Package 'yaps'

Title: Track Estimation using YAPS (Yet Another Positioning Solver)
Description: Estimate tracks of animals tagged with acoustic transmitters. 'yaps' was introduced in 2017 as a transparent open-source tool to estimate positions of fish (and other aquatic animals) tagged with acoustic transmitters. Based on registrations of acoustic transmitters on hydrophones positioned in a fixed array, 'yaps' enables users to synchronize the collected data (i.e. correcting for drift in the internal clocks of the hydrophones/receivers) and subsequently to estimate tracks of the tagged animals. The paper introducing 'yaps' is available in open access at Baktoft, Gjelland, Økland & Thygesen (2017) <doi:10.1038/s41598-017-14278-z>. Also check out our cookbook with a completely worked through example at Baktoft, Gjelland, Økland, Rehage, Rodemann, Corujo, Viadero & Thygesen (2019) <DOI:10.1101/2019.12.16.877688>. Additional tutorials will eventually make their way onto the project website at <https://baktoft.github.io/yaps/>.
Authors: Henrik Baktoft [cre, aut] , Karl Gjelland [aut] , Uffe H. Thygesen [aut] , Finn Økland [aut]
Maintainer: Henrik Baktoft <[email protected]>
License: GPL-3
Version: 1.2.5
Built: 2024-11-04 04:34:55 UTC
Source: https://github.com/ocean-tracking-network/yaps

Help Index

Align synced data with known burst sequence

Description

Identifies where in the sequence of known burst intervals the detected data is from. Add extra columns to data.table containing ping index of the burst sequence (seq_ping_idx) and expected time of ping (seq_epo). Only to be used for 'random' burst interval data when you know the burst sequence.

Usage

alignBurstSeq(
  synced_dat,
  burst_seq,
  seq_lng_min = 10,
  rbi_min,
  rbi_max,
  plot_diag = TRUE
)

Arguments

synced_dat

data.table obtained using applySync() on a detections_table
burst_seq

Vector containing known burst sequence
seq_lng_min

Minimum length of sequence of consecutive pings to use for the alignment. Finds first occurence of sequence of this length in the data and compare to the known burst sequence
rbi_min, rbi_max

Minimum and maximum burst interval of the transmitter. Used to identify sequence of consecutive pings in the data
plot_diag

Logical indicating if visual diagnosis plots should be created.

Value

data.table like the input synced_dat, but with extra columns seq_ping_idx and seq_epo

Examples

# Align data from a tag with known random burst interval to the burst interval sequence
# using the hald data included in `yapsdata` (see ?yapsdata::hald for info).
synced_dat_1315 <- dat_align$synced_dat_1315
seq_1315 <- dat_align$seq_1315
rbi_min <- 60
rbi_max <- 120
aligned_dat <- alignBurstSeq(synced_dat=synced_dat_1315, burst_seq=seq_1315, 
	rbi_min=rbi_min, rbi_max=rbi_max, plot_diag=TRUE)

Apply sync model to toa matrix to obtain synced data

Description

Apply sync model to toa matrix to obtain synced data

Usage

applySync(toa, hydros = "", sync_model)

Arguments

toa

Object containing data to be synchronized. Typically a data.table as e.g. ssu1$detections, but can also be a matrix dim=(n_ping, n_hydo).
hydros

data.table formatted as ssu1$hydros
sync_model

Synchronization model obtained using getSyncModel()

Value

A data.table with the now synchronized time-of-arrivals in column eposync.

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)

inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Check consistency of inp object obtained from getInp()

Description

Check consistency of inp object obtained from getInp()

Usage

checkInp(inp)

Arguments

inp

Object obtained using getInp()

Value

No return value, but prints errors/warnings if issues with inp is detected.

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)
inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Check consistency of inp_sync object obtained from getInpSync()

Description

Check consistency of inp_sync object obtained from getInpSync()

Usage

checkInpSync(inp_sync, silent_check)

Arguments

inp_sync

Object obtained using getInpSync()
silent_check

Logical whether to get output from checkInpSync(). Default is FALSE

Value

No return value, but prints errors/warnings if issues with inp_sync is detected.

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)
inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Example data for showcasing yaps function alignBurstSeq()

Description

Function alignBurstSeq() is used to align synced detection data with a sequence of known random burst intervals (BI).
This step is needed to take advantage of the extra information available when working with random BI data with a known sequence.
This small sample is obtained from the accompanying data package yapsdata.

Usage

dat_align

Format

A list containing 2 items:

synced_dat_1315

data.table containing synced detections of tag 1315.

synced_dat_1315

vector of small part of the complete sequence of known random BIs.

Fine-tune an already fitted sync_model Wrapper function to re-run getSyncModel() using the same data, but excluding outliers. Note dimensions of data might change if eps_threshold results in empty rows in the TOA-matrix.

Description

Fine-tune an already fitted sync_model Wrapper function to re-run getSyncModel() using the same data, but excluding outliers. Note dimensions of data might change if eps_threshold results in empty rows in the TOA-matrix.

Usage

fineTuneSyncModel(sync_model, eps_threshold, silent = TRUE)

Arguments

sync_model

sync_model obtained using getSyncModel()
eps_threshold

Maximum value of residual measured in meter assuming speed of sound = 1450 m/s
silent

logical whether to make getSyncModel() silent

Value

Fine tuned sync_model. See ?getSyncModel for more info.

