frflib.plots.utils

Module Contents

Functions

plot_get_timeserie_sum(field_data[, wf_list, ...])

param field_data:

get_count_per_method_multi(dict_multi_summary[, well_list])

function that generates the data for the bar plot - multi realisation runs per forecaster

get_result_summary_by_run(dict_multi_summary[, well_list])

get result summary by fluid and by run - to be used in front multi real foreacst page

get_result_distribution(dict_multi_summary[, well_list])

generate the result distribution

prepare_tornado(dict_multi_summary, df_runs[, ...])

generate a dataframe for a tornado chart with min and max values for parameters

get_main_group(df, groupby[, min_elements, select_top])

Select the top group for a category - all the remaining group will be casted to 'other'

map_list_to_color(group_list[, color_scale])

based on a list generate a color dictionary

get_group_colors(df, groupby[, min_elements, ...])

function allows to create a dict map of color / group

lighten_color(color[, amount])

Lightens the given color by multiplying (1-luminosity) by the given amount.

get_group_colors_from_map(input_map_color, cat_list)

allows to create a map category color - derived from an existing one
frflib.plots.utils.plot_get_timeserie_sum(field_data: frflib.data_class.input_data.InputData, wf_list=None, col_list=None, well_list=None)
Parameters:

  • field_data –

  • wf_list –

  • col_list –

  • well_list –

Returns:

frflib.plots.utils.get_count_per_method_multi(dict_multi_summary, well_list=None)

function that generates the data for the bar plot - multi realisation runs per forecaster

Parameters:

  • dict_multi_summary – attribute of multi real

  • well_list – well list - default None

Returns:

frflib.plots.utils.get_result_summary_by_run(dict_multi_summary, well_list=None)

get result summary by fluid and by run - to be used in front multi real foreacst page

Parameters:

  • dict_multi_summary –

  • well_list –

Returns:

frflib.plots.utils.get_result_distribution(dict_multi_summary, well_list=None)

generate the result distribution

Parameters:

  • dict_multi_summary –

  • well_list –

Returns:

frflib.plots.utils.prepare_tornado(dict_multi_summary, df_runs, well_list=None, run_names=None)

generate a dataframe for a tornado chart with min and max values for parameters

Parameters:

dict_multi_summary –

Returns:

dataframe tornado

frflib.plots.utils.get_main_group(df, groupby, min_elements=0, select_top=5)

Select the top group for a category - all the remaining group will be casted to ‘other’

Parameters:

  • df –

  • groupby –

  • min_elements –

  • select_top –

Returns:

frflib.plots.utils.map_list_to_color(group_list, color_scale='tab10')

based on a list generate a color dictionary

Parameters:

  • group_list –

  • color_scale –

Returns:

frflib.plots.utils.get_group_colors(df, groupby, min_elements=0, select_top=None, color_scale='tab10')

function allows to create a dict map of color / group it allows to keep the same colors while plotting different graphs

Parameters:

  • df – dataframe

  • groupby – groupby value -> to be used

  • min_elements – min elements for the group to be considered (otherwise => other)

  • select_top – select only top groups (otherwise =other)

  • color_scale – color scale (matplotlib names)

Returns:

dict category / color

frflib.plots.utils.lighten_color(color, amount=0.5)

Lightens the given color by multiplying (1-luminosity) by the given amount. Input can be matplotlib color string, hex string, or RGB tuple.

Examples: >> lighten_color(‘g’, 0.3) >> lighten_color(‘#F034A3’, 0.6) >> lighten_color((.3,.55,.1), 0.5)

frflib.plots.utils.get_group_colors_from_map(input_map_color, cat_list)

allows to create a map category color - derived from an existing one example using a color per field to create a map field_year the category needs to be separated by ‘_’ and the name needs to be similar to the name used in the static data

Parameters:

  • input_map_color –

  • cat_list –

Returns: