Reading and Writing Timeseries

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width=6, 
  fig.height=4
)
options(scipen = 9999)

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.

timeseries_file <- system.file('extdata/yahara_alb_gdp_file.csv', package = "ncdfgeom")

raw_data <- geoknife::parseTimeseries(timeseries_file, delim=',', with.units=TRUE)

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_file

Now we have a NetCDF file with reference spatial information for each time series, and a single timeseries variable.

The file has three dimensions.

  1. A dimension called "instance" that is the same length as the number of timeseries written,
  2. one called "time" that is the length of the number of time series, and
  3. one called "name_strlen" that is the length of the longest timeseries ID (which is stored as a string).
ncmeta::nc_dims(nc_file)

The file has variables for latitude, longitude, altitude, timeseries IDs, and a data variable.

ncmeta::nc_vars(nc_file)

The primary dimensions in the file are of length, number of time steps and number of time series.

ncmeta::nc_dims(nc_file)

The header of the resulting NetCDF file looks like:

try({ncdump <- system(paste("ncdump -h", nc_file), intern = TRUE)
cat(ncdump ,sep = "\n")}, silent = TRUE)

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, and alts are vectors that apply to the whole dataset.
  2. varmeta has one entry per timeseries variable read from the NetCDF file and contains the name and long_name attribute of each variable.
  3. data_unit and data_prec contain units and precision metadata for each variable.
  4. data_frames is a list containing one data.frame for each variable read from the NetCDF file.
  5. 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.
t <- file.remove(nc_file)


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ncdfgeom documentation built on Aug. 29, 2019, 1:03 a.m.