说的话。我目前正忙于我的项目的一部分，构建一个网络图，可视化谁在节目的任何特定剧集中与谁交谈。用户可以选择 select 一季，然后是一集，然后是网络图的 2 个字符。
到目前为止，这是我的代码：
import pandas as pd
import numpy as np
import dash
import os
from dash import dcc
from dash import html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import visdcc
import itertools as it
from sklearn.feature_extraction.text import CountVectorizer
#Load data
sheet_url = 'https://docs.google.com/spreadsheets/d/18wS5AAwOh8QO95RwHLS95POmSNKA2jjzdt0phrxeAE0/edit#gid=747974534'
url = sheet_url.replace('/edit#gid=', '/export?format=csv&gid=')
office_data = pd.read_csv(url)
office_data['season'] = 'Season ' + office_data['season'].astype(str)
office_data['episode'] = 'Episode ' + office_data['episode'].astype(str)
office_data['scene'] = 'Scene ' + office_data['scene'].astype(str)
#-----------Network Graph Prep----------#
#1.) Filter down to just data with main characters
office_data['main_ind'] = np.where(
(office_data['speaker']=='Pam')|
(office_data['speaker']=='Jan')|
(office_data['speaker']=='Kelly')|
(office_data['speaker']=='Phyllis')|
(office_data['speaker']=='Angela')|
(office_data['speaker']=='Erin')|
(office_data['speaker']=='Holly')|
(office_data['speaker']=='Meredith')|
(office_data['speaker']=='Michael')|
(office_data['speaker']=='Jim')|
(office_data['speaker']=='Kevin')|
(office_data['speaker']=='Oscar')|
(office_data['speaker']=='Stanley')|
(office_data['speaker']=='Toby')|
(office_data['speaker']=='Roy')|
(office_data['speaker']=='Ryan')|
(office_data['speaker']=='Andy')|
(office_data['speaker']=='Creed')|
(office_data['speaker']=='Darryl')|
(office_data['speaker']=='Dwight'),
1,0
)
#2.) Filter down to only scenes containing these people
size = office_data.groupby(['season','episode','scene']).size().reset_index()
sums = office_data.groupby(['season','episode','scene']).agg({'main_ind':'sum'}).reset_index()
main_metrics = pd.merge(size,sums,how='left',on=['season','episode','scene'])
main_metrics.rename(columns={0:'count'}, inplace=True )
office_data = pd.merge(office_data,main_metrics,how='left',on=['season','episode','scene'])
office_data['diff'] = office_data['count'] - office_data['main_ind_y']
data_for_ng = office_data[office_data['diff']==0]
#Create a season-character dictionary
season_character_dict = {'Season 1': ['Angela', 'Darryl', 'Dwight', 'Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer'],
'Season 2': ['Angela','Creed', 'Darryl', 'David Wallace', 'Dwight', 'Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer'],
'Season 3': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight', 'Jan', 'Jim','Karen','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer'],
'Season 4': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Holly', 'Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby'],
'Season 5': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Holly', 'Jan', 'Jim','Karen','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby'],
'Season 6': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe','Holly','Jan', 'Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Ryan','Stanley','Toby','Todd Packer'],
'Season 7': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe','Holly','Jan', 'Jim','Karen','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Ryan','Stanley','Toby','Todd Packer'],
'Season 8': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe', 'Jim','Kelly','Kevin','Meredith','Oscar','Pam','Phyllis','Ryan','Stanley','Toby','Todd Packer'],
'Season 9': ['Andy', 'Angela','Creed', 'Darryl', 'David Wallace', 'Dwight','Erin','Gabe','Jan','Jim','Kelly','Kevin','Meredith','Michael','Oscar','Pam','Phyllis','Roy','Ryan','Stanley','Toby','Todd Packer']
}
season_episode_dict = {'Season 1': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6'],
'Season 2': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22'],
'Season 3': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23'],
'Season 4': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14'],
'Season 5': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24','Episode 25','Episode 26'],
'Season 6': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24'],
'Season 7': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24'],
'Season 8': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23','Episode 24'],
'Season 9': ['Episode 1', 'Episode 2', 'Episode 3', 'Episode 4', 'Episode 5','Episode 6','Episode 7', 'Episode 8', 'Episode 9', 'Episode 10', 'Episode 11','Episode 12','Episode 13', 'Episode 14', 'Episode 15', 'Episode 16', 'Episode 17','Episode 18','Episode 19', 'Episode 20', 'Episode 21', 'Episode 22', 'Episode 23']
