Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(meteoland)
library(stars)
library(dplyr)
## ----points_to_interpolate_example--------------------------------------------
points_to_interpolate_example
## ----raster_to_interpolate_example--------------------------------------------
raster_to_interpolate_example
## ----meteoland_meteo_example--------------------------------------------------
meteoland_meteo_example
## ----meteo_names--------------------------------------------------------------
names(meteoland_meteo_example)
## ----quick_interpolation------------------------------------------------------
# creating the interpolator object
interpolator <- with_meteo(meteoland_meteo_example) |>
create_meteo_interpolator()
# performing the interpolation
points_interpolated <- points_to_interpolate_example |>
interpolate_data(interpolator)
points_interpolated
## ----non_mandatory_vars_in_meteo, error=TRUE----------------------------------
meteo_without_temp <- meteoland_meteo_example
meteo_without_temp[["MinTemperature"]] <- NULL
meteo_without_temp[["MaxTemperature"]] <- NULL
with_meteo(meteo_without_temp)
## ----interpolatior_params-----------------------------------------------------
# parameters
get_interpolation_params(interpolator)
## ----interpolated_data--------------------------------------------------------
# interpolated meteo for the first location
points_interpolated[["interpolated_data"]][1]
## ----long---------------------------------------------------------------------
tidyr::unnest(points_interpolated, cols = "interpolated_data")
## ----interpolator_class-------------------------------------------------------
class(interpolator)
## ----interpolator_description-------------------------------------------------
interpolator
## ----get_interpolation_params-------------------------------------------------
get_interpolation_params(interpolator)
## ----set_interpolation_params-------------------------------------------------
# wind_height parameter
get_interpolation_params(interpolator)$wind_height
# set a new wind_height parameter and check
interpolator <- set_interpolation_params(interpolator, params = list(wind_height = 5))
get_interpolation_params(interpolator)$wind_height
## ----writing_interpolator-----------------------------------------------------
temporal_folder <- tempdir()
write_interpolator(interpolator, file.path(temporal_folder, "interpolator.nc"))
# file should exists now
file.exists(file.path(temporal_folder, "interpolator.nc"))
## ----reading_interpolator-----------------------------------------------------
file_interpolator <- read_interpolator(file.path(temporal_folder, "interpolator.nc"))
# the read interpolator should be identical to the one we have already
identical(file_interpolator, interpolator)
## ----interpolator_calibration-------------------------------------------------
# min temperature N and alpha before calibration
get_interpolation_params(interpolator)$N_MinTemperature
get_interpolation_params(interpolator)$alpha_MinTemperature
# calibration
interpolator <- interpolator_calibration(
interpolator,
variable = "MinTemperature",
N_seq = c(5, 20),
alpha_seq = c(1, 10),
update_interpolation_params = TRUE
)
# parameters after calibration
get_interpolation_params(interpolator)$N_MinTemperature
get_interpolation_params(interpolator)$alpha_MinTemperature
## ----preparing_interpolator---------------------------------------------------
interpolator <- with_meteo(meteoland_meteo_example) |>
create_meteo_interpolator() |>
interpolator_calibration(
variable = "MinTemperature",
N_seq = c(5, 20),
alpha_seq = c(1, 10),
update_interpolation_params = TRUE
) |>
interpolator_calibration(
variable = "MaxTemperature",
N_seq = c(5, 20),
alpha_seq = c(1, 10),
update_interpolation_params = TRUE
) |>
interpolator_calibration(
variable = "DewTemperature",
N_seq = c(5, 20),
alpha_seq = c(1, 10),
update_interpolation_params = TRUE
) |>
write_interpolator(
filename = file.path(temporal_folder, "interpolator.nc"),
.overwrite = TRUE
)
## ----cross_validation---------------------------------------------------------
cross_validation <- interpolation_cross_validation(interpolator, verbose = FALSE)
cross_validation$errors
cross_validation$station_stats
cross_validation$dates_stats
cross_validation$r2
## ----summarise_interpolated_data----------------------------------------------
summarise_interpolated_data(
points_interpolated,
fun = "mean",
frequency = "week"
)
## ----erosivity_one_location---------------------------------------------------
precipitation_rainfall_erosivity(
points_interpolated$interpolated_data[[1]],
longitude = sf::st_coordinates(points_interpolated$geometry[[1]])[,1],
scale = 'month'
)
## ----erosivity_mutate---------------------------------------------------------
points_interpolated |>
mutate(erosivity = precipitation_rainfall_erosivity(
interpolated_data,
longitude = sf::st_coordinates(geometry)[,1],
scale = 'month'
))
## ----interpolation_piped------------------------------------------------------
points_interpolated <- points_to_interpolate_example |>
interpolate_data(interpolator) |>
summarise_interpolated_data(
fun = "mean",
frequency = "week"
) |>
summarise_interpolated_data(
fun = "max",
frequency = "month"
) |>
mutate(
monthly_erosivity = precipitation_rainfall_erosivity(
interpolated_data,
longitude = sf::st_coordinates(geometry)[,1],
scale = 'month'
)
)
points_interpolated
## ----raster_to_interpolate----------------------------------------------------
raster_to_interpolate_example
## ----raster_interpolation-----------------------------------------------------
raster_interpolated <- raster_to_interpolate_example |>
interpolate_data(interpolator)
raster_interpolated
## ----raster_temporal_agg------------------------------------------------------
summarise_interpolated_data(
raster_interpolated,
fun = "mean",
frequency = "week"
)
## ----raster_piped-------------------------------------------------------------
monthly_mean_temperature <- raster_to_interpolate_example |>
interpolate_data(interpolator, variables = "Temperature") |>
summarise_interpolated_data(
fun = "max",
frequency = "month",
variable = "MeanTemperature"
)
plot(monthly_mean_temperature)
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