## 1. Prepare inputs
data("MOD13A1")
df <- subset(MOD13A1$dt, date >= as.Date("2010-01-01") & date <= as.Date("2017-12-31"))
sitename <- "CA-NS6"
d <- subset(df, site == sitename)
nptperyear <- 23
nyear <- floor(nrow(d)/nptperyear)
file_y <- sprintf("TSM_%s_y.txt", sitename)
file_qc <- sprintf("TSM_%s_w.txt", sitename)
file_set <- sprintf("TSM_%s.set", sitename)
## 2. Update options
options <- list(
file_y = file_y, # Data file list/name
file_qc = file_qc, # Mask file list/name
nyear_and_nptperear = c(nyear, nptperyear), # No. years and no. points per year
ylu = c(0, 9999), # Valid data range (lower upper)
qc_1 = c(0, 0, 1), # Quality range 1 and weight
qc_2 = c(1, 1, 0.5), # Quality range 2 and weight
qc_3 = c(2, 3, 0.2), # Quality range 3 and weight
A = 0.1, # Amplitude cutoff value
output_type = c(1, 1, 0), # Output files (1/0 1/0 1/0), 1: seasonality data; 2: smoothed time-series; 3: original time-series
seasonpar = 1.0, # Seasonality parameter (0-1)
iters = 2, # No. of envelope iterations (3/2/1)
FUN = 2, # Fitting method (3/2/1)
half_win = 7, # half Window size for Sav-Gol.
meth_pheno = 1, # Season start / end method (4/3/2/1)
trs = c(0.5, 0.5) # Season start / end values
)
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