diff --git a/R/bankful_width.R b/R/bankful_width.R
index 1f782d6..c18e86d 100644
--- a/R/bankful_width.R
+++ b/R/bankful_width.R
@@ -72,10 +72,6 @@ calc_powerlaw_bankful_width <- function(total_drainage_area_sqkm) {
 #' @export
 add_powerlaw_bankful_width <- function(df, total_drainage_area_sqkm_col, min_bf_width) {
   
-  # df <- flowlines
-  # total_drainage_area_sqkm_col = "tot_drainage_areasqkm"
-  # MIN_BF_WIDTH <- 50
-  
   # Check if 'total_drainage_area_sqkm' is numeric or a numeric vector
   if (!is.character(total_drainage_area_sqkm_col)) {
     stop("'total_drainage_area_sqkm_col' must be a character")
@@ -88,9 +84,6 @@ add_powerlaw_bankful_width <- function(df, total_drainage_area_sqkm_col, min_bf_
      stop("'total_drainage_area_sqkm_col' ", total_drainage_area_sqkm_col, " must be a numeric column in input 'df'")
   }
   
-  # df[1, ][[total_drainage_area_sqkm_col]] <- NA
-  # df[[total_drainage_area_sqkm_col]]
-  
   # fill any NA values with the given default Bankful width value
   df[is.na(df[[total_drainage_area_sqkm_col]]), ][[total_drainage_area_sqkm_col]] <- min_bf_width
   
@@ -104,20 +97,3 @@ add_powerlaw_bankful_width <- function(df, total_drainage_area_sqkm_col, min_bf_
   return(df)
   
 }
-
-
-
-# flowlines %>% 
-#   dplyr::mutate(
-#     bf_width        = hydrofabric3D::calc_powerlaw_bankful_width(tot_drainage_areasqkm),
-#     # bf_width        = hydrofabric3D::calc_powerlaw_bankful_width(tot_drainage_areasqkm),
-#     # bf_width        = pmax(50, bf_width * 2)
-#     bf_width        = pmax(50, bf_width * 11)
-#   ) %>% 
-#   dplyr::select(
-#     hy_id = crosswalk_id, 
-#     # tot_drainage_areasqkm, 
-#     bf_width,
-#     # input_bf_width,
-#     geometry = geom
-#   ) 
\ No newline at end of file
diff --git a/R/bottom_finder.R b/R/bottom_finder.R
index 946e90a..89dff62 100644
--- a/R/bottom_finder.R
+++ b/R/bottom_finder.R
@@ -32,7 +32,6 @@ find_plateaus <- function(x) {
 
 # find local minima indices using plateau midpoints
 find_local_minima <- function(x) {
-  # x = c(1, 1, 1, 1)
   n <- length(x)
   if (n < 2) {
     return(integer(0))
@@ -187,20 +186,8 @@ find_local_maxima <- function(x) {
 # finds local minimas and then the neighboring local maximas, returns a list of the index values for those poitnts
 find_bottom_candidates <- function(x, index_only = TRUE) {
   
-  # x = Z_VALS
-  # index_only    <- T
-  # x = c(1, 1, 3,  1)
-  # x = c(1, 1, 3)
-  # x = c(1, 2, 1, 2, 3, 4, 5, 5, 3, 2, 4, 6, 8)
-  # x = c(1, 2, 1, 2, 3, 4, 5, 5, 3, 2, 4, 6)
-  # plot(x)
-  
   # get the middle of the vector 
   midpoint  <- (length(x) + 1) / 2
-  # midpoint  <- (length(x) + 1) %/% 2
-  # midpoint  <- length(x) / 2
-  # midpoint  <- length(x) %/% 2
-  # midpoint
   
   # get pts of local mins and maxs
   minima_idx <- find_local_minima(x)
@@ -279,26 +266,6 @@ find_bottom_candidates <- function(x, index_only = TRUE) {
   return(result)
 }
 
-# x = c(1, 1, 1, 1, 3, 4, 6, 7, 8, 7, 6, 6, 6, 5, 4, 3, 1, 3, 4, 5, 5, 6, 6, 5, 5, 2, 2, 0, 2, 2, 4, 6, 7, 8, 8, 
-#       9, 4, 3, 3, 2, 1, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 12, 13, 14, 15, 14, 16, 16
-# )
-# x = c(1, 2, 1, 1, 3, 4)
-# x = c(1, 1, 1, 1)
-
-# plot(x)
-
-# bucket_indexes <- 
-#   x %>% 
-#   find_bottom_candidates() %>% 
-#   rm_edge_buckets() %>% 
-#   anchor_picker()
-
-# anchor_pts <- list(
-#   L = bucket_indexes$left_max,
-#   M = bucket_indexes$minimum,
-#   R = bucket_indexes$right_maxixum
-# )
-
 # TODO: work in progress, getting this implemented into classify_points(), if this method fails, fall back to original method
 # TODO: that relies on the middle third of cross sections containing the bottom points / thalweg
 
@@ -312,12 +279,6 @@ anchor_picker <- function(bucket_indexes) {
       -sapply(bucket_indexes, `[[`, "width")
     )
     
-    # sort_order <- order(
-    #   -sapply(bucket_indexes, `[[`, "width"),
-    #   -sapply(bucket_indexes, `[[`, "depth"),
-    #   sapply(bucket_indexes, `[[`, "distance_to_center")
-    # )
-    
     anchor <- bucket_indexes[sort_order[1]]
     
     return(anchor)
@@ -326,9 +287,6 @@ anchor_picker <- function(bucket_indexes) {
   return(list())
 }
 
-# min_indexes <- find_bottom_candidates(Z_VALS)
-# min_indexes <- rm_edge_buckets(min_indexes)
-
 # removes buckets on the edges of the set of points
 rm_edge_buckets <- function(bucket_indexes) {
   if (length(bucket_indexes) > 0) {