Timon
e4e19df0c7
- Changed report date in CI report for cane supply to "2026-02-04". - Updated output file naming convention for agronomic support report to reflect new report date. - Enhanced map creation functions to allow customizable legend positions and improved layout settings. - Adjusted widths for map arrangements to ensure better visual representation. - Fixed minor issues in ggplot aesthetics for clearer legend positioning and improved readability. - Corrected field size unit from hectares to acres in KPI summary generation.
605 lines
22 KiB
R
605 lines
22 KiB
R
# 80_UTILS_CANE_SUPPLY.R
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# ============================================================================
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# CANE SUPPLY CLIENT-SPECIFIC UTILITIES (SCRIPT 80 - CLIENT TYPE: cane_supply)
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#
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# Contains ANGATA and other cane supply-specific KPI and reporting functions.
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#
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# Currently, CANE_SUPPLY clients use the common utilities from 80_utils_common.R:
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# - Weekly statistics (calculate_field_statistics, calculate_kpi_trends)
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# - Field analysis reporting (generate_field_analysis_summary)
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# - Excel export (export_field_analysis_excel)
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#
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# This file is structured to accommodate future ANGATA-specific functionality such as:
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# - Custom yield models
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# - Harvest readiness criteria
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# - Supply chain integration hooks
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# - ANGATA-specific alerting and messaging
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#
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# Orchestrator: (Placeholder - uses common functions)
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# Dependencies: 00_common_utils.R, 80_utils_common.R
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# Used by: 80_calculate_kpis.R (when client_type == "cane_supply")
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# ============================================================================
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library(terra)
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library(sf)
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library(dplyr)
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library(tidyr)
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library(readxl)
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library(writexl)
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# ============================================================================
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# ANGATA-SPECIFIC HELPER FUNCTIONS (Placeholder Section)
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# ============================================================================
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#' Calculate acreage for each field from geometry
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#' @param field_boundaries_sf sf object with field geometries
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#' @return data.frame with field and acreage columns
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calculate_field_acreages <- function(field_boundaries_sf) {
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tryCatch({
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lookup_df <- field_boundaries_sf %>%
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sf::st_drop_geometry() %>%
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as.data.frame() %>%
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mutate(
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geometry_valid = sapply(seq_len(nrow(field_boundaries_sf)), function(idx) {
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tryCatch({
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sf::st_is_valid(field_boundaries_sf[idx, ])
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}, error = function(e) FALSE)
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}),
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area_ha = 0
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)
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# Calculate area for valid geometries
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valid_indices <- which(lookup_df$geometry_valid)
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areas_ha <- vapply(valid_indices, function(idx) {
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tryCatch({
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area_m2 <- as.numeric(sf::st_area(field_boundaries_sf[idx, ]))
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area_m2 / 10000
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}, error = function(e) NA_real_)
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}, numeric(1))
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lookup_df$area_ha[valid_indices] <- areas_ha
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# Convert hectares to acres
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lookup_df %>%
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mutate(acreage = area_ha / 0.404686) %>%
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select(field, acreage)
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}, error = function(e) {
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message(paste("Warning: Could not calculate acreages from geometries -", e$message))
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data.frame(field = character(0), acreage = numeric(0))
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})
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}
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#' Calculate age in weeks from planting date
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#'
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#' @param planting_date Date of planting
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#' @param reference_date Date to calculate age relative to (typically end_date)
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#' @return Numeric age in weeks (rounded to nearest week)
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calculate_age_week <- function(planting_date, reference_date) {
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if (is.na(planting_date)) {
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return(NA_real_)
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}
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round(as.numeric(difftime(reference_date, planting_date, units = "weeks")), 0)
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}
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#' Assign crop phase based on age in weeks
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#'
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#' @param age_week Numeric age in weeks
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#' @return Character phase name
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calculate_phase <- function(age_week) {
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if (is.na(age_week)) return(NA_character_)
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if (age_week >= 0 & age_week < 4) return("Germination")