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)

inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Get a standard bounding box to impose spatial constraints

Description

Standard is a rectangle based on coordinates of outer hydros +- the buffer in meters

Usage

getBbox(hydros, buffer = 100, eps = 0.001, pen = 1e+06)

Arguments

hydros

Dataframe from simHydros() or Dataframe with columns hx and hy containing positions of the receivers. Translate the coordinates to get the grid centre close to (0;0).
buffer

Number of meters the spatial domain extends beyound the outer hydros.
eps

Specifies how well-defined the borders are (eps=1E-2 is very sharp, eps=100 is very soft).
pen

Specifies the penalty multiplier.

Value

Vector of lenght 6: c(x_min, x_max, y_min, y_max, eps, pen). Limits are given in UTM coordinates.

Examples

hydros <- ssu1$hydros
colnames(hydros) <- c('serial','hx','hy','hz','sync_tag','idx')
bbox <- getBbox(hydros)
plotBbox(hydros, bbox)

Get prepared inp-object for use in TMB-call

Description

Wrapper-function to compile a list of input needed to run TMB

Usage

getInp(
  hydros,
  toa,
  E_dist,
  n_ss,
  pingType,
  sdInits = 1,
  rbi_min = 0,
  rbi_max = 0,
  ss_data_what = "est",
  ss_data = 0,
  biTable = NULL,
  z_vec = NULL,
  bbox = NULL
)

Arguments

hydros

Dataframe from simHydros() or Dataframe with columns hx and hy containing positions of the receivers. Translate the coordinates to get the grid centre close to (0;0).
toa

TOA-matrix: matrix with receivers in rows and detections in columns. Make sure that the receivers are in the same order as in hydros, and that the matrix is very regular: one ping per column (inlude empty columns if a ping is not detected).
E_dist

Which distribution to use in the model - "Gaus" = Gaussian, "Mixture" = mixture of Gaussian and t or "t" = pure t-distribution
n_ss

Number of soundspeed estimates: one estimate per hour is usually enough
pingType

Type of transmitter to simulate - either stable burst interval ('sbi'), random burst interval ('rbi') or random burst interval but where the random sequence is known a priori
sdInits

If >0 initial values will be randomized around the normally fixed value using rnorm(length(inits), mean=inits, sd=sdInits)
rbi_min, rbi_max

Minimum and maximum BI for random burst interval transmitters
ss_data_what

What speed of sound (ss) data to be used. Default ss_data_what='est': ss is estimated by the model. Alternatively, if ss_data_what='data': ss_data must be provided and length(ss_data) == ncol(toa)
ss_data

Vector of ss-data to be used if ss_data_what = 'est'. Otherwise ss_data <- 0 (default)
biTable

Table of known burst intervals. Only used when pingType == "pbi". Default=NULL
z_vec

Vector of known depth values (positive real). Default=NULL is which case no 3D is assumed. If z_vec = "est" depth will be estimated.
bbox

Spatial constraints in the form of a bounding box. See ?getBbox for details.

Value

List of input data ready for use in runYaps()

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20


# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)

bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)
inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)


# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 


# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Get object inp for synchronization

Description

Get object inp for synchronization

Usage

getInpSync(
  sync_dat,
  max_epo_diff,
  min_hydros,
  time_keeper_idx,
  fixed_hydros_idx,
  n_offset_day,
  n_ss_day,
  keep_rate = 1,
  excl_self_detect = TRUE,
  lin_corr_coeffs = NA,
  ss_data_what = "est",
  ss_data = c(0),
  silent_check = FALSE
)

Arguments

sync_dat

List containing data.tables with hydrophone information and detections. See e.g. ?ssu1 for example
max_epo_diff

Sets the upper threshold for differences in TOA of sync tags. Best parameter value depends on burst rate of sync tags and how far apart the internal clocks of the hydros are prior to synchronization. A bit less than half of minimum sync tag burst rate is a good starting choice.
min_hydros

Sets the lower threshold of how many hydrophones need to detect each sync tag ping in order to be included in the sync process. Should be as high as possible while observing that all hydrosphones are contributing. If too low, isolated hydrophones risk falling out completely. Future versions will work towards automising this.
time_keeper_idx

Index of the hydrophone to use as time keeper. Could e.g. be the one with smallest overall clock-drift.
fixed_hydros_idx

Vector of hydro idx's for all hydrophones where the position is assumed to be known with adequate accuracy and precission. Include as many as possible as fixed hydros to reduce overall computation time and reduce overall variability. As a bare minimum two hydros need to be fixed, but we strongly advice to use more than two.
n_offset_day

Specifies the number of hydrophone specific quadratic polynomials to use per day. For PPM based systems, 1 or 2 is often adeqaute.
n_ss_day

Specifies number of speed of sound to estimate per day if no ss data is supplied. It is recommended to use logged water temperature instead. However, estimating SS gives an extra option for sanity-checking the final sync-model.
keep_rate

Syncing large data sets can take a really long time. However, there is typically an excess number of sync tag detections and a sub-sample is typically enough for good synchronization. This parameter EITHER specifies a proportion (0-1) of data to keep when sub-sampling OR (if keep_rate > 10) number of pings (approximate) to keep in each hydro X offset_idx combination if enough exists.
excl_self_detect

