When i spin up my dashboard, initially i will be thrown an “ValueError: Invalid value”, and only 1 of my 4 graphs will show. which one shows, is random for each time i run the dashboard.
However, if i change the “area-radio” button to another value and then change it back to the initial one (the exact same one as when i spin up the dashboard…) everything works fine.
Each callback works without any errors if i comment out the remaining ones (ie. test them out one at a time). But if they are there simultaniously i get an error as mentioned on startup.
The dashboard is fed a df on creation.
Has anyone experience anything similar, and how did you go about fixing it?
any help will be much appreciated!
layout
return html.Div([
dcc.RadioItems(
id="area-radio",
options=[
{"label": "Områder i produktion", "value": "oip"},
{"label": "Alle områder", "value": "ao"},
],
value="oip",
labelStyle={"display": "inline-block"},
),
html.Div(
[
dcc.DatePickerRange(
id="my-date-picker-range", # ID to be used for callback
calendar_orientation="horizontal", # vertical or horizontal
day_size=39, # size of calendar image. Default is 39
end_date_placeholder_text="Return", # text that appears when no end date chosen
with_portal=False, # if True calendar will open in a full screen overlay portal
first_day_of_week=0, # Display of calendar when open (0 = Sunday)
reopen_calendar_on_clear=True,
is_RTL=False, # True or False for direction of calendar
clearable=True, # whether or not the user can clear the dropdown
number_of_months_shown=1, # number of months shown when calendar is open
min_date_allowed=df.ml_log_date.astype("datetime64[ns]").min().date() - timedelta(days=1), # minimum date allowed on the DatePickerRange component
max_date_allowed=df.ml_log_date.astype("datetime64[ns]").max().date() + timedelta(days=1), # maximum date allowed on the DatePickerRange component
initial_visible_month=df.ml_log_date.astype("datetime64[ns]").max().date(), # the month initially presented when the user opens the calendar
start_date=df.ml_log_date.astype("datetime64[ns]").min().date(),
end_date=df.ml_log_date.astype("datetime64[ns]").max().date(),
display_format="D-M-Y", # how selected dates are displayed in the DatePickerRange component.
month_format="MMMM, YYYY", # how calendar headers are displayed when the calendar is opened.
minimum_nights=1, # minimum number of days between start and end date
persistence=True,
persisted_props=["start_date"],
persistence_type="session", # session, local, or memory. Default is 'local'
updatemode="singledate", # singledate or bothdates. Determines when callback is triggered
),
dcc.RadioItems(
id="timeperiod-radio",
options=[
{"label": "uge", "value": "uge"},
{"label": "dag", "value": "dag"},
],
value="dag",
labelStyle={"display": "inline-block"},
),
dcc.Graph(id="timeseries_predicted_count"),
html.Br(),
dcc.Graph(id="timeseries_change_share"),
html.Br(),
dcc.Graph(id="timeseries_predicted_timediff"),
html.Br(),
dcc.Graph(id="timeseries_blanks_selected"),
]
),
]
)
Callbacks
@dash_app.callback(
Output("timeseries_predicted_count", "figure"),
[
Input("my-date-picker-range", "start_date"),
Input("my-date-picker-range", "end_date"),
Input("area-radio", "value"),
Input("timeperiod-radio", "value"),
],
)
def handled_pr_area(
start_date, end_date, data_filter_value, date_filter_value
):
if check_pickle():
dff = prepare_pickle_df(df, start_date, end_date)
if data_filter_value == "oip":
dff = dff.loc[dff.ml_prediction != "empty_list"]
else:
dff = df.copy()
periode = toggle_week_day(date_filter_value)
dff = dff.groupby([periode, "Område"]).sum()
dff.reset_index(inplace=True)
dff_pivot = (
dff.loc[:, [periode, "Område", "count"]]
.pivot_table(
index=periode, columns="Område", values=["count"], fill_value=0
)
.reset_index()
).round(1)
dff_pivot.columns = [periode] + list(dff.Område.unique())
print(dff_pivot.dtypes, dff_pivot)
fig = px.line(
dff_pivot,
x=periode,
y=dff_pivot.columns,