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if (age_week >= 4 & age_week < 17) return("Tillering")
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if (age_week >= 17 & age_week < 39) return("Grand Growth")
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if (age_week >= 39) return("Maturation")
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NA_character_
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}
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#' Bin percentage into 10% intervals with special handling for 90-100%
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#'
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#' @param pct Numeric percentage value (0-100)
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#' @return Character bin label
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bin_percentage <- function(pct) {
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if (is.na(pct)) return(NA_character_)
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if (pct >= 95) return("95-100%")
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else if (pct >= 90) return("90-95%")
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else if (pct >= 80) return("80-90%")
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else if (pct >= 70) return("70-80%")
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else if (pct >= 60) return("60-70%")
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else if (pct >= 50) return("50-60%")
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else if (pct >= 40) return("40-50%")
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else if (pct >= 30) return("30-40%")
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else if (pct >= 20) return("20-30%")
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else if (pct >= 10) return("10-20%")
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else return("0-10%")
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}
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#' Calculate germination progress from CI threshold percentage
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#'
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#' @param pct_pixels_ci_gte_2 Percentage of pixels with CI >= 2
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#' @return Character bin label
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calculate_germination_progress <- function(pct_pixels_ci_gte_2) {
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bin_percentage(pct_pixels_ci_gte_2)
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}
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#' Categorize CV trend (long-term slope) into qualitative labels
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#'
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#' @param cv_slope Numeric slope from CV trend analysis
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#' @return Character category: "More uniform", "Stable uniformity", or "Less uniform"
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categorize_cv_trend_long_term <- function(cv_slope) {
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if (is.na(cv_slope)) {
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return(NA_character_)
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} else if (cv_slope < -0.01) {
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return("More uniform")
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} else if (cv_slope > 0.01) {
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return("Less uniform")
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} else {
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return("Stable uniformity")
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}
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}
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#' Determine status alert based on harvest probability and crop health
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#' Priority order:
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#' 1. Ready for harvest-check (imminent + mature ≥12 months)
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#' 2. Strong decline in crop health (drop ≥2 points but still >1.5)
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#' 3. Harvested/bare (Mean CI < 1.5)
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#' @param imminent_prob Numeric harvest probability
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#' @param age_week Numeric age in weeks
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#' @param weekly_ci_change Numeric weekly CI change
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#' @param mean_ci Numeric mean CI value
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#' @return Character status alert or NA
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calculate_status_alert <- function(imminent_prob, age_week, weekly_ci_change, mean_ci) {
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# Priority 1: Ready for harvest-check
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if (!is.na(imminent_prob) && imminent_prob > 0.5 && !is.na(age_week) && age_week >= 52) {
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return("Ready for harvest-check")
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}
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# Priority 2: Strong decline
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if (!is.na(weekly_ci_change) && weekly_ci_change <= -2.0 && !is.na(mean_ci) && mean_ci > 1.5) {
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return("Strong decline in crop health")
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}
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# Priority 3: Harvested/bare
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if (!is.na(mean_ci) && mean_ci < 1.5) {
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return("Harvested/bare")
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}
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# Fallback: no alert
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NA_character_
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}
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#' Build complete per-field KPI dataframe with all 22 columns
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#' @param current_stats data.frame with current week statistics from load_or_calculate_weekly_stats
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#' @param planting_dates data.frame with field_id and planting_date columns
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#' @param imminent_prob_data data.frame with Field_id and Imminent_prob_actual columns (or NULL)
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#' @param gap_scores_df data.frame with Field_id and gap_score columns (or NULL)
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#' @param field_boundaries_sf sf object with field geometries
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#' @param end_date Date object for current report date
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#' @return data.frame with all 22 KPI columns
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calculate_all_field_kpis <- function(current_stats,
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planting_dates,
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imminent_prob_data,
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gap_scores_df,
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field_boundaries_sf,
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end_date) {
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message("\nBuilding final field analysis output...")
