version 0.0.3

Increase speed with foreach
Point level abstaction across functions
new exports

.Rbuildignore

@@ -1,2 +1,4 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+inProgress
+sampleFiles

.gitignore

@@ -2,4 +2,5 @@
 .Rhistory
 .RData
 .Ruserdata
-sampleFiles
+sampleFiles
+inProgress

DESCRIPTION

@@ -2,7 +2,7 @@ Package: climateR
 Type: Package
 Title: climateR
 Description: A set of functions to subset climate data to an area of interest.
-Version: 0.0.2
+Version: 0.0.3
 Authors@R: person("Mike", "Johnson", role = c("aut", "cre"), email = "[email protected]")
 Maintainer: Mike Johnson <[email protected]>
 BugReports: https://github.com/mikejohnson51/climateR/issues
@@ -12,8 +12,11 @@ Depends:
     AOI
 Imports: 
     areal,
+    doParallel,
+    foreach,
+    httr,
     magrittr,
-    ncdf4,
+    RNetCDF,
     raster,
     sf
 VignetteBuilder: knitr

NAMESPACE

@@ -1,28 +1,28 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
-export(buildWeights)
-export(define.grid2)
 export(getBCCA)
+export(getBCSDvic)
+export(getCHIRPS)
+export(getDaymet)
+export(getGridMET)
+export(getLOCA)
 export(getMACA)
 export(getPRISM)
-export(makeGrid)
-export(zonal)
+export(getTerraClim)
 import(AOI)
-importFrom(crayon,"%+%")
-importFrom(crayon,blue)
-importFrom(crayon,cyan)
-importFrom(crayon,white)
-importFrom(crayon,yellow)
+importFrom(RNetCDF,open.nc)
+importFrom(RNetCDF,var.get.nc)
+importFrom(doParallel,registerDoParallel)
+importFrom(foreach,"%do%")
+importFrom(foreach,"%dopar%")
+importFrom(foreach,foreach)
+importFrom(httr,GET)
+importFrom(httr,write_disk)
 importFrom(magrittr,"%>%")
-importFrom(ncdf4,nc_close)
-importFrom(ncdf4,nc_open)
-importFrom(ncdf4,ncvar_get)
+importFrom(raster,brick)
 importFrom(raster,crop)
+importFrom(raster,crs)
 importFrom(raster,extent)
-importFrom(raster,intersect)
-importFrom(raster,mosaic)
 importFrom(raster,raster)
 importFrom(raster,stack)
-importFrom(raster,stackApply)
-importFrom(raster,writeRaster)

R/getBCCA.R

@@ -1,52 +1,44 @@
 #' @title Get BCCA Climate Data for an Area of Interest
 #' @description Bias Corrected Constructed Analogs (BCCA) V2 Daily Climate Projections.
-#' This archive contains daily BCCA CMIP3 and CMIP5 projections of precipitation, daily maximum, and daily minimum temperature over the contiguous
+#' This service offeres an interface containing daily BCCA CMIP3 and CMIP5 projections of precipitation, daily maximum, and daily minimum temperature over the contiguous
 #' United States. The dataset content is based on global climate projections from the World Climate Research Programme's (WCRP's) Coupled Model
 #' Intercomparison Project phase 3 (CMIP3) multi-model dataset referenced in the Intergovernmental Panel on Climate Change Fourth Assessment Report,
 #' and the phase 5 (CMIP5) multi-model dataset that is informing the IPCC Fifth Assessment.
+#' @param AOI a spatial polygon object (sf or sp)
 #' @param param a meterological parameter (see `param_meta$bcca`)
-#' @param model GMC model name (see `model_meta$bcca$name`)
+#' @param model GMC model name (see `model_meta$bcca`)
 #' @param scenario a climate scenario pathway (rcp45 or rcp85)
 #' @param startDate a start date given as "YYYY-MM-DD"
 #' @param endDate   an end date given as "YYYY-MM-DD"
 #' @author Mike Johnson
-#' @return a list of rasterStacks
+#' @return if AOI is an areal extent a list of rasterStacks, if AOI is a point then a data.frame of modeled records.
 #' @export
 
+
 getBCCA = function(AOI, param, model = 'CCSM4', scenario = 'rcp45', startDate, endDate = NULL){
 
