graph stops drawing sometimes

[dcsan]

I'm often working with a sankey and sometimes it will just refuse to draw:

i can mess around with the params and eventually it will draw again - eg with 21 connections instead of 20. It seems to somehow give the renderer a kick.

This looks to me like the CSS isn't loading or something? I can only guess there's some loading order issue to the widget? Are you doing a server-side render of the whole D3 graph?

In this case when I save a png it also comes out the same.

[ricklupton]

Is it possible there is a NaN or other funny value in your data for the flow that is causing the problem when you add it back in?

On 6 Oct 2021, at 10:57, DC @.***> wrote:

 I'm often working with a sankey and sometimes it will just refuse to draw:

i can mess around with the params and eventually it will draw again - eg with 21 connections instead of 20. It seems to somehow give the renderer a kick.

This looks to me like the CSS isn't loading or something? I can only guess there's some loading order issue to the widget? Are you doing a server-side render of the whole D3 graph?

In this case when I save a png it also comes out the same.

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[aluchuk]

Solution I had the wrong column names, needs to be "value" I have "amount".

~~I'm also getting the same error. ~~ here is my code (example from https://github.com/IKNL/oncoguide_sankey/blob/master/scripts/oncoguide_sankey.ipynb)

import os
import pathlib
import pandas as pd

from ipysankeywidget import SankeyWidget

os.chdir(os.getcwd())

PATH_DATA = pathlib.Path(r'./storage/shared/Documents')
PATH_RESULTS = pathlib.Path(r'./storage/shared/Documents')

if not PATH_DATA.exists():
    raise ValueError("Data directory not found.")

df = pd.read_csv(PATH_DATA/'flow.csv', low_memory=False)

SankeyWidget(links=df.to_dict('records'))

the csv file:

source,target,amount
wages,income,75000
interest,income,30
other,income,5000
income,expenses,5
income,liabilities,6000
income,loans,300
income,savings,200
expenses,Education,1000
expenses,Insurance,1999
expenses,Medical,123
expenses,utilities,240
liabilities,Chase,745
liabilities,Discover,137
loans,Education,2500
loans,Mortgage,17000
loans,Car,6000

and the result

P.S. to_dict seems to have the right layout:

df.to_dict('records')
[{'source': 'wages', 'target': 'income', 'amount': 75000},
 {'source': 'interest', 'target': 'income', 'amount': 30},
 {'source': 'other', 'target': 'income', 'amount': 5000},
 {'source': 'income', 'target': 'expenses', 'amount': 5},
 {'source': 'income', 'target': 'liabilities', 'amount': 6000},
 {'source': 'income', 'target': 'loans', 'amount': 300},
 {'source': 'income', 'target': 'savings', 'amount': 200},
 {'source': 'expenses', 'target': 'Education', 'amount': 1000},
 {'source': 'expenses', 'target': 'Insurance', 'amount': 1999},
 {'source': 'expenses', 'target': 'Medical', 'amount': 123},
 {'source': 'expenses', 'target': 'utilities', 'amount': 240},
 {'source': 'liabilities', 'target': 'Chase', 'amount': 745},
 {'source': 'liabilities', 'target': 'Discover', 'amount': 137},
 {'source': 'loans', 'target': 'Education', 'amount': 2500},
 {'source': 'loans', 'target': 'Mortgage', 'amount': 17000},
 {'source': 'loans', 'target': 'Car', 'amount': 6000}]