](https://baktoft.shinyapps.io/yapslive/)
+[
](https://baktoft.shinyapps.io/yapslive/)
## Dependencies
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+Welcome to the yaps repository. The yaps package is based on the original YAPS presented in Baktoft, Gjelland, Økland & Thygesen (2017): Positioning of aquatic animals based on time-of-arrival and random walk models using YAPS (Yet Another Positioning Solver)
To use yaps on own data, you need to compile a TOA-matrix based on synchronized hydrophone data and replace the hydros dataframe with actual hydrophone positions. A complete step-by-step guide on how to do this, can be found in our pre-print paper Opening the black box of fish tracking using acoustic telemetry. The example in this guide is based on data collected using a 69 kHz PPM-based system (Vemco VR2). We are working towards adding examples based on data collected using other manufacturers.
We are working towards true live tracking for aquatic animals. Check out our prototype of yaps-live (or click the screenshot below). The track estimation is done on-the-fly using yaps, but the live-stream of detection data is at the moment computer generated from manually downloaded data.
The yaps package requires devtools and TMB. Please see instructions on TMB installation. If working on Windows, you might also need to install Rtools as specified in the TMB documentation.
yaps obeys the fundamental rule of “garbage in, garbage out”. Therefore, DO NOT expect yaps to salvage a poorly designed study, nor to turn crappy data into gold.
+We have attempted to make both synchronization process and track estimation user-friendly. However, it is not trivial to synchronize hydrophones (let alone automating the process) based on detections in a variable and often noisy environment. Hydrophones might be replaced/shifted and if not fixed securely, hydrophones might move/be moved during a study. Additionally, hydrophone performance and output format varies considerably among (and within) manufacturers. On top of that, hydrophones don’t always behave and perform as expected. For instance, some hydrophone models autonomously initiate reboots causing perturbation of varying magnitude and/or duration of the internal clock at apparently random time intervals. Therefore, the functions in yaps might perform sub-optimal or even fail miserably when applied to new data. If/when this happens, please let us know through a direct message or leave a bug-report. Also note, the to-do list for improvements and tweaks is long and growing, so stay tuned for updates.
Make sure you have the newest version of yaps installed. For this, you need devtools installed - if not already installed, run install.packages('devtools').
+yaps relies heavily on use of Template Model Builder TMB for fitting the models, so make sure TMB is installed and working by following the simple TMB instructions.
+Then install the latest version of yaps with:
install.packages("devtools")
+ install.packages("TMB")
+ TMB::runExample(all=TRUE)
+ devtools::install_github("baktoft/yaps")The code below is identical to the example workflow presented in Opening the black box of fish tracking using acoustic telemetry. See the pre-print for further explantion.
+library(yaps)
+
+# set sync parameters
+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
+
+# 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)
+
+# fit the sync model
+sync_model <- getSyncModel(inp_sync, silent=TRUE)
+
+# Plot model residuals and model check plots to ensure the synchronization process was successful...
+plotSyncModelResids(sync_model, by='overall')
+plotSyncModelResids(sync_model, by='sync_tag')
+plotSyncModelResids(sync_model, by='hydro')
+
+plotSyncModelCheck(sync_model, by="sync_bin_sync")
+plotSyncModelCheck(sync_model, by="sync_bin_hydro")
+plotSyncModelCheck(sync_model, by="sync_tag")
+plotSyncModelCheck(sync_model, by="hydro")
+
+# Apply the synchronization model to all data
+detections_synced <- applySync(toa=ssu1$detections, hydros=ssu1$hydros, sync_model)
+
+# Prepare to estimate track using `yaps` on newly synchronized `ssu1` data
+hydros_yaps <- data.table::data.table(sync_model$pl$TRUE_H)
+colnames(hydros_yaps) <- c('hx','hy','hz')
+
+# Specify focal tag and tag specific min and max burst intervals
+focal_tag <- 15266
+rbi_min <- 20
+rbi_max <- 40
+
+# Extract relevant data from the synced data
+synced_dat_ssu1 <- detections_synced[tag == focal_tag]
+
+# Compile TOA-matrix to use for yaps
+toa_ssu1 <- getToaYaps(synced_dat_ssu1, hydros_yaps, rbi_min, rbi_max)
+
+# Compile all input data needed for yaps
+inp_ssu1 <- getInp(hydros_yaps, toa_ssu1, E_dist="Mixture", n_ss=2, pingType="rbi",
+ sdInits=1, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what="est", ss_data=0)
+
+# Run yaps to obtain estimated track
+yaps_out_ssu1 <- runYaps(inp_ssu1, silent=TRUE)
+
+# plot the estimated track
+plotYaps(inp=inp_ssu1, yaps_out=yaps_out_ssu1, type="map")
+# Add gps track for direct comparison
+lines(utm_y~utm_x, data=ssu1$gps, lty=2)
+
+par(mfrow=c(2,1))
+plotYaps(inp=inp_ssu1, yaps_out=yaps_out_ssu1, type="coord_X")
+lines(utm_x~ts, data=ssu1$gps, lty=2)
+plotYaps(inp=inp_ssu1, yaps_out=yaps_out_ssu1, type="coord_Y")
+lines(utm_y~ts, data=ssu1$gps, lty=2)devtools::install_github("baktoft/yaps")
+rm(list=ls())
+library(yaps)
+
+# Simulate true track of animal movement of n seconds
+trueTrack <- simTrueTrack(model='crw', n = 15000, deltaTime=1, shape=1, scale=0.5, addDielPattern=TRUE, ss='rw')
+
+# Simulate telemetry observations from true track.