-   base = 'https://cida.usgs.gov/thredds/dodsC/cmip5_bcca/'
+  id = 'bcca'
+  base = 'https://cida.usgs.gov/thredds/dodsC/cmip5_bcca/'
 
   d = define.dates(startDate, endDate)
   v = define.versions(dates = d$date, scenario = scenario, future.call = "future?", historic.call = "historical?")
-  p = define.param(param, service = 'bcca')
-  k = define.config(dataset = "bcca", model = model, ensemble = NA)
+  p = define.param(param, service = id)
+  k = define.config(dataset = id, model = model, ensemble = NA)
 
   tmp = expand.grid(min.date = v$min.date, model = k, call = p$call, stringsAsFactors = FALSE)
   fin = merge(v, tmp, "min.date")  %>% merge(p, "call") %>%  merge(model_meta$bcca, "model")
   fin = fin[fin$scenario %in% scenario,]
 
-  g = define.grid2(AOI, url = paste0(base, fin$calls[1]) )
-  s = define.initial(g, d, p, dataset = "bcca")
-
-  for(i in 1:NROW(fin)){
-
-    variable_call = paste0( "BCCA_0-125deg_",fin$call[i], "_day_", fin$model[i], "_", fin$ver[i], "_", fin$ensemble[i])
+  g = define.grid3(AOI, id)
 
-      nc = RNetCDF::open.nc(paste0(
-        base, fin$calls[i], variable_call,
-                          fin$time.index[i],
-                          g$lat.call,
-                          g$lon.call))
+  variable_call = paste0( "BCCA_0-125deg_",fin$call, "_day_", fin$model, "_", fin$ver, "_", fin$ensemble)
 
-      var = RNetCDF::var.get.nc(nc, variable_call )
+  urls = paste0(base, fin$calls, variable_call, fin$time.index, g$lat.call, g$lon.call)
 
-      RNetCDF::close.nc(nc)
+  s = fast.download(urls, params = variable_call, names = paste0(fin$model, "_",fin$ensemble, "_", fin$call), g, date.names = d$date, dataset = id)
 
-      s = process.var(group = s, g = g, var, dates = seq.Date(fin$min.date[i], fin$max.date[i],1),
-                      param = fin$common.name[i], name = paste0(fin$ver[i], "_", fin$model[i]))
-    }
+  s
 
   return(s)
-
 }
+

R/getBCSD.R

@@ -1,51 +1,38 @@
-#' @title Get BCSD VIC outputs Climate Data for an Area of Interest
-#' @description Multivariate Adaptive Constructed Analogs (MACA) is a statistical method for downscaling Global Climate Models
-#' (GCMs) from their native coarse resolution to a higher spatial resolution that captures
-#' reflects observed patterns of daily near-surface meteorology and simulated changes in GCMs experiments.
+#' @title Get BCSD Hydrologic (VIC) Outputs for an Area of Interest
+#' @description CMIP5 Monthly Hydrology Projections driven with BCSD climate forcings
 #' @param AOI a spatial polygon object (sf or sp)
-#' @param param a meterological parameter (see `param_meta$maca`)
-#' @param model GMC model name (see `model_meta$maca$name`)
+#' @param param a meterological parameter (see `param_meta$bcsd`)
+#' @param model GMC model name (see `model_meta$bcsd`)
 #' @param scenario a climate scenario pathway (rcp45 or rcp85)
 #' @param startDate a start date given as "YYYY-MM-DD"
 #' @param endDate   an end date given as "YYYY-MM-DD"
 #' @author Mike Johnson
-#' @return a list of rasterStacks
+#' @return if AOI is an areal extent a list of rasterStacks, if AOI is a point then a data.frame of modeled records.
 #' @export
 
 getBCSDvic = function(AOI, param, model = 'CCSM4', scenario = 'rcp45', startDate, endDate = NULL){
 
+id = "bcsd"
+base = "https://cida.usgs.gov/thredds/dodsC/BCSD_mon_VIC?"
+
 d = define.dates(startDate, endDate, baseDate = '1950-01-15')
 p = define.param(param, service = 'bcsd')
 k = define.config(dataset = 'bcca', model = model)
+g = define.grid3(AOI, id)
 
 fin = expand.grid(model = k, scenario = scenario, call = p$call)
 fin = merge(fin, p, "call")
 
