Timon
1da5c0d0a7
- Implemented `weather_api_comparison.py` to compare daily precipitation from multiple weather APIs for Arnhem, Netherlands and Angata, Kenya. - Integrated fetching functions for various weather data sources including Open-Meteo, NASA POWER, OpenWeatherMap, and WeatherAPI.com. - Added plotting functions to visualize archive and forecast data, including cumulative precipitation and comparison against ERA5 reference. - Created `90_rainfall_utils.R` for R to fetch rainfall data and overlay it on CI plots, supporting multiple providers with a generic fetch wrapper. - Included spatial helpers for efficient API calls based on unique geographical tiles.
370 lines
14 KiB
R
370 lines
14 KiB
R
# 90_RAINFALL_UTILS.R
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# ============================================================================
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# Rainfall data fetching and ggplot overlay utilities for SmartCane CI reports.
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#
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# Provider-agnostic design: to swap weather provider, implement a new
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# rain_fetch_<provider>() function and update the dispatch in rain_fetch().
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#
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# EXPORTS:
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# - rain_snap_to_tile() Snap lat/lon to Open-Meteo native 0.25° grid
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# - rain_fetch() Generic fetch wrapper (dispatches to provider)
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# - rain_fetch_for_fields() Batch fetch with spatial tile deduplication
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# - rain_join_to_ci() Join rain to latest-season CI data by Date
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# - rain_add_to_plot() Overlay rain on single CI plot (abs or cum)
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# - rain_add_to_faceted_plot() Overlay rain on faceted "both" CI plot
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# ============================================================================
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suppressPackageStartupMessages({
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library(dplyr)
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library(tidyr)
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library(httr2)
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library(purrr)
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})
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# ============================================================================
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# PROVIDER LAYER — swap here to change data source
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# ============================================================================
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#' Fetch daily precipitation from Open-Meteo ERA5 archive
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#'
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#' ERA5 covers 1940-present at 0.25° (~28 km) resolution.
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#' No API key required.
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#'
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#' @param latitude Numeric. WGS84 latitude.
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#' @param longitude Numeric. WGS84 longitude.
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#' @param start_date Date or "YYYY-MM-DD" string.
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#' @param end_date Date or "YYYY-MM-DD" string.
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#' @return data.frame with columns: date (Date), rain_mm (numeric)
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rain_fetch_open_meteo_archive <- function(latitude, longitude, start_date, end_date) {
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url <- paste0(
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"https://archive-api.open-meteo.com/v1/archive",
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"?latitude=", latitude,
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"&longitude=", longitude,
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"&daily=precipitation_sum",
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"&start_date=", format(as.Date(start_date), "%Y-%m-%d"),
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"&end_date=", format(as.Date(end_date), "%Y-%m-%d"),
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"&timezone=auto"
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)
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resp <- tryCatch(
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httr2::request(url) %>% httr2::req_timeout(30) %>% httr2::req_perform(),
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error = function(e) stop(paste("Rain archive request failed:", e$message), call. = FALSE)
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)
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if (httr2::resp_status(resp) != 200) {
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stop(paste("Rain archive API returned status", httr2::resp_status(resp)), call. = FALSE)
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}
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body <- httr2::resp_body_json(resp)
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if (is.null(body$daily) || is.null(body$daily$time)) {
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stop("Unexpected Open-Meteo archive response: missing daily/time.", call. = FALSE)
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}
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data.frame(
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date = as.Date(unlist(body$daily$time)),
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rain_mm = as.numeric(unlist(body$daily$precipitation_sum)),
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stringsAsFactors = FALSE
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)
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}
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# ============================================================================
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# GENERIC FETCH WRAPPER
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# ============================================================================
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#' Fetch daily precipitation via the configured provider
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#'
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#' @param latitude Numeric.
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#' @param longitude Numeric.
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#' @param start_date Date or "YYYY-MM-DD".
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#' @param end_date Date or "YYYY-MM-DD".
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#' @param provider Character. Currently only "open_meteo_archive".
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#' @return data.frame(date, rain_mm)
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rain_fetch <- function(latitude, longitude, start_date, end_date,
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provider = "open_meteo_archive") {
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# --- ADD NEW PROVIDERS HERE ---
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switch(provider,
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open_meteo_archive = rain_fetch_open_meteo_archive(
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latitude, longitude, start_date, end_date
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),
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stop(paste("Unknown rain provider:", provider), call. = FALSE)
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)
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}
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# ============================================================================
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# SPATIAL HELPERS
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# ============================================================================
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#' Snap a coordinate to the nearest ERA5 tile centre
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#'
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#' Open-Meteo ERA5 has 0.25° native resolution (~28 km). Snapping coordinates
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#' to this grid avoids redundant API calls for nearby fields.
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#'
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#' @param latitude Numeric.
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#' @param longitude Numeric.
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#' @param resolution Numeric. Grid resolution in degrees (default 0.25).
