Note that the combination of `land1` and `land2` are a key to the scores, i.e. if a bullet has six lands, each of the input vectors should have length 36.

compute_average_scores(land1, land2, score, addNA = FALSE)

Arguments

land1

(numeric) vector with land ids of bullet 1

land2

(numeric) vector with land ids of bullet 2

score

numeric vector of scores to be summarized into a single number

addNA

logical value. In case of missing lands, are scores set to 0 (addNA = FALSE) or set to NA (addNA = TRUE)

Value

numeric vector of average scores. Length is the same as the number of land engraved areas on the bullets.

Examples

# NOT RUN {
# Set the data up to be read in, cleaned, etc.
library(bulletxtrctr)
library(x3ptools)

bullets <- bullet_pipeline(
  location = list(
    Bullet1 = c(hamby252demo$bullet1),
    Bullet2 = c(hamby252demo$bullet2)
  ),
  x3p_clean = function(x) x %>%
      x3p_scale_unit(scale_by=10^6) %>%
      rotate_x3p(angle = -90) %>%
      y_flip_x3p()
) %>%
mutate(land = paste0(rep(1:2, each = 6), "-", rep(1:6, times = 2)))

comparisons <- data.frame(
  expand.grid(land1 = bullets$land, land2 = bullets$land),
  stringsAsFactors = FALSE)
comparisons <- comparisons %>%
  mutate(
    aligned = purrr::map2(.x = land1, .y = land2, .f = function(xx, yy) {
      land1 <- bullets$sigs[bullets$land == xx][[1]]
      land2 <- bullets$sigs[bullets$land == yy][[1]]
      land1$bullet <- "first-land"
      land2$bullet <- "second-land"

      sig_align(land1$sig, land2$sig)
    }),
    striae = purrr::map(aligned, sig_cms_max),
    features = purrr::map2(.x = aligned, .y = striae, extract_features_all),
    rfscore = purrr::map_dbl(features, rowMeans) # This is a hack until the new RF is fit...
  )

# Clean up a bit
comparisons <- comparisons %>%
  mutate(
    bulletA = gsub("(\\d)-\\d", "\\1", land1),
    landA = gsub("\\d-(\\d)", "\\1", land1),
    bulletB = gsub("(\\d)-\\d", "\\1", land2),
    landB = gsub("\\d-(\\d)", "\\1", land2)
  ) %>%
  group_by(bulletA, bulletB) %>% tidyr::nest() %>%
  mutate(
    bullet_score = data %>% purrr::map_dbl(
      .f = function(d) max(compute_average_scores(land1 = d$landA,
                                                  land2 = d$landB,
                                                  d$rfscore)))
  )
# }