Logical whether to excluded detections of sync tags on the hydros they are co-located with. Sometimes self detections can introduce excessive residuals in the sync model in which case they should be excluded.
lin_corr_coeffs

Matrix of coefficients used for pre-sync linear correction. ⁠dim(lin_corr_coeffs)=(#hydros, 2)⁠.
ss_data_what

Indicates whether to estimate ("est") speed of sound or to use data based on logged water temperature ("data").
ss_data

data.table containing timestamp and speed of sound for the entire period to by synchronised. Must contain columns 'ts' (POSIXct timestamp) and 'ss' speed of sound in m/s (typical values range 1400 - 1550).
silent_check

Logical whether to get output from checkInpSync(). Default is FALSE

Value

List of input data ready for use in getSyncModel()

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)

inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Quick overview to check if all hydros have enough data within each offset period.

Description

Quick overview to check if all hydros have enough data within each offset period.

Usage

getSyncCoverage(inp_sync, plot = FALSE)

Arguments

inp_sync

Object obtained using getInpSync()
plot

Logical indicating whether to plot a visual or not.

Value

A data.table containing number of pings included in each hydro x offset combination.

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)
inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Get sync model from inp_sync object obtained by getInpSync()

Description

Get sync model from inp_sync object obtained by getInpSync()

Usage

getSyncModel(
  inp_sync,
  silent = TRUE,
  fine_tune = FALSE,
  max_iter = 100,
  tmb_smartsearch = TRUE
)

Arguments

inp_sync

Input data prepared for the sync model using getInpSync()
silent

Keep TMB quiet
fine_tune

Logical. Whether to re-run the sync model excluding residual outliers. Deprecated use fineTuneSyncModel() instead.
max_iter

Max number of iterations to run TMB. Default=100 seems to work in most cases.
tmb_smartsearch

Logical whether to use the TMB smartsearch in the inner optimizer (see ?TMB::MakeADFun for info). Default and original implementation is TRUE. However, there seems to be an issue with some versions of Matrix that requires tmb_smartsearch=FALSE.

Value

List containing relevant data constituting the sync_model ready for use in fineTuneSyncModel() if needed or in applySync()

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)
inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Build TOA matrix from synced data.table - also do some pre-filtering of severe MP, pruning loose ends etc

Description

Build TOA matrix from synced data.table - also do some pre-filtering of severe MP, pruning loose ends etc

Usage

getToaYaps(synced_dat, hydros, rbi_min, rbi_max, pingType = NULL)

Arguments

synced_dat

data.table containing synchronized data formatted as output from/or obtained using applySync()
hydros

Dataframe from simHydros() or Dataframe with columns hx and hy containing positions of the receivers. Translate the coordinates to get the grid centre close to (0;0).
rbi_min

Minimum and maximum BI for random burst interval transmitters
rbi_max

Minimum and maximum BI for random burst interval transmitters
pingType

Type of transmitter to simulate - either stable burst interval ('sbi'), random burst interval ('rbi') or random burst interval but where the random sequence is known a priori

Value

Matrix of time-of-arrivals. One coloumn per hydro, one row per ping.

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)
inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Graphical representation of spatial constraints

Description

Graphical representation of spatial constraints

Usage

plotBbox(hydros, bbox)

Arguments

hydros

Dataframe from simHydros() or Dataframe with columns hx and hy containing positions of the receivers. Translate the coordinates to get the grid centre close to (0;0).
bbox

Spatial constraints in the form of a bounding box. See ?getBbox for details.

Value

No return value, called to plot graphic.

Examples

hydros <- ssu1$hydros
colnames(hydros) <- c('serial','hx','hy','hz','sync_tag','idx')
bbox <- getBbox(hydros)
plotBbox(hydros, bbox)

Plot to check how well the sync model is working

Description

Delta values indicate absolute difference between true and estimated distances based on pairwise relative distances to sync_tag. For instance, a ping from sync_tag t colocated with hydro Ht is detected by hydros H1 and H2. The pairwise relative distance to sync tag is then delta = abs((true_dist(Ht, H1) - true_dist(Ht, H2)) - (est_dist(Ht, H1) - est_dist(Ht, H2)))

Usage

plotSyncModelCheck(sync_model, by = "")

Arguments

sync_model

Synchronization model obtained using getSyncModel()
by

What to facet/group the plot by? Currently supports one of 'sync_bin_sync', 'sync_bin_hydro', 'sync_bin_sync_smooth', 'sync_bin_hydro_smooth', 'hydro', 'sync_tag'

Value

No return value, called to plot graphics.

Examples

sync_model <- ssu1$sync_model

plotSyncModelHydros(sync_model)

plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

plotSyncModelCheck(sync_model, by = "hydro")
plotSyncModelCheck(sync_model, by = "sync_tag")
plotSyncModelCheck(sync_model, by = "sync_bin_sync")
plotSyncModelCheck(sync_model, by = "sync_bin_hydro")

Plot hydrophone positions. Especially useful if some hydro re-positioned as part of the sync model.

Description

Plot hydrophone positions. Especially useful if some hydro re-positioned as part of the sync model.

Usage

plotSyncModelHydros(sync_model)

Arguments

sync_model

Synchronization model obtained using getSyncModel()

Value

No return value, called to plot graphics.

