OperationsNoDownload.R

# # A high-level outline of forecasting structure
# setwd("/var/www/ge585/PhenologyForecast/")
source("PlotDataTimeSeries.R")

# All site information is read from the file "site_metadata.csv". 
# Analysis will be performed on each site in that file.
num.sites = as.numeric(nrow(read.csv("site_metadata.csv")))

# For each site, we need to run a state-space model, and then the forecast model for the current year
for(site in 1:num.sites) {
  
#   # Step 1: Download/update site GCC and MODIS data
#   source("get.site.data.R")
#   get.site.data(site)
  
#   # Step 2: check to see if state space model has already been run
#   source("check.for.complete.SS.model.R")
#   SS.complete <- check.for.complete.SS.model(site) # SS.complete is TRUE/FALSE 
#   
#   # If not, then create the state space model for that site:
#   if(!SS.complete) {
    source("run.SS.model.R")
   # run.SS.model(site) # This should create some files which contain inputs for the forecast model
#   }
#   
  # Step 3: Check to see if the forecast model has been run (and has some output)
  # "FM" means forecast model, the particle filter in this case
#  source("check.for.FM.model.R")
#  FM.complete <- check.for.FM.model(site) # FM.created is TRUE/FALSE 
  
#   # If not, then create the forecast model for that site:
#   if(!FM.complete) {
    source("create.FM.model.R")
    create.FM.model(site) # This should create some files with forecasts and some other output
#   }
#   
  # Step 4: Check for new data not yet included in forecast model:
  source("update.FM.model.R")
  update.FM.model(site) # This should update the output from the forecast model for any new data
  
}

# # Also, plot all of the data: