checkInp: Check consistency of 'inp' object obtained from 'getInp()'

In baktoft/yaps: Track Estimation using YAPS (Yet Another Positioning Solver)

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)

baktoft/yaps documentation built on Nov. 12, 2023, 2:30 p.m.