-base = "https://cida.usgs.gov/thredds/dodsC/BCSD_mon_VIC?"
-
-g = define.grid2(AOI, url = base)
-s = define.initial(g, d)
-
+var.names = paste0(tolower(fin$model), "_",  fin$scenario, "_r1i1p1_",fin$call)
 
-for(i in 1:NROW(fin)){
+urls = paste0(base, var.names,
+              "[", min(d$month.index), ":1:" , max(d$month.index), "]",
+              g$lat.call,
+              g$lon.call )
 
-nc = ncdf4::nc_open(paste0("https://cida.usgs.gov/thredds/dodsC/BCSD_mon_VIC?",
-                           tolower(fin$model[i]),
-                           "_",
-                           fin$scenario[i],
-                           "_r1i1p1_",
-                           fin$call[i],
-                           "[", min(d$month.index), ":1:" , max(d$month.index), "]",
-                           g$lat.call,
-                           g$lon.call))
 
-var = ncdf4::ncvar_get(nc)
-ncdf4::nc_close(nc)
+fast.download(urls, params = var.names, names = paste0(fin$model, "_", fin$common.name), g, date.names = unique(format(d$date, "%Y-%m")), dataset = id)
 
-s = process.var(group = s, g = g, var,  dates = unique(format(d$date, "%Y-%m")),
-                param = fin$common.name[i], name = paste0(fin$model[i], "_", fin$scenario[i]))
 }
 
-return(s)
-}
 

R/getCHIRPS.R

@@ -1,23 +1,49 @@
+#' @title Get CHIRPS Precipitation Data for an Area of Interest
+#' @description Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. Spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present,
+#' CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
+#' @param AOI a spatial polygon object (sf or sp)
+#' @param startDate a start date given as "YYYY-MM-DD"
+#' @param endDate   an end date given as "YYYY-MM-DD"
+#' @author Mike Johnson
+#' @return a list of rasterStacks or dataframe of point values
+#' @export
+
 getCHIRPS = function(AOI, startDate, endDate = NULL  ){
 
   d = define.dates  (startDate, endDate)
-
-  bb = AOI::bbox_st(AOI)
-
   dates = paste0("%28", as.numeric(format(d$date, "%d")), "%20", month.abb[as.numeric(format(d$date, "%m"))], "%20", format(d$date, "%Y"))
+  g = define.grid3(AOI, 'chirps')
+
+  bb = g$AOI %>% AOI::bbox_st()
 
   urls = paste0('https://iridl.ldeo.columbia.edu/SOURCES/.UCSB/.CHIRPS/.v2p0/.daily-improved/.global/.0p05/.prcp/',
-               'Y/', bb$ymin, '/', bb$ymax,'/RANGEEDGES/',
-               'X/',bb$xmin, '/', bb$xmax,'/RANGEEDGES/',
-               'T/',dates,'%29VALUES/',
-               '%5BX/Y/%5D/palettecolor.tiff?filename=tmp.tiff')
+         'Y/', bb$ymin, '/', bb$ymax,'/RANGEEDGES/',
+         'X/',bb$xmin, '/', bb$xmax,'/RANGEEDGES/',
+         'T/',dates,'%29VALUES/%5BX/Y/%5D/palettecolor.tiff?filename=tmp.tiff')
+
+ `%dopar%` <- foreach::`%dopar%`
+  no_cores  <- parallel::detectCores() - 1
+  doParallel::registerDoParallel(no_cores)
+
+  if(any(sf::st_geometry_type(g$AOI) == 'POINT')) {
 
-  s = raster::stack()
+    var = foreach::foreach(i = 1:length(urls), .combine = 'c', .packages = 'raster') %dopar% { raster::raster(urls[i]) %>% as.matrix()}
 
-  for(i in 1:length(urls)){
-    r = raster::raster(urls[i])
-    names(r) = paste0("prcp_", d$string[i])
-    s = raster::addLayer(s, r )
+    l = length(d$date)
+    df = data.frame(source = rep('chirps', l), lat = rep(bb$ymax , l), lon = rep(bb$xmax, l),  date = d$date, stringsAsFactors = FALSE)
+
+    mat = matrix(var, nrow = l, byrow = F)
+    s = cbind(df, mat)
+    colnames(s) = c('source', 'lat', 'lon', 'date', 'prcp')
+
+  } else {
+
+    s = foreach::foreach(i = 1:length(urls), .combine = 'stack', .packages = 'raster') %dopar% { raster::raster(urls[i])}
+    names(s) = paste0("prcp_", d$string)
+    raster::crs(s) = sf::st_crs(g$AOI)[[2]]
   }
+
   return(s)
 }
+
+