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# Pre-calculate acreages
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acreage_lookup <- calculate_field_acreages(field_boundaries_sf)
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field_analysis_df <- current_stats %>%
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mutate(
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# Column 2: Farm_Section (user fills manually)
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Farm_Section = NA_character_,
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# Column 3: Field_name (from GeoJSON)
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Field_name = Field_id,
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# Column 4: Acreage (from geometry)
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Acreage = {
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acreages_vec <- acreage_lookup$acreage[match(Field_id, acreage_lookup$field)]
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if_else(is.na(acreages_vec), 0, acreages_vec)
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},
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# Column 8: Last_harvest_or_planting_date (from harvest.xlsx)
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Last_harvest_or_planting_date = {
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planting_dates$planting_date[match(Field_id, planting_dates$field_id)]
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},
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# Column 9: Age_week (calculated)
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Age_week = {
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sapply(seq_len(nrow(current_stats)), function(idx) {
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calculate_age_week(Last_harvest_or_planting_date[idx], end_date)
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})
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},
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# Column 10: Phase (based on Age_week)
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Phase = sapply(Age_week, calculate_phase),
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# Column 12: Germination_progress (binned Pct_pixels_CI_gte_2)
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Germination_progress = sapply(Pct_pixels_CI_gte_2, calculate_germination_progress),
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# Column 13: Imminent_prob (from script 31 or NA)
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Imminent_prob = {
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if (!is.null(imminent_prob_data)) {
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imminent_prob_data$Imminent_prob_actual[match(Field_id, imminent_prob_data$Field_id)]
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} else {
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rep(NA_real_, nrow(current_stats))
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}
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},
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# Column 14: Status_Alert (multi-priority logic)
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Status_Alert = {
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sapply(seq_len(nrow(current_stats)), function(idx) {
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calculate_status_alert(
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Imminent_prob[idx],
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Age_week[idx],
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Weekly_ci_change[idx],
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Mean_CI[idx]
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)
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})
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},
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# Column 19b: CV_Trend_Long_Term_Category (categorical slope)
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CV_Trend_Long_Term_Category = sapply(current_stats$CV_Trend_Long_Term, categorize_cv_trend_long_term),
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# Column 21: Cloud_pct_clear (binned into intervals)
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Cloud_pct_clear = sapply(Cloud_pct_clear, bin_percentage),
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# Column 22: Gap_score (2σ method)
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Gap_score = {
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if (!is.null(gap_scores_df) && nrow(gap_scores_df) > 0) {
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gap_scores_df$gap_score[match(current_stats$Field_id, gap_scores_df$Field_id)]
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} else {
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rep(NA_real_, nrow(current_stats))
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}
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}
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) %>%
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select(
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all_of(c("Field_id", "Farm_Section", "Field_name", "Acreage", "Status_Alert",
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"Last_harvest_or_planting_date", "Age_week", "Phase",
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"Germination_progress",
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"Mean_CI", "Weekly_ci_change", "Four_week_trend", "CI_range", "CI_Percentiles",
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"CV", "CV_Trend_Short_Term", "CV_Trend_Long_Term", "CV_Trend_Long_Term_Category",
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"Imminent_prob", "Cloud_pct_clear", "Cloud_category", "Gap_score"))
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)
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message(paste("✓ Built final output with", nrow(field_analysis_df), "fields and 22 columns"))
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return(field_analysis_df)
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}
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#' Aggregate per-field data into farm-level KPI summary
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#'
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#' @param field_analysis_df data.frame with per-field KPI data
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#' @param current_week Numeric current week number
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#' @param current_year Numeric current year
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#' @param end_date Date object for current report date
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#' @return List with phase_distribution, status_distribution, cloud_distribution, overall_stats
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calculate_farm_level_kpis <- function(field_analysis_df, current_week, current_year, end_date) {
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cat("\n=== CALCULATING FARM-LEVEL KPI SUMMARY ===\n")
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# Filter to only fields with actual data
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field_data <- field_analysis_df %>%
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filter(!is.na(Mean_CI) & !is.na(Acreage)) %>%
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filter(Acreage > 0)
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if (nrow(field_data) == 0) {
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message("No valid field data for farm-level aggregation")
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return(NULL)
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}
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farm_summary <- list()
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# 1. PHASE DISTRIBUTION
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phase_dist <- field_data %>%
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group_by(Phase) %>%
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summarise(
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num_fields = n(),
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acreage = sum(Acreage, na.rm = TRUE),
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.groups = 'drop'
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) %>%
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rename(Category = Phase)
