cluster_spatial
.cluster_spatial(
growth_df, global_or_country, scenario, admin_1 = False
)
Creates a spatial plot of the clusters
Arguments
- growth_df : a dataframe of the growth rate
- global_or_country : a string of either "global" or "US" that indicates the scale
Returns
None, but saves the plot
growth_rate_spatial_by_year
.growth_rate_spatial_by_year(
growth_df, global_or_country, scenario, optimal_growth_rate
)
Plots the growth rate by year. This includes the first three months without nuclear war, in the case of the first year
Arguments
- growth_df : a dataframe of the growth rate
Returns
None, but saves the plot
cluster_timeseries_all_parameters_q_lines
.cluster_timeseries_all_parameters_q_lines(
parameters, global_or_country, scenario, areas
)
Plots line plots for all clusters and all parameters
Arguments
- parameters : a dictionary of dataframes of all parameters
Returns
None, but saves the plot
compare_nw_scenarios
.compare_nw_scenarios(
areas, optimal_growth_rate
)
Compares the results of the nuclear war scenarios as weigthed median
Arguments
- areas : A dataframe containing the area of each grid cell
- eez : A eez around coastlines to only use those areas
Returns
None
compare_nutrient_subfactors
.compare_nutrient_subfactors(
nitrate, ammonium, phosphate, scenario, areas
)
Takes the weighted average of the nutrient subfactors globally and plots them in the same plot to be able to compare them.
Arguments
- nitrate : The nitrate subfactor
- ammonium : The ammonium subfactor
- phosphate : The phosphate subfactor
- scenario : The scenario to plot
- areas : The areas of the grid cells
Returns
None
plot
.plot(
scenario, global_or_country, optimal_growth_rate, admin_1 = False
)
Runs the other functions to read the data and make the plots
Arguments
- scenario : The scenario to plot
- global_or_country : Whether to plot the global or a country scenario
- optimal_growth_rate : The maximum growth rate
Returns
None