115 lines
3.9 KiB
R
115 lines
3.9 KiB
R
# 02b_CONVERT_CI_RDS_TO_CSV.R
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# ============================
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# Convert combined_CI_data.rds (output of script 02) to CSV format for Python
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# This script runs AFTER script 02 (CI extraction) and creates a CSV that Python
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# can use for harvest date detection WITHOUT requiring the 'model' column (which
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# comes from script 03 after interpolation and harvest dates are known).
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#
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# Usage: Rscript 02b_convert_ci_rds_to_csv.R [project_dir]
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# - project_dir: Project directory name (e.g., "esa", "chemba", "angata")
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#
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# Output: CSV file at laravel_app/storage/app/{project_dir}/Data/extracted_ci/cumulative_vals/ci_data_for_python.csv
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# Columns: field, sub_field, Date, FitData, DOY, value (alias for FitData)
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#
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suppressPackageStartupMessages({
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library(tidyverse)
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library(lubridate)
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library(here)
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})
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main <- function() {
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# Process command line arguments
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args <- commandArgs(trailingOnly = TRUE)
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# Get project directory
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if (length(args) >= 1 && !is.na(args[1])) {
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project_dir <- as.character(args[1])
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} else if (exists("project_dir", envir = .GlobalEnv)) {
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project_dir <- get("project_dir", envir = .GlobalEnv)
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} else {
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project_dir <- "esa"
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}
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# Make available globally
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assign("project_dir", project_dir, envir = .GlobalEnv)
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cat(sprintf("Converting CI RDS to CSV: project=%s\n", project_dir))
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# Initialize project configuration
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tryCatch({
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source("parameters_project.R")
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}, error = function(e) {
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warning("Default parameters_project.R not found. Attempting from 'r_app' directory.")
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tryCatch({
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source(here::here("r_app", "parameters_project.R"))
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}, error = function(e) {
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stop("Failed to source parameters_project.R from both default and 'r_app' directories.")
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})
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})
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# Define paths
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ci_data_dir <- here::here("laravel_app", "storage", "app", project_dir, "Data", "extracted_ci", "cumulative_vals")
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input_file <- file.path(ci_data_dir, "combined_CI_data.rds")
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output_file <- file.path(ci_data_dir, "ci_data_for_python.csv")
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# Check if input file exists
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if (!file.exists(input_file)) {
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stop(paste("Input file not found:", input_file))
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}
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cat(sprintf("Loading: %s\n", input_file))
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# Load RDS file
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ci_data <- readRDS(input_file) %>%
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as_tibble()
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cat(sprintf(" Loaded %d rows\n", nrow(ci_data)))
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cat(sprintf(" Columns: %s\n", paste(names(ci_data), collapse = ", ")))
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# Prepare data for Python
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ci_data_python <- ci_data %>%
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# Ensure standard column names
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rename(
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field = field,
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sub_field = sub_field,
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Date = Date,
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FitData = FitData,
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DOY = DOY
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) %>%
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# Add 'value' as an alias for FitData (sometimes needed)
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mutate(value = FitData) %>%
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# Keep only necessary columns
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select(field, sub_field, Date, FitData, DOY, value) %>%
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# Sort by field and date
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arrange(field, Date)
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# Validate data
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cat(sprintf("\nValidation:\n"))
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cat(sprintf(" Unique fields: %d\n", n_distinct(ci_data_python$field)))
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cat(sprintf(" Date range: %s to %s\n",
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min(ci_data_python$Date, na.rm = TRUE),
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max(ci_data_python$Date, na.rm = TRUE)))
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cat(sprintf(" FitData range: %.2f to %.2f\n",
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min(ci_data_python$FitData, na.rm = TRUE),
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max(ci_data_python$FitData, na.rm = TRUE)))
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cat(sprintf(" Missing FitData: %d rows\n", sum(is.na(ci_data_python$FitData))))
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# Save to CSV
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cat(sprintf("\nSaving to: %s\n", output_file))
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write_csv(ci_data_python, output_file)
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cat(sprintf("✓ Successfully created CSV with %d rows\n", nrow(ci_data_python)))
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cat("\nNext steps for Python harvest detection:\n")
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cat(" 1. Read this CSV file in Python\n")
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cat(" 2. Group by field to identify seasons\n")
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cat(" 3. Run LSTM model to detect harvest dates\n")
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cat(" 4. Save predicted harvest dates to Excel\n")
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cat(" 5. Use output in script 03 for interpolation\n")
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}
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if (sys.nframe() == 0) {
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main()
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}
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