ssb_sirius_dash.modals package

ssb_sirius_dash.modals.control module

class Control(ident, qualityreport_path)

Bases: object

Provides a layout and functionality for a modal that offers an overview of data checks and control results.

Parameters:

  • ident (str)

  • qualityreport_path (str)

ident

Name of the identification variable, e.g., ‘orgf’.

Type:

str

control_documentation_path

Path to the saved quality report in JSON format on Dapla.

Type:

str

callbacks()

Sets up interactivity for the control modal, including linking table clicks and navigation back to the main view.

Return type:

None

layout()

Generates the layout for the control modal.

Returns:

Layout containing the control modal and interactive components.

Return type:

dash.html.Div

ssb_sirius_dash.modals.hb_method module

class HBMethod(database, hb_get_data_func, selected_state_keys, selected_ident, variable)

Bases: object

Module for detecting outliers using the Hidiroglou-Berthelot (HB) method in a Dash application.

This module applies the HB method to identify potential outliers in time-series data by comparing values in the current period (t) with revised values from the previous period (t-1). It includes methods for preprocessing data, visualizing results, and managing interactions in a Dash app.

Parameters:

  • database (object)

  • hb_get_data_func (Callable[[...], DataFrame])

  • selected_state_keys (list[str])

  • selected_ident (str)

  • variable (str)

database

Database connection or interface for fetching data.

Type:

object

hb_get_data

Function for retrieving data based on selected parameters.

Type:

callable

References

More information about the HB method: https://rdrr.io/github/statisticsnorway/Kostra/man/Hb.html

callbacks(selected_state_keys, selected_ident, variable)

Registers callbacks for the HB method Dash app components.

Parameters:

  • selected_state_keys (list of str) – List of state keys for dynamic state configuration.

  • selected_ident (str) – Identifier used for grouping or filtering data.

  • variable (str) – Name of the value variable for HB method analysis.

Return type:

None

Notes

This method registers Dash callbacks for handling user interactions, including running the HB method, toggling the modal, and passing results to variabelvelger.

layout()

Generates the layout for the HB method Dash component.

Returns:

Div containing the modal and interactive elements for the HB method.

Return type:

dash.html.Div

make_hb_data(data_df, pc, pu, pa, ident, variable)

Processes data using the HB method for outlier detection.

Parameters:

  • data_df (pandas.DataFrame) – Input data containing variables for analysis.

  • pc (int) – Confidence interval control parameter.

  • pu (float) – Parameter for adjusting the variable’s level.

  • pa (float) – Parameter for small differences between quartiles and the median.

  • ident (str) – Identifier field name in the dataset.

  • variable (str) – Name of the value variable for analysis.

Returns:

Processed data with outlier results, sorted by “maxX”.

Return type:

pandas.DataFrame

make_hb_figure(data, variable)

Creates a Plotly figure for visualizing HB method results.

Parameters:

  • data (pandas.DataFrame) – Processed data from the HB method, including outlier and limit values.

  • variable (str) – Name of the value variable for the method.

Returns:

Plotly figure with scatter plots for observations and limits.

Return type:

plotly.graph_objects.Figure

ssb_sirius_dash.modals.quality_indicators module

class QualityIndicator(indicators)

Bases: object

A module for setting up the view for selected quality indicators.

Parameters:

indicators (list[Any])

indicators

A list of quality indicators. Example: [QualityIndicatorEditeringsandel(), QualityIndicatorEffektaveditering()]

Type:

list

Notes

All indicators assume a long format for the data with a minimum of ident, variabel, and verdi as columns.

callbacks()

Registers callbacks to enable the modal to be opened and closed.

Return type:

None

layout()

Creates the layout for the quality indicator modal.

Returns:

A Dash HTML Div element containing the modal and the sidebar button.

Return type:

html.Div

class QualityIndicatorEditeringsandel(get_current_data_func, get_change_data_func, var_name, ident_var, grouping_vars=None, key_vars=None)

Bases: object

Quality indicator for editing ratio.

Parameters:

  • get_current_data_func (Callable[[...], DataFrame])

  • get_change_data_func (Callable[[...], DataFrame])

  • var_name (str)

  • ident_var (str)

  • grouping_vars (list[str] | None)

  • key_vars (list[str] | None)

get_current_data_func

Function to retrieve current data.

Type:

Callable

get_change_data_func

Function to retrieve rows with changes.

Type:

Callable

periode

Current period for the data.

var_name

The name of the column in the dataset indicating the variable.

Type:

str

ident_var

Name of the identification variable. For example, “orgf”.

Type:

str

grouping_vars

Variables by which the dataset can be grouped, such as “nace” or “kommune”.

