fixed plot colouring and made ci_extraction resilient against none file;

This commit is contained in:
Martin Folkerts 2024-11-11 20:55:59 +01:00
parent c8ef406545
commit 576250982c
3 changed files with 16 additions and 4 deletions

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@ -343,7 +343,7 @@ ggplot(pred_ffs_rf, aes(y = predicted_Tcha, x = tonnage_ha)) +
y = "Predicted tonnage/ha (Tcha)") + y = "Predicted tonnage/ha (Tcha)") +
theme_minimal() theme_minimal()
ggplot(pred_rf_current_season, aes(x = Age_days, y = predicted_Tcha, col = field)) + ggplot(pred_rf_current_season, aes(x = Age_days, y = predicted_Tcha)) +
geom_point(size = 2, alpha = 0.6) + # Adjust point size and transparency geom_point(size = 2, alpha = 0.6) + # Adjust point size and transparency
labs(title = "Predicted Yields for Fields Over 300 Days \nOld Yet to Be Harvested", labs(title = "Predicted Yields for Fields Over 300 Days \nOld Yet to Be Harvested",
x = "Age (days)", x = "Age (days)",

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@ -48,11 +48,23 @@ raster_files <- list.files(planet_tif_folder,full.names = T, pattern = ".tif")
filtered_files <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x = raster_files)]) %>% filtered_files <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x = raster_files)]) %>%
compact() %>% compact() %>%
flatten_chr() flatten_chr()
head(filtered_files)
# Remove files that do not exist
existing_files <- filtered_files[file.exists(filtered_files)]
# Check if the list of existing files is empty
if (length(existing_files) == 0) {
message("No files exist for the given date(s).")
stop("Terminating script.")
}
# Continue with the rest of the script
print(existing_files)
vrt_list <- list() vrt_list <- list()
for (file in filtered_files) { for (file in existing_files) {
v_crop <- create_mask_and_crop(file, field_boundaries, merged_final) v_crop <- create_mask_and_crop(file, field_boundaries, merged_final)
emtpy_or_full <- global(v_crop, "notNA") emtpy_or_full <- global(v_crop, "notNA")
@ -109,7 +121,7 @@ if (!file.exists(file_path)) {
extracted_values <- map(dates$days_filter, ~ extracted_values[grepl(pattern = .x, x = extracted_values)]) %>% extracted_values <- map(dates$days_filter, ~ extracted_values[grepl(pattern = .x, x = extracted_values)]) %>%
compact() %>% compact() %>%
flatten_chr() flatten_chr()
pivot_stats <- extracted_values %>% pivot_stats <- extracted_values %>%
map(readRDS) %>% list_rbind() %>% map(readRDS) %>% list_rbind() %>%
group_by(sub_field) group_by(sub_field)