Frontend error (TypeError: e[r] is undefined) when updating graph after legends are selected #595
Description
I have attached a gif of the problem at the bottom of this comment.
I have a bar graph that I can update by two drop down menus (changing the X-axis time binning and the grouping of the bars). The bug occurs when I
- Unselected all bars using the legends
- Change one of the drop down menus
There are no errors in the back end console. On the front end console, the following error is displayed:
The bug makes the Dash application unresponsive until I click the Reset axes button on the Dash graph.
Gif of issue:
Code below:
raw_df = load_df_as_json()
app = dash.Dash(__name__)
raw_df_table_col_names = [
{'name': i, 'id': i} for i in raw_df.columns
]
app.layout = html.Div([
dcc.Dropdown(
id='freq_dropdown',
options=[
{'label': 'Daily', 'value': 'D'},
{'label': 'Weekly', 'value': 'W'},
{'label': 'Monthly', 'value': 'M'},
{'label': 'Quarterly', 'value': 'BQ-MAR'},
{'label': 'Yearly', 'value': 'Y'},
],
value='M',
clearable=False,
),
dcc.Dropdown(
id='colour_by_dropdown',
options=[
{'label': 'Machine ID', 'value': 'sequencerName'},
{'label': 'Machine Model', 'value': 'name_model'},
],
value=None,
placeholder='Colour By'
),
dcc.Graph(
id='bar_sum',
),
dcc.Tabs(id="table_tabs", value='grouped', children=[
dcc.Tab(label='Grouped Data', value='grouped'),
dcc.Tab(label='All Data', value='all'),
]),
html.Div(
id='table_tabs_content'
),
html.Div(
id='raw_df_json',
style={'display': 'none'},
children=raw_df.to_json(
date_format='iso', orient='records'
),
),
html.Div(
id='df_group_sum',
style={'display': 'none'},
children=raw_df.groupby(
[pandas.Grouper(key='startDate', freq='1M')]
).sum().reset_index().to_json(
date_format='iso', orient='split'
),
),
])
@app.callback(
Output('bar_sum', 'figure'),
[Input('df_group_sum', 'children'),
Input('colour_by_dropdown', 'value')]
)
def create_bar_sum_fig(df_group_sum, colour_by):
print('hello')
df = pandas.read_json(df_group_sum, orient='split')
if colour_by is None:
return {
'data': [go.Bar(
x=df['startDate'],
y=df['Total Yield (GB)']
)]
}
else:
traces = []
for name, data in df.groupby(colour_by):
t = go.Bar(
x=list(data['startDate']),
y=list(data['Total Yield (GB)']),
name=name
)
traces.append(t)
return {
'data': traces
}
@app.callback(
Output('table_tabs_content', 'children'),
[Input('table_tabs', 'value'),
Input('raw_df_json', 'children'),
Input('df_group_sum', 'children')]
)
def update_table_tab(selected_tab, raw_df_json, group_df_json):
if selected_tab == 'grouped':
df = pandas.read_json(group_df_json, orient='split')
if selected_tab == 'all':
df = pandas.read_json(raw_df_json, orient='records')
col_names = [{'name': i, 'id': i} for i in df.columns]
return dash_table.DataTable(
id='test',
columns=col_names,
data=df.to_dict('rows')
)
@app.callback(
Output('df_group_sum', 'children'),
[Input('raw_df_json', 'children'),
Input('freq_dropdown', 'value'),
Input('colour_by_dropdown', 'value')]
)
def update_grouped_df(raw_df_json, frequency, colour_grouper):