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#' @return Named list: tile_lat, tile_lon, tile_id
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rain_snap_to_tile <- function(latitude, longitude, resolution = 0.25) {
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tile_lat <- round(latitude / resolution) * resolution
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tile_lon <- round(longitude / resolution) * resolution
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list(
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tile_lat = tile_lat,
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tile_lon = tile_lon,
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tile_id = paste0(tile_lat, "_", tile_lon)
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)
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}
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# ============================================================================
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# BATCH FETCH WITH TILE DEDUPLICATION
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# ============================================================================
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#' Fetch rain for multiple fields, calling the API once per unique 0.25° tile
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#'
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#' Fields that fall on the same ERA5 tile share one API call. This handles
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#' estates spread over 100+ km (e.g. Angata) without over-calling the API.
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#'
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#' @param centroids_df data.frame with columns: field_id, latitude, longitude
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#' @param start_date Date or "YYYY-MM-DD". Start of fetch window.
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#' @param end_date Date or "YYYY-MM-DD". End of fetch window.
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#' @param provider Character. Passed to rain_fetch().
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#' @return data.frame with columns: field_id, date, rain_mm
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rain_fetch_for_fields <- function(centroids_df, start_date, end_date,
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provider = "open_meteo_archive") {
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required_cols <- c("field_id", "latitude", "longitude")
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if (!all(required_cols %in% names(centroids_df))) {
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stop(paste("centroids_df must have:", paste(required_cols, collapse = ", ")), call. = FALSE)
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}
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# Snap each field to its tile
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centroids_tiled <- centroids_df %>%
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dplyr::mutate(
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tile_info = purrr::map2(latitude, longitude, rain_snap_to_tile),
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tile_lat = purrr::map_dbl(tile_info, "tile_lat"),
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tile_lon = purrr::map_dbl(tile_info, "tile_lon"),
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tile_id = purrr::map_chr(tile_info, "tile_id")
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) %>%
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dplyr::select(-tile_info)
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# Fetch once per unique tile
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unique_tiles <- centroids_tiled %>%
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dplyr::distinct(tile_id, tile_lat, tile_lon)
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tile_rain <- unique_tiles %>%
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dplyr::mutate(rain_data = purrr::pmap(
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list(tile_lat, tile_lon),
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function(lat, lon) {
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tryCatch(
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rain_fetch(lat, lon, start_date, end_date, provider = provider),
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error = function(e) {
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safe_log(paste0("Rain fetch failed for tile ", lat, "_", lon, ": ", e$message), "WARNING")
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data.frame(date = as.Date(character(0)), rain_mm = numeric(0))
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}
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)
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}
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))
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# Join tile rain back to fields and unnest
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centroids_tiled %>%
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dplyr::left_join(tile_rain %>% dplyr::select(tile_id, rain_data), by = "tile_id") %>%
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dplyr::select(field_id, rain_data) %>%
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tidyr::unnest(rain_data)
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}
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# ============================================================================
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# PROCESSING
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# ============================================================================
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#' Join rain data to latest-season CI data by Date
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#'
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#' @param rain_df data.frame(date, rain_mm) for the field.
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#' @param ci_season_data data.frame with at minimum: Date, DAH, week columns
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#' (latest season only, one row per date).
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#' @return data.frame with Date, DAH, week, rain_mm, cum_rain_mm columns,
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#' ordered by Date. Only dates present in ci_season_data are returned.
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rain_join_to_ci <- function(rain_df, ci_season_data) {
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if (is.null(rain_df) || nrow(rain_df) == 0) return(NULL)
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ci_dates <- ci_season_data %>%
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dplyr::distinct(Date, DAH, week) %>%
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dplyr::arrange(Date)
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joined <- ci_dates %>%
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dplyr::left_join(
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rain_df %>% dplyr::rename(Date = date),
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by = "Date"
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) %>%
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dplyr::mutate(
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rain_mm = tidyr::replace_na(rain_mm, 0),
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cum_rain_mm = cumsum(rain_mm)
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)
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joined
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}
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# ============================================================================
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# PLOT OVERLAY — single plot (absolute or cumulative)
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# ============================================================================
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#' Add a rainfall overlay to a single-panel CI ggplot
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#'
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#' For absolute CI (mean_rolling_10_days): adds daily precipitation bars
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#' (steelblue, semi-transparent) scaled to the primary y-axis range, with a
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#' secondary right y-axis labelled in mm/day.
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#'
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#' For cumulative CI: adds a cumulative precipitation filled area scaled to the
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#' primary y-axis range, with a secondary right y-axis labelled in mm.
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#'
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#' The secondary axis is a linear transform of the primary axis (ggplot2
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#' requirement). The rain values are scaled so the maximum rain never exceeds
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#' y_max on screen. The right axis labels show the real mm values.
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#'
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#' @param g ggplot object.
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#' @param rain_joined Result of rain_join_to_ci(). NULL returns g unchanged.
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#' @param ci_type "mean_rolling_10_days" or "cumulative_CI".
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#' @param x_var Character: "DAH", "week", or "Date".
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#' @param y_max Numeric. Max value of the primary y-axis (used for scaling
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#' and for setting limits = c(0, y_max) on the primary axis).
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#' @return Modified ggplot object.