Examples

sync_model <- ssu1$sync_model

plotSyncModelHydros(sync_model)

plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

plotSyncModelCheck(sync_model, by = "hydro")
plotSyncModelCheck(sync_model, by = "sync_tag")
plotSyncModelCheck(sync_model, by = "sync_bin_sync")
plotSyncModelCheck(sync_model, by = "sync_bin_hydro")

Plot residuals of sync_model to enable check of model

Description

Plot residuals of sync_model to enable check of model

Usage

plotSyncModelResids(sync_model, by = "overall")

Arguments

sync_model

Synchronization model obtained using getSyncModel()
by

What to facet/group the plot by? Currently supports one of 'overall', 'sync_tag', 'hydro', 'quantiles', 'temporal', 'temporal_hydro', 'temporal_sync_tag'

Value

No return value, called to plot graphics.

Examples

sync_model <- ssu1$sync_model

plotSyncModelHydros(sync_model)

plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

plotSyncModelCheck(sync_model, by = "hydro")
plotSyncModelCheck(sync_model, by = "sync_tag")
plotSyncModelCheck(sync_model, by = "sync_bin_sync")
plotSyncModelCheck(sync_model, by = "sync_bin_hydro")

Basic plots of yaps output

Description

Basic plots of yaps output

Usage

plotYaps(yaps_out, type = "map", xlim = NULL, ylim = NULL, main = NULL)

Arguments

yaps_out

Output from succesful run of runYaps()
type

Plot type. type="map" prodces a basic map of estimated track and hydrophones; type="coord_X", type="coord_Y" produces plots of X and Y coordinated including +- 1 standard error.
xlim, ylim

Optional vectors of length 2 to set xlim and/or ylim.
main

Title of plot - optional.

Value

No return value, called to plot graphics.

Examples

library(yaps)
plotYaps(ssu1$yaps_out, type="map")
plotYaps(ssu1$yaps_out, type="coord_X")
plotYaps(ssu1$yaps_out, type="coord_Y")

Experimental! Prepare detections data.table from raw data - csv-files exported from vendor software

Description

Experimental! Prepare detections data.table from raw data - csv-files exported from vendor software

Usage

prepDetections(raw_dat, type)

Arguments

raw_dat

Data file from vendor supplied software
type

Type of the vendor file. Currently only 'vemco_vue' is supported.

Value

data.table containing detections extracted from manufacturer data file.

Examples

## Not run: 
prepped_detections <- prepDetections("path-to-raw-data-file", type="vemco_vue")

## End(Not run)

Function to run TMB to estimate track

Description

Function to run TMB to estimate track

Usage

runYaps(
  inp,
  maxIter = 1000,
  getPlsd = TRUE,
  getRep = TRUE,
  silent = TRUE,
  opt_fun = "nlminb",
  opt_controls = list(),
  tmb_smartsearch = TRUE
)

runTmb(
  inp,
  maxIter = 1000,
  getPlsd = TRUE,
  getRep = TRUE,
  silent = TRUE,
  opt_fun = "nlminb",
  opt_controls = list(),
  tmb_smartsearch = TRUE
)

Arguments

inp

inp-object obtained from getInp()
maxIter

Sets inner.control(maxit) of the TMB-call. Increase if model is not converging.
getPlsd, getRep

Whether or not to get sd estimates (plsd=TRUE) and reported values (getRep=TRUE).
silent

Logical whether to keep the optimization quiet.
opt_fun

Which optimization function to use. Default is opt_fun = 'nlminb' - alternative is opt_fun = 'nloptr' (experimental!). If using nloptr, opt_controls must be specified.
opt_controls

List of controls passed to optimization function. For instances, tolerances such as x.tol=1E-8.
If opt_fun = 'nloptr', opt_controls must be a list formatted appropriately. For instance:
opt_controls <- list( algorithm="NLOPT_LD_AUGLAG", xtol_abs=1e-12, maxeval=2E+4, print_level = 1, local_opts= list(algorithm="NLOPT_LD_AUGLAG_EQ", xtol_rel=1e-4) ).
See ?nloptr and the NLopt site https://nlopt.readthedocs.io/en/latest/ for more info. Some algorithms in nloptr require bounded parameters - this is not currently implemented.
tmb_smartsearch

Logical whether to use the TMB smartsearch in the inner optimizer (see ?TMB::MakeADFun for info). Default and original implementation is TRUE. However, there seems to be an issue with recent versions of Matrix that requires tmb_smartsearch=FALSE.

Value

List containing results of fitting yaps to the data.

pl

List containing all parameter estimates.

plsd

List containing standard errors of parameter estimates.

rep

List containing mu_toa.

obj

Numeric obj value of the fitted model obtained using obj$fn().

inp

List containing the inp object used in runYaps(). See ?getInp for further info.

conv_status

Integer convergence status.

conv_message

Text version of convergence status.

track

A data.table containing the estimated track including time-of-ping (top), standard errors and number of hydros detecting each ping (nobs).

