Invloedsfactoren

Attributes

DISTINCTIVE_COLORS

Functions

get_plot_order(geoprob_pipe)

Assigns color codes (indices) and color rgb-values to each stochast such that they can be plotted with the

_get_method_used(geoprob_pipe, worst_uittredepunt_id, ...)

get_influence_factors_for_vak(geoprob_pipe, ...)

Returns the influence factors for the worst result of a scenario within the vak. May be empty dataframe if

_get_data(geoprob_pipe)

_plot_data(geoprob_pipe, df_invloedsfactoren)

_update_layout(fig)

invloedsfactoren(geoprob_pipe[, export])

Module Contents

DISTINCTIVE_COLORS = ['(60, 180, 75)', '(230, 25, 75)', '(255, 225, 25)', '(0, 130, 200)', '(245, 130, 48)', '(145,...
get_plot_order(geoprob_pipe)

Assigns color codes (indices) and color rgb-values to each stochast such that they can be plotted with the same color and same order.

Parameters:

geoprob_pipe (Geoprobpipe)

Returns:

DataFrame with columns ‘variable’, ‘plot_order’ and ‘color’

Return type:

pandas.DataFrame

_get_method_used(geoprob_pipe, worst_uittredepunt_id, worst_scenario_id)
Parameters:

  • geoprob_pipe (Geoprobpipe)

  • worst_uittredepunt_id (int)

  • worst_scenario_id (str)

get_influence_factors_for_vak(geoprob_pipe, df_invloedsfactoren, vak_id)

Returns the influence factors for the worst result of a scenario within the vak. May be empty dataframe if vak has no results.

In the past weigh it for the uittredepunt, or average is among all uittredepunten, as was done in the past. The choice was made to keep the actual resulting influence factors because they sum up to 100%. There is also no physical reason to average them, and the worst case scenario is normative for the final result anyway.

Parameters:

  • geoprob_pipe (Geoprobpipe)

  • df_invloedsfactoren (pandas.DataFrame)

  • vak_id (int)

Returns:

Influence factors, worst case uittredepunt ID, worst case scenario label and method used.

Return type:

Tuple[Optional[pandas.DataFrame], int, str, str]

_get_data(geoprob_pipe)
Parameters:

geoprob_pipe (Geoprobpipe)

Return type:

pandas.DataFrame

_plot_data(geoprob_pipe, df_invloedsfactoren)
Parameters:

  • geoprob_pipe (Geoprobpipe)

  • df_invloedsfactoren (pandas.DataFrame)

Return type:

plotly.graph_objects.Figure

_update_layout(fig)
Parameters:

fig (plotly.graph_objects.Figure)

Return type:

plotly.graph_objects.Figure

invloedsfactoren(geoprob_pipe, export=False)
Parameters:
Return type:

plotly.graph_objects.Figure