Merge pull request #3530 from infotroph/dataland-check

Author: infotroph

modules/data.land/NAMESPACE

@@ -52,6 +52,7 @@ export(shp2kml)
 export(soil.units)
 export(soil2netcdf)
 export(soil_params)
+export(soil_params_ensemble_soilgrids)
 export(soil_process)
 export(soilgrids_soilC_extract)
 export(soilgrids_texture_extraction)
@@ -61,6 +62,7 @@ export(to.TreeCode)
 export(write_ic)
 export(write_veg)
 importFrom(dplyr,"%>%")
+importFrom(foreach,"%dopar%")
 importFrom(magrittr,"%>%")
 importFrom(ncdf4,ncvar_get)
 importFrom(rlang,"%||%")

modules/data.land/R/soil_params_ensemble.R

@@ -1,23 +1,27 @@
-##' A function to estimate individual alphas for Dirichlet distribution to approximate the observed quantiles with means as known moments
-##' for SoilGrids soil texture data. 
-##' Dirichlet distribution is assumed as soil texture data follow categorical distribution and the probability of each category is in the range 0 to 1, 
-##' and all must sum to 1.
-##'  
-##' @param means A vector of means of sand, clay, and silt proportion for one soil layer at one site from SoilGrids data
-##' @param quantiles A list of 5th, 50th, and 95th percentiles for sand, clay, and silt for one soil layer at one site from SoilGrids data
-##'
-##' @example
-##' \dontrun{
-##' means <- c(0.566,0.193,0.241) # means of sand, clay, and silt at one site and one depth
-##' quantiles <-list(
-##' q5 = c(0.127,0.034,0.052), # 5th percentile for each category: sand, clay, and silt at one site and one depth
-##' q50 = c(0.615,0.15,0.191), # 50th percentile (median) for each category: sand, clay, and silt at one site and one depth
-##' q95 = c(0.799,0.66,0.616))  # 95th percentile for each category: sand, clay, and silt at one site and one depth
-##' alpha_est <- estimate_dirichlet_parameters(means, quantiles)
-##' }
-##'  @return The individual alphas that work best to fit the observed quantiles
-##'  @export 
-##'  @author Qianyu Li
+#' A function to estimate individual alphas for Dirichlet distribution to
+#' approximate the observed quantiles with means as known moments for SoilGrids
+#' soil texture data.
+#' Dirichlet distribution is assumed as soil texture data follow categorical
+#' distribution and the probability of each category is in the range 0 to 1,
+#' and all must sum to 1.
+#'
+#' @param means A vector of means of sand, clay, and silt proportion for one
+#'  soil layer at one site from SoilGrids data
+#' @param quantiles A list of 5th, 50th, and 95th percentiles for sand, clay,
+#'  and silt for one soil layer at one site from SoilGrids data
+#'
+#' @examples
+#' \dontrun{
+#' # Means and percentiles for each category: sand, clay, and silt at one site and one depth
+#' means <- c(0.566,0.193,0.241)
+#' quantiles <-list(
+#'   q5 = c(0.127,0.034,0.052), # 5th percentile
+#'   q50 = c(0.615,0.15,0.191), # 50th percentile (median)
+#'   q95 = c(0.799,0.66,0.616))  # 95th percentile
+#' alpha_est <- estimate_dirichlet_parameters(means, quantiles)
+#' }
+#' @return The individual alphas that work best to fit the observed quantiles
+#' @author Qianyu Li
 estimate_dirichlet_parameters <- function(means, quantiles) {
 
   # A function to optimize alpha0, which is the sum of individual alphas.
@@ -31,13 +35,19 @@ estimate_dirichlet_parameters <- function(means, quantiles) {
       # Generate samples based on estimated alpha
       samples <- MCMCpack::rdirichlet(10000, alpha) # Generate samples
       # Compute differences with observed quantiles
-      estimated_quantiles <- apply(samples, 2, quantile, probs = c(0.05, 0.5, 0.95),na.rm = TRUE)
+      estimated_quantiles <- apply(
+        x = samples,
+        margin = 2,
+        FUN = stats::quantile,
+        probs = c(0.05, 0.5, 0.95),
+        na.rm = TRUE
+      )
       quantile_diff <- sum((estimated_quantiles - do.call(rbind, quantiles))^2)
       return(quantile_diff)
     }
 