Examples

library(yaps)
set.seed(42)

# # # Example using the ssu1 data included in package. See ?ssu1 for info.
# # # Set parameters to use in the sync model - these will differ per study
max_epo_diff <- 120
min_hydros <- 2
time_keeper_idx <- 5
fixed_hydros_idx <- c(2:3, 6, 8, 11, 13:17)
n_offset_day <- 2
n_ss_day <- 2
keep_rate <- 20

# # # Get input data ready for getSyncModel()
inp_sync <- getInpSync(sync_dat=ssu1, max_epo_diff, min_hydros, time_keeper_idx, 
    fixed_hydros_idx, n_offset_day, n_ss_day, keep_rate=keep_rate, silent_check=TRUE)

# # # Check that inp_sync is ok
checkInpSync(inp_sync, silent_check=FALSE)

# # # Also take a look at coverage of the sync data
getSyncCoverage(inp_sync, plot=TRUE)

# # # Fit the sync model
sync_model <- getSyncModel(inp_sync, silent=TRUE, max_iter=200, tmb_smartsearch = TRUE)

# # # On some systems it might work better, if we disbale the smartsearch feature in TMB
# # # To do so, set tmb_smartsearch = FALSE in getSyncModel()

# # # Visualize the resulting sync model
plotSyncModelResids(sync_model, by = "overall")
plotSyncModelResids(sync_model, by = "quantiles")
plotSyncModelResids(sync_model, by = "sync_tag")
plotSyncModelResids(sync_model, by = "hydro")
plotSyncModelResids(sync_model, by = "temporal_hydro")
plotSyncModelResids(sync_model, by = "temporal_sync_tag")

# # # If the above plots show outliers, sync_model can be fine tuned by excluding these.
# # # Use fineTuneSyncModel() for this.
# # # This should typically be done sequentially using eps_thresholds of e.g. 1E4, 1E3, 1E2, 1E2
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E3, silent=TRUE)
sync_model <- fineTuneSyncModel(sync_model, eps_threshold=1E2, silent=TRUE)

# # # Apply the sync_model to detections data.
detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)

# # # Prepare data for running yaps
hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
colnames(hydros_yaps) <- c('hx','hy','hz')
focal_tag <- 15266
rbi_min <- 20
rbi_max <- 40
synced_dat <- detections_synced[tag == focal_tag]
toa <- getToaYaps(synced_dat=synced_dat, hydros=hydros_yaps, pingType='rbi', 
	rbi_min=rbi_min, rbi_max=rbi_max)
bbox <- getBbox(hydros_yaps, buffer=50, pen=1e6)
inp <- getInp(hydros_yaps, toa, E_dist="Mixture", n_ss=5, pingType="rbi", 
	sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0, bbox=bbox)

# # # Check that inp is ok
checkInp(inp)

# # # Run yaps on the prepared data to estimate track
yaps_out <- runYaps(inp, silent=TRUE, tmb_smartsearch=TRUE, maxIter=5000) 

# # # Plot the results and compare to "the truth" obtained using gps

oldpar <- par(no.readonly = TRUE) 
par(mfrow=c(2,2))
plot(hy~hx, data=hydros_yaps, asp=1, xlab="UTM X", ylab="UTM Y", pch=20, col="green")
lines(utm_y~utm_x, data=ssu1$gps, col="blue", lwd=2)
lines(y~x, data=yaps_out$track, col="red")

plot(utm_x~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(x~top, data=yaps_out$track, col="red")
lines(x~top, data=yaps_out$track, col="red")
lines(x-2*x_sd~top, data=yaps_out$track, col="red", lty=2)
lines(x+2*x_sd~top, data=yaps_out$track, col="red", lty=2)

plot(utm_y~ts, data=ssu1$gps, col="blue", type="l", lwd=2)
points(y~top, data=yaps_out$track, col="red")
lines(y~top, data=yaps_out$track, col="red")
lines(y-2*y_sd~top, data=yaps_out$track, col="red", lty=2)
lines(y+2*y_sd~top, data=yaps_out$track, col="red", lty=2)

plot(nobs~top, data=yaps_out$track, type="p", main="#detecting hydros per ping")
lines(caTools::runmean(nobs, k=10)~top, data=yaps_out$track, col="orange", lwd=2)
par(oldpar)

Sim hydrophone array configuration

Description

Sim hydrophone array configuration

Usage

simHydros(auto = TRUE, trueTrack = NULL)

Arguments

auto

If TRUE, attempts to find a decent array configuration to cover the simulated true track.
trueTrack

Track obtained from simTrueTrack().

Value

data.frame containing X and Y for hydros

Examples

library(yaps)
set.seed(42)
# Simulate true track of animal movement of n seconds
trueTrack <- simTrueTrack(model='crw', n = 1000, deltaTime=1, shape=1, 
	scale=0.5, addDielPattern=TRUE, ss='rw')

# Simulate telemetry observations from true track.
# Format and parameters depend on type of transmitter burst interval (BI).
pingType <- 'sbi'

if(pingType == 'sbi') { # stable BI
    sbi_mean <- 30; sbi_sd <- 1e-4;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, sbi_mean=sbi_mean, sbi_sd=sbi_sd)
} else if(pingType == 'rbi'){ # random BI
    pingType <- 'rbi'; rbi_min <- 20; rbi_max <- 40;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, rbi_min=rbi_min, rbi_max=rbi_max)
}

# Simulate hydrophone array
hydros <- simHydros(auto=TRUE, trueTrack=trueTrack)
toa_list <- simToa(teleTrack, hydros, pingType, sigmaToa=1e-4, pNA=0.25, pMP=0.01)
toa <- toa_list$toa