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farm_summary$phase_distribution <- phase_dist
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# 2. STATUS ALERT DISTRIBUTION
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status_dist <- field_data %>%
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group_by(Status_Alert) %>%
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summarise(
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num_fields = n(),
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acreage = sum(Acreage, na.rm = TRUE),
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.groups = 'drop'
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) %>%
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rename(Category = Status_Alert)
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farm_summary$status_distribution <- status_dist
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# 3. CLOUD COVERAGE DISTRIBUTION
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cloud_dist <- field_data %>%
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group_by(Cloud_category) %>%
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summarise(
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num_fields = n(),
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acreage = sum(Acreage, na.rm = TRUE),
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.groups = 'drop'
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) %>%
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rename(Category = Cloud_category)
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farm_summary$cloud_distribution <- cloud_dist
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# 4. OVERALL STATISTICS
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farm_summary$overall_stats <- data.frame(
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total_fields = nrow(field_data),
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total_acreage = sum(field_data$Acreage, na.rm = TRUE),
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mean_ci = round(mean(field_data$Mean_CI, na.rm = TRUE), 2),
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median_ci = round(median(field_data$Mean_CI, na.rm = TRUE), 2),
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mean_cv = round(mean(field_data$CV, na.rm = TRUE), 4),
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week = current_week,
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year = current_year,
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date = as.character(end_date)
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)
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# Print summaries
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cat("\n--- PHASE DISTRIBUTION ---\n")
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print(phase_dist)
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cat("\n--- STATUS TRIGGER DISTRIBUTION ---\n")
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print(status_dist)
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cat("\n--- CLOUD COVERAGE DISTRIBUTION ---\n")
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print(cloud_dist)
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cat("\n--- OVERALL FARM STATISTICS ---\n")
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print(farm_summary$overall_stats)
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return(farm_summary)
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}
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# ============================================================================
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# ORCHESTRATOR FOR CANE_SUPPLY WORKFLOWS
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# ============================================================================
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#' Main orchestrator for CANE_SUPPLY per-field KPI workflow
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#'
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#' This function coordinates all KPI calculations for the per-field analysis workflow.
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#' It loads historical data, calculates current/previous week statistics, computes
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#' all 22 KPI columns, and aggregates farm-level summaries.
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#'
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#' @param setup List with directory paths (kpi_reports_dir, data_dir, etc.)
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#' @param client_config List with workflow configuration (script_91_compatible, outputs)
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#' @param end_date Date object for current report date
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#' @param project_dir Character project identifier
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#' @param weekly_mosaic Character path to weekly mosaic directory
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#' @param daily_vals_dir Character path to daily values directory
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#' @param field_boundaries_sf sf object with field geometries
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#' @param data_dir Character path to data directory
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#' @return List with field_analysis_df, farm_kpi_results, export_paths
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calculate_field_analysis_cane_supply <- function(setup,
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client_config,
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end_date,
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project_dir,
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weekly_mosaic,
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daily_vals_dir,
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field_boundaries_sf,
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data_dir) {
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message("\n", strrep("=", 70))
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message("CANE_SUPPLY WORKFLOW: PER-FIELD ANALYSIS (Script 91 compatible)")
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message(strrep("=", 70))
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reports_dir <- file.path(setup$reports_dir, "kpis")
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# ========== PHASE 1: WEEKLY ANALYSIS SETUP ==========
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message("\n", strrep("-", 70))
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message("PHASE 1: PER-FIELD WEEKLY ANALYSIS ")
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message(strrep("-", 70))
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weeks <- calculate_week_numbers(end_date)
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current_week <- weeks$current_week
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current_year <- weeks$current_year
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previous_week <- weeks$previous_week
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previous_year <- weeks$previous_year
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message(paste("Week:", current_week, "/ Year (ISO 8601):", current_year))
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# Find per-field weekly mosaics
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message("Finding per-field weekly mosaics...")
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if (!dir.exists(weekly_mosaic)) {
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stop(paste("ERROR: weekly_mosaic directory not found:", weekly_mosaic,
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"\nScript 40 (mosaic creation) must be run first."))
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}
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field_dirs <- list.dirs(weekly_mosaic, full.names = FALSE, recursive = FALSE)
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field_dirs <- field_dirs[field_dirs != ""]
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if (length(field_dirs) == 0) {
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stop(paste("ERROR: No field subdirectories found in:", weekly_mosaic,
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"\nScript 40 must create weekly_mosaic/{FIELD}/ structure."))