     # Optimize alpha0
-    result <- optim(
+    result <- stats::optim(
       par = 1, # Initial guess for alpha0
       fn = objective_function,
       method = "L-BFGS-B",
@@ -56,31 +66,34 @@ estimate_dirichlet_parameters <- function(means, quantiles) {
 
 
 
-##' A function to estimate the soil parameters based on SoilGrids soil texture data and write the parameter paths into settings
-##' 
-##' @param settings A multi-site settings
-##' @param sand A data frame containing sand fraction in percentage from SoilGrids250m v2.0 with columns "Depth", "Quantile", "Siteid", and "Value"
-##' @param clay A data frame containing clay fraction in percentage from SoilGrids250m v2.0 with columns "Depth", "Quantile", "Siteid", and "Value"
-##' @param silt A data frame containing silt fraction in percentage from SoilGrids250m v2.0 with columns "Depth", "Quantile", "Siteid", and "Value"
-##' @param outdir Provide the path to store the parameter files
-##' @param write_into_settings Whether to write the path of parameter file into the setting. The default is TRUE
-##' 
-##' @example
-##' \dontrun{
-##' 
-##' outdir <- "/projectnb/dietzelab/Cherry/SoilGrids_texture/39NEON"
-##' sand <- readRDS("/projectnb/dietzelab/Cherry/SoilGrids_texture/sand_percent.rds") #sand fraction in percentage
-##' clay <- readRDS("/projectnb/dietzelab/Cherry/SoilGrids_texture/clay_percent.rds") #clay fraction in percentage
-##' silt <- readRDS("/projectnb/dietzelab/Cherry/SoilGrids_texture/silt_percent.rds") #silt fraction in percentage
-##' settings <-read.settings("/projectnb/dietzelab/Cherry/xml/pecan_monthly_SDA_soilwater.xml")
-##' soil_params_ensemble_soilgrids(settings,sand,clay,silt,outdir)
-##'  }
-##'  
-##'  @return Ensemble soil parameter files defined in outdir and file paths in xml file
-##'  @export 
-##'  @author Qianyu Li
-##'  @importFrom magrittr %>%
-##'  
+#' A function to estimate the soil parameters based on SoilGrids soil texture
+#'  data and write the parameter paths into settings
+#'
+#' @param settings A multi-site settings
+#' @param sand,clay,silt Data frames containing fraction in percentage from SoilGrids250m
+#'  v2.0, each with columns "Depth", "Quantile", "Siteid", and "Value"
+#' @param outdir Provide the path to store the parameter files
+#' @param write_into_settings Whether to write the path of parameter file into
+#'  the setting. The default is TRUE
+#'
+#' @examples
+#' \dontrun{
+#'
+#' outdir <- "/projectnb/dietzelab/Cherry/SoilGrids_texture/39NEON"
+#' # each file contains percent salt, silt, or clay
+#' sand <- readRDS("/path/to/SoilGrids_texture/sand_percent.rds")
+#' clay <- readRDS("/path/to/SoilGrids_texture/clay_percent.rds")
+#' silt <- readRDS("/path/to/SoilGrids_texture/silt_percent.rds")
+#' settings <-read.settings("/path/to/pecan_monthly_SDA_soilwater.xml")
+#' soil_params_ensemble_soilgrids(settings,sand,clay,silt,outdir)
+#' }
+#'
+#' @return Ensemble soil parameter files defined in outdir and file paths in xml file
+#' @export
+#' @author Qianyu Li
+#' @importFrom magrittr %>%
+#' @importFrom rlang .data
+#' @importFrom foreach %dopar%
 
 soil_params_ensemble_soilgrids <- function(settings,sand,clay,silt,outdir,write_into_settings=TRUE){
 
