# select-events

    library(scenfire)

C:4sNiUS3e103ab43d8c-events.R

    # Dummy data for demonstration (replace with your actual data)
    set.seed(123)
    historical_data_for_target <- floor(fit_powerlaw(n = 500, alpha = 2, xmin = 10, xmax = 10000))
    event_surfaces <- fit_powerlaw(n = 10000, alpha = 2, xmin = 10, xmax = 10000)
    # Discard simulated fires that are too large (below 110% max historical size)
    event_surfaces <- event_surfaces[event_surfaces<max(historical_data_for_target)*1.1]
    event_probabilities <- rnorm(length(event_surfaces))
    event_probabilities <- (event_probabilities-min(event_probabilities))/
                            (max(event_probabilities)-min(event_probabilities))

    y <- 100 #number of years spanning historical fire data
    check_fire_data(fires_hist_size = historical_data_for_target,
                    sim_perimeters_size = event_surfaces,
                    n_years = y)
    #> Sufficient simulated perimeters and burned area. Maximum surface threshold: 1726.
    #>  Recommended surface threshold: 172
    #> [1] 431

    reference_surface_example <- sum(historical_data_for_target)/y
    surface_threshold_example <- check_fire_data(fires_hist_size = historical_data_for_target,
                                                 sim_perimeters_size = event_surfaces,
                                                 n_years = 10)
    #> Sufficient simulated perimeters and burned area. Maximum surface threshold: 172.
    #>  Recommended surface threshold: 17
    tolerance_example <- 0.1

    # Create a dummy target histogram (assuming 'event_surfaces' from historical data)
    # For a real scenario, 'event_surfaces' here would be your historical fire sizes.

    target_info_example <- build_target_hist(num_bins = 10, logaritmic = TRUE,
                                             sizes = historical_data_for_target,
                                             event_surfaces = event_surfaces)
    target_hist <- target_info_example$target_hist
    bins <- target_info_example$bins

    foreach::registerDoSEQ()

    # Run the selection process
    selected_events_result <- select_events(
      event_sizes = event_surfaces,
      event_probabilities = event_probabilities,
      target_hist = target_hist,
      bins = bins,
      reference_surface = reference_surface_example,
      surface_threshold = surface_threshold_example,
      tolerance = tolerance_example,
      max_it = 2 # Reduced iterations for example
    )

    # Stop the parallel cluster when done
    doParallel::stopImplicitCluster()

C:4sNiUS3e103ab43d8c-events.R