diff --git a/build_mosaic.sh b/build_mosaic.sh
index ef9ff2a..812d20a 100755
--- a/build_mosaic.sh
+++ b/build_mosaic.sh
@@ -20,6 +20,8 @@ for arg in "$@"; do
     shift
 done
 
+echo "Weeks ago: $weeks_ago"
+
 # Check if required arguments are set
 if [ -z "$weeks_ago" ] || [ -z "$data_dir" ]; then
     echo "Missing arguments. Use: build_mosiac.sh --weeks_ago=3 --data_dir=chemba"
diff --git a/r_app/CI_report_dashboard_planet.Rmd b/r_app/CI_report_dashboard_planet.Rmd
index 86f6ba2..1c03956 100644
--- a/r_app/CI_report_dashboard_planet.Rmd
+++ b/r_app/CI_report_dashboard_planet.Rmd
@@ -200,7 +200,7 @@ subchunkify <- function(g, fig_height=7, fig_width=5) {
   ), collapse = '')
 
   sub_chunk <- paste0("
-  `","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", echo=FALSE}",
+`","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", echo=FALSE}",
   "\n(",
     g_deparsed
     , ")()",
@@ -283,36 +283,10 @@ cum_ci_plot <- function(pivotName){
                                                                                 max_date = max(Date),
                                                                                 days = max_date - min_date)
 
-  # perfect_pivot_raw <- CI_quadrant %>% group_by(Field) %>% filter(Field == "5.1" & season == "2022") %>%
-  #   group_by(DOY) %>% summarise(cumulative_CI = mean(cumulative_CI)) %>% mutate(CI_rate = cumulative_CI/DOY) %>%
-  #   mutate(mean_rolling10 = rollapplyr(CI_rate  , width = 10, FUN = mean, partial = TRUE))
-
   unique_seasons <- unique(date_preperation_perfect_pivot$season)
 
-   # if(length(unique_seasons) == 3) {
-   #   unique_seasons <- unique_seasons[c(2,3)]
-   # } else if(length(unique_seasons) == 4) {
-   #   unique_seasons <- unique_seasons[c(3,4)]
-   # } else {
-   #   unique_seasons <- unique_seasons
-   # }
 
-  # perfect_pivot <- perfect_pivot_raw
-  # 
-  # for (s in unique_seasons) {
-  #   season_dates <- seq(from = date_preperation_perfect_pivot$min_date[date_preperation_perfect_pivot$season == s],
-  #                       to = date_preperation_perfect_pivot$min_date[date_preperation_perfect_pivot$season == s] + nrow(perfect_pivot_raw) - 1,
-  #                       by = "1 day")
-  #   col_name <- as.character(s)
-  #   perfect_pivot <- dplyr::bind_cols(perfect_pivot, tibble(!!col_name := season_dates))
-  # }
-  # 
-  # perfect_pivot <- perfect_pivot %>%
-  #   pivot_longer(cols = -c("DOY", "cumulative_CI", "CI_rate", "mean_rolling10"),
-  #                names_to = "season", values_to = "Date")
-
-  g <- ggplot(data= data_ci2 %>% filter(season %in% unique_seasons)) +
-       #   facet_wrap(~Year, scales = "free_x") +
+  g <- ggplot(data= data_ci2)  +
           geom_line( aes(Date, mean_rolling10, col = subField)) +
           # geom_line(data= perfect_pivot, aes(Date , mean_rolling10, col = "Model CI (p5.1 Data 2022, \n date x axis is fictive)"), lty="11",size=1) +
           labs(title = paste("14 day rolling MEAN CI rate - Pivot ", pivotName))+
@@ -323,7 +297,6 @@ cum_ci_plot <- function(pivotName){
                           legend.title = element_text(size = 8),
                           legend.text = element_text(size = 8)) +
           guides(color = guide_legend(nrow = 2, byrow = TRUE))
-  # options(repr.plot.width = 2, repr.plot.height = 2)
   subchunkify(g, 3.2, 10)
 
 }
@@ -375,40 +348,42 @@ tm_shape(CI, unit = "m")+
 ```
 
 \newpage
-
 ```{r plots_ci_estate, eval=FALSE, fig.height=3.8, fig.width=10, message=FALSE, warning=FALSE, include=FALSE, results='asis'}
 # # pivots <- AllPivots_merged %>% filter(pivot != c("1.1", "1.17"))
-pivots_estate <- AllPivots_merged  %>% filter(pivot %in% c("1.1", "1.2", "1.3", "1.4", "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13", "1.14" , "1.16" , "1.17" , "1.18" , "6.1", "6.2", "DL1.1", "DL1.3")) %>% filter(pivot != "1.17")
+pivots_estate <- AllPivots_merged  %>% filter(Field %in% c("6.2")) #, "1.2", "1.3", "1.4")) #, "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13", "1.14" , "1.16" , "1.17" , "1.18" , "6.1", "6.2", "DL1.1", "DL1.3")) %>% filter(pivot != "1.17")
 
 
 # pivots <- AllPivots_merged %>% filter(pivot != c("1.1", "1.17"))
 # pivots_estate <- AllPivots_merged %>% filter(pivot %in% c("1.1", "1.2", "1.7")) %>% filter(pivot != "1.17")
 
-walk(pivots_estate$pivot, ~ {
+
+walk(pivots_estate$Field, ~ {
+  cat("# Hello!!!")
   cat("\n")  # Add an empty line for better spacing
   ci_plot(.x)
  # cum_ci_plot(.x)
 })
 ```
 
-
-```{r looping over sub_area,  echo=FALSE, fig.height=3.8, fig.width=10, message=FALSE, warning=FALSE, results='asis'}
-pivots_grouped <- AllPivots_merged %>%
-  group_by(sub_area) %>%
-  arrange(sub_area)  # Optional: arrange the groups alphabetically by sub_area
+```{r looping_over_sub_area,  echo=FALSE, fig.height=3.8, fig.width=10, message=FALSE, warning=FALSE, results='asis', eval=TRUE}
+pivots_grouped <- AllPivots_merged # %>%
+#  group_by(sub_area) %>%
+#  arrange(sub_area)  # Optional: arrange the groups alphabetically by sub_area
 
 # Iterate over each subgroup
 for (subgroup in unique(pivots_grouped$sub_area)) {
-  cat("\n")
-  cat("# Subgroup: ", subgroup, "\n")  # Add a title for the subgroup
+cat("# HELLO!!!")
+print(" PRINT")
+#  cat("\n")
+#  cat("# Subgroup: ", subgroup, "\n")  # Add a title for the subgroup
   subset_data <- filter(pivots_grouped, sub_area == subgroup)
-  cat('\\pagebreak')
+#  cat("\\pagebreak")
   walk(subset_data$Field, ~ {
-    cat("\n")  # Add an empty line for better spacing
+  #  cat("\n")  # Add an empty line for better spacing
     ci_plot(.x)
-    cat("\n")
+  #  cat("\n")
     cum_ci_plot(.x)
-    cat("\n")
+  #  cat("\n")
   })
 }
 ```
diff --git a/r_app/Rplots.pdf b/r_app/Rplots.pdf
index de7759a..43ef5b7 100644
Binary files a/r_app/Rplots.pdf and b/r_app/Rplots.pdf differ