First, we’ll load up some time series data.
attribute_file<-system.file('extdata/yahara_alb_attributes.csv', package = "ncdfgeom")
attributes <- read.csv(attribute_file, colClasses='character')
lats <- as.numeric(attributes$YCOORD)
lons <- as.numeric(attributes$XCOORD)
alts <- rep(1,length(lats)) # Making up altitude for the sake of demonstration.We now have vectors of latitudes, longitudes, altitudes for each of our time series.
# can use geoknife from github
# timeseries_file <- system.file('extdata/yahara_alb_gdp_file.csv', package = "ncdfgeom")
# raw_data <- geoknife::parseTimeseries(timeseries_file, delim=',', with.units=TRUE)
raw_data <- readRDS(system.file('extdata/yahara_alb_gdp_file.rds', package = "ncdfgeom"))
timeseries_data <- raw_data[2:(ncol(raw_data) - 3)]
time <- raw_data$DateTime
long_name <- paste(raw_data$variable[1], 'area weighted', raw_data$statistic[1], 'in',
raw_data$units[1], sep=' ')
meta <- list(name=raw_data$variable[1], long_name=long_name)Now we have the timeseries_data data.frame of timeseries
data, the time vector of timesteps, and a bit of metadata
for the timeseries variable that we will write into the NetCDF file.
nc_summary<-'example summary'
nc_date_create<-'2099-01-01'
nc_creator_name='example creator'
nc_creator_email='example@test.com'
nc_project='example ncdfgeom'
nc_proc_level='just an example no processing'
nc_title<-'example title'
global_attributes<-list(title = nc_title,
summary = nc_summary,
date_created = nc_date_create,
creator_name = nc_creator_name,
creator_email = nc_creator_email,
project = nc_project,
processing_level = nc_proc_level)
ncdfgeom::write_timeseries_dsg(nc_file = "demo_nc.nc",
instance_names = names(timeseries_data),
lats = lats,
lons = lons,
alts = alts,
times = time,
data = timeseries_data,
data_unit = raw_data$units[1],
data_prec = 'double',
data_metadata = meta,
attributes = global_attributes) -> nc_fileNow we have a NetCDF file with reference spatial information for each time series, and a single timeseries variable.
The file has three dimensions.
ncmeta::nc_dims(nc_file)
#> # A tibble: 3 × 4
#> id name length unlim
#> <int> <chr> <dbl> <lgl>
#> 1 0 instance 71 FALSE
#> 2 1 time 730 FALSE
#> 3 2 instance_name_char 2 FALSEThe file has variables for latitude, longitude, altitude, timeseries IDs, and a data variable.
ncmeta::nc_vars(nc_file)
#> # A tibble: 6 × 5
#> id name type ndims natts
#> <int> <chr> <chr> <int> <int>
#> 1 0 instance_name NC_CHAR 2 2
#> 2 1 time NC_DOUBLE 1 4
#> 3 2 lat NC_DOUBLE 1 4
#> 4 3 lon NC_DOUBLE 1 4
#> 5 4 alt NC_DOUBLE 1 4
#> 6 5 BCCA_0-125deg_pr_day_ACCESS1-0_rcp45_r1i1p1 NC_DOUBLE 2 4The primary dimensions in the file are of length, number of time steps and number of time series.
ncmeta::nc_dims(nc_file)
#> # A tibble: 3 × 4
#> id name length unlim
#> <int> <chr> <dbl> <lgl>
#> 1 0 instance 71 FALSE
#> 2 1 time 730 FALSE
#> 3 2 instance_name_char 2 FALSEThe header of the resulting NetCDF file looks like:
#> netcdf demo_nc {
#> dimensions:
#> instance = 71 ;
#> time = 730 ;
#> instance_name_char = 2 ;
#> variables:
#> char instance_name(instance, instance_name_char) ;
#> instance_name:long_name = "Station Names" ;
#> instance_name:cf_role = "timeseries_id" ;
#> double time(time) ;
#> time:units = "days since 1970-01-01 00:00:00" ;
#> time:missing_value = -999. ;
#> time:long_name = "time of measurement" ;
#> time:standard_name = "time" ;
#> double lat(instance) ;
#> lat:units = "degrees_north" ;
#> lat:missing_value = -999. ;
#> lat:long_name = "latitude of the measurement" ;
#> lat:standard_name = "latitude" ;
#> double lon(instance) ;
#> lon:units = "degrees_east" ;
#> lon:missing_value = -999. ;
#> lon:long_name = "longitude of the measurement" ;
#> lon:standard_name = "longitude" ;
#> double alt(instance) ;
#> alt:units = "m" ;
#> alt:missing_value = -999. ;
#> alt:long_name = "altitude of the measurement" ;
#> alt:standard_name = "height" ;
#> double BCCA_0-125deg_pr_day_ACCESS1-0_rcp45_r1i1p1(instance, time) ;
#> BCCA_0-125deg_pr_day_ACCESS1-0_rcp45_r1i1p1:units = "mm/d" ;
#> BCCA_0-125deg_pr_day_ACCESS1-0_rcp45_r1i1p1:missing_value = -2147483648. ;
#> BCCA_0-125deg_pr_day_ACCESS1-0_rcp45_r1i1p1:long_name = "BCCA_0-125deg_pr_day_ACCESS1-0_rcp45_r1i1p1 area weighted MEAN in mm/d" ;
#> BCCA_0-125deg_pr_day_ACCESS1-0_rcp45_r1i1p1:coordinates = "time lat lon alt" ;
#>
#> // global attributes:
#> :Conventions = "CF-1.8" ;
#> :featureType = "timeSeries" ;
#> :cdm_data_type = "Station" ;
#> :standard_name_vocabulary = "CF-1.8" ;
#> :title = "example title" ;
#> :summary = "example summary" ;
#> :date_created = "2099-01-01" ;
#> :creator_name = "example creator" ;
#> :creator_email = "example@test.com" ;
#> :project = "example ncdfgeom" ;
#> :processing_level = "just an example no processing" ;
#> }This file can be read back into R with the function
read_timeseries_dsg. The response is a list of variables as
shown below.
timeseries_dataset <- ncdfgeom::read_timeseries_dsg(nc_file)
names(timeseries_dataset)
#> [1] "time" "lats" "lons"
#> [4] "alts" "varmeta" "data_unit"
#> [7] "data_prec" "data_frames" "global_attributes"time, lats, lons, and
alts are vectors that apply to the whole dataset.varmeta has one entry per timeseries variable read from
the NetCDF file and contains the name and
long_name attribute of each variable.data_unit and data_prec contain units and
precision metadata for each variable.data_frames is a list containing one
data.frame for each variable read from the NetCDF
file.global_attributes contains standard global attributes
found in the file. All of the variables that have one element per
timeseries variable, are named the same as the NetCDF variable names so
they can be accessed by name as shown below.