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}
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# Verify we have mosaics for this week
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single_file_pattern <- sprintf("week_%02d_%d\\.tif", current_week, current_year)
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per_field_files <- c()
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for (field in field_dirs) {
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field_mosaic_dir <- file.path(weekly_mosaic, field)
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files <- list.files(field_mosaic_dir, pattern = single_file_pattern, full.names = TRUE)
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if (length(files) > 0) {
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per_field_files <- c(per_field_files, files)
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}
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}
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if (length(per_field_files) == 0) {
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stop(paste("ERROR: No mosaics found for week", current_week, "year", current_year,
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"\nExpected pattern:", single_file_pattern,
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"\nChecked:", weekly_mosaic))
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}
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message(paste(" ✓ Found", length(per_field_files), "per-field weekly mosaics"))
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# ========== PHASE 2: LOAD HISTORICAL DATA ==========
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message("\nLoading historical field data for trend calculations...")
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num_weeks_to_load <- max(WEEKS_FOR_FOUR_WEEK_TREND, WEEKS_FOR_CV_TREND_LONG)
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message(paste(" Attempting to load up to", num_weeks_to_load, "weeks of historical data..."))
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allow_auto_gen <- !exists("_INSIDE_AUTO_GENERATE", envir = .GlobalEnv)
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historical_data <- load_historical_field_data(
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project_dir, current_week, current_year, reports_dir,
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num_weeks = num_weeks_to_load,
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auto_generate = allow_auto_gen,
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field_boundaries_sf = field_boundaries_sf,
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daily_vals_dir = daily_vals_dir
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)
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# ========== PHASE 3: LOAD PLANTING DATES ==========
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message("\nLoading harvest data from harvest.xlsx for planting dates...")
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harvest_file_path <- file.path(data_dir, "harvest.xlsx")
|
||
|
||
harvesting_data <- tryCatch({
|
||
if (file.exists(harvest_file_path)) {
|
||
harvest_raw <- readxl::read_excel(harvest_file_path)
|
||
harvest_raw$season_start <- as.Date(harvest_raw$season_start)
|
||
harvest_raw$season_end <- as.Date(harvest_raw$season_end)
|
||
message(paste(" ✓ Loaded harvest data:", nrow(harvest_raw), "rows"))
|
||
harvest_raw
|
||
} else {
|
||
message(paste(" WARNING: harvest.xlsx not found at", harvest_file_path))
|
||
NULL
|
||
}
|
||
}, error = function(e) {
|
||
message(paste(" ERROR loading harvest.xlsx:", e$message))
|
||
NULL
|
||
})
|
||
|
||
planting_dates <- extract_planting_dates(harvesting_data, field_boundaries_sf)
|
||
|
||
if (is.null(planting_dates) || nrow(planting_dates) == 0) {
|
||
message("WARNING: No planting dates available. Using NA for all fields.")
|
||
planting_dates <- data.frame(
|
||
field_id = field_boundaries_sf$field,
|
||
planting_date = rep(as.Date(NA), nrow(field_boundaries_sf)),
|
||
stringsAsFactors = FALSE
|
||
)
|
||
}
|
||
|
||
# ========== PHASE 4: CALCULATE WEEKLY STATISTICS ==========
|
||
message("\nUsing modular RDS-based approach for weekly statistics...")
|
||
|
||
# Current week
|
||
message("\n1. Loading/calculating CURRENT week statistics (week", current_week, ")...")
|
||
current_stats <- load_or_calculate_weekly_stats(
|
||
week_num = current_week,
|
||
year = current_year,
|
||
project_dir = project_dir,
|
||
field_boundaries_sf = field_boundaries_sf,
|
||
mosaic_dir = weekly_mosaic,
|
||
reports_dir = reports_dir,
|
||
report_date = end_date
|
||
)
|
||
message(paste(" ✓ Loaded/calculated stats for", nrow(current_stats), "fields in current week"))
|
||
|
||
# Previous week
|
||
message("\n2. Loading/calculating PREVIOUS week statistics (week", previous_week, ")...")
|
||
prev_report_date <- end_date - 7
|
||
|
||
prev_stats <- load_or_calculate_weekly_stats(
|
||
week_num = previous_week,
|
||
year = previous_year,
|
||
project_dir = project_dir,
|
||
field_boundaries_sf = field_boundaries_sf,
|
||
mosaic_dir = weekly_mosaic,
|
||
reports_dir = reports_dir,
|
||
report_date = prev_report_date
|
||
)
|
||
message(paste(" ✓ Loaded/calculated stats for", nrow(prev_stats), "fields in previous week"))
|
||
|
||
# ========== PHASE 5: CALCULATE TRENDS ==========
|
||
message("\n3. Calculating trend columns...")