}
character_choices = office_data['speaker'].sort_values().unique()
season_choices = office_data['season'].sort_values().unique()
episode_choices = office_data['episode'].sort_values().unique()
app = dash.Dash(__name__,assets_folder=os.path.join(os.curdir,"assets"))
server = app.server
app.layout = html.Div([
dbc.Row([
dbc.Col(
dcc.Dropdown(
id='dropdown4',
options=[{'label': i, 'value': i} for i in season_choices],
value=season_choices[0]
), width=3
),
dbc.Col(
dcc.Dropdown(
id='dropdown7',
options=[{'label': i, 'value': i} for i in episode_choices],
value=episode_choices[0]
), width=3
),
dbc.Col(
dcc.Dropdown(
id='dropdown5',
options=[{'label': i, 'value': i} for i in character_choices],
value=character_choices[0]
), width=3
),
dbc.Col(
dcc.Dropdown(
id='dropdown6',
options=[{'label': i, 'value': i} for i in character_choices],
value=character_choices[1]
), width=3
)
]),
dbc.Row([
dbc.Col(
visdcc.Network(
id='net',
options = dict(
height='600px',
width='100%',
physics={'barnesHut': {'avoidOverlap': 0.5}},
maxVelocity=0,
stabilization={
'enabled': 'true',
'iterations': 15,
'updateInterval': 50,
'onlyDynamicEdges': 'false',
'fit': 'true'
},
)
)
)
])
])
@app.callback(
Output('dropdown5', 'options'),
Output('dropdown5', 'value'),
Input('dropdown4', 'value') #--> choose season
)
def set_character_options2(selected_season):
return [{'label': i, 'value': i} for i in season_character_dict[selected_season]], season_character_dict[selected_season][0],
@app.callback(
Output('dropdown6', 'options'),
Output('dropdown6', 'value'),
Input('dropdown4', 'value') #--> choose season
)
def set_character_options2(selected_season):
return [{'label': i, 'value': i} for i in season_character_dict[selected_season]], season_character_dict[selected_season][1],
@app.callback(
Output('dropdown7', 'options'), #--> filter episodes
Output('dropdown7', 'value'),
Input('dropdown4', 'value') #--> choose season
)
def set_episode_options(selected_season):
return [{'label': i, 'value': i} for i in season_episode_dict[selected_season]], season_episode_dict[selected_season][0],
@app.callback(
Output('net','data'),
Input('dropdown4','value'),
Input('dropdown7','value'),
Input('dropdown5','value'),
Input('dropdown6','value'),
)
def network(season_select, episode_select, character_select1, character_select2):
filtered = data_for_ng[['season','episode','scene','speaker']]
filtered = filtered[filtered['season']==season_select]
filtered = filtered[filtered['episode']==episode_select]
def assets_pairs(speakers):
unique_speakers = set(speakers)
if len(unique_speakers) == 1:
x = speakers.iat[0] # get the only unique asset
pairs = [[x, x]]
else:
pairs = it.permutations(unique_speakers, r=2) # get all the unique pairs without repeated elements
return pd.DataFrame(pairs, columns=['Source', 'Target'])
df_pairs = (
filtered.groupby(['season', 'episode', 'scene'])['speaker']
.apply(assets_pairs) # create asset pairs per group
.groupby(['Source', 'Target'], as_index=False) # compute the weights by
.agg(Weights = ('Source', 'size')) # counting the unique ('Source', 'Target') pairs
)
new_df = df_pairs[(df_pairs['Source']==character_select1)|(df_pairs['Source']==character_select2)]
node_list = list(
set(new_df['Source'].unique().tolist()+new_df['Target'].unique().tolist())
)
nodes = [{
'id': node_name,
'label': node_name,
#'color':#i_dont_know_what_to_put_here,
'shape':'dot',
'size':15
}
for i, node_name in enumerate(node_list)]
#Create edges from df
edges=[]
for row in new_df.to_dict(orient='records'):
source, target = row['Source'], row['Target']
edges.append({
'id':source + "__" + target,
'from': source,
'to': target,
'width': 2
})
data = {'nodes':nodes, 'edges': edges}
return data
app.run_server(host='0.0.0.0',port='8051')
我遇到的问题是，当有很多连接时，很难看到源节点在哪里（2 个 selected 字符）。所以，我希望能够更改这些节点的颜色，使这些图表更易于理解。但是，我还没有想出一种方法来更改特定节点的颜色 - 到目前为止，似乎只能更改所有节点的颜色。
我找到了网络功能的文档页面，但不确定如何实现我在此处找到的一些内容：https://visjs.github.io/vis-network/docs/network/nodes.html#
谁能帮我弄清楚如何让图表的这一小部分正常工作？如有任何帮助，我们将不胜感激！
谢谢！
您只需要将源节点和目标节点与其他节点区分开即可。
一个快速解决方法是在创建节点列表时添加一个 if 条件，如下所示：
nodes = [
({
'id': node_name,
'label': node_name,
'color': "red",
'shape':'dot',
'size':15
})
if node_name == character_select1 or node_name == character_select2
else
({
'id': node_name,
'label': node_name,
'color': "green",
'shape':'dot',
'size':15
})
for _, node_name in enumerate(node_list)
]
这会将指定的节点着色为红色，intermediate/irrelevant 的节点着色为绿色。