125 lines
3.8 KiB
R
125 lines
3.8 KiB
R
library(sf)
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library(terra)
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library(tidyverse)
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library(lubridate)
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library(exactextractr)
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# Vang alle command line argumenten op
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args <- commandArgs(trailingOnly = TRUE)
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# Converteer het tweede argument naar een string waarde
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project_dir <- as.character(args[1])
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# Controleer of data_dir een geldige waarde is
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if (!is.character(project_dir)) {
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project_dir <- "chemba"
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}
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source("parameters_project.R")
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source("ci_extraction_utils.R")
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# Check if the file exists
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file_path <- here(cumulative_CI_vals_dir, "combined_CI_data.rds")
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pivot_stats2 <- data.frame()
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if (file.exists(file_path)) {
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pivot_stats2 <- readRDS(file_path) %>%
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ungroup() %>%
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group_by(field, sub_field) %>%
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summarise(across(everything(), ~ first(na.omit(.))), .groups = "drop")
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}
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head(pivot_stats2)
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#%>% drop_na(pivot_quadrant)
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# gather data into long format for easier calculation and visualisation
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pivot_stats_long <- pivot_stats2 %>%
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tidyr::gather("Date", value, -field, -sub_field ) %>%
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mutate(#Date = right(Date, 8),
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Date = lubridate::ymd(Date)
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) %>%
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drop_na(c("value","Date")) %>%
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mutate(value = as.numeric(value))%>%
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filter_all(all_vars(!is.infinite(.))) %>%
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# rename(field = pivot_quadrant,
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# sub_field = field) %>%
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unique()
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years <- harvesting_data %>%
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filter(!is.na(season_start)) %>%
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distinct(year) %>%
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pull(year)
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extract_CI_data <- function(field_names, harvesting_data, field_CI_data, season) {
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# Filter harvesting data for the given season and field names
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filtered_harvesting_data <- harvesting_data %>%
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filter(year == season, sub_field %in% field_names)
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# Filter field CI data for the given field names
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filtered_field_CI_data <- field_CI_data %>%
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filter(sub_field %in% field_names)
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# Return an empty data frame if no CI data is found
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if (nrow(filtered_field_CI_data) == 0) {
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return(data.frame())
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}
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# Create a linear interpolation function for the CI data
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ApproxFun <- approxfun(x = filtered_field_CI_data$Date, y = filtered_field_CI_data$value)
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Dates <- seq.Date(ymd(min(filtered_field_CI_data$Date)), ymd(max(filtered_field_CI_data$Date)), by = 1)
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LinearFit <- ApproxFun(Dates)
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# Combine interpolated data with the original CI data
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CI <- data.frame(Date = Dates, FitData = LinearFit) %>%
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left_join(., filtered_field_CI_data, by = "Date") %>%
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filter(Date > filtered_harvesting_data$season_start & Date < filtered_harvesting_data$season_end)
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# If CI is empty after filtering, return an empty dataframe
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if (nrow(CI) == 0) {
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message ('CI empty after filtering')
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return(data.frame())
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}
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# Add additional columns if data exists
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CI <- CI %>%
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mutate(DOY = seq(1, n(), 1),
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model = paste0("Data", season, " : ", field_names),
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season = season,
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subField = field_names)
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return(CI)
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}
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message(harvesting_data)
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CI_all <- map_df(years, function(yr) {
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# yr = 2021
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message(yr)
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# Get the fields harvested in this year
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sub_fields <- harvesting_data %>%
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filter(year == yr) %>%
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filter(!is.na(season_start)) %>%
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pull(sub_field)
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# Filter sub_fields to only include those with value data in pivot_stats_long
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valid_sub_fields <- sub_fields %>%
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keep(~ any(pivot_stats_long$sub_field == .x))
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# Extract data for each valid field
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map(valid_sub_fields, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = yr)) %>%
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list_rbind()
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})
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# it will crash here if CI_all is empty and will not overwrite the rds rendering growth_model.R useless
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# if(nrow(CI_all) > 0){
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CI_all <- CI_all %>%
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group_by(model) %>%
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mutate(CI_per_day = FitData - lag(FitData), cumulative_CI = cumsum(FitData))
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# }
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saveRDS(CI_all, here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
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message('rds saved')
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