labels=dict(value="antal", variable="område", date="tid", week_year="tid"),
title="Håndterede emails",
)
fig.update_xaxes(dtick="D1", tickformat="%Y-%d-%m")
return fig
@dash_app.callback(
Output("timeseries_change_share", "figure"),
[
Input("my-date-picker-range", "start_date"),
Input("my-date-picker-range", "end_date"),
Input("area-radio", "value"),
Input("timeperiod-radio", "value"),
],
)
def handled_pr_area_change(
start_date, end_date, data_filter_value, date_filter_value
):
if check_pickle():
dff = prepare_pickle_df(df, start_date, end_date)
if data_filter_value == "oip":
dff = dff.loc[dff.ml_prediction != "empty_list"]
else:
dff = df.copy()
periode = toggle_week_day(date_filter_value)
dff = dff.groupby([periode, "Område"]).sum()
dff["andel"] = dff["Antal ændrede Email templates"] / dff["count"] * 100
dff.reset_index(inplace=True)
dff_pivot = (
dff.loc[:, [periode, "Område", "andel"]]
.pivot_table(
index=periode, columns="Område", values=["andel"], fill_value=0
)
.reset_index()
).round(1)
dff_pivot.columns = [periode] + list(dff.Område.unique())
print(dff_pivot.dtypes, dff_pivot)
fig = px.line(
dff_pivot,
x=periode,
y=dff_pivot.columns,
labels=dict(value="%", variable="område", date="tid", week_year="tid"),
title="Email templates der er blevet ændret - andel",
)
fig.update_xaxes(dtick="D1", tickformat="%Y-%d-%m")
return fig
@dash_app.callback(
Output("timeseries_predicted_timediff", "figure"),
[
Input("my-date-picker-range", "start_date"),
Input("my-date-picker-range", "end_date"),
Input("area-radio", "value"),
Input("timeperiod-radio", "value"),
],
)
def time_avg_pr_area(
start_date, end_date, data_filter_value, date_filter_value
):
if check_pickle():
dff = prepare_pickle_df(df, start_date, end_date)
if data_filter_value == "oip":
dff = dff.loc[dff.ml_prediction != "empty_list"]
else:
dff = df.copy()
periode = toggle_week_day(date_filter_value)
dff = dff.groupby([periode, "Område"]).mean().reset_index()
dff_pivot = pd.pivot_table(
dff,
index=periode,
columns="Område",
values=["timediff_seconds"],
fill_value=0,
).reset_index().round(1)
dff_pivot.columns = [periode] + list(dff.Område.unique())
print(dff_pivot.dtypes, dff_pivot)
fig = px.line(
dff_pivot,
x=periode,
y=dff_pivot.columns,
labels=dict(
value="sekunder", variable="område", date="tid", week_year="tid"
),
title="Email håndtering - sekunder",
)
fig.update_xaxes(dtick="D1", tickformat="%Y-%d-%m")
return fig
@dash_app.callback(
Output("timeseries_blanks_selected", "figure"),
[
Input("my-date-picker-range", "start_date"),
Input("my-date-picker-range", "end_date"),
Input("area-radio", "value"),
Input("timeperiod-radio", "value"),
],
)
def blank_selected_pr_area_share(
start_date, end_date, data_filter_value, date_filter_value
):
if check_pickle():
dff = prepare_pickle_df(df, start_date, end_date)
if data_filter_value == "oip":
dff = dff.loc[dff.ml_prediction != "empty_list"]
else:
dff = df.copy()
periode = toggle_week_day(date_filter_value)
dff_tom = dff.loc[dff.selectedTemplate == "tom"]
dff_tom = dff_tom.groupby([periode, "Område"]).sum()
dff_tom.reset_index(inplace=True)
dff_pivot_tom = dff_tom.loc[:, [periode, "Område", "count"]].pivot_table(
index=periode, columns="Område", values=["count"], fill_value=0
)
dff_alle = dff.copy()
dff_alle = dff_alle.groupby([periode, "Område"]).sum()
dff_alle.reset_index(inplace=True)
dff_pivot_alle = dff_alle.loc[:, [periode, "Område", "count"]].pivot_table(
index=periode, columns="Område", values=["count"], fill_value=0
)
dff_kombineret = ((dff_pivot_tom / dff_pivot_alle) * 100).fillna(0)
dff_kombineret = dff_kombineret.reset_index().round(1)
dff_kombineret.columns = [periode] + list(
dff_alle.Område.sort_values().unique()
)
print(dff_kombineret.dtypes, dff_kombineret)
fig = px.line(
dff_kombineret,
x=periode,
y=dff_kombineret.columns,
labels=dict(value="%", variable="område", date="tid", week_year="tid"),
title="Blanke templates - andel",
)
fig.update_xaxes(dtick="D1", tickformat="%Y-%d-%m")
return fig