# Specify whether to use ss_data from measured water temperature (ss_data_what <- 'data') or 
#	to estimate ss in the model (ss_data_what <- 'est')
ss_data_what <- 'data'
if(ss_data_what == 'data') {ss_data <- teleTrack$ss} else {ss_data <- 0}


if(pingType == 'sbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		ss_data_what=ss_data_what, ss_data=ss_data)
} else if(pingType == 'rbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what=ss_data_what, ss_data=ss_data)
} 

pl <- c()
maxIter <- ifelse(pingType=="sbi", 500, 5000)
outTmb <- runYaps(inp, maxIter=maxIter, getPlsd=TRUE, getRep=TRUE)

# Estimates in pl
pl <- outTmb$pl
# Correcting for hydrophone centering
pl$X <- outTmb$pl$X + inp$inp_params$Hx0
pl$Y <- outTmb$pl$Y + inp$inp_params$Hy0


# Error estimates in plsd
plsd <- outTmb$plsd

# plot the resulting estimated track
plot(y~x, data=trueTrack, type="l", xlim=range(hydros$hx), ylim=range(hydros$hy), asp=1)
lines(y~x, data=teleTrack)
points(hy~hx, data=hydros, col="green", pch=20, cex=3)
lines(pl$Y~pl$X, col="red")

Simulate telemetry track based on known true track obtained using simTrueTrack

Description

Based on a known true track obtained using simTrueTrack, this function will give true positions at time-of-pings, which are also in the output. TOPs are determined by user-specified transmitter type. Number of pings are determined automatically based on track length and transmitter specifications.

Usage

simTelemetryTrack(
  trueTrack,
  pingType,
  sbi_mean = NULL,
  sbi_sd = NULL,
  rbi_min = NULL,
  rbi_max = NULL
)

Arguments

trueTrack

Know track obtained using simTrueTrack
pingType

Type of transmitter to simulate - either stable burst interval ('sbi'), random burst interval ('rbi') or random burst interval but where the random sequence is known a priori
sbi_mean, sbi_sd

Mean and SD of burst interval when pingType = 'sbi'
rbi_min

Minimum and maximum BI for random burst interval transmitters
rbi_max

Minimum and maximum BI for random burst interval transmitters

Value

data.frame containing time of ping and true positions

Examples

library(yaps)
set.seed(42)
# Simulate true track of animal movement of n seconds
trueTrack <- simTrueTrack(model='crw', n = 1000, deltaTime=1, shape=1, 
	scale=0.5, addDielPattern=TRUE, ss='rw')

# Simulate telemetry observations from true track.
# Format and parameters depend on type of transmitter burst interval (BI).
pingType <- 'sbi'

if(pingType == 'sbi') { # stable BI
    sbi_mean <- 30; sbi_sd <- 1e-4;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, sbi_mean=sbi_mean, sbi_sd=sbi_sd)
} else if(pingType == 'rbi'){ # random BI
    pingType <- 'rbi'; rbi_min <- 20; rbi_max <- 40;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, rbi_min=rbi_min, rbi_max=rbi_max)
}

# Simulate hydrophone array
hydros <- simHydros(auto=TRUE, trueTrack=trueTrack)
toa_list <- simToa(teleTrack, hydros, pingType, sigmaToa=1e-4, pNA=0.25, pMP=0.01)
toa <- toa_list$toa

# Specify whether to use ss_data from measured water temperature (ss_data_what <- 'data') or 
#	to estimate ss in the model (ss_data_what <- 'est')
ss_data_what <- 'data'
if(ss_data_what == 'data') {ss_data <- teleTrack$ss} else {ss_data <- 0}


if(pingType == 'sbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		ss_data_what=ss_data_what, ss_data=ss_data)
} else if(pingType == 'rbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what=ss_data_what, ss_data=ss_data)
} 

pl <- c()
maxIter <- ifelse(pingType=="sbi", 500, 5000)
outTmb <- runYaps(inp, maxIter=maxIter, getPlsd=TRUE, getRep=TRUE)

# Estimates in pl
pl <- outTmb$pl
# Correcting for hydrophone centering
pl$X <- outTmb$pl$X + inp$inp_params$Hx0
pl$Y <- outTmb$pl$Y + inp$inp_params$Hy0


# Error estimates in plsd
plsd <- outTmb$plsd

# plot the resulting estimated track
plot(y~x, data=trueTrack, type="l", xlim=range(hydros$hx), ylim=range(hydros$hy), asp=1)
lines(y~x, data=teleTrack)
points(hy~hx, data=hydros, col="green", pch=20, cex=3)
lines(pl$Y~pl$X, col="red")

Sim TOA matrix for the supplied telemetryTrack

Description

Provides the TOA matrix for the specified telemetryTrack. Probability of NA (pNA) and observation noise (sigmaToa) can be specified.

Usage

simToa(telemetryTrack, hydros, pingType, sigmaToa, pNA, pMP, tempRes = NA)

Arguments

telemetryTrack

Dataframe obtained from simTelemetryTrack
hydros

Dataframe obtained from getHydros
pingType

Type of transmitter to simulate - either stable burst interval ('sbi'), random burst interval ('rbi') or random burst interval but where the random sequence is known a priori
sigmaToa

Detection uncertainty
pNA

Probability of missing detection 0-1
pMP

Probability of multipath propagated signal 0-1
tempRes

Temporal resolution of the hydrophone. PPM systems are typially 1/1000 sec. Other systems are as high as 1/19200 sec.

