229 lines
6.5 KiB
R
229 lines
6.5 KiB
R
source(testthat::test_path("..", "..", "R", "rain_db.R"))
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test_that("normalise_rainfall_data converts API payload into unified metric rows", {
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raw_data <- data.frame(
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POSTE = c("1001", "1001"),
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DATE = c(202401020000, 202401020006),
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RR6 = c(0, 1.5),
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Nom_usuel = c("Station A", "Station A"),
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stringsAsFactors = FALSE
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)
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cache_rows <- normalise_rainfall_data(
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raw_data = raw_data,
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location_id = "vignasses",
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fetched_at = as.POSIXct("2026-04-08 09:00:00", tz = "UTC")
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)
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expect_identical(
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names(cache_rows),
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c(
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"location_id",
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"station_id",
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"station_name",
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"observed_at",
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"observed_day",
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"metric_id",
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"metric_label",
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"unit",
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"source_name",
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"value_num",
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"fetched_at"
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)
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)
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expect_equal(cache_rows$metric_id[1], "rain_6m")
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expect_equal(cache_rows$observed_at[2], "2024-01-02T00:06:00Z")
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expect_equal(cache_rows$fetched_at[1], "2026-04-08T09:00:00Z")
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})
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test_that("normalise_observation_package_data extracts temperature and other metrics", {
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raw_data <- data.frame(
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geo_id_insee = c("1001", "1001"),
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validity_time = c("2026-04-08T09:24:00Z", "2026-04-08T09:30:00Z"),
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t = c(293.15, 294.15),
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td = c(289.15, 290.15),
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u = c(40, 42),
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ff = c(1.5, 2.0),
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fxi10 = c(3.5, 4.0),
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pres = c(101325, 101425),
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pmer = c(101525, 101625),
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stringsAsFactors = FALSE
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)
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cache_rows <- normalise_observation_package_data(
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raw_data = raw_data,
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location_id = "vignasses",
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station_name = "Station A",
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fetched_at = as.POSIXct("2026-04-08 10:00:00", tz = "UTC")
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)
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expect_true(all(c(
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"air_temperature",
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"dew_point",
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"humidity",
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"wind_speed",
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"wind_gust",
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"station_pressure",
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"sea_level_pressure"
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) %in% unique(cache_rows$metric_id)))
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expect_equal(cache_rows$value_num[cache_rows$metric_id == "air_temperature"][1], 20)
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expect_equal(cache_rows$value_num[cache_rows$metric_id == "station_pressure"][1], 1013.2)
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})
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test_that("SQLite cache upserts and queries multiple metrics from one dataset", {
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skip_if_not(nzchar(Sys.which("sqlite3")), "sqlite3 is required for cache tests.")
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db_path <- tempfile(fileext = ".sqlite")
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on.exit(unlink(c(db_path, paste0(db_path, c("-shm", "-wal")))), add = TRUE)
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ensure_weather_db(db_path)
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rain_rows <- normalise_rainfall_data(
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raw_data = data.frame(
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POSTE = c("1001", "1001"),
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DATE = c(202401020000, 202401020006),
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RR6 = c(1.25, 0.5),
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Nom_usuel = c("Station A", "Station A"),
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stringsAsFactors = FALSE
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),
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location_id = "vignasses",
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fetched_at = as.POSIXct("2026-04-08 09:00:00", tz = "UTC")
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)
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obs_rows <- normalise_observation_package_data(
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raw_data = data.frame(
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geo_id_insee = c("1001", "1001"),
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validity_time = c("2024-01-02T00:00:00Z", "2024-01-02T06:00:00Z"),
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t = c(293.15, 295.15),
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u = c(40, 44),
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ff = c(1.5, 2.5),
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pres = c(101325, 101225),
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stringsAsFactors = FALSE
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),
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location_id = "vignasses",
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station_name = "Station A",
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fetched_at = as.POSIXct("2026-04-08 09:00:00", tz = "UTC")
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)
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expect_equal(upsert_weather_measurements(rain_rows, db_path), 2)
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expect_true(upsert_weather_measurements(obs_rows, db_path) >= 6)
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updated_rain <- rain_rows[1, , drop = FALSE]
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updated_rain$value_num <- 2