raw = pandas.read_json(
raw_df_json, orient='records', convert_dates=['startDate']
)
if colour_grouper is None:
grouper = [
pandas.Grouper(key='startDate', freq=frequency)
]
else:
grouper = [
pandas.Grouper(key='startDate', freq=frequency),
colour_grouper
]
return raw.groupby(
grouper
).sum().reset_index().to_json(
date_format='iso', orient='split',
)
if __name__ == '__main__':
app.run_server(debug=True)
Python module versions:
aiohttp==2.3.9
alabaster==0.7.12
appdirs==1.4.3
astroid==1.6.3
async-timeout==2.0.0
atomicwrites==1.2.1
attrs==18.2.0
autopep8==1.4
Babel==2.6.0
backcall==0.1.0
beautifulsoup4==4.6.0
black==18.9b0
bleach==2.1.3
brewer2mpl==1.4.1
certifi==2018.11.29
chardet==3.0.4
click==6.7
cloudpickle==0.5.2
colormath==3.0.0
Crimson==0.4.0
cycler==0.10.0
dash==0.35.1
dash-core-components==0.42.1
dash-html-components==0.13.4
dash-renderer==0.13.1
dash-table==3.1.11
decorator==4.3.0
docopt==0.6.2
docutils==0.14
entrypoints==0.2.3
Flask==1.0.2
Flask-Compress==1.4.0
future==0.16.0
ggplot==0.11.5
greenlet==0.4.15
grip==4.5.2
gunicorn==19.9.0
html5lib==1.0.1
idna==2.8
imagesize==1.1.0
interop==1.1.6
ipykernel==4.8.2
ipython==6.3.1
ipython-genutils==0.2.0
ipywidgets==7.2.1
isort==4.3.4
itsdangerous==1.1.0
jedi==0.12.0
Jinja2==2.10
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.2.3
jupyter-console==5.2.0
jupyter-contrib-core==0.3.3
jupyter-contrib-nbextensions==0.5.1
jupyter-core==4.4.0
jupyter-highlight-selected-word==0.2.0
jupyter-latex-envs==1.4.6
jupyter-nbextensions-configurator==0.4.1
lazy-object-proxy==1.3.1
lxml==4.2.6
lzstring==0.1
Markdown==2.6.11
MarkupSafe==1.1.0
matplotlib==2.1.0
mccabe==0.6.1
mistune==0.8.3
more-itertools==4.3.0
msgpack==0.5.6
multidict==4.0.0
multiqc==1.5
mypy==0.630
mypy-extensions==0.4.1
nbconvert==5.3.1
nbformat==4.4.0
neovim==0.2.6
networkx==2.1
notebook==5.5.0
numexpr==2.6.5
numpy==1.15.4
numpydoc==0.8.0
packaging==18.0
pandas==0.23.4
pandocfilters==1.4.2
parso==0.2.0
path-and-address==2.0.1
patsy==0.5.1
pexpect==4.5.0
pickleshare==0.7.4
plotly==3.3.0
pluggy==0.7.1
prompt-toolkit==1.0.15
psutil==5.4.5
ptyprocess==0.5.2
py==1.7.0
pybedtools==0.7.10
pycodestyle==2.4.0
pydocstyle==2.1.1
pyflakes==1.6.0
Pygments==2.3.0
pylint==1.8.4
pyls-black==0.2.1
pyls-isort==0.1.1
pyls-mypy==0.1.3
PyOpenGL==3.1.0
pyparsing==2.3.0
PyQt5==5.9.2
pysam==0.15.0
pytest==4.0.2
python-dateutil==2.7.5
python-jsonrpc-server==0.0.2
python-language-server==0.21.2
pytz==2018.7
PyYAML==3.12
pyzmq==17.0.0
QtAwesome==0.4.4
qtconsole==4.3.1
QtPy==1.4.2
requests==2.21.0
retrying==1.3.3
rope==0.10.7
scipy==1.2.0
seaborn==0.9.0
Send2Trash==1.5.0
simplegeneric==0.8.1
simplejson==3.13.2
sip==4.19.8
six==1.12.0
snowballstemmer==1.2.1
spectra==0.0.11
Sphinx==1.8.2
sphinx-rtd-theme==0.4.2
sphinxcontrib-websupport==1.1.0
spyder==3.2.8
statsmodels==0.9.0
tables==3.4.4
terminado==0.8.1
testpath==0.3.1
toml==0.9.6
tornado==5.0.2
traitlets==4.3.2
typed-ast==1.1.0
urllib3==1.24.1
wcwidth==0.1.7
webencodings==0.5.1
Werkzeug==0.14.1
widgetsnbextension==3.2.1
wrapt==1.10.11
yapf==0.24.0
yarl==1.1.0