plot_satisfaction_results

source

.plot_satisfaction_results(
   clusters, percent_need, scenario, location
)

Plots the results of the model

Arguments

  • cluster_df (pd.DataFrame) : The results of the model
  • percent_need (int) : The percent of the population that needs to be satisfied
  • scenario (str) : The scenario name

Returns

None, but plots and saves the results

plot_scenario_comparison

source

.plot_scenario_comparison(
   percent_need, scenario_max_growth_rates_df, location
)

Plots the results of the model from all scenarios and compares the cluster with the highest growth rate for a given scenario.

Arguments

  • percent_need (int) : The percent of the population that needs to be satisfied

Returns

None, but plots and saves the results

plot_area_results

source

.plot_area_results(
   clusters, scenario, location
)

Plots how much area the different growth rates need

Arguments

  • clusters (dict) : The seaweed scale up area results sorted by cluster

Returns

None, but plots and saves the results

plot_self_shading

source

.plot_self_shading()

Plots the self shading used in the model. Based on James and Boriah (2010).

Arguments

None

Returns

None

create_plots

source

.create_plots(
   location, scenarios, consumption_aim, number_of_clusters,
   with__shading = False, with_comparison = True
)

Main function to run the plotter and read the data

Arguments

  • location (str) : The location to plot
  • consumption_aim (float) : The consumption aim in percent
  • with_self_shading (bool) : Whether to plot the self shading factor
  • with_comparison (bool) : Whether to plot the scenario comparison

Returns

None