# Daymet Example

    library(rgeedim)
    library(terra)

    project_id <- Sys.getenv("GOOGLE_CLOUD_QUOTA_PROJECT", "rgeedim-demo")
    gd_initialize(project = project_id)

    ## Sacramento county area
    # b <- soilDB::fetchSDA_spatial("CA067", "areasymbol", geom="sapolygon")
    # sapply(terra::ext(b), as.numeric)

    b <- gd_bbox(
      xmin = -121.9,
      ymax = 38.8,
      xmax = -121.0,
      ymin = 38.0
    )

    collection_name <- "NASA/ORNL/DAYMET_V4"

    # search collection for spatial and date range
    res <- collection_name |>
      gd_collection_from_name() |>
      gd_search(region = b,
                start_date = "2020-01-01",
                end_date = "2020-02-01")

    # inspect table of IDs and dates
    gd_properties(res)
    td <- tempdir()

    # download each daily image in collection as separate GeoTIFF (no compositing)
    # Note: `filename` is a directory
    res |>
      gd_download(filename = td,
                  composite = FALSE,
                  region = b,
                  crs = "EPSG:5070",
                  scale = 1000)

    # each EE image is named YYYYMMDD_1km, so geotiffs have same base name
    r <- rast(list.files(td, pattern = "\\d{8}_1km.tif$", full.names = TRUE))
    r2 <- r[[which(names(r) == "prcp")]]

    # inspect: sum daily precip values -> monthly total
    plot(sum(r2))

    ## optional: create animated GIF w/ {gifski}

    # library(gifski)
    #
    # r3 <- cumsum(r2 + 1e-3)
    # gifski::save_gif({
    #   res <- lapply(seq_len(nlyr(r3)), function(i) {
    #     plot(
    #       r3[[i]],
    #       type = "continuous",
    #       main = paste0("2020-01-", formatC(i, width = 2, flag = 0)),
    #       range = c(0, 100)
    #     )
    #   })
    # }, delay = 0.25,
    #    width = 400,
    #    height = 400)
    #
    # browseURL("animation.gif")