Value

List containing TOA matrix (toa) and matrix indicating, which obs are multipath (mp_mat)

Examples

library(yaps)
set.seed(42)
# Simulate true track of animal movement of n seconds
trueTrack <- simTrueTrack(model='crw', n = 1000, deltaTime=1, shape=1, 
	scale=0.5, addDielPattern=TRUE, ss='rw')

# Simulate telemetry observations from true track.
# Format and parameters depend on type of transmitter burst interval (BI).
pingType <- 'sbi'

if(pingType == 'sbi') { # stable BI
    sbi_mean <- 30; sbi_sd <- 1e-4;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, sbi_mean=sbi_mean, sbi_sd=sbi_sd)
} else if(pingType == 'rbi'){ # random BI
    pingType <- 'rbi'; rbi_min <- 20; rbi_max <- 40;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, rbi_min=rbi_min, rbi_max=rbi_max)
}

# Simulate hydrophone array
hydros <- simHydros(auto=TRUE, trueTrack=trueTrack)
toa_list <- simToa(teleTrack, hydros, pingType, sigmaToa=1e-4, pNA=0.25, pMP=0.01)
toa <- toa_list$toa

# Specify whether to use ss_data from measured water temperature (ss_data_what <- 'data') or 
#	to estimate ss in the model (ss_data_what <- 'est')
ss_data_what <- 'data'
if(ss_data_what == 'data') {ss_data <- teleTrack$ss} else {ss_data <- 0}


if(pingType == 'sbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		ss_data_what=ss_data_what, ss_data=ss_data)
} else if(pingType == 'rbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what=ss_data_what, ss_data=ss_data)
} 

pl <- c()
maxIter <- ifelse(pingType=="sbi", 500, 5000)
outTmb <- runYaps(inp, maxIter=maxIter, getPlsd=TRUE, getRep=TRUE)

# Estimates in pl
pl <- outTmb$pl
# Correcting for hydrophone centering
pl$X <- outTmb$pl$X + inp$inp_params$Hx0
pl$Y <- outTmb$pl$Y + inp$inp_params$Hy0


# Error estimates in plsd
plsd <- outTmb$plsd

# plot the resulting estimated track
plot(y~x, data=trueTrack, type="l", xlim=range(hydros$hx), ylim=range(hydros$hy), asp=1)
lines(y~x, data=teleTrack)
points(hy~hx, data=hydros, col="green", pch=20, cex=3)
lines(pl$Y~pl$X, col="red")

Simulate a known movement track for subsequent estimation using YAPS

Description

Produces a simulated regular time-spaced track following the specified movement model. Linear movement between consecutive observations is assumed. The output contains x, y, time and sound speed at each simulated position.

Usage

simTrueTrack(
  model = "rw",
  n,
  deltaTime = 1,
  D = NULL,
  shape = NULL,
  scale = NULL,
  addDielPattern = TRUE,
  ss = "rw",
  start_pos = NULL
)

Arguments

model

Movement model: 'rw': Two-dimension random walk (X,Y)
n

Number of steps in the simulated track
deltaTime

Number of time units (seconds) between each location
D

Diffusivity of the animal movement - only used if model='rw'
shape

Shape of the Weibull distribution - only used when model='crw'.
scale

Scale of the Weibull distribution - only used when model='crw'.
addDielPattern

Adds a realistic(?) diel pattern to movement. Periods of both low and high movement
ss

Simulations model for Speed of Sound - defaults to 'rw' = RW-model.
start_pos

Specify the starting position of the track with c(x0, y0)

Value

data.frame containing a simulated track

Examples

library(yaps)
set.seed(42)
# Simulate true track of animal movement of n seconds
trueTrack <- simTrueTrack(model='crw', n = 1000, deltaTime=1, shape=1, 
	scale=0.5, addDielPattern=TRUE, ss='rw')

# Simulate telemetry observations from true track.
# Format and parameters depend on type of transmitter burst interval (BI).
pingType <- 'sbi'

if(pingType == 'sbi') { # stable BI
    sbi_mean <- 30; sbi_sd <- 1e-4;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, sbi_mean=sbi_mean, sbi_sd=sbi_sd)
} else if(pingType == 'rbi'){ # random BI
    pingType <- 'rbi'; rbi_min <- 20; rbi_max <- 40;
    teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, rbi_min=rbi_min, rbi_max=rbi_max)
}

# Simulate hydrophone array
hydros <- simHydros(auto=TRUE, trueTrack=trueTrack)
toa_list <- simToa(teleTrack, hydros, pingType, sigmaToa=1e-4, pNA=0.25, pMP=0.01)
toa <- toa_list$toa