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rain_add_to_plot <- function(g, rain_joined, ci_type, x_var, y_max) {
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if (is.null(rain_joined) || nrow(rain_joined) == 0) {
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# Still enforce y limits even without rain
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return(g + ggplot2::scale_y_continuous(limits = c(0, y_max)))
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}
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if (ci_type == "mean_rolling_10_days") {
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# --- Daily precipitation bars ---
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max_rain <- max(rain_joined$rain_mm, na.rm = TRUE)
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if (is.na(max_rain) || max_rain == 0) {
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return(g + ggplot2::scale_y_continuous(limits = c(0, y_max)))
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}
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scale_factor <- y_max / max_rain
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bar_width <- switch(x_var,
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"DAH" = 1,
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"week" = 0.5,
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1 # Date: 1 day width handled by ggplot
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)
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g +
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ggplot2::geom_col(
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data = rain_joined,
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ggplot2::aes(x = .data[[x_var]], y = rain_mm * scale_factor),
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fill = "steelblue",
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alpha = 0.65,
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width = bar_width,
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inherit.aes = FALSE
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) +
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ggplot2::scale_y_continuous(
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limits = c(0, y_max),
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sec.axis = ggplot2::sec_axis(
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~ . / scale_factor,
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name = tr_key("lbl_rain_mm_day", "Precipitation (mm/day)")
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)
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)
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} else if (ci_type == "cumulative_CI") {
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# --- Cumulative precipitation area ---
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max_cum_rain <- max(rain_joined$cum_rain_mm, na.rm = TRUE)
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if (is.na(max_cum_rain) || max_cum_rain == 0) {
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return(g)
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}
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scale_factor <- (y_max * 0.30) / max_cum_rain
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g +
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ggplot2::geom_line(
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data = rain_joined,
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ggplot2::aes(x = .data[[x_var]], y = cum_rain_mm * scale_factor),
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color = "steelblue",
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linewidth = 0.8,
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inherit.aes = FALSE
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) +
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ggplot2::scale_y_continuous(
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limits = c(0, y_max),
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sec.axis = ggplot2::sec_axis(
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~ . / scale_factor,
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name = tr_key("lbl_cum_rain_mm", "Cumulative Rain (mm)")
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)
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)
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} else {
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g
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}
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}
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# ============================================================================
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# PLOT OVERLAY — faceted "both" plot
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# ============================================================================
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#' Add rainfall overlays to a faceted (plot_type = "both") CI ggplot
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#'
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#' ggplot2 does not support per-facet secondary axes with free_y scales.
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#' This function adds the rain layers to the correct facets by setting
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#' ci_type_label to match the facet strip, but omits secondary axis labels
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#' to avoid misleading scale information across facets.
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#'
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#' Rolling mean facet receives: daily precipitation bars.
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#' Cumulative facet receives: cumulative precipitation filled area.
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#'
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#' @param g ggplot object with facet_wrap(~ci_type_label).
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#' @param rain_joined Result of rain_join_to_ci().
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#' @param rolling_mean_label Character. Must match the strip label for the
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#' rolling mean facet (from tr_key).
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#' @param cumulative_label Character. Must match the strip label for the
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#' cumulative CI facet (from tr_key).
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#' @param x_var Character: "DAH", "week", or "Date".
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#' @param y_max_abs Numeric. Primary y-axis max for rolling mean facet (typically 7).
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#' @param y_max_cum Numeric. Primary y-axis max for cumulative CI facet.
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#' @return Modified ggplot object.
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rain_add_to_faceted_plot <- function(g, rain_joined, rolling_mean_label, cumulative_label,
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x_var, y_max_abs, y_max_cum) {
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if (is.null(rain_joined) || nrow(rain_joined) == 0) return(g)
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# --- Bars in rolling mean facet ---
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max_rain <- max(rain_joined$rain_mm, na.rm = TRUE)
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if (!is.na(max_rain) && max_rain > 0) {
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scale_abs <- y_max_abs / max_rain
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bar_width <- switch(x_var, "DAH" = 1, "week" = 0.5, 1)
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rain_bars <- rain_joined %>%
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dplyr::mutate(
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ci_type_label = rolling_mean_label,
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y_scaled = rain_mm * scale_abs
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)
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g <- g +
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ggplot2::geom_col(
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data = rain_bars,
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ggplot2::aes(x = .data[[x_var]], y = y_scaled),
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fill = "steelblue",
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alpha = 0.65,
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width = bar_width,
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inherit.aes = FALSE
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)
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}
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# --- Area in cumulative facet ---
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max_cum_rain <- max(rain_joined$cum_rain_mm, na.rm = TRUE)
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if (!is.na(max_cum_rain) && max_cum_rain > 0) {
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scale_cum <- (y_max_cum * 0.30) / max_cum_rain
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rain_area <- rain_joined %>%
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dplyr::mutate(
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ci_type_label = cumulative_label,
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y_scaled = cum_rain_mm * scale_cum
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)
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g <- g +
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ggplot2::geom_line(
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data = rain_area,
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ggplot2::aes(x = .data[[x_var]], y = y_scaled),
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color = "steelblue",
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linewidth = 0.8,
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inherit.aes = FALSE
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)
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}
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g
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}
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