Jupyter interactive widgets

Created on Mon Aug 9 14:46:11 2021

To define Jupyter widgets which accept user input and can update dataframes and show variables.

A user can define new widgets to suit the need for her use case. This requires some familliarity with the ipywidget library https://ipywidgets.readthedocs.io/en/stable/

This module is a revision of the modelwidget module. Modelwidget is still around and is used for legacy and output widgets.

Ipywidgets are used to define widgets which can be used in jupyter notebooks and in shiny.

Tabwidget and basewidget:

defines a container of widgets.

updatawidget:

defines a widget to update variables using the widgets defined in the classes mentioned abowe

keep_plot_widget:

Defines a widget to display the results from the model run made in the updatewidget

sheetwidget slidewidget ect:

defines a basic widget which can be dispayed and which might have the ability to update a dataframe

Some methods and properties of widgets.

datawidget:

The ipywidget which can be dispayed and might accept input used for updating a dataframe

datachildren:

A list of widgets in a container widget

update_df:

a function which updates a dataframe

reset:

A function which resets the input values to the initial values.

@author: Ib

modelwidget_input

class modelwidget_input.singelwidget(widgetdef)

Bases: object

Parent class for widgets

Will do some common stuff

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

update_df(df, current_per)

will update container widgets

reset(g)

will reset container widgets

class modelwidget_input.basewidget(widgetdef)

Bases: singelwidget

basis for widget updating in jupyter

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

update_df(df, current_per)

will update container widgets

reset(g)

will reset container widgets

class modelwidget_input.tabwidget(widgetdef)

Bases: singelwidget

A widget to create tab or acordium contaners

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

update_df(df, current_per)

will update container widgets

reset(g)

will reset container widgets

class modelwidget_input.sheetwidget(widgetdef)

Bases: singelwidget

class defefining a widget with lines of slides

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

update_df(df, current_per=None)

will update container widgets

reset(g)

will reset container widgets

class modelwidget_input.slidewidget(widgetdef)

Bases: singelwidget

class defefining a widget with lines of slides

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

reset(g)

will reset container widgets

update_df(df, current_per)

updates a dataframe with the values from the widget

set_slide_value(g)

updates the new values to the self.current_vlues will be used in update_df

class modelwidget_input.sumslidewidget(widgetdef)

Bases: singelwidget

class defefining a widget with lines of slides the sum of the input variables can max be the parameter maxsum (default=1.0)

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

reset(g)

will reset container widgets

update_df(df, current_per)

updates a dataframe with the values from the widget

set_slide_value(g)

updates the new values to the self.current_vlues will be used in update_df

class modelwidget_input.radiowidget(widgetdef)

Bases: singelwidget

class defining a widget with a number of radiobutton groups

When df_update is executed the cheked variable will be set to 1 the other to 0

{‘<heading 1> [[‘text1’,’variable1’],

[‘text2’,’variable2’]

…..]

‘heading 2’ [[‘text1’,’variable1’],

[‘text2’,’variable2’]

…..]

}

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

reset(g)

will reset container widgets

update_df(df, current_per)

updates a dataframe with the values from the widget

class modelwidget_input.checkwidget(widgetdef)

Bases: singelwidget

class defefining a widget with a number of radiobutton groups

When df_update is executed the cheked variable will be set to 1 the other to 0

{

‘<heading 1>:[‘variable1 variable2 —‘,value ], ‘<heading 2>:[‘variable10 variable20 —‘,value ] }

Some common properties and methods are defines.

Sets these properties:

• self.widgetdef = widgetdef

• self.content = self.widgetdef[‘content’] the content of this widget

• self heading = self.widgetdef[‘heading’] the heading for this widget.

Parameters:

widgetdef (dict) – A dict with the descrition of a widget

Return type:

None.

reset(g)

will reset container widgets

update_df(df, current_per)

updates a dataframe with the values from the widget

class modelwidget_input.updatewidget(mmodel: any, datawidget: any, basename: str = 'Business as usual', keeppat: str = '*', varpat: str = '*', showvarpat: bool = True, exodif: any = <factory>, lwrun: bool = True, lwupdate: bool = False, lwreset: bool = True, lwsetbas: bool = True, outputwidget: str = 'jupviz', display_first: any = None, prefix_dict: dict = <factory>, vline: list = <factory>, relativ_start: int = 0, short: bool = False)