Type:

list[str]

key_vars

Key variables relevant to the quality indicator.

Type:

list[str]

callbacks()

Sets up callbacks for opening the detail view and selecting grouping for details.

Return type:

None

editeringsandel()

Calculates the editing ratio.

Returns:

The editing ratio as a percentage.

Return type:

float

editeringsandel_details(group)

Calculates the editing ratio for different subsets of the dataset.

Parameters:

group (list[str] | str) – Variable(s) by which to group the dataset.

Returns:

A DataFrame with editing ratios for each subset.

Return type:

pd.DataFrame

class QualityIndicatorEffektaveditering(get_current_data_func, get_original_data_func, periode, ident_var, key_vars, grouping_vars)

Bases: object

Indicator to display the effect of editing.

Parameters:

  • get_current_data_func (Callable[[...], DataFrame])

  • get_original_data_func (Callable[[...], DataFrame])

  • periode (str | int)

  • ident_var (str)

  • key_vars (list[str])

  • grouping_vars (str | list[str])

get_current_data_func

Function to fetch the current updated data.

Type:

Callable

get_original_data_func

Function to fetch the original received data.

Type:

Callable

periode

The period for the data.

Type:

str | int

ident_var

Name of the identification variable, e.g., “orgf”.

Type:

str

key_vars

List of key variables relevant to the indicator.

Type:

list[str]

grouping_vars

List of grouping variables for stratification.

Type:

list[str]

callbacks()

Sets up callbacks for opening and closing the detailed view.

Return type:

None

get_comparison_data(periode, grouping=None)

Calculates the effect of editing using the provided functions.

Parameters:

  • periode (str | int) – The period for the data.

  • grouping (list[str] | None) – Variables for grouping. Defaults to None.

Returns:

A DataFrame containing the calculated effect of editing.

Return type:

pd.DataFrame

class QualityIndicatorKontrollutslagsandel(control_documentation=None, qualityreport_path=None)

Bases: object

Indicator for displaying the percentage of possible control outcomes that trigger a flag.

The control documentation must be a dataset with the following columns: kontroll_id, Enheter kontrollert, Kontrollutslag.

Parameters:

  • control_documentation (QualityReport | None)

  • qualityreport_path (str | None)

control_documentation

The quality report used for calculations.

Type:

QualityReport | None

qualityreport_path

File path to a saved quality report in JSON format on Dapla.

Type:

str | None

callbacks()

Sets up callbacks for opening and closing the detailed view.

Return type:

None

kontrollutslag()

Calculates the proportion of control outcomes that trigger a flag.

Returns:

  • The total proportion of control outcomes as a float.

  • A DataFrame with detailed proportions for each control.

Return type:

tuple[float, pd.DataFrame]

class QualityIndicatorTreffsikkerhet(get_edits_list_func, quality_report=None, qualityreport_path=None)

Bases: object

Indicator to display the accuracy of the controls being run.

Parameters:

  • get_edits_list_func (Callable[[...], list[tuple[str, str]]])

  • quality_report (QualityReport | None)

  • qualityreport_path (str | None)

get_edits_list_func

Function that retrieves a list of changes made to the data. It returns a list of tuples describing the fields changed. Used to check against control outcomes to determine if a control outcome likely resulted in an edit. Example: [(orgnr_1, variabel_1), (orgnr_1, variabel_2), (orgnr_2, variabel_1)].

Type:

Callable

quality_report

The quality report used for calculations.

Type:

QualityReport | None

qualityreport_path

File path to a saved quality report in JSON format on Dapla.

Type:

str | None

beregn_treffsikkerhet()

Calculates the accuracy indicator based on the quality report.

Returns:

A dictionary where keys are control names and values are the accuracy percentage.

Includes a “total” key for overall accuracy.

Return type:

dict

callbacks()

Sets up callbacks for opening and closing the detailed view.

Return type:

None

ssb_sirius_dash.modals.visualizationbuilder module

class VisualizationBuilder(database)

Bases: object

A module for creating and visualizing data queries and graphs interactively.

Parameters:

database (object)

database

The database connection or interface for executing queries.

Type:

object

callbacks()

Registers Dash callbacks for the Visualiseringsbygger module.

Return type:

None

Notes

  • sqlmodal_toggle: Toggles the visibility of the query modal.

  • sql_query: Executes the SQL query and updates the table and dropdown options.

  • update_graph: Generates graphs based on selected columns and graph type.

layout()

Generates the layout for the Visualiseringsbygger module.

Returns:

A Div element containing components for querying data and visualizing graphs.

Return type:

html.Div