|
||
current_stats <- calculate_kpi_trends(
|
||
current_stats, prev_stats,
|
||
project_dir = project_dir,
|
||
reports_dir = reports_dir,
|
||
current_week = current_week,
|
||
year = current_year
|
||
)
|
||
message(paste(" ✓ Added Weekly_ci_change, CV_Trend_Short_Term, Four_week_trend, CV_Trend_Long_Term, nmr_of_weeks_analysed"))
|
||
|
||
# ========== PHASE 6: LOAD HARVEST PROBABILITIES ==========
|
||
message("\n4. Loading harvest probabilities from script 31...")
|
||
harvest_prob_dir <- file.path(data_dir, "..", "reports", "kpis", "field_stats")
|
||
harvest_prob_file <- file.path(harvest_prob_dir,
|
||
sprintf("%s_harvest_imminent_week_%02d_%d.csv", project_dir, current_week, current_year))
|
||
message(paste(" Looking for:", harvest_prob_file))
|
||
|
||
imminent_prob_data <- tryCatch({
|
||
if (file.exists(harvest_prob_file)) {
|
||
prob_df <- readr::read_csv(harvest_prob_file, show_col_types = FALSE,
|
||
col_types = readr::cols(.default = readr::col_character()))
|
||
message(paste(" ✓ Loaded harvest probabilities for", nrow(prob_df), "fields"))
|
||
prob_df %>%
|
||
select(field, imminent_prob, detected_prob) %>%
|
||
rename(Field_id = field, Imminent_prob_actual = imminent_prob, Detected_prob = detected_prob)
|
||
} else {
|
||
message(paste(" INFO: Harvest probabilities not available (script 31 not run)"))
|
||
NULL
|
||
}
|
||
}, error = function(e) {
|
||
message(paste(" WARNING: Could not load harvest probabilities:", e$message))
|
||
NULL
|
||
})
|
||
|
||
# ========== PHASE 7: CALCULATE GAP SCORES ==========
|
||
gap_scores_df <- calculate_gap_scores(per_field_files, field_boundaries_sf)
|
||
|
||
# ========== PHASE 8: BUILD FINAL PER-FIELD DATAFRAME ==========
|
||
field_analysis_df <- calculate_all_field_kpis(
|
||
current_stats = current_stats,
|
||
planting_dates = planting_dates,
|
||
imminent_prob_data = imminent_prob_data,
|
||
gap_scores_df = gap_scores_df,
|
||
field_boundaries_sf = field_boundaries_sf,
|
||
end_date = end_date
|
||
)
|
||
|
||
# ========== PHASE 9: EXPORT PER-FIELD RESULTS ==========
|
||
export_paths <- export_field_analysis_excel(
|
||
field_analysis_df,
|
||
NULL,
|
||
project_dir,
|
||
current_week,
|
||
current_year,
|
||
reports_dir
|
||
)
|
||
|
||
# cat("\n--- Per-field Results (first 10) ---\n")
|
||
# available_cols <- c("Field_id", "Acreage", "Age_week", "Mean_CI", "Four_week_trend", "Status_Alert", "Cloud_category")
|
||
# available_cols <- available_cols[available_cols %in% names(field_analysis_df)]
|
||
# if (length(available_cols) > 0) {
|
||
# print(head(field_analysis_df[, available_cols], 10))
|
||
# }
|
||
|
||
# # ========== PHASE 10: CALCULATE FARM-LEVEL KPIS ==========
|
||
# farm_kpi_results <- calculate_farm_level_kpis(
|
||
# field_analysis_df,
|
||
# current_week,
|
||
# current_year,
|
||
# end_date
|
||
# )
|
||
}
|
||
|
||
# ============================================================================
|
||
# FUTURE EXTENSION POINTS
|
||
# ============================================================================
|
||
|
||
# Placeholder for ANGATA-specific utilities that may be added in future:
|
||
# - Custom yield models based on ANGATA historical data
|
||
# - Field condition thresholds specific to ANGATA growing practices
|
||
# - Integration with ANGATA harvest scheduling system
|
||
# - WhatsApp messaging templates for ANGATA supply chain stakeholders
|
||
# - Cost/benefit analysis for ANGATA operational decisions
|
||
|
||
# These functions can be added here as ANGATA requirements evolve.
|