Bases: object

class to input and run a model

• display(wtotal) to activate the widget

mmodel: any

datawidget: any

basename: str = 'Business as usual'

keeppat: str = '*'

varpat: str = '*'

showvarpat: bool = True

exodif: any

lwrun: bool = True

lwupdate: bool = False

lwreset: bool = True

lwsetbas: bool = True

outputwidget: str = 'jupviz'

display_first: any = None

prefix_dict: dict

vline: list

relativ_start: int = 0

short: bool = False

update(g)

run(g)

setbasis(g)

reset(g)

modelwidget_input.fig_to_image(fig, format='svg')

class modelwidget_input.keep_plot_widget(mmodel: any, smpl: ~typing.Tuple[str, str] = ('', ''), relativ_start: int = 0, selected: str = '', selectfrom: str = '*', showselectfrom: bool = True, legend: bool = False, dec: str = '', use_descriptions: bool = True, select_width: str = '', select_height: str = '200px', vline: any = None, var_groups: dict = <factory>, use_var_groups: bool = True, add_var_name: bool = False, short: any = 0, select_scenario: bool = True, displaytype: str = 'tab', save_location: str = './graph', switch: bool = False, use_smpl: bool = False, init_dif: bool = False)

Bases: object

Provides an interactive widget to plot data from ModelFlow model instances. It allows for selection of variables, scenarios, and display types, and can show differences between scenarios in various formats.

Parameters:

• mmodel – A ModelFlow model instance.

• smpl (Tuple[str, str], optional) – Sample period for plotting (start, end). Defaults to (‘’, ‘’).

• use_smpl (bool, optional) – If True, enables a sample period selection slider. Defaults to False.

• selectfrom (str, optional) – Space-separated string of variable names to select from. If empty, all kept variables are available. Defaults to ‘*’.

• legend (bool, optional) – If True, displays legends next to the plots. Defaults to False.

• dec (str, optional) – Format string for decimal places on the y-axis. Defaults to ‘’.

• use_descriptions (bool, optional) – If True, uses variable descriptions from the model. Defaults to True.

• vline (any, optional) – List of vertical lines for the plot (position, text). Defaults to None.

• add_var_name (bool, optional) – If True, adds variable names to descriptions. Defaults to False.

• short (any, optional) – If set, reduces the number of input fields. Defaults to 0.

• select_scenario (bool, optional) – If True, allows selecting scenarios to display. Defaults to True.

• switch (bool, optional) – If True, uses scenarios from mmodel.basedf and mmodel.lastdf. Defaults to False.

• var_groups (dict, optional) – Dictionary of variable patterns for selection, like country groups. If empty use mmodel.var_groups. Defaults to an empty dict.

• use_var_groups (bool, optional) – If True, enables selection using var_groups. Defaults to True.

• displaytype (str, optional) – Type of display (‘tab’, ‘accordion’, or other). Defaults to ‘tab’.

• save_location (str, optional) – Default location for saving plots. Defaults to ‘./graph’.

• use_smpl – If True, enables a sample selection slider. Defaults to False.

Properties:

show: Displays the widget. datawidget: The actual interactive widget.

Returns:

An instance of keep_plot_widget. This instance’s ‘keep_wiz_figs’ property is set to a dictionary containing the figures, which can be used for creating publication-quality files.

mmodel: any

smpl: Tuple[str, str] = ('', '')

relativ_start: int = 0

selected: str = ''

selectfrom: str = '*'

showselectfrom: bool = True

legend: bool = False

dec: str = ''

use_descriptions: bool = True

select_width: str = ''

select_height: str = '200px'

vline: any = None

var_groups: dict

use_var_groups: bool = True

add_var_name: bool = False

short: any = 0

select_scenario: bool = True

displaytype: str = 'tab'

save_location: str = './graph'

switch: bool = False

use_smpl: bool = False

init_dif: bool = False

property show

explain(i_smpl=None, selected_vars=None, diff=None, showtype=None, scale=None, legend=None)

trigger(g)

class modelwidget_input.savefigs_widget(figs: dict = <factory>, location: str = './graph', addname: str = '')

Bases: object

Provides a widget for saving matplotlib figures from a dictionary to files.

The widget allows the user to specify the save location, file format(s), and additional naming details for the saved figures. It also includes an option to open the save location in the file explorer after saving.

figs

A dictionary containing matplotlib figures. Defaults to an empty dict.

Type:

dict, optional

location

The default directory where figures will be saved. Defaults to ‘./graph’.

Type:

str, optional

addname

An additional suffix to append to the figure filenames. Defaults to an empty string.

Type:

str, optional

The widget layout includes:

• A button to initiate the save process.

• Input fields to specify the experiment name, save location, and filename suffix.

• A multiple-selection dropdown to choose the file formats (e.g., svg, pdf, png, eps).

• A checkbox to optionally open the save location after saving.

• An output field displaying the final save location.

figs: dict

location: str = './graph'

addname: str = ''

class modelwidget_input.shinywidget(a_widget: any, widget_id: str)

Bases: object

A to translate a widget to shiny widget

a_widget: any

widget_id: str

update_df(df, current_per)

will update container widgets

reset(g)

will reset container widgets

modelwidget_input.make_widget(widgetdef)