Growing season division

Source: R/deprecated_season.R, R/season_mov.R

Divide growing seasons according to rough fitting (rFUN) result .

For season, rough fitting is applied for whole. For season_mov rough fitting is applied in every year, during which maxExtendMonth is extended.

season(
  INPUT,
  rFUN,
  wFUN,
  iters = 2,
  wmin = 0.1,
  lambda,
  nf = 3,
  frame = floor(INPUT$nptperyear/5) * 2 + 1,
  minpeakdistance = NULL,
  ypeak_min = 0.1,
  r_max = 0.2,
  r_min = 0.05,
  rtrough_max = 0.6,
  MaxPeaksPerYear = 2,
  MaxTroughsPerYear = 3,
  calendarYear = FALSE,
  adj.param = TRUE,
  rm.closed = TRUE,
  is.continuous = TRUE,
  .check_season = TRUE,
  verbose = FALSE,
  ...
)

stat_season(INPUT, d_fit)

Arguments

INPUT

A list object with the elements of t, y, w, Tn (optional) and ylu, returned by check_input().

rFUN

character, the name of rough curve fitting function, can be one of c("smooth_wSG", "smooth_wWHIT", "smooth_wHANTS"), which are corresponding to smooth_wSG(), smooth_wWHIT() and smooth_wHANTS().

wFUN

weights updating function, can be one of .

iters

integer, the number of rough fitting iterations

wmin

double, minimum weigth (i.e. weight of snow, ice and cloud).

lambda

The smoothing parameter of smooth_wWHIT(). For season_mov(), if lambda is NULL, init_lambda() will be used. Generally, it was set as 10000, 15, and 5 for daily, 8-day and 16-day inputs respectively.

nf

The parameter of smooth_wHANTS(), number of frequencies to be considered above the zero frequency.

frame

The parameter of smooth_wSG(), moving window size. Suggested by TIMESAT, default frame = floor(nptperyear/7)*2 + 1.

minpeakdistance

double, in points (default as nptperyear/6). The minimum distance of two peaks. If the distance of two maximum extreme value less than minpeakdistance, only the real maximum value will be left.

ypeak_min

y_peak >= ypeak_min

r_max

Similar as r_min, The maximum threshold should be greater than r_max.

r_min

Threshold is defined as the difference of peak value with trough value. There are two threshold (left and right). The minimum threshold should be greater than r_min.

rtrough_max

ytrough <= rtrough_max*A, A is the amplitude of y.

MaxPeaksPerYear

This parameter is used to adjust lambda in iterations. If PeaksPerYear > MaxPeaksPerYear, then lambda = lambda*2.

MaxTroughsPerYear

This parameter is used to adjust lambda in iterations. If TroughsPerYear > MaxTroughsPerYear, then lambda = lambda*2.

calendarYear

If true, only one static calendar growing season will be returned.

adj.param

, .

rm.closed

boolean. Whether check the two closest peaks (or troughs).

is.continuous

boolean. Whether the input is continuous? This parameter is for fluxsite site-year data.

.check_season

not used (only for debug)

verbose

whether to print options_season into console?

...

ignored.

d_fit

A data.frame with the columns of t, y, witer... and ziter....

Value

  • fit: A data.table of Rough fitting result, with the columns of (t, y, witer1, ..., witerN, ziter1, ..., ziterN).

  • dt: A data.table of growing season division information, with the columns of (beg, peak, end, y_beg, y_peak, y_end, len, year, season, flag).

Details

Before growing season division, INPUT should be added a year in head and tail first by add_HeadTail.

Finally, use findpeaks() to get local maximum and local minimum values. Two local minimum define a growing season. If two local minimum(maximum) are too closed, then only the smaller(biger) is left.

See also

findpeaks().

Examples

data("CA_NS6")
d <- CA_NS6

nptperyear <- 23
INPUT <- check_input(d$t, d$y, d$w,
    QC_flag = d$QC_flag,
    nptperyear = nptperyear, south = FALSE,
    maxgap = nptperyear / 4, alpha = 0.02, wmin = 0.2
)
# plot_input(INPUT)

wFUN <- "wTSM"
# all year as a whole
options = list(rFUN = "smooth_wWHIT", wFUN = wFUN, lambda = 10)
brks <- season(INPUT, lambda = 10)
plot_season(INPUT, brks, d)


brks2 = season_input(INPUT, options)
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
all.equal(brks2, brks)
#> [1] TRUE

c(d_fit, info_peak) %<-% roughFit(INPUT)
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
d_season = find_season.peaks(d_fit, info_peak)

c(t, ypred) %<-% d_fit[, .(t, ziter2)]
d_season = find_season.default(ypred, t)
all.equal(brks$dt, d_season)
#> [1] TRUE

# opt <- .options$season
# brks$fit - d_fit # function passed test

# curve fitting by year
brks_mov <- season_mov(INPUT,
    options = list(
        rFUN = "smooth_wWHIT", wFUN = wFUN,
        lambda = 10,
        r_min = 0.05, ypeak_min = 0.05,
        verbose = TRUE
    )
)
#>   [season_mov] running 1 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 2 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 3 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 4 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 5 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 6 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 7 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
plot_season(INPUT, brks_mov)


rfit <- brks2rfit(brks_mov)
r <- get_pheno(rfit)