@@ -154,21 +167,28 @@ soil_params_ensemble_soilgrids <- function(settings,sand,clay,silt,outdir,write_
     # Estimate Dirichlet parameters for each depth at each site
     for (depths in sort(unique(texture_all$soil_depth))) {
       quantiles <- list(
-        q5 = dplyr::filter(dat[[i]], quantile == "0.05", soil_depth == depths) %>% dplyr::select(
-          fraction_of_sand_in_soil,
-          fraction_of_clay_in_soil,
-          fraction_of_silt_in_soil), # 5th percentile for each category
-        q50 = dplyr::filter(dat[[i]], quantile == "0.5", soil_depth == depths) %>% dplyr::select(
-          fraction_of_sand_in_soil,
-          fraction_of_clay_in_soil,
-          fraction_of_silt_in_soil), # 50th percentile (median) for each category
-        q95 = dplyr::filter(dat[[i]], quantile == "0.95", soil_depth == depths) %>% dplyr::select(
-          fraction_of_sand_in_soil,
-          fraction_of_clay_in_soil,
-          fraction_of_silt_in_soil))  # 95th percentile for each category
+        q5 = dplyr::filter(dat[[i]], .data$quantile == "0.05", soil_depth == depths) %>%
+          dplyr::select(
+            "fraction_of_sand_in_soil",
+            "fraction_of_clay_in_soil",
+            "fraction_of_silt_in_soil"), # 5th percentile for each category
+        q50 = dplyr::filter(dat[[i]], .data$quantile == "0.5", soil_depth == depths) %>%
+          dplyr::select(
+            "fraction_of_sand_in_soil",
+            "fraction_of_clay_in_soil",
+            "fraction_of_silt_in_soil"), # 50th percentile (median) for each category
+        q95 = dplyr::filter(dat[[i]], .data$quantile == "0.95", soil_depth == depths) %>%
+          dplyr::select(
+            "fraction_of_sand_in_soil",
+            "fraction_of_clay_in_soil",
+            "fraction_of_silt_in_soil"))  # 95th percentile for each category
 
       # Extract the means
-      means <- dplyr::filter(dat[[i]], quantile == "Mean", soil_depth == depths) %>% dplyr::select(fraction_of_sand_in_soil,fraction_of_clay_in_soil,fraction_of_silt_in_soil)
+      means <- dplyr::filter(dat[[i]], .data$quantile == "Mean", soil_depth == depths) %>%
+        dplyr::select(
+          "fraction_of_sand_in_soil",
+          "fraction_of_clay_in_soil",
+          "fraction_of_silt_in_soil")
       soil_rescaled <-rescale_sum_to_one(means$fraction_of_sand_in_soil,means$fraction_of_clay_in_soil,means$fraction_of_silt_in_soil)
 
       # Replace the original means with the rescaled ones
@@ -182,7 +202,7 @@ soil_params_ensemble_soilgrids <- function(settings,sand,clay,silt,outdir,write_
       # Generate the ensemble soil texture data based on the ensemble size (ens_n) defined in the settings
       samples <- MCMCpack::rdirichlet(ens_n, alpha_est)
       colnames(samples) <-c("fraction_of_sand_in_soil","fraction_of_clay_in_soil","fraction_of_silt_in_soil")
-      samples <-list(samples) %>% setNames(depths)
+      samples <-list(samples) %>% stats::setNames(depths)
       samples_ens <- append(samples_ens, samples)
     }
     # Generate soil parameter file for each one in ensemble soil texture data
@@ -211,7 +231,7 @@ soil_params_ensemble_soilgrids <- function(settings,sand,clay,silt,outdir,write_
       settings[[ind]]$run$inputs$soil_physics$ensemble <- ens_n
       settings[[ind]]$run$inputs$soil_physics$path <-create_mult_list(rep("path", ens_n), PATH[[i]])
     }
-    write.settings(settings,outputdir = settings$outdir,outputfile = "pecan.xml")
+    PEcAn.settings::write.settings(settings,outputdir = settings$outdir,outputfile = "pecan.xml")
   }
 }
 
@@ -224,7 +244,7 @@ reformat_soil_list <- function(samples_all_depth) {
       "fraction_of_silt_in_soil")
 
   # Initialize a new list to store reformatted data
-  reformatted <-setNames(vector("list", length(fractions)), fractions)
+  reformatted <- stats::setNames(vector("list", length(fractions)), fractions)
 
   # Extract data for each fraction
   for (fraction in fractions) {

modules/data.land/man/estimate_dirichlet_parameters.Rd

@@ -13,7 +13,7 @@
 * package encoding: UTF-8
 * checking package namespace information ... OK
 * checking package dependencies ... NOTE
-Imports includes 33 non-default packages.
+Imports includes 36 non-default packages.
 Importing from so many packages makes the package vulnerable to any of
 them becoming unavailable.  Move as many as possible to Suggests and
 use conditionally.