WaverideR (version 0.4.1)

curve2time_unc_anchor: Convert the re-tracked curve results to a depth time space with uncertainty

Description

Converts the re-tracked curve results from retrack_wt_MC function to a depth time space using an anchor date while also taking into account the uncertainty of the tracked astronomical cycle

Usage

curve2time_unc_anchor(
  age_constraint = NULL,
  tracked_cycle_curve = NULL,
  tracked_cycle_period = NULL,
  tracked_cycle_period_unc = NULL,
  tracked_cycle_period_unc_dist = "n",
  n_simulations = 20,
  gap_constraints = NULL,
  proxy_data = NULL,
  cycles_check = NULL,
  uncer_cycles_check = NULL,
  max_runs = 1000,
  run_multicore = FALSE,
  verbose = FALSE,
  genplot = FALSE,
  keep_nr = 2,
  keep_all_time_curves = FALSE,
  dj = 1/200,
  lowerPeriod = 1,
  upperPeriod = 4600,
  omega_nr = 6,
  seed_nr = 1337,
  dir = TRUE
)

Value

The output is a list of 3 or 4 elements if keep_all_time_curves is set to TRUE then the list consist of the x-axis, all the fitted curves in a matrix format, the astrochronologically fitted age of the anchor, all the generated depth time curves if keep_all_time_curves is set to TRUE then the list consists of the x-axis, all the fitted curves in a matrix format and the astrochronologically fitted age of the anchor If genplot=TRUE then 3 plots stacked on top of each other will be plotted. Plot 1: the original data set. Plot 2: the depth time plot. Plot 3: the data set in the time domain. #'

Arguments

age_constraint

age constrains for the modelling run Input should be a data frame with 7 columns, the first columns are the ID names the second column are the ages (usually in kyr) the third column is the uncertainty (usually in kyr) given as the fourth column is the distribution which is either "n" for a normal distribution or "u" for a uniform distribution. The fifth column is the location in the depth domain of the age constraint. the sixth column is the location/thickness uncertainty of the age_constraint in the depth domain. The seventh column is the uncertainty distribution of the age_constrain in the depth domain

tracked_cycle_curve

Curve of the cycle tracked using the retrack_wt_MC function
Any input (matrix or data frame) with 3 columns in which column 1 is the x-axis, column 2 is the mean tracked frequency (in cycles/metres) column 3 1 standard deviation

tracked_cycle_period

Period of the tracked curve in kyr.

tracked_cycle_period_unc

uncertainty in the period of the tracked cycle

tracked_cycle_period_unc_dist

distribution of the uncertainty of the tracked cycle value need to be either "u" for uniform distribution or "n" for normal distribution Default="n"

n_simulations

number of time series to be modeled Default=20

gap_constraints

gap parameters for the modelling run input should be a data frame with

proxy_data

proxy data to be tune and check preservation of astronomical cycles

cycles_check

astronomical cycles which are checked for their presence after tuning

uncer_cycles_check

uncertainty of astronomical cycles to be check for after tunning

max_runs

maximum runs before one of the age constraints is dropped Default=1000

run_multicore

Run function using multiple cores Default="FALSE"

verbose

Print text Default=FALSE.

genplot

generate plot codeDefault=FALSE

keep_nr

minimal number of age constraints to be kept Default=2

keep_all_time_curves

weather to keep all the generated age curves including the ones rejected from the modelling run Default=FALSE

dj

Spacing between successive scales. The CWT analyses analyses the signal using successive periods which increase by the power of 2 (e.g.2^0=1,2^1=2,2^2=4,2^3=8,2^4=16). To have more resolution in-between these steps the dj parameter exists, the dj parameter specifies how many extra steps/spacing in-between the power of 2 scaled CWT is added. The amount of steps is 1/x with a higher x indicating a smaller spacing. Increasing the increases the computational time of the CWT Default=1/200.

lowerPeriod

Lowest period to be analyzed Default=2. The CWT analyses the signal starting from the lowerPeriod to the upperPeriod so the proper selection these parameters allows to analyze the signal for a specific range of cycles. scaling is done using power 2 so for the best plotting results select a value to the power or 2.