+# Format and parameters depend on type of transmitter burst interval (BI) - stable (sbi) or random (rbi).
+pingType <- 'sbi'
+
+if(pingType == 'sbi') { # stable BI
+ sbi_mean <- 30; sbi_sd <- 1e-4;
+ teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, sbi_mean=sbi_mean, sbi_sd=sbi_sd)
+} else if(pingType == 'rbi'){ # random BI
+ pingType <- 'rbi'; rbi_min <- 20; rbi_max <- 40;
+ teleTrack <- simTelemetryTrack(trueTrack, pingType=pingType, rbi_min=rbi_min, rbi_max=rbi_max)
+}
+
+# Simulate hydrophone array
+hydros <- simHydros(auto=TRUE, trueTrack=trueTrack)
+toa_list <- simToa(teleTrack, hydros, pingType, sigmaToa=1e-4, pNA=0.25, pMP=0.01)
+toa <- toa_list$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$ss} else {ss_data <- 0}
+
+
+if(pingType == 'sbi'){
+ inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, ss_data_what=ss_data_what, ss_data=ss_data)
+} else if(pingType == 'rbi'){
+ inp <- getInp(hydros, toa, E_dist="Mixture", n_ss=10, pingType=pingType, sdInits=0, rbi_min=rbi_min, rbi_max=rbi_max, ss_data_what=ss_data_what, ss_data=ss_data)
+}
+str(inp)
+
+pl <- c()
+maxIter <- ifelse(pingType=="sbi", 500, 5000)
+outTmb <- runTmb(inp, maxIter=maxIter, getPlsd=TRUE, getRep=TRUE)
+str(outTmb)
+
+# Estimates in pl
+pl <- outTmb$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")Baktoft, H., Gjelland, K.Ø., Økland, F., Rehage, J.S., Rodemann, J.R., Corujo, R.S., Viadero, N., Thygesen, U.H. (2019). Opening the black box of fish tracking using acoustic telemetry bioRxiv 2019.12.16.877688; doi: https://doi.org/10.1101/2019.12.16.877688
Silva, A.T., Bærum, K.M., Hedger, R.D., Baktoft, H., Fjeldstad, H., Gjelland, K.Ø., Økland, F. Forseth, T. (2019). Science of the Total Environment The effects of hydrodynamics on the three-dimensional downstream migratory movement of Atlantic salmon. Science of the Total Environment, 135773. https://doi.org/10.1016/j.scitotenv.2019.135773
Szabo-Meszaros, M., Forseth, T., Baktoft, H., Fjeldstad, H.-P., Silva, A.T., Gjelland, K.Ø., Økland, F., Uglem, I., Alfredsen, K. (2019). Modelling mitigation measures for smolt migration at dammed river sections. Ecohydrology, e2131. https://doi.org/10.1002/eco.2131
](https://baktoft.shinyapps.io/yapslive/)
+[](https://baktoft.shinyapps.io/yapslive/)
## Dependencies
diff --git a/vignettes/yaps-live.PNG b/vignettes/yaps_live.png
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