getInpSync: Get object inp for synchronization

Description Usage Arguments Value Examples

View source: R/getInpSync.R

Description

Get object inp for synchronization

Usage

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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

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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)

baktoft/yaps documentation built on April 18, 2021, 7:33 a.m.