upperPeriod

Upper period to be analyzed Default=1024. The CWT analyses the signal starting from the lowerPeriod to the upperPeriod so the proper selection these parameters allows to analyze the signal for a specific range of cycles. scaling is done using power 2 so for the best plotting results select a value to the power or 2.

omega_nr

Number of cycles contained within the Morlet wavelet

seed_nr

The seed number of the Monte-Carlo simulations. Default=1337

dir

time direction of tuning e.g. does time increase or decrease with depth

Author

Part of the code is based on the sedrate2time function of the 'astrochron' R package

References

Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>

Examples

if (FALSE) {


Bisciaro_al <- Bisciaro_XRF[, c(1, 61)]
Bisciaro_al <-
 astrochron::sortNave(Bisciaro_al, verbose = FALSE, genplot = FALSE)
Bisciaro_al <-
 astrochron::linterp(Bisciaro_al,
                     dt = 0.01,
                     verbose = FALSE,
                     genplot = FALSE)
Bisciaro_al <- Bisciaro_al[Bisciaro_al[, 1] > 2, ]

Bisciaro_al_wt <-
 analyze_wavelet(
   data = Bisciaro_al,
   dj = 1 / 200 ,
   lowerPeriod = 0.01,
   upperPeriod = 50,
   verbose = FALSE,
   omega_nr = 8
 )
# Bisciaro_al_wt_track <-
#   track_period_wavelet(
#     astro_cycle = 110,
#     wavelet = Bisciaro_al_wt,
#     n.levels = 100,
#     periodlab = "Period (metres)",
#     x_lab = "depth (metres)"
#   )
#
# Bisciaro_al_wt_track <- completed_series(
#   wavelet = Bisciaro_al_wt,
#   tracked_curve = Bisciaro_al_wt_track,
#   period_up = 1.2,
#   period_down = 0.8,
#   extrapolate = TRUE,
#   genplot = FALSE,
#   keep_editable = FALSE
# )
#
# Bisciaro_al_wt_track <-
#   loess_auto(
#     time_series = Bisciaro_al_wt_track,
#     genplot = FALSE,
#     print_span = FALSE,
#     keep_editable = FALSE
#   )



Bisciaro_ca <- Bisciaro_XRF[, c(1, 55)]
Bisciaro_ca <-
 astrochron::sortNave(Bisciaro_ca, verbose = FALSE, genplot = FALSE)
Bisciaro_ca <-
 astrochron::linterp(Bisciaro_ca,
                     dt = 0.01,
                     verbose = FALSE,
                     genplot = FALSE)
Bisciaro_ca <- Bisciaro_ca[Bisciaro_ca[, 1] > 2, ]

Bisciaro_ca_wt <-
 analyze_wavelet(
   data = Bisciaro_ca,
   dj = 1 / 200 ,
   lowerPeriod = 0.01,
   upperPeriod = 50,
   verbose = FALSE,
   omega_nr = 8
 )


#
# Bisciaro_ca_wt_track <-
#   track_period_wavelet(
#     astro_cycle = 110,
#     wavelet = Bisciaro_ca_wt,
#     n.levels = 100,
#     periodlab = "Period (metres)",
#     x_lab = "depth (metres)"
#   )
#
# Bisciaro_ca_wt_track <- completed_series(
#   wavelet = Bisciaro_ca_wt,
#   tracked_curve = Bisciaro_ca_wt_track,
#   period_up = 1.2,
#   period_down = 0.8,
#   extrapolate = TRUE,
#   genplot = FALSE,
#   keep_editable = FALSE
# )
#
# Bisciaro_ca_wt_track <-
#   loess_auto(
#     time_series = Bisciaro_ca_wt_track,
#     genplot = FALSE,
#     print_span = FALSE,
#     keep_editable = FALSE
#   )


Bisciaro_sial <- Bisciaro_XRF[, c(1, 64)]
Bisciaro_sial <-
 astrochron::sortNave(Bisciaro_sial, verbose = FALSE, genplot = FALSE)
Bisciaro_sial <-
 astrochron::linterp(Bisciaro_sial,
                     dt = 0.01,
                     verbose = FALSE,
                     genplot = FALSE)
Bisciaro_sial <- Bisciaro_sial[Bisciaro_sial[, 1] > 2, ]