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expect_true(upsert_rainfall_observations(updated_rain, db_path) >= 1)
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rain_raw <- query_cached_rainfall(
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location_id = "vignasses",
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start_date = "2024-01-02",
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end_date = "2024-01-02",
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aggregate = "raw",
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db_path = db_path
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)
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rain_daily <- query_cached_rainfall(
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location_id = "vignasses",
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start_date = "2024-01-02",
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end_date = "2024-01-02",
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aggregate = "daily",
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db_path = db_path
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)
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temperature_raw <- query_cached_metric(
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location_id = "vignasses",
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metric_id = "air_temperature",
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start_date = "2024-01-02",
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end_date = "2024-01-02",
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aggregate = "raw",
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db_path = db_path
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)
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temperature_daily <- query_cached_metric(
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location_id = "vignasses",
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metric_id = "air_temperature",
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start_date = "2024-01-02",
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end_date = "2024-01-02",
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aggregate = "daily",
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db_path = db_path
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)
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latest_temperature <- query_latest_metric_values(
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location_id = "vignasses",
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metric_id = "air_temperature",
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db_path = db_path
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)
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expect_equal(nrow(rain_raw), 2)
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expect_equal(rain_raw$rain_mm[1], 2)
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expect_equal(rain_daily$rain_mm[1], 2.5)
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expect_equal(nrow(temperature_raw), 2)
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expect_equal(temperature_daily$value_num[1], 21)
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expect_equal(latest_temperature$value_num[1], 22)
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expect_equal(
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as.character(get_sync_start_date("vignasses", db_path, end_date = as.Date("2024-01-10"))),
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"2023-12-21"
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)
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expect_equal(
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as.character(get_sync_start_date("vignasses", db_path, end_date = as.Date("2024-01-01"))),
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"2023-12-12"
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)
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})
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test_that("sync ranges are chunked predictably", {
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ranges <- split_sync_ranges(
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start_date = as.Date("2024-01-01"),
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end_date = as.Date("2024-05-15"),
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chunk_days = 60L
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)
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expect_equal(nrow(ranges), 3)
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expect_equal(as.character(ranges$start_date[1]), "2024-01-01")
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expect_equal(as.character(ranges$end_date[3]), "2024-05-15")
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})
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test_that("weekly and monthly rainfall aggregates collapse long windows", {
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skip_if_not(nzchar(Sys.which("sqlite3")), "sqlite3 is required for cache tests.")
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db_path <- tempfile(fileext = ".sqlite")
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on.exit(unlink(c(db_path, paste0(db_path, c("-shm", "-wal")))), add = TRUE)
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ensure_weather_db(db_path)
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rain_rows <- normalise_rainfall_data(
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raw_data = data.frame(
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POSTE = c("1001", "1001", "1001"),
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DATE = c(202401020000, 202401090000, 202402010000),
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RR6 = c(1, 2, 3),
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Nom_usuel = c("Station A", "Station A", "Station A"),
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stringsAsFactors = FALSE
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),
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location_id = "vignasses",
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fetched_at = as.POSIXct("2026-04-08 09:00:00", tz = "UTC")
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)
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expect_equal(upsert_weather_measurements(rain_rows, db_path), 3)
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weekly_rain <- query_cached_rainfall(
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location_id = "vignasses",
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start_date = "2024-01-01",
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end_date = "2024-02-01",
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aggregate = "weekly",
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db_path = db_path
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)
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monthly_rain <- query_cached_rainfall(
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location_id = "vignasses",
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start_date = "2024-01-01",
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end_date = "2024-02-01",
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aggregate = "monthly",
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db_path = db_path
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)
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expect_equal(as.character(weekly_rain$observed_day), c("2024-01-01", "2024-01-08", "2024-01-29"))
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expect_equal(weekly_rain$rain_mm, c(1, 2, 3))
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expect_equal(as.character(monthly_rain$observed_day), c("2024-01-01", "2024-02-01"))
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expect_equal(monthly_rain$rain_mm, c(3, 3))
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})
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