R/getDaymet.R

@@ -1,11 +1,26 @@
+#' @title Get Daymet Climate Data for an Area of Interest
+#' @description This dataset provides Daymet Version 3 model output data as gridded estimates of daily weather parameters for North America.
+#' Daymet output variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation,
+#' vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980 to December 31 of the most recent full calendar year. Each subsequent year is processed individually at the close of a calendar year after
+#' allowing adequate time for input weather station data to be of archive quality.  Daymet variables are continuous surfaces provided as individual files, by year, at a 1-km x 1-km spatial resolution and a daily temporal resolution. Data are in a Lambert Conformal
+#' Conic projection for North America and are in a netCDF file format compliant with Climate and Forecast (CF) metadata conventions.
+#' @param AOI a spatial polygon object (sf or sp)
+#' @param param a meterological parameter (see `param_meta$daymet`)
+#' @param startDate a start date given as "YYYY-MM-DD"
+#' @param endDate   an end date given as "YYYY-MM-DD"
+#' @author Mike Johnson
+#' @return if AOI is an areal extent a list of rasterStacks, if AOI is a point then a data.frame of modeled records.
+#' @export
+#'
 getDaymet = function(AOI, param, startDate, endDate = NULL){
 
-  daymet.proj = "+proj=lcc +lon_0=-100 +lat_0=42.5 +x_0=0 +y_0=0 +a=6378137 +rf=298.257223563 +lat_1=25 +lat_2=60 +datum=WGS84"
+  id = 'daymet'
+  base = 'https://thredds.daac.ornl.gov/thredds/dodsC/ornldaac/1328/'
 
-  d = define.dates(startDate, endDate)
-  g = define.grid(AOI, service = 'daymet', proj = daymet.proj)
-  p = define.param(param, service = 'daymet')
-  s = define.initial(grid = g, date = d)
+  d = define.dates(startDate, endDate, baseDate = "1980-01-01")
+  d = d[d$julien < 366, ]
+  p = define.param(param, id)
+  g = define.grid3(AOI, id)
 
   y = data.frame(year = unique(d$year), minJul = NA, maxJul = NA, min.date = as.Date(NA), max.date = as.Date(NA), stringsAsFactors = FALSE)
 
@@ -20,33 +35,15 @@ getDaymet = function(AOI, param, startDate, endDate = NULL){
   tmp = expand.grid(year = y$year, call = p$call, stringsAsFactors = FALSE)
   fin = merge(tmp, p, "call") %>% merge(y, "year")
 
-  for(i in 1:NROW(fin)){
+  urls = paste0(base, fin$year, '/daymet_v3_',  fin$call, '_', fin$year, '_na.nc4?', fin$call,
+                '[', fin$minJul - 1 ,   ':1:', fin$maxJul - 1, ']',
+                g$lat.call,
+                g$lon.call)
 
-      nc = ncdf4::nc_open(paste0('https://thredds.daac.ornl.gov/thredds/dodsC/ornldaac/1328/',
-                          fin$year[i],
-                          '/daymet_v3_',
-                          fin$call[i],
-                          '_',
-                          fin$year[i],
-                          '_na.nc4?',
-                          fin$call[i],
-                          '[', fin$minJul[i] -1 ,   ':1:', fin$maxJul[i] -1, ']',
-                          g$lat.call,
-                          g$lon.call ))
+  s = fast.download(urls, params = fin$call, names = fin$call, g, date.names =  d$date, dataset = id, fun = 'r')
 
-      var = ncdf4::ncvar_get(nc)
+  s
 
-      ncdf4::nc_close(nc)
-
-      s = process.var(group = s, g = g, var, dates = seq.Date(as.Date(fin$min.date[i]), as.Date(fin$max.date[i]), 1),
-                      param = fin$common.name[i], name = fin$year[i], proj = daymet.proj, fun = 't')
-  }
-
-  ss = define.initial(grid = g, date = d)
-
-  for(i in 1:NROW(p)){ ss[[p$common.name[i]]] = raster::stack(s[grepl(p$common.name[i], names(s))]) }
-
-  return(ss)
+  return(s)
 }
 
-