Bisciaro_sial_wt <-
 analyze_wavelet(
   data = Bisciaro_sial,
   dj = 1 / 200 ,
   lowerPeriod = 0.01,
   upperPeriod = 50,
   verbose = FALSE,
   omega_nr = 8
 )

#Bisciaro_sial_wt_track <-
#   track_period_wavelet(
#     astro_cycle = 110,
#     wavelet = Bisciaro_sial_wt,
#     n.levels = 100,
#     periodlab = "Period (metres)",
#     x_lab = "depth (metres)"
#   )
#
#
# Bisciaro_sial_wt_track <- completed_series(
#   wavelet = Bisciaro_sial_wt,
#   tracked_curve = Bisciaro_sial_wt_track,
#   period_up = 1.2,
#   period_down = 0.8,
#   extrapolate = TRUE,
#   genplot = FALSE,
#   keep_editable = FALSE
# )
#
# Bisciaro_sial_wt_track <-
#   loess_auto(
#     time_series = Bisciaro_sial_wt_track,
#     genplot = FALSE,
#     print_span = FALSE,
#     keep_editable = FALSE
#   )


Bisciaro_Mn <- Bisciaro_XRF[, c(1, 46)]
Bisciaro_Mn <-
 astrochron::sortNave(Bisciaro_Mn, verbose = FALSE, genplot = FALSE)
Bisciaro_Mn <-
 astrochron::linterp(Bisciaro_Mn,
                     dt = 0.01,
                     verbose = FALSE,
                     genplot = FALSE)
Bisciaro_Mn <- Bisciaro_Mn[Bisciaro_Mn[, 1] > 2, ]

Bisciaro_Mn_wt <-
 analyze_wavelet(
   data = Bisciaro_Mn,
   dj = 1 / 200 ,
   lowerPeriod = 0.01,
   upperPeriod = 50,
   verbose = FALSE,
   omega_nr = 8
 )

# Bisciaro_Mn_wt_track <-
#   track_period_wavelet(
#     astro_cycle = 110,
#     wavelet = Bisciaro_Mn_wt,
#     n.levels = 100,
#     periodlab = "Period (metres)",
#     x_lab = "depth (metres)"
#   )
#
#
# Bisciaro_Mn_wt_track <- completed_series(
#   wavelet = Bisciaro_Mn_wt,
#   tracked_curve = Bisciaro_Mn_wt_track,
#   period_up = 1.2,
#   period_down = 0.8,
#   extrapolate = TRUE,
#   genplot = FALSE,
#   keep_editable = FALSE
# )
# Bisciaro_Mn_wt_track <-
#   loess_auto(
#     time_series = Bisciaro_Mn_wt_track,
#     genplot = FALSE,
#     print_span = FALSE,
#     keep_editable = FALSE
#   )

Bisciaro_Mg <- Bisciaro_XRF[, c(1, 71)]
Bisciaro_Mg <-
 astrochron::sortNave(Bisciaro_Mg, verbose = FALSE, genplot = FALSE)
Bisciaro_Mg <-
 astrochron::linterp(Bisciaro_Mg,
                     dt = 0.01,
                     verbose = FALSE,
                     genplot = FALSE)
Bisciaro_Mg <- Bisciaro_Mg[Bisciaro_Mg[, 1] > 2, ]

Bisciaro_Mg_wt <-
 analyze_wavelet(
   data = Bisciaro_Mg,
   dj = 1 / 200 ,
   lowerPeriod = 0.01,
   upperPeriod = 50,
   verbose = FALSE,
   omega_nr = 8
 )