# Specify whether to use ss_data from measured water temperature (ss_data_what <- 'data') or 
#	to estimate ss in the model (ss_data_what <- 'est')
ss_data_what <- 'data'
if(ss_data_what == 'data') {ss_data <- teleTrack$ss} else {ss_data <- 0}


if(pingType == 'sbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		ss_data_what=ss_data_what, ss_data=ss_data)
} else if(pingType == 'rbi'){
    inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, 
		rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what=ss_data_what, ss_data=ss_data)
} 

pl <- c()
maxIter <- ifelse(pingType=="sbi", 500, 5000)
outTmb <- runYaps(inp, maxIter=maxIter, getPlsd=TRUE, getRep=TRUE)

# Estimates in pl
pl <- outTmb$pl
# Correcting for hydrophone centering
pl$X <- outTmb$pl$X + inp$inp_params$Hx0
pl$Y <- outTmb$pl$Y + inp$inp_params$Hy0


# Error estimates in plsd
plsd <- outTmb$plsd

# plot the resulting estimated track
plot(y~x, data=trueTrack, type="l", xlim=range(hydros$hx), ylim=range(hydros$hy), asp=1)
lines(y~x, data=teleTrack)
points(hy~hx, data=hydros, col="green", pch=20, cex=3)
lines(pl$Y~pl$X, col="red")

Test data from Florida Bay

Description

Small data set collected for positioning using acoustic telemetry and YAPS.
The data are part of a feasibility study using YAPS on Vemco PPM style data to track fish in shallow parts of Florida Bay. Data were collected using VR2 (Vemco) hydrophones.
Included in yaps with permission from J.S. Rehage, FIU Florida International University.

Usage

ssu1

Format

A list containing 3 data.tables:

hydros

  • serial Hydrophone serial number.

  • x,y,z Position of hydrophones in UTM.

  • sync_tag ID of co-located sync tag. Must be identical to entries in data.table detections$tag.

  • idx Unique values from 1:nrow(hydros).

detections

  • ts Timestamp of detection in POSIXct().

  • tag ID of detected tag.

  • epo Timestamp as number of seconds since Unix epoch. Can be obtained using as.numeric(ts).

  • frac Sub-second part of detection timestamp in fractions of second (0-1).

  • serial Serial number of detecting hydrophone. Must match entry in data.table hydros.

gps

  • ts Timestamp of gps position in POSIXct().

  • utm_x, utm_y Coordinates of position. Same projection and coordinate system as used in hydros.

Calculate speed of sound from water temperature, salinity and depth Based on H. Medwin (1975) Speed of sound in water: A simple equation for realistic parameters. (https://doi.org/10.1121/1.380790)

Description

Calculate speed of sound from water temperature, salinity and depth Based on H. Medwin (1975) Speed of sound in water: A simple equation for realistic parameters. (https://doi.org/10.1121/1.380790)

Usage

tempToSs(temp, sal, depth = 5)

Arguments

temp

Water temperature in degrees Celcius
sal

Water slinity in parts per thousand (promille)
depth

Depth in meters - default = 5 m - can typically be ignored

Value

Vector of estimated speed of sound in water.

Examples

water_temp <- rnorm(100, 20, 2)
ss <- tempToSs(temp=water_temp, sal=0, depth=5)

Test YAPS core functionality

Description

Run testYaps() to check that the core functions of YAPS is working correctly. Output should be a random simulated (black) and estimated (red) track.

Usage

testYaps(
  silent = TRUE,
  pingType = "sbi",
  est_ss = TRUE,
  opt_fun = "nlminb",
  opt_controls = list(),
  return_yaps = FALSE,
  tmb_smartsearch = TRUE
)

Arguments

silent

Logical whether to print output to the console
pingType

Type of transmitter to simulate - either stable burst interval ('sbi'), random burst interval ('rbi') or random burst interval but where the random sequence is known a priori
est_ss

Logical whether to test using ss_data_what = 'est' (est_ss = TRUE) or ss_data_what = 'data' (est_ss = FALSE)
opt_fun

Which optimization function to use. Default is opt_fun = 'nlminb' - alternative is opt_fun = 'nloptr' (experimental!). If using nloptr, opt_controls must be specified.
opt_controls

List of controls passed to optimization function. For instances, tolerances such as x.tol=1E-8.
If opt_fun = 'nloptr', opt_controls must be a list formatted appropriately. For instance:
opt_controls <- list( algorithm="NLOPT_LD_AUGLAG", xtol_abs=1e-12, maxeval=2E+4, print_level = 1, local_opts= list(algorithm="NLOPT_LD_AUGLAG_EQ", xtol_rel=1e-4) ).
See ?nloptr and the NLopt site https://nlopt.readthedocs.io/en/latest/ for more info. Some algorithms in nloptr require bounded parameters - this is not currently implemented.
return_yaps

Logical whether to return the fitted yaps model. Default=FALSE.
tmb_smartsearch

Logical whether to use the TMB smartsearch in the inner optimizer (see ?TMB::MakeADFun for info). Default and original implementation is TRUE. However, there seems to be an issue with recent versions of Matrix that requires tmb_smartsearch=FALSE.

Value

If return_yaps == TRUE, the fitted yaps object. See ?runYaps for further info.

Examples

#' # To test  basic functionality of yaps using simulated data
testYaps()
# # # Three pingTypes are availabe: 
# # #	fixed burst interval (testYaps(pingType='sbi')), 
# # # 	random burst interval with UNKNOWN burst interval sequence('testYaps(pingType='rbi')), 
# # # 	random burst interval with KNOWN burst interval sequence (testYaps(pingType='pbi'))