# Bisciaro_Mg_wt_track <-
#   track_period_wavelet(
#     astro_cycle = 110,
#     wavelet = Bisciaro_Mg_wt,
#     n.levels = 100,
#     periodlab = "Period (metres)",
#     x_lab = "depth (metres)"
#   )
#
#
# Bisciaro_Mg_wt_track <- completed_series(
#   wavelet = Bisciaro_Mg_wt,
#   tracked_curve = Bisciaro_Mg_wt_track,
#   period_up = 1.2,
#   period_down = 0.8,
#   extrapolate = TRUE,
#   genplot = FALSE,
#   keep_editable = FALSE
# )
#
# Bisciaro_Mg_wt_track <-
#   loess_auto(
#     time_series = Bisciaro_Mg_wt_track,
#     genplot = FALSE,
#     print_span = FALSE,
#     keep_editable = FALSE
#   )




wt_list_bisc <- list(Bisciaro_al_wt,
                    Bisciaro_ca_wt,
                    Bisciaro_sial_wt,
                    Bisciaro_Mn_wt,
                    Bisciaro_Mg_wt)


data_track_bisc <- cbind(
 Bisciaro_al_wt_track[, 2],
 Bisciaro_ca_wt_track[, 2],
Bisciaro_sial_wt_track[, 2],
 Bisciaro_Mn_wt_track[, 2],
 Bisciaro_Mg_wt_track[, 2]
)

x_axis_bisc <- Bisciaro_al_wt_track[, 1]

bisc_retrack <- retrack_wt_MC(
wt_list = wt_list_bisc,
 data_track = data_track_bisc,
 x_axis = x_axis_bisc,
 nr_simulations = 500,
 seed_nr = 1337,
 verbose = TRUE,
 genplot = FALSE,
 keep_editable = FALSE,
 create_GIF = FALSE,
 plot_GIF = FALSE,
 width_plt =  600,
 height_plt = 450,
 period_up  =  1.5,
 period_down = 0.5,
 plot.COI = TRUE,
 n.levels = 100,
 palette_name = "rainbow",
 color_brewer = "grDevices",
periodlab = "Period (metres)",
x_lab = "depth (metres)",
add_avg = FALSE,
time_dir = TRUE,
file_name = "TEST",
run_multicore = TRUE,
output = 5,
 n_imgs = 50,
 plot_horizontal = TRUE,
 empty_folder = FALSE
)

proxy_list_bisc <- list(Bisciaro_al,
                    Bisciaro_ca,
                    Bisciaro_sial,
                    Bisciaro_Mn,
                    Bisciaro_Mg)




id <- c("CCT18_322", "CCT18_315", "CCT18_311")
ages <- c(20158, 20575, 20857)
ageSds <- c(28, 40, 34)
ages_unc_dist <- c("n", "n", "n")
position <- c(13.3, 7.25, 3.2)
anchor_thick <- c(0.2, 0.1, 0.1)
anchor_thick_unc_dist <- c("u", "u", "u")

ash_Bisc <-
 as.data.frame(
   cbind(
     id,
     ages,
     ageSds,
     ages_unc_dist,
     position,
     anchor_thick,
     anchor_thick_unc_dist
   )
 )

gap_dur = c(10, 20)
gap_unc = c(3, 10)
gap_depth = c(2.5, 9)
gap_unc_dist = c("n", "n")


gap_constraints_Bisc <-
 as.data.frame(cbind(gap_dur, gap_unc, gap_depth, gap_unc_dist))

cycles_checks <- c(110,40,22)
uncer_cycles_checks <- c(20,5,7)

curve2time_unc_anchor_res <-
 curve2time_unc_anchor(
 age_constraint =  ash_Bisc,
  tracked_cycle_curve =  bisc_retrack,
  tracked_cycle_period = 110,
   tracked_cycle_period_unc = ((135 - 110) + (110 - 95)) / 2,
  tracked_cycle_period_unc_dist = "n",
   n_simulations = 20,
   gap_constraints = gap_constraints_Bisc,
   proxy_data = proxy_list_bisc,
   cycles_check = NULL,
   uncer_cycles_check = NULL,
   cycles_check = cycles_checks,
   uncer_cycles_check = uncer_cycles_checks,
  max_runs = 1000,
   run_multicore = FALSE,
   verbose = FALSE,
   genplot = FALSE,
   keep_nr = 2,
   keep_all_time_curves = FALSE,
   dj = 1/200,
   lowerPeriod =1,
   upperPeriod =2500,
   omega_nr = 6,
   seed_nr=1337,
   dir=TRUE
 )

}